breast cancer research journal articles

Double-negative T cells with a distinct transcriptomic profile are abundant in the peripheral blood of patients with breast cancer

• Xiao-Fei Liu

Breast Cancer Risk Assessment Tool (BCRAT) and long-term breast cancer mortality in the Women's Health Initiative

• Rebecca A. Nelson
• Rowan T. Chlebowski
• Laura Kruper

Breast cancer recurrence in relation to mode of detection: implications on personalized surveillance

• Hanna Sartor
• Oskar Hagberg
• Charlotta Wadsten

Risk-reducing surgeries for breast cancer in Brazilian patients undergoing multigene germline panel: impact of results on decision making

• Bárbara Narciso Duarte
• Christine Elisabete Rubio Alem
• Cesar Cabello

CD68 positive and/or CD163 positive tumor-associated macrophages and PD-L1 expression in breast phyllodes tumor

• Hye Min Kim
• Ja Seung Koo

Overcoming technical hurdles in mobile health: insights from the Fit2ThriveMB breast cancer study

Letter to the editor: “frequency of zoledronate administration in early breast cancer”.

• Wei-Zhen Tang
• Wen-Xing Deng
• Tai-Hang Liu

Baseline sLAG-3 levels in Caucasian and African-American breast cancer patients

• Helen Swede
• Sharif M. Ridwan
• Henry M. Smilowitz

Microcalcifications in benign breast biopsies: association with lesion type and risk

• Sarah Schrup
• Heather Hardway
• Mark E. Sherman

Correction: FBLN2 is associated with basal cell markers Krt14 and ITGB1 in mouse mammary epithelial cells and has a preferential expression in molecular subtypes of human breast cancer

• Amr Ahmed WalyEldeen
• Salwa Sabet
• Ayman M. Ibrahim

Barriers and facilitators to breast cancer screening among high-risk women: a qualitative study

• Claire C. Conley
• Alaina Anderson
• Susan T. Vadaparampil

Outcomes from low-risk ductal carcinoma in situ: a systematic review and meta-analysis

• Ian Campbell
• Sandar Tin Tin

Evaluation of tumor infiltrating lymphocytes as a prognostic biomarker in patients with ductal carcinoma in situ of the breast

• Camila Vitola Pasetto
• Fernando Nalesso Aguiar
• Rodrigo Gonçalves

Phosphoenolpyruvate carboxykinase-2 (PCK2) is a therapeutic target in triple-negative breast cancer

• Vignesh Gunasekharan
• Hao-Kuen Lin
• Lajos Pusztai

The effects of the-optimal-lymph-flow health IT system application on treatment-related high risk lymphedema in breast cancer patients: a randomized controlled trial

Trends in HR+ metastatic breast cancer survival before and after CDK4/6 inhibitor introduction in the United States: a SEER registry analysis of patients with HER2− and HER2+ metastatic breast cancer

• Adam Brufsky
• Marilyn L. Kwan
• Ravi K. Goyal

SGLT2 inhibition improves PI3Kα inhibitor–induced hyperglycemia: findings from preclinical animal models and from patients in the BYLieve and SOLAR-1 trials

• Manuel Ruiz Borrego
• Yen-Shen Lu
• Stephen Chia

Patient characteristics and treatment patterns of patients with locally advanced or metastatic HER2-low breast cancer, a single site descriptive study

• Connor Willis
• Chia Jie Tan
• David Stenehjem

Cytokine levels in breast cancer are highly dependent on cytomegalovirus (CMV) status

• Juliet V. Spencer
• Jianfang Liu
• Stella Somiari

Polycystic ovary syndrome and risk of breast cancer in premenopausal and postmenopausal women: a nationwide population-based cohort study

• Clarissa L. B. Frandsen
• Allan Jensen

Patterns in use and tolerance of adjuvant neratinib in patients with hormone receptor (HR)-positive, HER2-positive early-stage breast cancer

• Julia Blanter
• Elena Baldwin
• Amy Tiersten

Prognostic and predictive impact of NOTCH1 in early breast cancer

• Julia Engel
• Vanessa Wieder
• Martina Vetter

Endocrine therapy initiation among women diagnosed with ductal carcinoma in situ from 2001 to 2018

• Erin J. Aiello Bowles
• Gretchen L. Gierach

The landscape of use of NCCN-guideline chemotherapy regimens in stage I-IIIA breast cancer in an integrated healthcare delivery system

• Jenna Bhimani
• Kelli O’Connell
• Elizabeth D. Kantor

Effect of minocycline on changes in affective behaviors, cognitive function, and inflammation in breast cancer survivors undergoing chemotherapy: a pilot randomized controlled trial

• Zihan Melink
• Maryam B. Lustberg
• Tonya S. Orchard

Reply to: “Surgery of the primary tumor in patients with de novo metastatic breast cancer: a nationwide population-based retrospective cohort study in Belgium and the National Cancer Database (NCDB)”

• Mandeep Kaur
• Harriet T. Rothschild
• Rita A. Mukhtar

Variation in surgical treatment by body mass index in patients with invasive lobular carcinoma of the breast

• Israel O. Falade
• Kayla M. Switalla

Letter to the BRCT editor

• Pierre Bourgeois

Different distant breast cancer metastases might show discordant hormone receptor status in the same patient

• Kadri Altundag

Integrating social work and shared decision-making for equity in metastatic breast cancer care

• Zhaoning Li

Efficacy of everolimus plus hormonal treatment after cyclin-dependent kinase inhibitor; real-life experience, A TOG study

• İsmail Beypınar
• Hacer Demir
• Cengiz Karaçin

Correction: The novel TP53 3′-end methylation pattern associated with its expression would be a potential biomarker for breast cancer detection

• Jianfu Heng

Monitoring of estradiol levels in premenopausal women receiving adjuvant abemaciclib and ovarian function suppression

• Alaina J. Kessler
• Julie Fasano

Barrier films or dressings for the prevention of acute radiation dermatitis in breast cancer: a systematic review and network meta-analysis

• Henry C. Y. Wong
• Shing Fung Lee
• Edward Chow

FBLN2 is associated with basal cell markers Krt14 and ITGB1 in mouse mammary epithelial cells and has a preferential expression in molecular subtypes of human breast cancer

Phenotypes of carriers of two mutated alleles in major cancer susceptibility genes

• Yael Laitman
• Anni Niskakoski
• Eitan Friedman

Oral minoxidil for late alopecia in cancer survivors

• Alyce Mei-Shiuan Kuo
• Rachel E. Reingold
• Mario E. Lacouture

Fragmentation of care in breast cancer: greater than the sum of its parts

• Hadley D. Freeman
• Linnea C. Burke
• Sharon S. Lum

Influence of tumour grade on disease survival in male breast cancer patients: a systematic review

• Stephen Kinsey-Trotman
• Alain Nguyen
• Wendy V. Ingman

Cost containment analysis of superparamagnetic iron oxide (SPIO) injection in patients with ductal carcinoma in situ

• Odette Solís
• Jamin Addae
• Kelly Hewitt

Receptor Discordance in Metastatic Breast Cancer; a review of clinical and genetic subtype alterations from primary to metastatic disease

• Gavin P. Dowling
• Stephen Keelan
• Arnold D. K. Hill

A randomised trial comparing 6-monthly adjuvant zoledronate with a single one-time dose in patients with early breast cancer

• Arif Ali Awan
• Carol Stober
• the REaCT Investigators

Correction: Ferroptosis as a promising targeted therapy for triple negative breast cancer

• Kasra Mokhtarpour
• Sepideh Razi
• Nima Rezaei

Alterations in the expression of homologous recombination repair (HRR) genes in breast cancer tissues considering germline BRCA1/2 mutation status

• Laczmanska Izabela
• Matkowski Rafal
• Sasiadek Maria

Efficacy of antiobesity medications among breast cancer survivors taking aromatase inhibitors

• Wissam Ghusn
• Maria D. Hurtado Andrade

Association between homologous recombination deficiency status and carboplatin treatment response in early triple-negative breast cancer

• Xiaosong Chen

Information needs persist after genetic counseling and testing for BRCA1/2 and Lynch Syndrome

• Lucy A. Peipins
• Sabitha Dasari
• Juan L. Rodriguez

Genomic and transcriptomic profiling of inflammatory breast cancer reveals distinct molecular characteristics to non-inflammatory breast cancers

• Kaiwen Zhou
• Mengmeng Zhang

CD133 expression is associated with less DNA repair, better response to chemotherapy and survival in ER-positive/HER2-negative breast cancer

• Takumi Sato
• Masanori Oshi
• Kazuaki Takabe

Feasibility and preliminary effects of the Fit2ThriveMB pilot physical activity promotion intervention on physical activity and patient reported outcomes in individuals with metastatic breast cancer

• Siobhan M. Phillips
• Julia Starikovsky
• William Gradishar

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Advances in Breast Cancer Research

A polyploid giant cancer cell (PGCC) from triple-negative breast cancer.

NCI-funded researchers are working to advance our understanding of how to prevent, detect, and treat breast cancer. They are also looking at how to address disparities and improve quality of life for survivors of the disease.

This page highlights some of what's new in the latest research for breast cancer, including new clinical advances that may soon translate into improved care, NCI-supported programs that are fueling progress, and research findings from recent studies.

Early Detection of Breast Cancer

Breast cancer is one of a few cancers for which an effective screening  test, mammography , is available. MRI ( magnetic resonance imaging ) and  ultrasound  are also used to detect breast cancer, but not as routine screening tools for people with average risk.

Ongoing studies are looking at ways to enhance current breast cancer screening options. Technological advances in imaging are creating new opportunities for improvements in both screening and early detection.

One technology advance is 3-D mammography , also called breast tomosynthesis . This procedure takes images from different angles around the breast and builds them into a 3-D-like image. Although this technology is increasingly available in the clinic, it isn’t known whether it is better than standard 2-D mammography , for detecting cancer at a less advanced stage.

NCI is funding a large-scale randomized breast screening trial, the Tomosynthesis Mammographic Imaging Screening Trial (TMIST) , to compare the number of advanced cancers detected in women screened for 5 years with 3-D mammography with the number detected in women screened with 2-D mammography. 

Two concerns in breast cancer screening, as in all cancer screening, are:

• the potential for diagnosing tumors that would not have become life-threatening ( overdiagnosis )
• the possibility of receiving false-positive test results, and the anxiety that comes with follow-up tests or procedures

As cancer treatment is becoming more individualized, researchers are looking at ways to personalize breast cancer screening. They are studying screening methods that are appropriate for each woman’s level of risk and limit the possibility of overdiagnosis.

For example, the Women Informed to Screen Depending on Measures of Risk (WISDOM) study aims to determine if risk-based screening—that is, screening at intervals that are based on each woman’s risk as determined by her genetic makeup, family history , and other risk factors—is as safe, effective, and accepted as standard annual screening mammography.

WISDOM is also making a focused effort to enroll Black women in the trial. Past studies  tended to contain a majority of White women and therefore, there is less data on how screening can benefit Black women. Researchers are taking a number of steps to include as many Black women as possible in the study while also increasing the diversity of all women enrolled.

Breast Cancer Treatment

The mainstays of breast cancer treatment are surgery , radiation , chemotherapy , hormone therapy , and targeted therapy . But scientists continue to study novel treatments and drugs, along with new combinations of existing treatments.

It is now known that breast cancer can be divided into subtypes based on whether they:

• are hormone receptor (HR) positive which means they express  estrogen and/or progesterone receptors  ( ER , PR )

Shortening Radiation Therapy for Some with Early Breast Cancer

A condensed course was as effective and safe as the standard course for women with higher-risk early-stage breast cancer who had a lumpectomy.

As we learn more about the subtypes of breast cancer and their behavior, we can use this information to guide treatment decisions. For example:

• The NCI-sponsored TAILORx clinical trial. The study, which included patients with ER-positive, lymph node-negative breast cancer, found that a test that looks at the expression of certain genes can predict which women can safely avoid chemotherapy.
• The RxPONDER trial found that the same gene expression test can also be used to determine treatment options in women with more advanced breast cancer. The study found that some postmenopausal women with HR positive, HER-2 negative breast cancer that has spread to several lymph nodes and has a low risk of recurrence do not benefit from chemotherapy when added to their hormone therapy. 
• The OFSET trial is comparing the addition of chemotherapy to usual treatment ( ovarian function suppression plus hormone therapy) to usual treatment alone in treating premenopausal estrogen receptor (ER)-positive/HER2-negative breast cancer patients who are at high risk of their cancer returning. This will help determine whether or not adding chemotherapy helps prevent the cancer from returning.  

Genomic analyses, such as those carried out through  The Cancer Genome Atlas (TCGA) , have provided more insights into the molecular diversity of breast cancer and eventually could help identify even more breast cancer subtypes. That knowledge, in turn, may lead to the development of therapies that target the genetic alterations that drive those cancer subtypes.

HR-Positive Breast Cancer Treatment 

Hormone therapies have been a mainstay of treatment for HR-positive cancer. However, there is a new focus on adding targeted therapies to hormone therapy for advanced or metastatic HR-positive cancers. These treatments could prolong the time until chemotherapy is needed and ideally, extend survival. Approved drugs include:

Drug Combo Effective for Metastatic Breast Cancer in Younger Women

Ribociclib plus hormone therapy were superior to standard chemotherapy combos in a recent trial.

• Palbociclib (Ibrance) ,  ribociclib (Kisqali) , and  everolimus (Afinitor) have all been approved by the FDA for use with hormone therapy for treatment of advanced or metastatic breast cancer. Ribociclib has been shown to increase the survival of patients with metastatic breast cancer . It has also shown to slow the growth of metastatic cancer in younger women when combined with hormone therapy.
• Elacestrant (Orserdu) is approved for HR-positive and HER2-negative breast cancer that has a mutation in the ESR1 gene, and has spread. It is used in postmenopausal women and in men whose cancer has gotten worse after at least one type of hormone therapy.
• Abemaciclib (Verzenio) can be used with or after hormone therapy to treat advanced or metastatic HR-positive, HER2-negative breast cancer. In October 2021, the Food and Drug Administration ( FDA ) approved abemaciclib in combination with hormone therapy to treat some people who have had surgery for early-stage HR-positive, HER2-negative breast cancer.
• Alpelisib (Piqray)  is approved to be used in combination with hormone therapy to treat advanced or metastatic HR-positive, HER2-negative breast cancers that have a mutation in the PIK3CA gene .
• Sacituzumab govitecan-hziy (Trodelvy) is used for HR-positive and HER2-negative breast cancer that has spread or can't be removed with surgery. It is used in those who have received hormone therapy and at least two previous treatments. It has shown to extend the amount of time that the disease doesn't get worse ( progression-free survival ) and also shown to improve overall survival .

HER2-Positive Breast Cancer Treatment 

The FDA has approved a number of targeted therapies to treat HER2-positive breast cancer , including:

• Trastuzumab (Herceptin) has been approved to be used to prevent a relapse in patients with early-stage HER2-positive breast cancer. 
• Pertuzumab (Perjeta) is used to treat metastatic HER2-positive breast cancer, and also both before surgery ( neoadjuvant ) and after surgery ( adjuvant therapy ). 
• Trastuzumab and pertuzumab together can be used in combination with chemotherapy to prevent relapse in people with early-stage HER2-positive breast cancer.  Both are also used together in metastatic disease, where they delay progression and improve overall survival. 
• Trastuzumab deruxtecan (Enhertu) is approved for patients with advanced or metastatic HER2-positive breast cancer who have previously received a HER2-targeted treatment. A 2021 clinical trial showed that the drug lengthened the time that people with metastatic HER2-positive breast cancer lived without their cancer progressing. The trial also showed that it was better at shrinking tumors than another targeted drug, trastuzumab emtansine (Kadcyla).
• Tucatinib (Tukysa) is approved to be used in combination with trastuzumab and capecitabine (Xeloda) for HER2-positive breast cancer that cannot be removed with surgery or is metastatic. Tucatinib is able to cross the blood–brain barrier, which makes it especially useful for HER2-positive metastatic breast cancer, which tends to spread to the brain. 
• Lapatinib (Tykerb)  has been approved for treatment of some patients with HER2-positive advanced or metastatic breast cancer, together with capecitabine or letrozole.
• Neratinib Maleate (Nerlynx) can be used in patients with early-stage HER2-positive breast cancer and can also be used together with capecitabine (Xeloda) in some patients with advanced or metastatic disease.
• Ado-trastuzumab emtansine (Kadcyla) is approved to treat patients with metastatic HER2-positive breast cancer who have previously received trastuzumab and a taxane . It's also used in some patients with early-stage HER2-positive breast cancer who have completed therapy before surgery ( neoadjuvant ) and have residual disease at the time of surgery.

HER2-Low Breast Cancer

 A newly defined subtype, HER2-low, accounts for more than half of all metastatic breast cancers. HER2-low tumors are defined as those whose cells contain lower levels of the HER2 protein on their surface. Such tumors have traditionally been classified as HER2-negative because they did not respond to drugs that target HER2. 

However, in a clinical trial, trastuzumab deruxtecan (Enhertu) improved the survival of patients with HER2-low breast cancer compared with chemotherapy , and the drug is approved for use in such patients. 

Immunotherapy Improves Survival in Triple-Negative Breast Cancer

For patients whose tumors had high PD-L1 levels, pembrolizumab with chemo helped them live longer.

Triple-Negative Breast Cancer Treatment 

Triple-negative breast cancers (TNBC) are the hardest to treat because they lack both hormone receptors and HER2 overexpression , so they do not respond to therapies directed at these targets. Therefore, chemotherapy is the mainstay for treatment of TNBC. However, new treatments are starting to become available. These include:

• Sacituzumab govitecan-hziy (Trodelvy)  is approved to treat patients with TNBC that has spread to other parts of the body . Patients must have received at least two prior therapies before receiving the drug.
• Pembrolizumab (Keytruda)  is an immunotherapy drug that is approved to be used in combination with chemotherapy for patients with locally advanced or metastatic TNBC that has the PD-L1 protein. It may also be used before surgery (called neoadjuvant ) for patients with early-stage TNBC, regardless of their PD-L1 status.
• PARP inhibitors, which include olaparib (Lynparza) and talazoparib (Talzenna) , are approved to treat metastatic HER2-negative or triple-negative breast cancers in patients who have inherited a harmful BRCA gene mutation. Olaparib is also approved for use in certain patients with early-stage HER2-negative or triple-negative breast cancer. 
• Drugs that block the androgen receptors  or prevent androgen production are being tested in a subset of TNBC that express the androgen receptor.

For a complete list of drugs for breast cancer, see Drugs Approved for Breast Cancer .

NCI-Supported Breast Cancer Research Programs

Many NCI-funded researchers working at the NIH campus, as well as across the United States and world, are seeking ways to address breast cancer more effectively. Some research is basic, exploring questions as diverse as the biological underpinnings of cancer and the social factors that affect cancer risk. And some are more clinical, seeking to translate this basic information into improving patient outcomes. The programs listed below are a small sampling of NCI’s research efforts in breast cancer.

TMIST is a randomized breast screening trial that compares two Food and Drug Administration (FDA)-approved types of digital mammography, standard digital mammography (2-D) with a newer technology called tomosynthesis mammography (3-D).

The  Breast Specialized Programs of Research Excellence (Breast SPOREs)  are designed to quickly move basic scientific findings into clinical settings. The Breast SPOREs support the development of new therapies and technologies, and studies to better understand tumor resistance, diagnosis, prognosis, screening, prevention, and treatment of breast cancer.

The NCI Cancer Intervention and Surveillance Modeling Network (CISNET) focuses on using modeling to improve our understanding of how prevention, early detection, screening, and treatment affect breast cancer outcomes.

The Confluence Project , from NCI's Division of Cancer Epidemiology and Genetics (DCEG) , is developing a research resource that includes data from thousands of breast cancer patients and controls of different races and ethnicities. This resource will be used to identify genes that are associated with breast cancer risk, prognosis, subtypes, response to treatment, and second breast cancers. (DCEG conducts other breast cancer research as well.)

The Black Women’s Health Study (BWHS) Breast Cancer Risk Calculator allows health professionals to estimate a woman’s risk of developing invasive breast cancer over the next 5 years. With the NCI-funded effort, researchers developed a tool to estimate the risk of breast cancer in US Black women. The team that developed the tool hopes it will help guide more personalized decisions on when Black women—especially younger women—should begin breast cancer screening. 

The goal of the Breast Cancer Surveillance Consortium (BCSC) , an NCI-funded program launched in 1994, is to enhance the understanding of breast cancer screening practices in the United States and their impact on the breast cancer's stage at diagnosis, survival rates, and mortality.

There are ongoing programs at NCI that support prevention and early detection research in different cancers, including breast cancer. Examples include:

• The  Cancer Biomarkers Research Group , which promotes research in cancer biomarkers and manages the Early Detection Research Network (EDRN) . EDRN is a network of NCI-funded institutions that are collaborating to discover and validate early detection biomarkers. Within the EDRN, the Breast and Gynecologic Cancers Collaborative Group conducts research on breast and ovarian cancers.
• NCI's Division of Cancer Prevention  houses the Breast and Gynecologic Cancer Research Group which conducts and fosters the development of research on the prevention and early detection of  breast and gynecologic cancers.

Breast Cancer Survivorship Research

NCI’s Office of Cancer Survivorship, part of the Division of Cancer Control and Population Sciences (DCCPS), supports research projects throughout the country that study many issues related to breast cancer survivorship. Examples of studies funded include the impact of cancer and its treatment on physical functioning, emotional well-being, cognitive impairment , sleep disturbances, and cardiovascular health. Other studies focus on financial impacts, the effects on caregivers, models of care for survivors, and issues such as racial disparities and communication.

Breast Cancer Clinical Trials

NCI funds and oversees both early- and late-phase clinical trials to develop new treatments and improve patient care. Trials are available for breast cancer prevention , screening , and treatment . 

Breast Cancer Research Results

The following are some of our latest news articles on breast cancer research and study updates:

• How Breast Cancer Risk Assessment Tools Work
• Can Some People with Breast Cancer Safely Skip Lymph Node Radiation?
• Study Adds to Debate about Mammography in Older Women
• Pausing Long-Term Breast Cancer Therapy to Become Pregnant Appears to Be Safe
• A Safer, Better Treatment Option for Some Younger Women with Breast Cancer
• Shorter Course of Radiation Is Effective, Safe for Some with Early-Stage Breast Cancer

View the full list of Breast Cancer Research Results and Study Updates .

• Research article
• Open access
• Published: 01 October 2013

Critical research gaps and translational priorities for the successful prevention and treatment of breast cancer

• Suzanne A Eccles 17 ,
• Eric O Aboagye 1 ,
• Simak Ali 1 ,
• Annie S Anderson 2 ,
• Jo Armes 7 ,
• Fedor Berditchevski 4 ,
• Jeremy P Blaydes 3 ,
• Keith Brennan 5 ,
• Nicola J Brown 6 ,
• Helen E Bryant 6 ,
• Nigel J Bundred 5 ,
• Joy M Burchell 7 ,
• Anna M Campbell 2 ,
• Jason S Carroll 9 ,
• Robert B Clarke 5 ,
• Charlotte E Coles 34 ,
• Gary JR Cook 7 ,
• Angela Cox 6 ,
• Nicola J Curtin 10 ,
• Lodewijk V Dekker 11 ,
• Isabel dos Santos Silva 12 ,
• Stephen W Duffy 13 ,
• Douglas F Easton 9 ,
• Diana M Eccles 3 ,
• Dylan R Edwards 15 ,
• Joanne Edwards 14 ,
• D Gareth Evans 5 ,
• Deborah F Fenlon 3 ,
• James M Flanagan 1 ,
• Claire Foster 3 ,
• William M Gallagher 16 ,
• Montserrat Garcia-Closas 17 ,
• Julia M W Gee 18 ,
• Andy J Gescher 28 ,
• Vicky Goh 7 ,
• Ashley M Groves 8 ,
• Amanda J Harvey 33 ,
• Michelle Harvie 5 ,
• Bryan T Hennessy 20 ,
• Stephen Hiscox 18 ,
• Ingunn Holen 6 ,
• Sacha J Howell 5 ,
• Anthony Howell 5 ,
• Gill Hubbard 21 ,
• Nick Hulbert-Williams 22 ,
• Myra S Hunter 7 ,
• Bharat Jasani 18 ,
• Louise J Jones 13 ,
• Timothy J Key 23 ,
• Cliona C Kirwan 5 ,
• Anthony Kong 23 ,
• Ian H Kunkler 24 ,
• Simon P Langdon 24 ,
• Martin O Leach 17 ,
• David J Mann 1 ,
• John F Marshall 13 ,
• Lesley Ann Martin 17 ,
• Stewart G Martin 11 ,
• Jennifer E Macdougall 25 ,
• David W Miles 7 ,
• William R Miller 24 ,
• Joanna R Morris 4 ,
• Sue M Moss 13 ,
• Paul Mullan 26 ,
• Rachel Natrajan 17 ,
• James PB O’Connor 5 ,
• Rosemary O’Connor 27 ,
• Carlo Palmieri 31 ,
• Paul D P Pharoah 9 ,
• Emad A Rakha 11 ,
• Elizabeth Reed 29 ,
• Simon P Robinson 17 ,
• Erik Sahai 32 ,
• John M Saxton 15 ,
• Peter Schmid 30 ,
• Matthew J Smalley 18 ,
• Valerie Speirs 19 ,
• Robert Stein 8 ,
• John Stingl 9 ,
• Charles H Streuli 5 ,
• Andrew N J Tutt 7 ,
• Galina Velikova 19 ,
• Rosemary A Walker 28 ,
• Christine J Watson 9 ,
• Kaye J Williams 5 ,
• Leonie S Young 20 &
• Alastair M Thompson 2  

Breast Cancer Research volume  15 , Article number:  R92 ( 2013 ) Cite this article

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Introduction

Breast cancer remains a significant scientific, clinical and societal challenge. This gap analysis has reviewed and critically assessed enduring issues and new challenges emerging from recent research, and proposes strategies for translating solutions into practice.

More than 100 internationally recognised specialist breast cancer scientists, clinicians and healthcare professionals collaborated to address nine thematic areas: genetics, epigenetics and epidemiology; molecular pathology and cell biology; hormonal influences and endocrine therapy; imaging, detection and screening; current/novel therapies and biomarkers; drug resistance; metastasis, angiogenesis, circulating tumour cells, cancer ‘stem’ cells; risk and prevention; living with and managing breast cancer and its treatment. The groups developed summary papers through an iterative process which, following further appraisal from experts and patients, were melded into this summary account.

The 10 major gaps identified were: (1) understanding the functions and contextual interactions of genetic and epigenetic changes in normal breast development and during malignant transformation; (2) how to implement sustainable lifestyle changes (diet, exercise and weight) and chemopreventive strategies; (3) the need for tailored screening approaches including clinically actionable tests; (4) enhancing knowledge of molecular drivers behind breast cancer subtypes, progression and metastasis; (5) understanding the molecular mechanisms of tumour heterogeneity, dormancy, de novo or acquired resistance and how to target key nodes in these dynamic processes; (6) developing validated markers for chemosensitivity and radiosensitivity; (7) understanding the optimal duration, sequencing and rational combinations of treatment for improved personalised therapy; (8) validating multimodality imaging biomarkers for minimally invasive diagnosis and monitoring of responses in primary and metastatic disease; (9) developing interventions and support to improve the survivorship experience; (10) a continuing need for clinical material for translational research derived from normal breast, blood, primary, relapsed, metastatic and drug-resistant cancers with expert bioinformatics support to maximise its utility. The proposed infrastructural enablers include enhanced resources to support clinically relevant in vitro and in vivo tumour models; improved access to appropriate, fully annotated clinical samples; extended biomarker discovery, validation and standardisation; and facilitated cross-discipline working.

Conclusions

With resources to conduct further high-quality targeted research focusing on the gaps identified, increased knowledge translating into improved clinical care should be achievable within five years.

Globally, breast cancer is the most frequently diagnosed cancer in women, with an estimated 1.38 million new cases per year. Fifty thousand cases in women and 400 in men are recorded each year in the UK alone. There are 458,000 deaths per year from breast cancer worldwide making it the most common cause of female cancer death in both the developed and developing world [ 1 ].

In the UK, the age-standardised incidence of breast cancer in women has increased by 6% over the last decade, between 1999 to 2001 and 2008 to 2010 [ 2 ]. It is estimated that around 550,000-570,000 people are living with or after a diagnosis of breast cancer in the UK [ 3 ] and, based on current projections, this figure is expected to triple by 2040 due to an ageing population and continued improvements in survival [ 4 ]. Recent research indicates that the annual cost of breast cancer to the UK economy is £1.5bn, with just over a third of that cost (£0.6bn) from healthcare alone [ 5 ]. Yet the annual spend on breast cancer research by partners of the National Cancer Research Institute has reduced in recent years despite the level of cancer research spend being generally maintained [ 6 ].

In 2006, the charity Breast Cancer Campaign facilitated a meeting of leading breast cancer experts in the United Kingdom to explore which gaps in research, if filled, would make the most impact on patient benefit. The subsequent paper [ 7 ] has helped shape the direction of breast cancer research since that time. One overarching need identified was the ‘lack of access to appropriate and annotated clinical material’, which directly led to the formation of the UK’s first multi-centre, breast-specific tissue bank [ 8 ].

This new gap analysis represents an expanded, evidence-based follow-on developed collaboratively by clinicians, scientists and healthcare professionals. The aim is to ensure that the roadmap for breast cancer research remains a relevant, consensual and authoritative resource to signpost future needs. It builds upon the previous gap analysis by briefly reviewing the current status of key areas, critically assessing remaining issues and new challenges emerging from recent research findings and proposes strategies to aid their translation into practice. Whilst a survey of progress during the last five years is not the intention of this article, the preparatory detailed discussions and data analysis could provide the basis for such a retrospective review.

During 2012, Breast Cancer Campaign facilitated a series of workshops, each covering a specialty area of breast cancer (Figure  1 ). These working groups covered genetics, epigenetics and epidemiology; molecular pathology and cell biology; hormonal influences and endocrine therapy; imaging, detection and screening; current and novel therapies and associated biomarkers; drug resistance; invasion, metastasis, angiogenesis, circulating tumour cells, cancer ‘stem’ cells; breast cancer risk and prevention; living with and managing breast cancer and its treatment. Working group leaders and their multidisciplinary teams (comprising a representative cross-section of breast cancer clinicians, scientists, and healthcare professionals) participated in iterative cycles of presentation and discussion, offering a subjective consideration of the recent relevant peer-reviewed literature. Summary reports were prepared by each group, collated, condensed and edited into a draft, which was critically appraised by an external Executive Advisory Board of international experts. This position paper highlights the key gaps in breast cancer research that were identified, together with detailed recommendations for action.

Gap analysis methodology. The flow chart illustrates the concept, processes and procedures devised to generate the gap analysis review.

Genetics, epigenetics and epidemiology

Current status, genetic predisposition.

Our knowledge of the heritability of breast cancer has increased significantly since 2007. Known breast cancer genes (BRCA1, BRCA2, CHEK2, ATM, PALB2, BRIP1, TP53, PTEN, CDH1 and STK11) make up 25 to 30% of the heritability [ 9 ]. Genome-wide association studies (GWAS) and the recent international collaborative analyses have confirmed 77 common polymorphisms individually associated with breast cancer risk, which add a further 14% [ 9 – 11 ]. Evidence from an Illumina collaborative oncological gene-environment study (iCOGS) experiment suggests that further single nucleotide polymorphisms (SNPs) may contribute at least 14% to the heritability, leaving only approximately 50% as ‘missing heritability’ (Figure  2 ).

Familial cancer genetics. The proportion of the familial component of breast cancers that can be ascribed to specific genetic defects. The difference between June 2007 and 2013 shows the impact of genome-wide association studies (GWAS) that have now identified 77 common low-risk SNPs. Courtesy of Professor Douglas Easton (University of Cambridge). Reprinted by permission from Macmillan Publishers Ltd: Nature Genetics (45,345-348), copyright 2013.

If we assume the risk estimates for polygenic markers are log additive, the cumulative risk associated with these SNPs has a median of 9% to age 80 (95% confidence intervals 5 to 15%). In the familial setting, we have learnt that common genetic SNPs can modify the risk associated with BRCA2, which may be relevant when considering risk-reducing surgery [ 12 , 13 ].

BRCA1 and BRCA2

There is improved understanding of the function of BRCA1 and BRCA2 in relation to DNA repair and therapeutic responses. For example, BRCA2 functions in RAD51 loading and BRCA1 in countering 53BP1-mediated blocking of homologous recombinational (HR)-DNA repair; hence poly (ADP-ribose) polymerase (PARP) inhibitors have been developed and trialled against BRCA-driven cancers [ 14 ]. Several additional genes associated with breast cancer risk are part of the BRCA network and there is a clear relationship with the Fanconi pathway [ 9 ]. Genes in this network point to reduced HR-DNA repair as the mechanism underlying cancer susceptibility, although the precise functions of associated signalling proteins (for example PTEN, CHK2, ATM and N-terminal BRCA1) that relate to cancer development are unknown. Gene interactions of some higher risk alleles are recognised to be sub-multiplicative, whereas low risk alleles are log-additive [ 15 ]. Some susceptibility SNPs may function at the level of chromatin remodelling/enhancer activity related to nearby gene expression.

Epigenetics

Epigenetic alterations are frequent and cancer-specific methylation in circulating tumour (ct)DNA in serum can be used as an early detection biomarker, or as a prognostic indicator [ 16 , 17 ]. The recent ENCODE study provided a wide-ranging analysis of epigenetic marks on a small fraction of the genome [ 18 ]. The first candidate gene epigenetic risk factor that could usefully be included in breast cancer risk models (once fully validated) has been identified [ 19 ]. Epigenetic factors also provide molecular measures of long-term exposure to potentially oncogenic agents. Epigenetic alterations are reversible; preclinical and recent clinical testing of epigenetic-targeted therapies such as etinostat (a DNA methylation inhibitor) and vorinostat (a histone deacetylase inhibitor) indicate that such drugs may prove effective in combination with other therapies [ 20 , 21 ].

Psychosocial considerations

Predictive genetic testing for breast cancer predisposition genes can increase distress in the short term (which reduces over time) for those identified as gene carriers, whilst non-carriers report lower levels of concern following genetic testing [ 22 ]. A number of interventions have now been developed and tested to support the genetic testing process and have been shown to reduce distress, improve the accuracy of the perceived risk of breast cancer, and increase knowledge about breast cancer and genetics [ 23 ]. Examples introduced since the last gap analysis include education using tailored information technology to prepare women for genetic counselling [ 24 ]; interventions to support women’s decisions about whether or not to have genetic testing [ 25 ] and support for gene carriers thus identified [ 12 ].

What are the key gaps in our knowledge and how might they be filled?

Moderate risk alleles.

Remaining ‘moderate risk’ alleles will be found within the short term by exome sequencing and extended GWAS studies will identify additional lower risk alleles. If up to 28% of the risk from known SNPs could be explained, while the median of the risk distribution changes little, confidence limits would change dramatically, such that the women in the top 5% at risk would have >15% lifetime risk, compared with <3% lifetime risk at the lower end. A prospective analysis will be required to show that genetic risk assessment can predict risk when combined with mammographic screening. We need to determine if or how common SNPs modify the contributions of BRCA1-associated and moderate risk genes (such as CHEK2, ATM) and whether this is influenced by oestrogen levels or risk management using, for example, lifestyle or chemopreventive approaches.

Functional implications of unclassified variants in BRCA1/BRCA2, fine-mapping of risk-associated variants (from GWAS) and understanding the functional impact of the more common SNPs such as TOX3 and the role of FOXA1 remain to be determined. Similarly, deconvoluting the functional interactions between susceptibility genes and known breast cancer-associated proteins require systems biology approaches. Can we achieve a clear clinical use of the knowledge gained by GWAS, SNP and BRCA studies by validation of risk models incorporating SNPs and moderate risk alleles (in particular in the familial setting) to improve risk management? A randomised trial for population screening with mammography stratified on individual genetic risk estimates (combined with other key risk factors) is warranted.

BRCA1 and 2

A scheme to define categories of risk for variants in BRCA (and other) cancer genes is needed to provide specific clinical recommendations. BRCA variants of uncertain significance occur in approximately 5% of all genetic tests for BRCA1/BRCA2 mutations [ 26 ]. A range of in silico and functional assays is available to provide evidence for or against a genetic variant being pathogenic. A calculation combining all lines of evidence can estimate the posterior probability that a particular gene variant is predisposing to disease. The expression of breast cancer genes in normal breast tissue and pathways that may underlie cancer risk (such as DNA damage response) could be used to identify tractable markers and to direct treatment choice. Additional BRCA-deficient human tumour cell lines and animal models of breast cancer are required.

There is a gap in our understanding of cause or consequence between epigenetic traits and gene transcription. Translational studies are needed to investigate epigenetic patterns in clinical material and from clinical trials to identify and validate prognostic markers. The extent to which epigenetic markers can be incorporated into risk models alongside genetic and lifestyle factors is not yet known. Understanding how cancer risk factors impact on the epigenome and whether this provides a mechanism for increased risk associated with those exposures is poorly understood.

Further research is needed to support informed decision making about risk management options and to assess the psychosocial implications of changing behaviour and anxiety about cancer [ 27 ]. Interventions to support discussions with those newly diagnosed with breast cancer are being developed to improve understanding of risk to individuals and their families [ 28 ]. Interventions are also required to support conversations within the family about genetic risk and its implications, given that the onus is often on the patient [ 29 ]. Research involving women at increased genetic risk for breast cancer should assess the psychosocial impact on partners and the implications for their relationships [ 30 ]. Evidence from this research needs to inform services and direct resources to support those at increased risk of breast cancer.

Risk and prevention

Risk estimation.

We know little about the exact cause(s) of the majority of breast cancers. The major challenge for prevention is to identify women at risk as precisely as possible and then to apply measures such as chemoprevention and lifestyle changes. Current models can predict probable numbers of breast cancer cases in specific risk factor strata, but have modest discriminatory accuracy at the individual level [ 31 ]. The publication of more than 70 common genetic susceptibility factors via large-scale collaborative efforts [ 10 , 32 ] and the realisation that mammographic density is a major risk factor is important, but the major gap in our knowledge is how to incorporate these factors into our current risk prediction models [ 33 ].

Automated methods for estimation of mammographic density require further evaluation for its potential use as a biomarker for risk stratification in screening and changes in density as a biomarker of responsiveness to preventive approaches. Studies of chest irradiation for lymphomas and carcinogens in rodent models suggest the importance of exposure to radiation during puberty [ 34 , 35 ].

There is a need to assess the value of several new approaches to discovering biomarkers including adductomics, transcriptomics, metabolomics [ 36 ] and epigenomics and to determine how well-established measurements (for example oestrogen levels) can be incorporated into risk models [ 37 ].

Chemoprevention

An overview of all trials of selective oestrogen receptor modulators (SERMs) as chemopreventive agents indicates that risk is reduced by 38% for up to 10 years from the start of five years’ treatment [ 38 ]. An issue is predicting those women who will benefit from SERM treatment. Lasofoxifene appears to be the most active SERM and its further development is desirable [ 39 ]. In postmenopausal women, the MA P3 trial indicated that exemestane reduced risk by 65% after 35 months median follow-up [ 40 ] requiring confirmation with additional aromatase inhibitor (AI) prevention studies. The value of low-dose tamoxifen and fenretinide also needs to be established [ 41 ]. Since SERMs and AIs reduce only oestrogen receptor positive (ER+ve) disease, there is a need for agents to prevent ER negative (ER-ve) disease, to distinguish between ER- and progesterone receptor (PR)-related disease [ 42 ] and to develop better animal models [ 43 ]. There is a need to confirm that oestrogen-only hormone replacement therapy (HRT) reduces risk whereas combined HRT increases risk in the Women’s Health Initiative (WHI) trials and to establish the mechanism of this dichotomy [ 44 , 45 ].

Lifestyle changes

Most studies related to breast cancer risk and lifestyles are observational. Favourable changes in lifestyle including reduction of calorie excess, increasing exercise, reducing alcohol intake and less environmental exposures to disturbance of circadian rhythm could reduce breast cancer by one third [ 46 – 49 ]. Communicating the potential benefits of lifestyle change, identifying teachable moments and using health services to endorse lifestyle change for prevention will require additional studies to determine why health beliefs translate poorly into action [ 50 ].

Marked adult weight gain in premenopausal women is associated with a doubling of risk of postmenopausal breast cancer compared with no or little weight gain [ 51 ]. Conversely, weight loss of 3kg or more is associated with a 25 to 40% reduction of cancer in older women compared with those who continue to gain weight. [ 52 – 54 ]. It is not clear whether to focus on all overweight women, those with gynoid or abdominal obesity or those with metabolic syndrome. Weight gain after surgery for breast cancer increases risk of relapse [ 55 ]; there is a need for further randomised trials to determine whether reducing weight in the overweight, or preventing weight gain after surgery prevents relapse. Weight management strategies seeking efficacy in the long term may be particularly difficult to sustain.

The effect of individual components of diet is controversial. The risk of ER-ve tumours may be reduced by high vegetable intake [ 56 ] while lowering fat intake may reduce both breast cancer risk and relapse after surgery. However, two of the three randomised trials of lower fat intake are confounded by concomitant weight loss [ 57 , 58 ] and the one study without weight loss showed no effect of reduction of fat intake on breast cancer relapse after surgery [ 59 ].

There is evidence for breast cancer prevention with habitual exercise [ 60 ]. Observational evidence shows that a physically active lifestyle after cancer treatment prevents relapse and reduces the risk of all-cause mortality [ 61 ]. The optimal exercise regime and timing are uncertain and randomised trials are required to assess the preventive benefits. There is a need to understand the mechanism of the apparent beneficial effects of caloric restriction and exercise.

Effective and sustainable lifestyle changes (diet, exercise and weight) need to be agreed and effective routes to initiation and maintenance identified. Further work needs to be undertaken in chemoprevention strategies and adherence to effective agents.

Prospective cohort studies are needed to develop and validate risk models, which may need to incorporate polygenic risks, mammographic density and measures of body composition. Risks may be refined by the discovery and validation of novel biomarkers such as epigenetic markers [ 19 ] and prospective validation of known markers such as serum oestrogen [ 62 , 63 ]. Effectiveness and cost-effectiveness, analyses to evaluate possible personalised screening and prevention programmes [ 64 ] and pilot studies to evaluate delivery options followed by large randomised trials are required. Polygenic and other biomarkers should be used to distinguish between the development of ER +ve, ER+ve/PR +ve and ER–ve cancers.

Many breast cancers arise in women without apparent risk factors; current studies suggest that polygenic risk factors and mammographic density add only a little to the Gail model [ 65 ]. Precision is required using polygenic approaches to decide whether or not to give preventive tamoxifen. Currently, about 10% of breast cancers arise in women with a 10-year risk above 5%. Taking this at-risk group and increasing the frequency of screening would be of some benefit, but more effective risk-adapted screening will depend upon a better definition of risk.

Further improvement and cost-effectiveness of the NHS breast cancer screening programme could include tomography, ultrasound and automated methods for the measurement of volumetric mammographic density (using software programs such as Quantra or Volpara) and automatically using these for risk stratification to adapt screening interval to risk. Experimentally, there are now opportunities for determining whether high breast density alters the response of breast epithelial cells to DNA damage or oncogene activation. This may provide prognostic value if we can define novel biomarkers to distinguish which women with high mammographic density will develop cancer [ 66 , 67 ].

Uptake of tamoxifen and raloxifene is variable and optimal methods need to be developed to explain risk, the benefit/risk ratio of treatment and to identify women who will benefit. The benefit from tamoxifen may be determined by changes in mammographic density [ 68 ] but needs confirmation. Identification of women who could develop ER-ve tumours should become possible (for example by polygenic scores). Work is required to corroborate the efficacy of lasofoxifene; the use of AIs in the preventive setting should be clarified by the International Breast Cancer Intervention Study II (IBIS II) trial, while the use of low-dose tamoxifen and retinoids also await trial results. Further studies are required to develop new preventive agents; those which might be pursued further include rexinoids, omega 3 fatty acids, sulphorophane, antiprogestins and insulin-like growth factor 1 (IGF1) inhibitors [ 409 ].

The widespread introduction of preventive agents depends upon efficient methods for identifying risk and effective counselling. Neither has been widely taken up, particularly in postmenopausal women, but the recently published NICE guidelines may signal a change for the use of tamoxifen in chemoprevention. Identification within screening programmes may be a valid approach [ 64 ]. However, since trials of chemoprevention require long duration and are costly, the development of biomarkers as indicators of effectiveness and their acceptance by regulatory agencies is attractive.

Lifestyle change for breast cancer prevention

A precise definition of interventions for diet and exercise and the relative importance for reduction of ER+ve or ER-ve breast cancer is unclear. The effect of caloric restriction by age and the duration of interventions remain unknown as do the underlying mechanisms of action. Identifying successful methods to translate prevention evidence into public health policy including effective behaviour change programmes and convincing clinicians to change practice in favour of prevention are required. Most evidence for lifestyle change is observational and confirmatory data from prospective randomised controlled trials (RCTs) with long-term follow-up and clinical endpoints may be needed. A breast cancer prevention trial using exercise would require a sample size of 25,000 to 35,000 and an eight to ten-year follow-up to observe a 20 to 25% decrease in risk for a moderate-to-vigorous physical activity programme. Such a large-scale study is not currently possible so the focus has been on a RCT of exercise in breast cancer patients to determine how exercise influences survival. The AMBER cohort study in 1,500 breast cancer patients measures physical activity, fitness and other indicators to determine exactly how physical activity influences survival [ 69 ].

Nevertheless, the beneficial effects demonstrated in randomised trials to prevent diabetes and cardiovascular disease need to be balanced against the enormous size and cost that would be required for such trials in breast cancer. For secondary prevention of disease recurrence after surgery, trials are due to report on caloric restriction and exercise in 2014 and 2018 [ 70 , 71 ].

There are teachable moments within the breast screening programmes for links to prevention through changes in lifestyle [ 50 , 64 ]. Reduction in alcohol consumption using community/class/cultural approaches, analogous to those for smoking, needs to be explored using social marketing approaches within a research context. It is likely that energy restriction and exercise will not be a complete answer to prevention and efforts should be made to design lifestyle prevention trials with and without energy restriction mimetic agents such as mTOR inhibitors, resveratrol, and metformin. mTOR inhibitors such as everolimus (RAD001) are effective in advanced breast cancer [ 72 ] although toxicities will prevent its use as a preventive agent; rapamycin in animal models reduces tumour incidence and increases longevity [ 73 ]. There is a need to translate these important findings into the clinic, perhaps by low dose or intermittent regimens to avoid toxicity [ 74 ]. Metformin is in clinical trial as an adjuvant for breast cancer treatment and demonstration of effectiveness in this situation could lead to assessment for prevention including in prediabetic populations [ 75 ].

Molecular pathology

Breast cancer classification and issues of heterogeneity.

During the last five years several high-profile studies have significantly advanced the molecular subclassification of breast cancer (reviewed in [ 76 ] and [ 77 ]). Intratumoral heterogeneity in both pre-malignant and invasive breast cancer is well documented. It is likely that both genetic and epigenetic instability, combined with microenvironmental and therapy-induced selective pressures lead to clonal evolution, which continues during metastatic progression. However, whether heterogeneity arises from cancer stem cell plasticity and a hierarchy of aberrant differentiation or stochastic events is a moot point (Figure 3 ). Genomic studies have been used to develop both prognostic biomarkers and to identify biomarkers to predict response to therapy. Nevertheless, ‘driver’ genetic changes in breast cancer will need to be filtered from the background, clinically inconsequential changes [ 78 ].

Tumour heterogeneity. (A) Recent molecular and genetic profiling has demonstrated significant intratumoural heterogeneity that can arise through genomic instability (leading to mutations), epigenetic events and/or microenvironmental influences. The stem cell hypothesis proposes that tumour-initiating cells are pluripotent and can thus give rise to progeny of multiple phenotypes; alternatively heterogeneity could be due to stochastic events. Temporal heterogeneity can be exacerbated by therapy (theoretically due to clonal evolution as some clones are eliminated whilst others expand). The significant molecular/genetic differences between cells in different areas within individual cancers, between primary and metastatic tumours (and potentially between cancer cells that successfully colonise different organs) have implications for the reliability of primary tumour biopsies for diagnosis, seeking biomarkers for treatment planning and responses to therapy. In addition, there is substantial inter-tumour heterogeneity. (B) shows images of two patients who presented with breast cancers of identical histological type and biochemical parameters. Four years later, one patient is clear of disease, while the other has evidence of multiple distant metastases, illustrative of between-patient heterogeneity in terms of response to therapy (clinical images kindly provided by Professor William Gallagher, with thanks to Dr Rut Klinger and Dr Donal Brennan (UCD Conway Institute).

Exploring the diversity and inter-tumour heterogeneity of breast cancer has led to the development of a novel classification that integrates genomic and transcriptomic information to classify 10 subtypes with distinct clinical outcomes [ 79 ]. Triple-negative breast cancer (TNBC) in particular is now recognised to demonstrate heterogeneity at the molecular, pathological and clinical levels. [ 80 ]. Such analyses, together with advanced next-generation sequencing have significant implications for improved understanding of basic tumour biology and will potentially enable the identification of new molecular targets for personalised treatment plans [ 81 , 82 ] Additionally, identification of non-coding RNAs is showing potential in diagnosis, prognosis and therapy [ 83 ].

Microenvironmental influences and tumour - host interactions

Breast development is critically reliant upon cell polarity [ 84 ], choreographed cell death pathways and interactions between epithelial cells and stroma; all processes which when deregulated are implicated in oncogenesis and tumour progression [ 85 – 87 ]. The tumour microenvironment, comprising a community of both malignant and non-malignant cells, significantly influences breast cancer cell behaviour [ 88 , 89 ]. Recently, progress has been made in understanding the bidirectional interplay between tumours and surrounding stromal cells/extracellular matrix (ECM), which can potentiate resistance to targeted therapies including endocrine therapy [ 90 , 91 ]. Consequently, components of the tumour microenvironment may represent targets for therapeutic intervention alongside the tumour to improve response to treatment [ 92 ].

Hypoxia reflects dynamic microenvironmental conditions in solid tumours, limits responses to radiotherapy [ 93 ] and some chemotherapeutic and anti-endocrine agents [ 94 , 95 ], drives genomic instability and is generally associated with progression to invasive/metastatic disease [ 96 , 97 ]. Tumour-stromal interactions change under hypoxic conditions to promote tumour progression via the activity of enzymes such as LOX [ 98 ], angiogenic factors and infiltrating macrophages [ 99 , 100 ]. A stem-like breast cancer cell subpopulation with an epithelial-mesenchymal transition (EMT) phenotype is expanded during repetitive hypoxia/reoxygenation cycles [ 101 ]. Hypoxia also contributes to cancer stem cell plasticity and niche formation [ 102 ] potentially explaining the relationship between hypoxia and chemotherapy resistance [ 103 ]. Finally, at the physiological level, host metabolic, inflammatory and immunological factors can impact on cancer development and progression, and these processes are further modified by the physical environments in which we live (Figure  4 ).

Microenvironmental influences on breast cancer. Breast cancer biology, progression and response to therapy is influenced at many levels from epigenetic effects on gene expression (for example methylation) through soluble and cell-mediated stromal interactions, intratumoural inflammatory and angiogenic components, hypoxia, host endocrinological and immunological status through to exposure to multiple agents in the environment in which we live.

What are the key gaps in our knowledge and how might these be filled?

Normal breast development and the origins of cancer.

It is not known how many breast epithelial cell subpopulations function as stem cells (capable of self-renewal) or progenitor cells (which proliferate expansively) [ 104 – 106 ]. Clearer understanding of cell lineages, changes in transcription factor expression during breast development and definition of the nature of stem and progenitor cells is fundamental to delineating relationships between normal and malignant cells.

Current cancer stem cell (CSC) assays have limitations: dormant cells cannot be detected and cell subpopulations that give rise to clones in vivo may not be active in ‘mammosphere’ cultures. There is no clear consensus on markers that define functional breast CSC in mouse and human. Indeed, they may not represent a fixed subpopulation, but instead exist in specific niches in flexible equilibrium with non-CSCs, with the balance depending on interactions between them as well as external selective pressures [ 107 – 109 ]. Understanding this plasticity [ 110 ] and its therapeutic implications are key areas for future investigation.

Breast cancer subtypes: genomics and bioinformatics

Several large-scale, cross-sectional, integrated molecular studies have established comprehensive molecular portraits of invasive primary breast cancers [ 111 – 114 ]. The International Cancer Genome Consortium (ICGC), The Cancer Genome Atlas (TCGA) and individual studies have released sequence data; however, gaining access to and interrogating this information requires expert bioinformatic collaborations. Relating these advances in genomic knowledge to improving clinical care has yet to be achieved. Knowledge of genetic, epigenetic and host factors underpinning distinct subtypes of breast cancer (plus their associated aberrant signalling pathways) and predictive biomarkers will be essential in targeting new therapeutic agents to the right patients.

For ductal carcinoma in situ (DCIS), an increased understanding is required of molecular markers of prognosis, thus providing key information to avoid overtreatment. We need to know which DCIS lesions will recur if adequate surgery is performed with wide, clear margins. Biological markers of DCIS should aim at defining which lesions are likely to progress, in order to avoid radiotherapy or even surgery if the risk of invasive cancer is sufficiently remote [ 115 ]. Markers for response to radiotherapy or endocrine therapy and the need for these therapies (particularly in low-risk patients) remain unclear.

Tumour microenvironment and stromal influences

Paget’s venerable ‘seed and soil’ analogy - recognising that tumour-initiating cells require a permissive host environment to thrive - is beginning to be deciphered at the molecular level. [ 42 ]. The composition and biophysical characteristics of the breast matrisome [ 116 ] and how it controls different stages of gland development and in early breast cancer requires definition. It is important to identify the transcription factors that define luminal and myoepithelial cells and to understand whether additional microenvironmental factors such as the ECM and fibroblast growth factor (FGF), Notch or Wnt signalling can switch their fate. Specialised niches defined by specific cell-cell/cell-matrix interactions in the microenvironment together with soluble, ECM-bound and microvesicle-associated host factors regulate CSC activation [ 117 ]. Further research on such CSC niches, their role in dormancy and the complex relationships between CSCs and metastasis is essential [ 118 – 120 ].

Stromal changes predict early progression of disease [ 121 ] and in-depth knowledge of how these conditions can be manipulated for therapeutic benefit is required [ 122 ]. Advances in the field of mechanotransduction are shedding light on the mechanisms by which altered matrix density or ‘stiffness’ can influence cell behaviour, and enzymes such as lysyl oxidases (LOX) are potential targets for therapy [ 123 ].

There is a need for better biomarkers of hypoxia including gene expression profiles [ 124 ] serum proteins, circulating tumour cells (CTCs) or functional imaging that could be used non-invasively in patients to enable more rigorous testing of its prognostic/predictive value. Although hypoxia-targeted therapies have proven disappointing to date, new approaches are emerging. In common with other targeted therapies for systemic disease, methods for measuring efficacy will need to be redesigned [ 124 – 126 ].

Tumours have an increased dependence on aerobic glycolysis. We need to understand how hypoxia affects the tumour metabolome and thus may determine therapeutic responses [ 96 ]. The dependence of metabolically adapted breast cancer cells on altered biochemical pathways presents new therapeutic targets linked to aerobic glycolysis, acidosis and the hypoxic response [ 127 , 128 ]. Since these pathways also interact with classical survival and proliferation signalling pathways via PKB/mTOR, there are opportunities to develop new combinatorial therapeutic strategies.

Breast cancer development and progression

Mammary stem cells.

There is increased understanding of stem cell hierarchies and their potential roles in breast development [ 129 – 131 ], but debate continues on the relationship between normal stem and progenitor cells, their dysregulation in cancer and the nature of putative CSCs [ 132 – 135 ]. Most data suggest that breast CSCs are a defined population with basal-like or mesenchymal-like features [ 136 – 138 ]. There is emerging data from cell line models that the CSC state is dynamic and can be induced by the tumour microenvironment [ 110 ], and this requires further investigation in human cancers. It is not known whether there are differences in CSC phenotype between breast cancer subtypes such as luminal vs. TNBC [ 139 , 140 ]. An emerging consensus is that CSCs initiate metastases and tumour regrowth after therapy, but do not necessarily generate the majority cell population in primary tumours.

Circulating tumour cells

Blood-borne tumour cells are routinely identified in breast cancer patients but their scoring can depend upon the method used [ 141 ]. Their relationship to disseminated tumour cells (DTCs) in tissues is unclear, although a recent publication showed that the presence of CD44+CD24 -/lo cells (putative CSCs) in the bone marrow is an independent adverse prognostic indicator in patients with early stage breast cancer [ 142 ]. A population of CTCs from patients with primary luminal cancer (expressing EPCAM, CD44, CD47 and MET) generated multi-site metastases when injected into mice. Hence it is likely that a subset of CTCs have metastatic potential [ 143 ], which may equate to CSCs. CTCs may occur in heterogeneous emboli of multiple cell types; perhaps those containing stem-like cells and/or ‘feeder’ cells are more likely to survive and grow at distant sites.

This key hallmark of breast cancer occurs when cancer cells access lymphatic and vascular systems, enabling dissemination via lymph nodes and then via the venous and arterial vascular system to distant organs. Once the disease has spread, it becomes life-threatening and patients require systemic treatment. Metastatic relapse typically occurs many months to decades after surgery, thus we need a greater understanding of the processes that occur following tumour cell dissemination, including the phenomenon of dormancy. Recent mathematical modelling using relapse data has provided interesting insights and proposals for hypothesis testing [ 144 ]. CTCs and DTCs that generate metastases are, by definition, tumour-initiating cells; hence their study needs to relate to CSC research [ 145 , 146 ]. Since the last gap analysis, there has been a paradigm shift in this area with the discovery of ‘pre-metastatic niches’ (analogous to stem cell niches) in organs destined to develop metastases [ 147 , 148 ].

In addition, seminal research using animal models has identified tumour and host genes associated with metastatic capacity (quite distinct from tumorigenic potential), and also organotropism [ 149 – 151 ]. The relevance of these experimental observations to human breast cancer and the translation of these findings into clinical studies require confirmation but may provide additional predictive value [ 152 ].

Reversible EMT, regulated by many factors including transforming growth factor beta (TGFβ) signalling, Slug and Snail transcription factors and hypoxia may be linked to invasion, dissemination and drug resistance [ 153 – 156 ]. The role of EMT in human cancer metastasis is still controversial and the underlying molecular mechanisms are not fully understood [ 157 ]. However, mesenchymal/stromal gene signatures have been identified which relate to TNBC subtypes, bone metastasis and resistance to neoadjuvant therapies [ 158 ].

Circulating tumour cells and nucleic acids

It is unclear whether CTCs originate from primary tumours, micro-metastases or multiple primary and secondary sites. Indeed, CTCs from distant metastases can potentially reseed the primary tumour [ 159 , 160 ]. More research is needed to define the origins of these cells. Importantly, analysis of CTCs needs to be carried out as far as possible in the clinical context, where their biology can be correlated with patient outcomes. CTCs and ctDNA are particularly useful where accessible breast cancer material is not available, or to obtain serial samples during therapy, providing a window on response and relapse.

To enable further progress, systems and protocols for isolating and characterising CTCs need to be rigorously defined and standardised, with an analysis of whether all systems identify/isolate the same cells (or indeed all CTCs, since EMT may preclude identification using epithelial markers [ 141 , 161 – 163 ]). We need to know the proportion of live, quiescent and apoptotic CTCs, their characteristics and malignant potential and to understand their relationship to the primary tumour and whether different subsets of CTCs have different predictive value.

The use of ctDNA is increasing as a potentially useful further source of information on breast cancer biology and response to therapy [ 164 – 166 ]. miRNAs identified in the systemic circulation (free or exosome-associated) [ 167 ] may also serve as diagnostic or prognostic biomarkers and/or as therapeutic targets. Indeed, it has been suggested that exosomes themselves, with their emerging roles in bidirectional signalling, immune suppression, subversion of targeted therapy and potentiation of metastasis [ 168 ] could be removed (for example by plasmapheresis) for therapeutic benefit [ 169 ].

Metastatic disease

Metastasis is the major cause of treatment failure, but it is far from clear why some patients with apparently similar disease succumb and not others [ 170 ]. We need to identify key signalling pathways linked to organotropism [ 171 ] and to develop new therapies for micro-and macro-metastatic disease [ 172 ]. Given the multiple breast cancer subtypes (and associated oncogenic drivers), it will be important to try to align genotypes/epigenotypes to metastatic patterns, in order to predict likely sites of relapse. Treatment decisions are generally based on the profile of the primary cancer, but information about the evolution of the disease from CTC, DTC or (where possible) metastases at different sites is essential, since both gains and losses of potential therapeutic targets have been observed in these distinct tumour cell populations.

We need to understand how the host microenvironment at secondary sites influences tumour cell survival and to define similarities and differences between ‘permissive’ microenvironments in organs favoured by breast cancer cells such brain, bone or liver. We have learned a good deal since the last gap analysis about the ‘vicious cycle’ of bone metastasis, whereby tumour cell interactions within this unique microenvironment mutually promote metastatic outgrowth and bone remodelling via hormonal, immunological and inflammatory mediators. These findings need to be translated into new therapies targeting both tumour and host components [ 173 ] with the paradigm extended to other specialised sites such as brain [ 174 ].

Current therapies

Clinical therapies.

Current clinical therapies for breast cancer are offered on an individual patient basis via a multidisciplinary team and comprise surgery, radiotherapy and drug therapies targeting oncogenic processes. Selection of therapy is based on Level 1 evidence from large RCTs or meta-analyses of such RCTs [ 175 – 177 ]. Increasingly, correlative translational studies are integrated prospectively into clinical trials, aiming to define the optimal target population and provide insight into mechanisms of resistance. The individualisation of treatment, optimal duration of treatments, prediction of metastasis or drug resistance remain challenging and reflect incomplete understanding of the underlying biology of breast cancer. However, up-to-date guidelines are useful to determine the best therapy for individual patients [ 178 ].

Immunohistochemical (IHC) analyses for selecting therapeutic options generally lack reproducibility and standardization resulting in poor concordance between laboratories. The Quality Assurance programme for ER, PR and human epidermal growth factor receptor 2 (HER2) in the UK has to some extent addressed this, but for other biomarkers, including Ki67, there clearly remain problems. We need to develop standardised protocols for better quantification of biomarkers [ 179 ], especially optimised methods of sample collection/storage to ensure that unstable or transient biomarkers (such as phosphoproteins or histone marks) are retained. This is especially important for predictive markers such as HER2, together with those which report on the efficacy of HER2-directed therapies and other emerging targets.

Health inequalities remain in relation to treatment. Older people diagnosed with cancer are more likely to experience undertreatment, potentially having poorer clinical outcomes than younger women for example [ 180 , 181 ]. Indeed, there is a lack of data to inform decision making about treatment for the elderly patient with breast cancer in part attributable to their under-representation in trials, but clinical teams may make inadvertent ageist decisions [ 182 , 183 ]. In addition, breast cancer and its treatment can have a considerable impact on women and their families [ 184 ]. Psychological distress is common, although not inevitable, and is associated with poorer quality of life [ 185 , 186 ]. Regular distress screening is recommended as a core component of good quality cancer care [ 187 , 188 ] in order to provide appropriate support.

Surgery remains the primary treatment for most women, with breast conservation (plus whole breast radiotherapy) providing similar outcomes to mastectomy. Following mastectomy, breast reconstruction should be considered, although uptake is incomplete. Axillary surgery has moved from clearance via node sampling techniques to sentinel node biopsy as the preferred means for assessment of axillary metastasis in early breast cancer. Neoadjuvant therapy, initially implemented to down-stage inoperable cancers, is increasingly used to assess drug efficacy in individuals and to reduce the extent of surgery required in good responders [ 189 ].

Radiotherapy

Radiotherapy is both clinically effective and cost-effective in the adjuvant and palliative settings. The Oxford overview of adjuvant radiotherapy trials [ 177 ] showed a halving of risk of first recurrence in all risk groups and favourable effects of local control on long-term survival. There is long-term confirmation of the value of boost irradiation to the site of excision after breast-conserving surgery in all subgroups, including women >60 years [ 190 ]. The long-term safety and efficacy of hypo-fractionated radiotherapy after breast-conserving surgery and mastectomy for operable breast cancer has recently been confirmed: (10-year results of Canadian [ 191 ] and Standardisation of Breast Radiotherapy (START) trials also suggesting generalisability to all subgroups of patients [ 192 , 193 ].

Trials of partial breast irradiation evaluating intraoperative radiotherapy in comparison to external beam radiotherapy [ 194 , 195 ] or brachytherapy [ 196 ] have short follow-up, but guidelines on partial breast irradiation [ 197 , 198 ] have encouraged off-study use of partial breast irradiation in advance of clinical trial results. Omission of postoperative radiotherapy after breast-conserving surgery in older, lower-risk women suggests the differential in local recurrence rates may be acceptable with a cumulative in breast recurrence of 2.5% in breast conservation surgery alone vs. 0.7% for surgery and postoperative radiotherapy (median follow-up 53 months age 55 to 75 years [ 199 ]) and at 10 years local recurrence, nine for conservation alone vs. 2% for surgery and radiotherapy in the =/>70 years, ER+ve group [ 200 ].

Decision making

Clinical decision-making tools to support individualised treatment can influence patients’ treatment choices and experiences [ 201 ] and communication training for oncology professionals is now widely available throughout the UK to improve the delivery of information and support to patients [ 202 ]. A recent national survey of over 40,000 patients with a broad range of cancers identified the fact that younger patients and ethnic minorities in particular reported substantially less positive experiences of involvement in decision making [ 203 ].

Overtreatment

A significant number of patients are overtreated to achieve the improved survival overall in early breast cancer, since we cannot define individual risks of disease recurrence or sensitivity to treatment. For survivors, the long-term side effects of treatment may be significant; individualised treatment so that patients only receive the treatment they require to achieve cure remains elusive. This is relevant to surgery, radiotherapy, chemotherapy and endocrine therapy.

With the widespread adoption of sentinel node biopsy (SNB)-limiting surgery to the axilla has substantially reduced arm morbidity [ 204 ]. A detailed understanding of underlying tumour biology is required to support decisions around surgical management, (for example axillary node clearance or not after positive sentinel nodes). No further axillary surgery even for one to two positive nodes [ 205 ] and the equivalence of axillary clearance to axillary radiotherapy for local disease recurrence (despite the differing morbidities) in the presence of a low disease burden [ 206 ] demonstrate further progress in this surgical setting. However, the optimal design of radiation treatment fields for SNB-positive patients is not known.

For postoperative radiotherapy after breast-conserving therapy, we do not have reliable ways of identifying low risk, particularly in elderly patients for whom radiotherapy might be omitted. While even low-risk patients have an approximately 50% reduction in first recurrence [ 177 ], the absolute gain for low-risk breast cancer patients (older age, small, ER+ve cancers) after breast-conserving surgery is very modest. We need reliable molecular markers of identifying such low-risk groups or individuals.

Further work is required to clarify whether the response to neoadjuvant chemotherapy can be used to guide the selection of patients for regional nodal irradiation [ 207 ] or whether patients who are clinically node positive before neoadjuvant chemotherapy and are converted to node negative after neoadjuvant chemotherapy on SNB require axillary nodal irradiation.

Individualisation of treatment

Understanding the optimal treatment strategies for an individual patient remains elusive. A number of genomic (for example Mammaprint, Oncotype Dx, PAM50) and immunohistochemical (for example IHC 4) tests have been developed to predict prognosis and latterly, response to chemotherapy; however, prospective trial evidence is still awaited [ 208 ]. Recently, serum metabolite profiling using a combination of nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS) correctly identified 80% of breast cancer patients whose tumours failed to respond adequately to chemotherapy, showing promise for more personalized treatment protocols [ 209 ].

Increased understanding of the dynamic changes that occur over time is critical and will require repeated assessment of tumour profiles. Genomic tests predict response to endocrine or chemotherapy and those at highest risk of relapse [ 210 – 212 ], but prospective trials are required to determine whether axillary clearance or chemotherapy can be avoided in node-positive patients. Similarly, biological markers of radiosensitivity (tumour and normal tissue) require better characterisation and implementation into clinical strategies to allow personalisation of treatment and avoidance of late radiation-induced toxicity [ 213 ].

CNS metastatic disease

As a result of improved outcome for patients with metastatic breast cancer (MBC), central nervous system (CNS) metastatic disease is an increasing therapeutic challenge [ 214 ]. Optimal treatment strategies have yet to be defined including sequencing or combination of stereotactic and whole brain radiotherapy, systemic treatments, intrathecal treatment approaches for leptomeningeal disease and prophylactic interventions.

Bone metastatic disease

Bisphosphonates reduce the risk of developing breast cancer in osteoporotic and osteopenic women by approximately 30% and the risk of recurrence in early breast cancer when used at the time of diagnosis [ 215 , 216 ].The interaction between the internal endocrine environment and the effect of bisphosphonates is complex and poorly understood. While negative results overall were reported in the large UK AZURE trial [ 217 ] women more than five years postmenopausal benefitted, consistent with data from the NSABP-34 trial [ 218 ]. In premenopausal women, bisphosphonates can abrogate the bone loss associated with use of an AI. In addition, recurrence and death rates were reduced when used in combination with either tamoxifen or an AI after treatment with the LHRH agonist goserelin (ABCSG12: [ 219 ]. Taken together, these studies suggest that a bisphosphonate may have its greatest effect in a low-oestrogen environment.

The impact of bone-targeted therapy on extra-skeletal metastases and locoregional relapse also highlights the need to better understand experimental observations concerning reseeding of tumours from dormant cells within the bone microenvironment [ 220 ]. Additionally, the role of RANK-RANKL signalling in mammary stem cell biology allows for the possibility that targeting this pathway with agents such as denosumab may offer a prevention strategy for bone metastasis [ 221 , 222 ].

Oligometastatic disease

The role of localised treatment of oligometastatic disease for example in the form of selective stereotactic body radiotherapy, radiofrequency ablation or surgery is currently unclear. The impact of irradiating the primary tumour, biological communications between treated primary site and distant metastases and whether radiation therapy can convert the primary tumour into an in situ vaccine [ 223 ] are relatively unexplored. Prospective randomised trials are required, which should ideally incorporate comprehensive molecular studies to define subtypes most likely to respond; a related question is how to treat primary breast cancer in patients presenting with metastatic disease.

The molecular basis of chemo-radiosensitivity, biomarkers (including specific gene signatures, proteomic markers) of tumour and/or normal tissue sensitivity is required to allow selection of patients who may benefit from adjuvant radiotherapy and avoid toxicity to those who will not. Explanations for the mechanism(s) of favourable impacts of locoregional control from radiotherapy (RT) on survival are needed [ 224 ] and may include in vivo real time biosensors of tumour biology to capture transient changes in the tumour microenvironment that drive metastasis.

Hypofractionated adjuvant radiotherapy

Even shorter-dose fractionation schedules (that is one week of whole breast radiotherapy) might achieve equivalent locoregional control with comparable toxicity [ 225 , 226 ]. Partial breast irradiation appears promising, but the long-term safety and efficacy is still uncertain [ 197 , 198 ]. In addition, it appears likely that there is a subgroup of low-risk, older patients from whom postoperative radiotherapy can be safely omitted [ 227 , 228 ]. The role of postmastectomy radiotherapy in intermediate risk breast cancer [ 229 ], axillary irradiation in sentinel node positive macro- or micro-metastases [ 230 ] or boost dose in DCIS following breast-conserving surgery [ 231 ] are all currently unclear. Further definition of the role of stereotactic body radiotherapy, accounting for tumour motion [ 232 ], in combination with neoadjuvant systemic therapy, to liver or bone metastases for oligometastatic disease are required. Similarly, the optimal dose fractionation for locally advanced disease needs to be established [ 233 ].

Molecularly targeted therapies

Anti-endocrine agents.

Multiple lines of clinical and translational evidence have increased our knowledge of the risk of recurrence, particularly for ER+ve disease [ 212 , 234 – 236 ]. The optimal duration of treatment remains incompletely defined but several RCTs have provided important new data: eight to ten years of adjuvant treatment for ER+ve breast cancers is more effective than five years of letrozole or tamoxifen [ 237 – 239 ].

Endocrine therapy resistance

Comprehensive guidelines to define endocrine resistance have now been agreed [ 240 ]. Clinical studies of various agents alone and in combination with signalling inhibitors have been completed since the last gap analysis. [ 241 – 243 ]. The biology of ERs, including the importance of phosphorylation [ 244 ], ER co-regulators [ 245 ], cross-talk with kinases [ 246 ] and altered ER-binding events [ 247 ] nevertheless requires further elucidation. MicroRNAs regulate ER activity and endocrine responses, [ 248 ], while epigenetic events promote ER loss or tumour suppressor silencing [ 249 ]. Cancer stem cells may also be implicated in endocrine resistance [ 250 ].

The multiple cell-signalling changes driving resistance and associated disease progression, nevertheless reveal potential cancer cell vulnerabilities [ 251 ] for example mTOR [ 72 ], EGFR/HER2 [ 252 ] and Src kinase [ 253 ]. New methodologies such as large-scale siRNA screens have also provided novel therapeutic targets such as CDK10 and fibroblast growth factor receptor 1(FGFR1) [ 254 , 255 ].

Oncogenic signalling inhibitors

Several molecularly targeted therapies have been licensed since the last gap analysis including lapatinib and pertuzumab in HER2+ cancers [ 31 ] and the mTOR inhibitor everolimus in ER+ve disease [ 72 , 256 ], which can overcome endocrine resistance [ 257 ]. Agents targeting signal transduction pathways (notably HER2) have had a significant impact in the treatment of certain breast cancer subtypes [ 258 ]. However, there is still limited understanding of the oncogenic pathways that control the progression of premalignant breast diseases or rare, but often aggressive, breast cancers (for example metaplastic breast cancer) [ 259 ]. Molecules may have distinct functions in different cellular contexts, therefore rigorous target validation is critical [ 260 , 261 ]; if a signalling protein has a scaffold function, disruption of protein-protein interactions may be required for efficacy. This requires a detailed biophysical analysis of protein structures and their key interactions.

For HER-2 positive disease, dual HER-receptor blockade is more effective than monotherapy and may help prevent or overcome resistance [ 262 , 263 ]. Two years of adjuvant trastuzumab offers no benefit over one year [ 264 ] but the utility of shorter trastuzumab therapy is, as yet, unconfirmed [ 265 ]. In metastatic breast cancer, serum metabolomic analyses may help to select patients with HER2+ cancers with greater sensitivity to paclitaxel plus lapatinib [ 266 ]. Multiple clinical trials are evaluating PI3K pathway inhibitors; other new agents under development include HSP90 inhibitors (for example NVP-AUY922 and ganetespib); panHER, irreversible inhibitors including neratinib and afatinib; monoclonal antibodies directed against human epidermal growth factor receptor 3 (HER3) and Src inhibitors such as saracatinib.

Resistance to signalling inhibitors

Resistance to targeted signal transduction agents is common, arising via multiple mechanisms including utilisation of compensatory feedback loops or alternative signalling pathways. Systems biology applications have begun to describe these dynamic changes [ 267 , 268 ], and are critical to identify key target points for effective therapeutic intervention.

Robust guidelines (akin to REMARK) are not yet employed in studies assessing the efficacy of novel therapeutics. Such rigour is essential to ensure that both appropriate models and quantitative outputs are fully utilised. The best drug combinatorial approaches could then be developed based on mechanistic insight into opportunities afforded by synthetic lethality [ 269 , 270 ]. More sophisticated experimental models of DNA-damage response (DDR) defects and those that accurately reflect mechanisms of therapy resistance will enable the design of targeted therapies to overcome these clinically relevant issues.

Drug responses

We lack a comprehensive understanding of the exact mechanisms (both on- and off-target) by which drugs exert anti-cancer effects in vivo ; this is exacerbated by our incomplete appreciation of networks, cross-talk and redundancy in cell signalling. Given that multiple inhibitors of specific pathways are now available (for example PI3K/PKB/mTOR), harmonised approaches to prioritisation of specific inhibitors/inhibitor classes and of research objectives in clinical trials are required.

Clinical determinants of intrinsic and acquired resistance

There is incomplete understanding of the role of diverse gene expression, epigenetic, protein and non-coding RNA changes in the heterogeneous manifestations of clinical resistance, [ 271 ]. There is a lack of equivalence between clinical, pathological, proliferative and molecular resistance that needs to be addressed and single genes or a canonical pathway are unlikely to be responsible. Furthermore, multiple mechanisms have also been implicated in acquired resistance, but their relationship to intrinsic resistance remains to be defined. Figure  5 illustrates the heterogeneity in patterns of gene expression in clinical endocrine resistance, suggesting that at least three major molecular mechanisms could be involved [ 272 ].

Molecular heterogeneity of endocrine resistance. Unsupervised hierarchical clustering of mRNA from 60 endocrine-resistant breast cancers shows heterogeneity in gene expression suggesting a multiplicity of underlying mechanisms including changes in oestrogen and interferon signalling and stromal genes. Courtesy of Professor William Miller and Dr Alexey Larionov, based on a poster presentation at the thirty-second annual CTRC-AACR San Antonio Breast Cancer Symposium, Dec 10–13, 2009 [ 272 ].

There is a need to understand the clinical impact of additional hormone receptors besides ERα, especially the progesterone receptor (PR): whilst PR is prognostic, the TEAM study has not demonstrated a predictive value [ 273 ]. Similar considerations apply to ERβ [ 274 , 275 ] and the androgen receptor (AR) [ 276 ], since trials of anti-androgens are currently underway in metastatic breast cancer [ 277 ].

It is not clear whether there are differences in ER+ve premenopausal vs. postmenopausal endocrine resistance [ 278 ]. As with other targeted therapies, the microenvironment, therapy-induced signalling reprogramming and stem cells are likely to play key roles. Proteomic profiling and protein functionality are particularly poorly characterised in the clinical resistance setting and such measurements remain challenging but essential.

It is important to define the contribution of CSCs to relapse on endocrine therapy, determine their sensitivity to existing agents or identify the unique signalling pathways that sustain their clonogenic potential. Diagnostic or prognostic tests based on ‘whole’ tumour samples may fail to address these potentially significant minority subpopulations of cells.

The few prospective studies to date have demonstrated that changes in management for one in six patients could be advised based on changes in breast cancer biomarkers on relapse, particularly ER, PR and HER2 [ 279 – 281 ]. Consequently, important clinical questions such as whether changes in the frequency of drug administration or alternating drug therapy could avoid or contribute to this process need to be addressed. Considering host factors such as adherence to medication [ 282 ], drug metabolism [ 283 ] and immune mechanisms [ 284 ], alongside molecular characteristics of tumours and the host microenvironment is essential.

Combinations and sequencing of targeted agents with conventional agents

Despite high-level evidence for isolated treatment situations (for example adjuvant treatment with AIs) [ 210 , 285 , 286 ], these have not been integrated into sequential treatment strategies, for example for adjuvant or first- or second-line palliative treatment. As treatment standards change (with AIs as standard adjuvant therapy), the sequence of tamoxifen as adjuvant therapy with AIs for first-line metastatic ER+ve disease may require adaptation. Such trials apply standard treatments that manufacturers may have little interest in supporting; new ways of supporting these trials will need to be explored.

Models are needed for the longitudinal study of hypoxic ‘microniches’ to inform timing of delivery of sequential targeted therapies or chemotherapy with radiation; to test real-time robotically controlled RT delivery to motion-affected hypoxic regions of primary breast tumours; and RT in combination with novel agents targeting pH regulatory mechanisms. Similarly, novel early-phase clinical trials of preoperative RT + targeted therapy or neoadjuvant hormonal therapy with baseline on-treatment biopsies for markers and gene signatures of radiosensitivity (the window of opportunity design) could complement the development of trials of stereotactic body RT to primary + neoadjuvant systemic therapy for limited-volume metastases in liver and bone.

Practical considerations include the risk/benefit of combining signalling inhibitors with anti-hormones, sequencing of tamoxifen and AIs [ 287 ] and targeting additional steroidogenic enzymes [ 288 ]. Recent randomised clinical studies have demonstrated substantial benefits for combinations of targeted agents such as endocrine therapy and mTOR inhibitors in ER+ve MBC [ 72 ] or horizontal dual HER-receptor blockade [ 289 – 292 ]. This results in several new challenges. Many patients benefit from single agent endocrine therapy or HER2-blockade and could avoid, at least initially, the toxicity of combination therapy if these cancers could be identified. There is a clear need to identify patients who respond adequately to targeted therapy (for example anti-HER-2 agents +/− endocrine agents) and do not need chemotherapy. Rational combinations need to be explored in the appropriate setting, taking into consideration compensatory induction of alternative signal transduction pathways bypassing targeted treatments. Treatment benefits in MBC or the neoadjuvant setting need converting into a potential survival benefit in early breast cancer.

New therapeutic approaches

Although phenotypically similar to BRCA1 mutant breast cancers, TNBC are heterogeneous and lack of expression of ER, PR and HER2 is not a good predictor of homologous recombination repair (HRR) status [ 293 ] Prognostic and predictive biomarkers of response for TNBC are obvious gaps which need to be addressed [ 294 ], complemented by an expanded and representative panel of fully characterised tumour cell lines and models [ 295 ]. More emphasis should be directed at developing markers of drug resistance and markers of resistance to current basal-like breast cancer/TNBC therapies [ 296 ]. Better biomarker-led characterisation could assist in patient stratification and hopefully improved treatment responses. Similarly, additional targets are required for other molecular subtypes that fail to respond to existing therapies.

Lymphangiogenesis and angiogenesis

Current understanding the role of lymphangiogenesis in metastasis (and thus its potential as a therapeutic target akin to neoangiogenesis) is limited [ 297 ]. In contrast, given the morbidity associated with lymphoedema following extensive lymph node dissection, identifying a means of inducing local regeneration of lymphatic vessels postoperatively could be envisaged. The contribution of the lymphatic system to immune responses to tumours is also underexplored [ 298 ]. Better in vitro and in vivo models are required to understand the cellular and molecular complexities of pathological angiogenesis and lymphangiogenesis, tumour cell intravasation, extravasation, organ colonisation and strategies for effective therapeutic interventions [ 299 ].

Anti-angiogenic therapies have been extensively trialled but have not yet lived up to their promise, with bevacizumab no longer approved for breast cancer by the FDA [ 300 – 302 ]. Tumour vasculature is heterogeneous [ 303 ] and multiple, temporally dynamic mechanisms contribute to the lack of durable responses [ 304 ]. The main focus has been vascular endothelial growth factor (VEGF)-driven angiogenesis but there is considerable redundancy in angiogenic signalling pathways [ 305 ]. Also, there are no validated biomarkers of response to anti-angiogenic therapies and it is likely that the vasculature of anatomically dispersed metastases will demonstrate further functional heterogeneity.

Exploiting the immune system

Although generally considered to be immunosuppressive, some chemotherapeutic agents (and indeed monoclonal antibodies) may involve an immune element; thus the combination of immunotherapy and chemotherapy becomes a real possibility [ 306 , 307 ]. In node-positive, ER-/HER2- disease, lymphocytic infiltration was associated with good prognosis in the BIG 02–98 adjuvant phase III trial [ 284 ]. There needs to be a systematic quantification of immune infiltration of breast cancer subtypes and how this relates to tumour progression, response to therapy or changes during treatment.

Cancer immunotherapy is gaining ground, whether antibody-based or cell-based, with an increasing emphasis on targeting the tumour microenvironment (for example macrophages or cancer-associated fibroblast (CAFs)) with DNA vaccines [ 308 ]. In addition, several immunogenic antigens (such as cancer testis antigens) have been detected in poor-prognosis breast cancers, which may serve as targets for therapy or chemoprevention [ 309 , 310 ]. New strategies for enhancing natural immunity or eliminating suppressor functions are required. There is a need for better animal models for evaluating immunotherapeutic strategies and in deciphering possible contributions to lack of responsiveness.

Living with and managing breast cancer and its treatment

Survivorship.

Cancer and its treatment have a considerable and long-term impact on everyday life [ 311 – 313 ]. Consequences may be physical (for example pain, fatigue, lymphoedema, hot flushes, night sweats and sexual problems), or psychological (cognitive function, anxiety, depression, fear of recurrence) and directly affect relationships, social activities and work. The relationship between the cancer patient and his/her partner will have a bearing on the level of distress: if communication is good, psychological distress will be lower [ 314 ]. Women may feel abandoned once treatment is completed with low confidence as a result [ 312 , 315 ]. The current system does not meet their needs [ 184 ] and the National Cancer Survivorship Initiative has been established to investigate new models of aftercare.

A recent framework publication highlights the importance of providing support to enable people to self-manage their aftercare [ 315 ]. Patients benefit from improved sense of control and ability to effect change together with an increased likelihood of seeking health information [ 316 , 317 ].

Living with advanced breast cancer

Quality of life in women with metastatic breast cancer is poor [ 318 ] with many experiencing uncontrolled symptoms [ 319 ]. Pain is a significant problem throughout the illness, not just with the end of life [ 318 ]. Depression, anxiety and traumatic stress also require intervention [ 320 , 321 ]. Those with metastatic breast cancer receiving social support report more satisfaction and a sense of fulfilment. Fewer avoidance-coping strategies are associated with better social functioning and a larger social network. Social stress has been found to increase pain and mood disturbance and has been associated with isolation. In addition, self-image and a decrease in sexual functioning challenge self-esteem and relationships at a time when support is most needed [ 322 ].

The impact of medical management on quality of life and decision making regarding palliative chemotherapy [ 323 , 324 ] and a lack of rehabilitation services [ 325 , 326 ] has been recognised. The convergence of palliative treatments and the end of life may impact on symptom control and care provision as well as place of death [ 327 , 328 ].

Supportive interventions

The main physical symptoms associated with breast cancer treatment are fatigue, pain, hot flushes, night sweats, cognitive and sexual problems and lymphoedema. Some interventions have demonstrated benefit with specific side effects [ 329 – 331 ]. Meta-analysis demonstrates that psychological interventions can reduce distress and anxiety [ 332 ], provide some physiological benefit, but with weak evidence regarding survival benefit [ 333 ]. Overall the evidence focuses on short-term benefit while the longer-term implications are unknown.

Group interventions are less effective in reducing anxiety and depression than individualised interventions such as cognitive behaviour therapy (CBT); [ 334 ], but do result in social and emotional improvements [ 335 ] and greater patient satisfaction [ 336 ]. Psycho-educational interventions show improvements in physical and psychosocial wellbeing [ 337 ] and reduced anxiety [ 338 ].

CBT reduces fatigue [ 339 ], insomnia [ 340 ] improves physical activity and quality of life [ 341 ]. CBT appears to be effective at all stages of breast cancer: group CBT can significantly reduce the impact of menopausal symptoms in breast cancer patients [ 342 , 343 ] with effects maintained over six months. Care packages to help improve coping skills, including group counselling sessions and/or telephone-based prompts has shown supportive care in the extended and permanent phases of survival to be effective [ 344 ]. Mindfulness-based stress reduction and cognitive therapy can improve mood, endocrine-related quality of life, and wellbeing at least in the short term [ 345 ].

Much evidence demonstrates the benefits of physical activity for breast cancer patients [ 346 ]. RCTs show that physical activity interventions during treatment show small to moderate beneficial effects on cardiovascular fitness, muscular strength and can reduce deconditioning. Post treatment, physical activity interventions result in a reduction in body fat and increase in fat-free mass, a moderate to large effect on cardiovascular and muscular strength, small to moderate effect on quality of life, fatigue, anxiety and depression and some evidence of reduced lymphoedema and osteoporosis [ 347 , 348 ].

The translation of physical activity research into clinical practice is a challenge. Currently, exercise-based cancer rehabilitation is not routinely incorporated into breast cancer care. However, from the National Cancer Survivorship Initiative, Macmillan Cancer Support is evaluating around 12 physical activity programmes and evaluating physical, psychological and cost benefits. One exercise intervention during therapy reassessed participants after five years and showed that those from the exercise group were still incorporating approximately 2.5 hours more physical activity a week and were more positive than control patients [ 349 ]. Furthermore, other charities are starting up similar programmes, such as Breast Cancer Care’s ‘Best Foot Forward’. There are very few intervention studies involving women with advanced metastatic cancer; these predominantly focus on supportive-expressive therapy and have been found to reduce distress [ 350 ] but the benefits are not maintained in the long term [ 334 ].

Inadequate translation of research findings into practice

While the problems are well recognised, there is inadequate clinical translation: for example, recognising the benefits of physical activity requires incorporating and testing intervention(s) in clinical practice. There is also a lack of representation and sensitivity to the needs of diverse groups. Similarly, the impact of breast cancer goes beyond the patient; more attention should be paid to their families, partners and children.

CBT is becoming integrated into clinical practice with training for clinical nurse specialists but there is still a need to consider how CBT and other interventions can be better integrated to widen access. Novel interventions must be developed and validated using methods based upon sound theoretical principles, with demonstrable effectiveness (both clinical and financial) that can be deployed as widely as possible to maximise benefit. A clear understanding of the components of interventions that promote uptake, adherence and long-term benefit is required. Funding for research into living with and managing the consequences of breast cancer and its treatment is very limited, adversely impacting the building of research capacity and expertise.

Establishing a multidisciplinary research consortium to develop a theoretical framework to inform research addressing the needs of those living with and managing the broad ranging consequences of breast cancer and its treatment would inform choice of outcome measures, innovative approaches to intervention design and testing. Alternative trial designs to RCTs need to be considered that incorporate patient preferences. It would also be of great benefit to the field to draw up guidance on implementing successful evidence into clinical practice.

Longitudinal studies are required to assess the recovery of health and wellbeing and the long-term adjustment of women and men who have a diagnosis of breast cancer. This will allow investigation of how unmet psychosocial needs and psychological morbidity during diagnosis and treatment relate to quality of life, sexuality, physical wellbeing and the effects of other illnesses later in life. The long-term impacts of breast cancer and therapy on everyday life need further investigation [ 351 ]. There are implications for cardiac functioning, osteoporosis, neuropathy, cognitive dysfunction, lymphoedema and shoulder mobility on the ability to maintain independence [ 352 ].

There is insufficient epidemiological data on the problems of women who have recurrence and metastatic disease. Research into integrated oncology and palliative care models are needed to determine which approaches improve quality of life, psychological wellbeing, palliation of symptoms, treatment decisions and end of life care. The needs of the families of women with advanced metastatic cancer and how to support them and their carers most effectively are unclear. Decision making at the end of life and the development of tools to assist women and healthcare professionals to choose appropriate treatment and place of death is needed.

Specialist breast care nurses have also been found to enhance the supportive care of women with metastatic breast cancer. [ 353 ]. However, there is a need to identify the active components of interventions and an individual’s preference for different types of interventions to determine what works best for him or her.

Development of mindfulness and third-wave approaches (for example Acceptance and Commitment Therapy) may be effective. More RCTs of theory-based interventions for treatment-related symptoms and innovative trial designs are needed (with longer follow-up, analysis of moderators and mediators and identified components) to support women to manage their everyday lives. Interventions to address specific psychological needs such as low self-confidence and fear of recurrence also need to be tested. Interventions are required to support women to increase their physical activity, reduce the risk of recurrence and examine the impact on late effects. The frequency, intensity, type and timing of physical activity for maximum benefit needs to be established. Effective means are required to support women to manage impaired sexuality/sexual function, altered body image, lymphoedema, weight gain [ 354 ], fear of recurrence, hormone therapy-related symptoms [ 341 , 343 , 355 , 356 ], cognitive problems [ 357 ][ 358 ] and post-surgical problems [ 359 , 360 ]. Alternative delivery of intervention needs to be explored, such as self-management, telephone or online support and non-specialist delivery: for example comparison of home-based versus hospital-based interventions on physical activity levels, patient satisfaction and motivation.

Strategic approaches to enable progress

Experimental models of breast cancer, improved tissue culture models.

There is now a greater appreciation of the importance of employing appropriate human cancer cells. [ 361 ]. Commonly used breast cancer cell lines are derived from metastases or pleural effusions and fail to adequately represent the diversity and complexity of breast cancer [ 362 ]. It has proven difficult to establish human tumour cell cultures representative of the major subtypes and to maintain their genomic and phenotypic integrity. In addition, inter-patient variability and inadvertent selection of the most malignant subtypes, skews availability of representative material.

Better representation of breast cancer subtypes is required. Material from normal mammary tissue, premalignant breast conditions, different ER+ve (and rare) subtypes of breast cancers and ideally metastases from all major sites are needed to cover the full spectrum of breast cancer development and progression. Primary or minimally passaged cell cultures will avoid issues of misidentification, contamination or long-term culture artefacts. Ideally, a central repository of well-annotated human primary breast cancer cells, associated host cells and cell lines should be available to researchers linked to a searchable, open-access database. Maintaining breast tumour tissue in culture with its essential characteristics intact will enable prognostic screening and testing of potential therapeutic agents.

Reliable cell-type-specific markers are required and it is also important to be able to recognise cancer stem cell subpopulations (or transient phenotypes). Identification of promoters for distinct cell subpopulations will enhance the number and scope of available in vitro models. [ 363 ] and enable conditional genetic modifications for mechanistic and target validation studies [ 364 ]. Ideally, co-cultures (of both normal and precancerous breast cells) with host cell populations such as fibroblasts, myoepithelial cells, macrophages, adipocytes or vascular endothelial cells are needed for studies of cellular interactions within the appropriate ECM microenvironment.

Three-dimensional culture models can recapitulate the tissue architecture of the breast and its characteristic invasion patterns [ 89 , 365 ] especially if host stromal components are incorporated [ 366 ]. Three-dimensional heterotypic model systems are also enabling dissection of the effect of cell-cell interactions and stromal elements in drug resistance. Three-dimensional cultures require additional refinement, higher throughput, quantitative assays [ 367 ] and a move towards more physiologically relevant conditions, for example by the use of bioreactors, enabling long-term cultures under flow conditions; especially appropriate for invasion assays [ 368 , 369 ].

Animal tumour models

In the last five years there has been an expansion in the use of orthotopic (anatomically correct) breast cancer xenografts [ 370 ] and significant advances in developing patient-derived xenografts (PDX) [ 371 ]. These models better reflect the human cancers from which they were derived and ER+ve tumours respond appropriately to oestrogen ablation [ 372 ]. Increased use of genetically engineered mouse (GEM) models driven by relevant abnormalities such as BRCA mutations, HER2 overexpression and so on have enabled the study of naturally occurring tumours in immunocompetent hosts and evaluation of new targeted therapies such as PARP inhibitors and the emergence of resistance [ 373 ]. Pros and cons of different models are shown in Figure  6 .

Comparative properties of experimental tumour models. In vitro assays of tumour growth and response to therapy can be conducted in two dimensions or three dimensions - the latter more closely approximating the biology of solid tumours than a simple monolayer. Cultures can be enhanced by the addition of matrix proteins and/or host cells and can be adapted to measure not only tumour cell proliferation, but also additional cancer hallmarks such as invasion. Standard in vivo assays depend upon the transplantation of established human tumour cell lines into athymic (immune-incompetent) hosts. These models are relatively simple and easy to use, but are increasingly complemented by genetically engineered mice harbouring targeted genetic mutations which render them susceptible to developing mammary cancers. The figure summarises key advantages and disadvantages of each model and means by which their clinical relevance and utility might be enhanced. Based on a figure provided courtesy of Claire Nash in Dr Valerie Speirs’ group (University of Leeds).

Expansion of PDX models will be required to cover all the main breast cancer phenotypes [ 374 ] and to address the contribution of ethnic diversity [ 375 ]. Advanced GEM models with multiple genetic abnormalities, able to generate both hormone sensitive and insensitive tumours and in which metastasis occurs at clinically relevant sites will also be a desirable refinement [ 376 , 377 ]. However, all such animal models will require validation of any findings in the clinical setting [ 296 , 378 , 379 ]. Models are also required to investigate mechanisms of the induction of (and escape from) long-term tumour dormancy [ 380 ], a unique feature of breast cancer.

Invasive behaviour does not occur uniformly or synchronously within a tumour [ 381 ] and this heterogeneity is not easily reproduced in vitro . Improved tumour models and methods are required to understand the localised and possibly transient factors involved in temporal and spatial heterogeneity that promote invasion and metastasis.

Models for testing novel targeted agents against disseminated disease

Novel agents designed for systemic administration are rarely tested against established invasive/metastatic disease in preclinical animal models [ 382 , 383 ]. There is an urgent need to develop better models for the discovery and development of therapies targeting metastases that are effective against all sites of disease [ 384 ].

In around 20% of women, complete resection of primary tumours does not prevent distant metastases because dissemination has already occurred. In these cases, agents targeting cell motility or invasion may have limited value. It is therefore critical that preclinical models used for testing such therapies incorporate established micrometastases [ 385 ]. Similarly, there is a preponderance of lung metastasis models in routine use. Other important sites of breast cancer metastasis (for example bone, brain and, liver) are relatively poorly represented, and this needs remedying in preclinical drug evaluation [ 386 – 388 ]. Human tissue (such as bone) transplanted into mice can provide a more relevant microenvironment [ 389 ].

Preclinical or clinical trials focused on tumour shrinkage are not appropriate for testing the efficacy of anti-invasive or anti-metastatic agents that may reduce metastasis without significantly impacting primary tumour growth [ 390 ]. Such approaches would likely fail current response evaluation criteria in solid tumors (RECIST) criteria and show little activity in the neoadjuvant setting or in late stage patients with advanced metastatic disease. The potential to utilise veterinary models for testing novel therapies or RT-systemic therapy combinations and cross-disciplinary collaboration with other scientific disciplines to develop real-time in vivo biosensors of tumour biology offer novel opportunities for significant progress.

Modelling drug resistance

While challenging, establishing cell lines, tissue slice models and PDX from relapsed and resistant cancers should be the ultimate goal in order to provide a window on the mechanisms that occur in patients where therapies fail. This would also allow ex vivo targeting studies, employing signalling analyses and imaging systems to track resistance mechanisms and progression.

Preclinical endocrine resistant models have largely been derived from ER+ve MCF7 cells in vitro , either by transfection of potential signalling molecules such as HER2 or from continuous exposure to anti-endocrine agents. Extensive panels of relapsed human tumour cell lines are required to reflect the heterogeneity of clinical resistant disease. This will allow assessment of the impact of genetic background, duration, sequence and type of endocrine agent (including AI) and rational evaluation of agents to reverse resistance [ 391 ]. It is critical to validate mechanisms identified in vitro with clinical resistance.

Longitudinal clinical samples and associated biological studies

Biobanking has substantially improved and is seen as a significant outcome of the last gap analysis [ 7 ] but the systematic analysis of clinical material collected from serial tumour biopsies/ fine-needle aspiration (FNA) (or ideally less invasive means such as ‘liquid biopsy’) before, during and following resistance development is lacking. Procurement of matched materials remains challenging but is critical to establishing clinically relevant signalling mechanisms that culminate in acquired resistance, allowing tracking of the dynamics and prevalence of molecular events during response through to any subsequent relapse. Care must be taken to provide adequate sampling of inherently heterogeneous tumours in their primary, recurrent and disseminated settings, which may also provide material for study of site-specific metastasis. [ 392 ] and samples must be full annotated, ideally with ‘omics’ profiling and immunohistochemistry. The biopsy of metastatic lesions is challenging and will require systematic introduction of a ‘warm autopsy’ programme [ 393 ]. A more realistic alternative is to further exploit the preoperative neoadjuvant setting, despite the potential issues of heterogeneity and sampling [ 394 ]. Collection of such samples is a particularly valuable resource to address mechanisms of intrinsic resistance and to track early therapy-associated signalling changes (Figure  7 ).

Longitudinal sampling and enhanced biobanks. The longitudinal collection of blood and samples from normal breasts, primary cancers and relapsed/metastatic/treatment-resistant disease is essential in order to address the origins, heterogeneity and evolution of breast cancers. Samples are required from as broad a patient population as possible to understand ethnic, age-related and gender differences in incidence, molecular subtypes, prognosis and response to treatment. Sequential samples (ideally patient-matched) from primary tumours and metastases will enable detailed studies of tumour evolution/progression and provide material for generating new cell lines and patient-derived xenografts for translational research. Multimodality imaging and metabolomic analyses will add further dimensions of valuable information. Based on a figure provided courtesy of Professor William Gallagher, with thanks to Dr Rut Klinger (UCD Conway Institute).

Increased use of clinical relapse material will determine the relevance of preclinical findings and identify potential candidates for detailed mechanistic evaluation in appropriate tumour model systems. Ultimately the goal is to determine if patients can be better stratified to allow rational, personalised choices for further therapy. This aspiration requires better integration between clinicians and scientists, trial providers and pharmaceutical companies and would benefit from data sharing. Tissue-based analyses from clinical trials need to be expanded to incorporate all of the next generation sequencing studies for research. These initiatives need to be co-ordinated with cancer registry/ British Association of Surgical Oncology (BASO) breast cancer data.

Blood samples for early diagnosis, monitoring treatment response, early indicators of disease relapse (and revealing increased heterogeneity) are imperative as our ability to generate new biomarkers through emerging technologies increases. These include detection of CTCs, miRNAs, ctDNA, exosomes, and so on. Serum HER2 measurement may be another promising biomarker with prognostic and predictive value [ 395 – 398 ].

Biomarkers of response or relapse

With the exception of ER and HER2, the availability of biomarkers to accurately identify which patients will receive benefit from targeted treatment, and indicators of patients at high risk of progression or relapse remains limited. Further advances in molecularly targeted and anti-endocrine therapy require clinically applicable predictive biomarkers to enable appropriate patient recruitment and to track responses to treatment [ 399 , 400 ]. These analyses should be applied both to primary tumours and recurrent/metastatic lesions to accommodate the profound heterogeneity within individual cancers, which increases further during disease progression. Understanding which molecular markers are ‘drivers’ of breast cancer and their functional roles at different stages of disease will be key to designing more effective targeted agents.

Validation of predictive markers for drug response could be better facilitated by the routine inclusion of such approaches into clinical trials rather than retrospective analyses of archived material. Any new biomarkers should have well-defined cut-off points, be thoroughly validated and robust. We require biomarkers to identify patients who will not respond to trastuzumab (primary resistance) in addition to the development of secondary acquired resistance. Discriminatory biomarkers are required for combination therapies such as lapatinib and trastuzumab in HER2-positive breast cancers. We lack preclinical data that can predict which combination of anti-HER2 therapies is optimal. There is also a need for biomarkers that can identify patients who may be more suitably treated with a tyrosine kinase inhibitor (TKI) rather than trastuzumab or combination anti-HER2 therapy. New irreversible TKIs currently in clinical trials, (for example afatinib and neratinib) have shown increased potency in preclinical studies - could these now become the mainstay for HER2-positive tumours?

Knowledge of the therapeutic benefits of mTOR inhibitors and of newer PI3K pathway inhibitors in breast cancer subtypes is rudimentary and we have no biomarkers that can be used to optimise their therapeutic index. In addition, knowledge of how important genomic (for example PIK3CA mutations) and proteomic (for example PTEN loss) biomarkers impact the efficacy of specific PI3K pathway inhibitors in the clinical setting is limited. Further preclinical research on the functional proteomic effects of genomic abnormalities in the PI3K pathway in breast cancer is essential.

ER+ve tumour heterogeneity remains a challenge: luminal A vs. luminal B subgroups impact on prognosis; however, the mechanisms of endocrine failure remain largely unknown. In ER+ve disease there is a lack of accepted biomarkers/signatures to distinguish endocrine-sensitive patients from those with intrinsic insensitivity or who will develop early or late resistance.

There is a need to develop non-invasive means of detecting risk of subsequent relapse. In addition to serial tumour samples, serum samples are warranted as these may ultimately provide less invasive indicators of acquisition of resistance. It remains unclear if single or multiple biomarkers or transcriptional profiles are optimal, or even if basic endocrinological markers may prove valuable in the context of predicting resistance.

While imaging (at least with some modalities) is routinely applied to the early detection and follow-up of breast cancers, there is a need to increase the use of functional screening techniques to better understand tumour heterogeneity, identify features associated with response or resistance to treatment and more rapidly translate promising new preclinical methodologies to clinical evaluation. It is important to evaluate emerging imaging biomarkers of primary and metastatic breast cancer and there is a requirement for new, more specific and clinically translatable radiotracers for positron emission tomography/single-photon emission computed tomography (PET/SPECT) [ 401 , 402 ]. We also need to identify and assess the utility of imaging biomarkers associated with other hallmarks of cancer beyond proliferation for example invasion, altered metabolism, hypoxia. Attention needs to be given as to how to validate novel imaging biomarkers in adequately powered multi-centre clinical trials. The funding available from most grant-awarding bodies is insufficient to cover this, suggesting the need to consider larger collaborative trials funded by more than one agency.

Imaging may also be able to report on intratumoural heterogeneity and identify the most significant region (for example more aggressive/invasive areas via diffusion-weighted magnetic resonance imaging (MRI)), to more accurately direct biopsies or radiotherapy. EMT could be addressed by the increased use of cluster, histogram and/or texture analyses, but it will be necessary to define the correct metrics to assess and quantify such phenotypes [ 403 ]. It would be desirable to extend these techniques to define different tumour subtypes such as DCIS, luminal or TNBC non-invasively (which may identify mixed lesions missed by homogenised or limited sample analyses) and assess heterogeneity between metastases. Ideally, imaging studies (both preclinical and clinical) should be co-registered with linked genomic and proteomic information in order to fully interpret the biological relevance of the images obtained [ 404 – 406 ]. However, tissue collection is often not co-ordinated with imaging studies and the added benefit not always appreciated.

A key achievable goal is to non-invasively evaluate predictive biomarkers of therapeutic responses. Increased adoption of more clinically relevant orthotopic xenograft and transgenic murine models of primary and metastatic breast cancer will demand robust preclinical imaging approaches. The use of such models in imaging-embedded trials of novel agents will improve the accuracy of preclinical data, accelerating the development of promising drugs, or enabling early closure of suboptimal programmes. Such refined preclinical trial designs will also prove highly informative in establishing combination and/or sequential treatment regimes.

Clinical trial design and patient involvement

Clinical trial design should be adapted to use preoperative and neoadjuvant models to allow novel therapies to be tested in patients [ 394 , 407 ], identify de novo resistant cancers and investigate how such resistance can be counteracted. These approaches are particularly relevant for therapeutic strategies that target cancer stem cells, residual (dormant) cancer cells or influence the tumour microenvironment. Future trial design will also have to incorporate dynamic strategies, such as using the response to short-term treatment to guide the use of additional preoperative treatment. Given the increasing focus on small target populations (for example molecular subtypes of breast cancer), clinical trial strategies for effective patient stratification or selection based on molecular characteristics are required to allow routine integration into large-scale clinical trials. In addition, the relatively long period between surgery and relapse in breast cancer patients impacts negatively on the economic feasibility of such clinical trials. New thinking will be required to modify clinical trial design, and to consider biomarkers that relate to invasive and metastatic phenotypes, for example as in trials with denosumab where the development of skeletal-related events (SRE) was an accepted and measurable endpoint [ 221 ].

Patient reported outcomes

There is a need to incorporate standardised patient-reported outcome measures (PROMs) both within clinical trials and in everyday clinical practice. Currently, many trial reports are reliant on the common terminology criteria for adverse events (CTCAE) gradings about side effects, which show alarming discrepancies with data actually collected from patients [ 408 ].

Further research is needed to support the use of decision aids around surgery and treatment and to define any benefits. There is also a need for prospective research to identify consequences of treatment and the impact of co-morbidities on the lives of women with breast cancer so that future patients can consider these as part of their decision making. The experiences of minority ethnic groups, younger (<45 years) and older (>70 years) women in relation to their treatment choices and management need further research. Addressing non-adherence to endocrine therapy and understanding the biological mechanisms of significant side effects such as menopausal symptoms are poorly understood. The value of incorporating lifestyle recommendations as part of routine care and its impact on recovery and quality of life should be further explored.

Multidisciplinary collaborations and resources

Increased resources are required to support core (for example biochemical/IHC) as well as new ‘omics technologies; to develop improved in vitro / in vivo / ex vivo model development, serial clinical sample collection, advanced bioinformatic/systems biology analysis, clinical biomarker validation and ‘bench to bedside’ drug development. Stronger multidisciplinary collaborations between laboratory scientists, clinicians, bioinformaticians and engineers (and in turn with funding bodies and industry) must be encouraged. Much better integration of computer science, database engineering, data analytics and visualisation, hardware and software engineering within biological research will be essential to effectively read and translate increasingly complex data. Convincing drug companies of the benefits of a co-ordinated approach (tissue collection before, during and after treatments) in clinical trials of new drugs is problematic, and access of material for research purposes is limited. Companies must be convinced of the benefits of accurate biomarkers to allow for the better stratification of patients. Even though this will limit their target population, this should be offset by higher response rates and faster regulatory approval.

Continued support is required for basic biological research and understanding of cell signalling processes with emphasis on interactions, cross-talk and microenvironmental regulation. It is important that approaches in this area are linked to systematic investigations and precise analyses of cell responses to a wide range (and combination) of inhibitors, tested in clinically relevant breast cancer model systems. A key element is open discussion and learning from negative results to avoid unnecessary duplication of research. Sharing of information, best practice, optimised model systems, technologies and resources is essential, perhaps through developing web-based analysis portals. Such approaches are needed to integrate and interpret diverse sources of data to understand the plasticity of signalling emerging during treatment though to resistance (Figure  8 ).

Integrated vision of multidisciplinary research. Enhanced integration and utilisation of the vast amount of clinical and experimental observations relating to breast cancer is urgently required. Clinical observations generate hypotheses relating to the origins of cancer, its underlying molecular pathology and potential vulnerabilities that could be exploited for therapeutic benefit. Such insights provide opportunities for testing and validation in in vitro, in vivo and in silico models. Drug discovery aims to provide inhibitors of major oncogenic ‘drivers’ for use singly or in combination with conventional therapies; such personalised medicine requires the co-development of predictive and pharmacodynamic biomarkers of response. Results from preclinical therapy studies and clinical trials should be fed back into searchable databases to reveal reasons for treatment failure and allow new strategies to be tested and deployed. Based on a figure provided courtesy of Professor William Gallagher, with thanks to Professor Walter Kolch (UCD Conway Institute).

A co-operative network of advanced radiotherapy facilities, analogous to the Experimental Cancer Medicine Centres is needed to ensure adequate patient numbers for clinical trials. Engaging patients and healthcare teams is critical to enable complex biological studies (especially longitudinal biomarker studies). Lack of academic clinicians (particularly in radiation oncology), radiobiology and physics staff nationally and rising service pressures on NHS staff are all detrimental to delivery of clinical translational research.

While substantial advances have been made in breast cancer research and treatment in the last five years, there remain significant gaps in translating this newly acquired knowledge into clinical improvements.

Understanding the specific functions and contextual interactions of genetic and epigenetic advances and applying this knowledge to clinical practice, including tailored screening, will require deeper understanding of molecular mechanisms and prospective clinical validation. Even with clinically actionable tests, decision making, support for patients and their families and overcoming the barriers to lifestyle change (diet, exercise and weight) alongside chemopreventive strategies are required to optimise health outcomes.

Genomic profiling of sequential clinical samples (primary, relapsed and secondary cancers, CTC, ctDNA, before, during and following therapy) is required to identify specific biomarkers of inter-/intra-tumour spatial and temporal heterogeneity, metastatic potential, sensitivity to radiotherapy and different forms of chemotherapy, de novo or acquired resistance. This will significantly improve patient stratification for existing therapies and identify key nodes in these dynamic processes as potential new therapeutic targets. Validated markers of these processes (including minimally invasive multimodality imaging and metabolomics methodologies) will benefit from synergies between laboratory and clinical interactions. Improved understanding of the interactions, duration, sequencing and optimal combinations of therapy should allow better stratification of patients and reduce overtreatment (or undertreatment) enhancing prevention or survival while reducing morbidity.

Further genetic, epigenetic and molecular profiling of breast cancers and their associated stroma would be significantly enhanced by expanded panels of cell lines representing all major breast cancer subtypes and three-dimensional tumour-host heterotypic co-culture systems. This would enable increased understanding of the molecular drivers behind specific cancer subtypes and their role (together with microenvironmental modifiers) in treatment resistance and metastasis. Deciphering tumour-stromal interactions incorporating metabolic and immunological host mechanisms and intracellular/extracellular signalling pathways would have therapeutic implications for prevention and therapy. Advanced high-content analytical methods will enable consideration of additional key cancer ‘hallmarks’ beyond proliferation (for example cell motility and invasion) and enable screening for inhibitors under more physiologically relevant conditions. Better preclinical animal models (for example genetically engineered mice expressing relevant human oncogenes, which develop widespread metastases; patient-derived xenografts) are required. Such models would enable testing of hypotheses derived from clinical observations and rigorous target validation and evaluation of novel therapies in the metastatic setting (and where desirable in immunocompetent hosts).

Underpinning these advances, optimised multimodality imaging for diagnosis and therapeutic monitoring should enable better evaluation of primary and metastatic disease. Clinically annotated tissues for translational research must be linked to bioinformatics as key contributors to interdisciplinary research, essential for rapid future advances. Increasing numbers of women and men are surviving breast cancer. Alongside advances in understanding the disease and using that knowledge for prevention, earlier detection and successful treatment of breast cancer, interventions to improve the survivorship experience require innovative approaches to address the consequences of diagnosis and treatment.

Top 10 gaps:

Understanding the specific functions and contextual interactions of genetic and epigenetic changes in the normal breast and the development of cancer

Effective and sustainable lifestyle changes (diet, exercise and weight) alongside chemopreventive strategies

Tailored screening approaches including clinically actionable tests

Molecular drivers behind breast cancer subtypes, treatment resistance and metastasis

Mechanisms of tumour heterogeneity, tumour dormancy, de novo or acquired resistance; how to target the key nodes in these dynamic processes

Validated markers of chemosensitivity and radiosensitivity

Interactions, duration, sequencing and optimal combinations of therapy for improved individualisation of treatment

Optimised multimodality imaging for diagnosis and therapeutic monitoring should enable better evaluation of primary and metastatic disease

Interventions and support to improve the survivorship experience including physical symptoms such as hot flushes and lymphoedema

Clinically annotated tissues for translational research including tumour, non-tumour and blood based materials from primary cancers, relapsed and metastatic disease

Proposed strategic solutions:

For significant progress to be made in treating and supporting those impacted by breast cancer (and ultimately preventing and overcoming this disease) basic and translational research scientists in academia and industry, funding bodies, government and patients need to work together to achieve the following key strategic solutions

To reverse the decline in resources targeted towards breast cancer research, funding must be increased and strategically directed to enhance our current knowledge, develop the talent pool, and apply evidence-based findings to improve clinical care

A fully cohesive and collaborative infrastructure must be developed to support breast cancer research; this requires improved access to appropriate, well-annotated clinical material including longitudinal sample collection with expert bioinformatics support and data sharing.

Building on sound investment and infrastructure, all stakeholders (researchers, funders, government, industry and patients) must work together on the clinical development and translation of research knowledge to patient benefit. For example, enhanced, clinically relevant, in vitro and in vivo models are required for evaluation of new therapies together with validated biomarkers, which should then be embedded in clinical practice.

Research funders, government and industry should provide innovative programmes to encourage collaborative cross-disciplinary working practices, including the training of more physician-scientists and integration of physical sciences, technology and engineering.

Improving clinical trial methodologies, including patient involvement, recognising that a changing global environment is required to ensure that all clinical developments can be tested and ultimately implemented for patient benefit.

Abbreviations

Aromatase inhibitor

Androgen receptor

Ataxia telangiectasia mutated

British Association of Surgical Oncology

Cancer-associated fibroblast

Cognitive behavioural therapy

Cyclin-dependent kinase 10

CHK2 checkpoint homolog

Checkpoint kinase 2

Central nervous system

Cancer stem cell

Circulating tumour cell (in blood)

Common terminology criteria for adverse events

Circulating tumour DNA

Ductal carcinoma in situ

DNA damage response

Deoxyribonucleic acid

Disseminated tumour cell (usually in marrow nodes or tissue)

Extracellular matrix

Epithelial-mesenchymal transition

Oestrogen receptor

Fibroblast growth factor

Fibroblast growth factor receptor 1

Fine-needle aspiration

Forkhead box protein A1

Genetically engineered mouse

Genome-wide association studies

Human epidermal growth factor receptor 2

Human epidermal growth factor receptor 3

Homologous recombination repair

Hormone replacement therapy

Heat shock protein 90

Ipsilateral breast tumour recurrence

International Cancer Genome Consortium

Illumina collaborative oncological gene-environment study

Insulin-like growth factor 1

Immunohistochemical

Induced pluripotent stem cells

Chromatography-mass spectrometry

Metastatic breast cancer

Magnetic resonance imaging

Nuclear magnetic resonance

Representing the whole HER family

Poly (ADP-ribose) polymerase

Patient-derived xenografts

Positron emission tomography/single-photon emission computed tomography

Phosphatidylinositide-3 kinase

Gene encoding PI3 kinase alpha

Protein kinase B

Progesterone receptor

Patient-reported outcome measures

Randomised controlled trial

Response evaluation criteria in solid tumors

Ribonucleic acid

Selective oestrogen receptor modulators

Short inhibitory RNAs

Sentinel node biopsy

Single nucleotide polymorphism

Skeletal-related events

Standardisation of Breast Radiotherapy (START) trial A

Standardisation of Breast Radiotherapy (START) trial B

The Cancer Genome Atlas

Transforming growth factor beta

Tyrosine kinase inhibitor

Tissue microarray

Triple-negative breast cancer

Vascular endothelial growth factor

Women’s Health Initiative.

Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM: Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010, 127: 2893-2917.

Article   CAS   PubMed   Google Scholar  

Breast cancer incidence statistics. http://www.cancerresearchuk.org/cancer-info/cancerstats/types/breast/incidence/#trends ,

Maddams JBD, Gavin A, Steward J, Elliott J, Utley M, Møller H: Cancer prevalence in the United Kingdom: estimates for 2008. Br J Cancer. 2009, 101: 541-547.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Maddams J, Utley M, Moller H: Projections of cancer prevalence in the United Kingdom, 2010–2040. Br J Cancer. 2012, 107: 1195-1202.

Leal J: The economic burden of cancer across the European Union. Proceedings of the National Cancer Research Institute Conference: 4–7. 2012, Liverpool, November

Google Scholar  

Data package. http://www.ncri.org.uk/includes/Publications/general/Data_package_12.xls ,

Thompson A, Brennan K, Cox A, Gee J, Harcourt D, Harris A, Harvie M, Holen I, Howell A, Nicholson R, Steel M, Streuli C: Evaluation of the current knowledge limitations in breast cancer research: a gap analysis. Breast Cancer Res : BCR. 2008, 10: R26-

Article   PubMed   PubMed Central   Google Scholar  

Tissue Bank. http://breastcancertissuebank.org/about-tissue-bank.php ,

Melchor L, Benitez J: The complex genetic landscape of familial breast cancer. Hum Genet. 2013

Michailidou K, Hall P, Gonzalez-Neira A, Ghoussaini M, Dennis J, Milne RL, Schmidt MK, Chang-Claude J, Bojesen SE, Bolla MK, Wang Q, Dicks E, Lee A, Turnbull C, Rahman N, Fletcher O, Peto J, Gibson L, Dos Santos Silva I, Nevanlinna H, Muranen TA, Aittomäki K, Blomqvist C, Czene K, Irwanto A, Liu J, Waisfisz Q, Meijers-Heijboer H, Adank M, Breast and Ovarian Cancer Susceptibility Collaboration, et al: Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat Genet. 2013, 45: 353-361. 361e351-352

Sakoda LC, Jorgenson E, Witte JS: Turning of COGS moves forward findings for hormonally mediated cancers. Nat Genet. 2013, 45: 345-348.

Antoniou AC, Beesley J, McGuffog L, Sinilnikova OM, Healey S, Neuhausen SL, Ding YC, Rebbeck TR, Weitzel JN, Lynch HT, Isaacs C, Ganz PA, Tomlinson G, Olopade OI, Couch FJ, Wang X, Lindor NM, Pankratz VS, Radice P, Manoukian S, Peissel B, Zaffaroni D, Barile M, Viel A, Allavena A, Dall'Olio V, Peterlongo P, Szabo CI, Zikan M, Claes K, et al: Common breast cancer susceptibility alleles and the risk of breast cancer for BRCA1 and BRCA2 mutation carriers: implications for risk prediction. Cancer Res. 2010, 70: 9742-9754.

Ingham S, Warwick J, Byers H, Lalloo F, Newman W, Evans D: Is multiple SNP testing in BRCA2 and BRCA1 female carriers ready for use in clinical practice? Results from a large Genetic Centre in the UK. Clin Genet. 2013, 84: 37-42.

Audeh MW, Carmichael J, Penson RT, Friedlander M, Powell B, Bell-McGuinn KM, Scott C, Weitzel JN, Oaknin A, Loman N, Lu K, Schmutzler RK, Matulonis U, Wickens M, Tutt A: Oral poly(ADP-ribose) polymerase inhibitor olaparib in patients with BRCA1 or BRCA2 mutations and recurrent ovarian cancer: a proof-of-concept trial. Lancet. 2010, 376: 245-251.

Turnbull C, Seal S, Renwick A, Warren-Perry M, Hughes D, Elliott A, Pernet D, Peock S, Adlard JW, Barwell J, Berg J, Brady AF, Brewer C, Brice G, Chapman C, Cook J, Davidson R, Donaldson A, Douglas F, Greenhalgh L, Henderson A, Izatt L, Kumar A, Lalloo F, Miedzybrodzka Z, Morrison PJ, Paterson J, Porteous M, Rogers MT, Shanley S, et al: Gene-gene interactions in breast cancer susceptibility. Hum Mol Genet. 2012, 21: 958-962.

Muller HM, Widschwendter A, Fiegl H, Ivarsson L, Goebel G, Perkmann E, Marth C, Widschwendter M: DNA methylation in serum of breast cancer patients: an independent prognostic marker. Cancer Res. 2003, 63: 7641-7645.

PubMed   Google Scholar  

Yazici H, Terry MB, Cho YH, Senie RT, Liao Y, Andrulis I, Santella RM: Aberrant methylation of RASSF1A in plasma DNA before breast cancer diagnosis in the Breast Cancer Family Registry. Cancer Epidemiol Biomarkers Prev. 2009, 18: 2723-2725.

Bernstein BE, Birney E, Dunham I, Green ED, Gunter C, Snyder M, ENCODE Project Consortium: An integrated encyclopedia of DNA elements in the human genome. Nature. 2012, 489: 57-74.

Article   CAS   Google Scholar  

Brennan K, Garcia-Closas M, Orr N, Fletcher O, Jones M, Ashworth A, Swerdlow A, Thorne H, Investigators KC, Riboli E, Vineis P, Dorronsoro M, Clavel-Chapelon F, Panico S, Onland-Moret NC, Trichopoulos D, Kaaks R, Khaw KT, Brown R, Flanagan JM: Intragenic ATM methylation in peripheral blood DNA as a biomarker of breast cancer risk. Cancer Res. 2012, 72: 2304-2313.

Azad N, Zahnow CA, Rudin CM, Baylin SB: The future of epigenetic therapy in solid tumours–lessons from the past. Nat Rev Clin Oncol. 2013, 10: 256-266.

Tsai HC, Li H, Van Neste L, Cai Y, Robert C, Rassool FV, Shin JJ, Harbom KM, Beaty R, Pappou E, Harris J, Yen RW, Ahuja N, Brock MV, Stearns V, Feller-Kopman D, Yarmus LB, Lin YC, Welm AL, Issa JP, Minn I, Matsui W, Jang YY, Sharkis SJ, Baylin SB, Zahnow CA: Transient low doses of DNA-demethylating agents exert durable antitumor effects on hematological and epithelial tumor cells. Cancer Cell. 2012, 21: 430-446.

Foster C, Watson M, Eeles R, Eccles D, Ashley S, Davidson R, Mackay J, Morrison PJ, Hopwood P, Evans DG, Psychosocial Study Collaborators: Predictive genetic testing for BRCA1/2 in a UK clinical cohort: three-year follow-up. Br J Cancer. 2007, 96: 718-724.

Hilgart JS, Coles B, Iredale R: Cancer genetic risk assessment for individuals at risk of familial breast cancer. Cochrane Database Syst Rev. 2012, 2: CD003721

Albada A, Werrett J, Van Dulmen S, Bensing JM, Chapman C, Ausems MG, Metcalfe A: Breast cancer genetic counselling referrals: how comparable are the findings between the UK and the Netherlands?. J Comm Gen. 2011, 2: 233-247.

Article   Google Scholar  

Wakefield CE, Meiser B, Homewood J, Peate M, Taylor A, Lobb E, Kirk J, Young MA, Williams R, Dudding T, Tucker K, AGenDA Collaborative Group: A randomized controlled trial of a decision aid for women considering genetic testing for breast and ovarian cancer risk. Breast Cancer Res Treat. 2008, 107: 289-301.

Article   PubMed   Google Scholar  

Lindor NM, Goldgar DE, Tavtigian SV, Plon SE, Couch FJ: BRCA1/2 Sequence variants of uncertain significance: a primer for providers to assist in discussions and in medical management. Oncol. 2013, 18: 518-524.

Hallowell N, Baylock B, Heiniger L, Butow PN, Patel D, Meiser B, Saunders C, Price MA, kConFab Psychosocial Group on behalf of the kConFab I: Looking different, feeling different: women’s reactions to risk-reducing breast and ovarian surgery. Fam Cancer. 2012, 11: 215-224.

Watts KJ, Meiser B, Mitchell G, Kirk J, Saunders C, Peate M, Duffy J, Kelly PJ, Gleeson M, Barlow-Stewart K, Rahman B, Friedlander M, Tucker K, TFGT Collaborative Group: How should we discuss genetic testing with women newly diagnosed with breast cancer? Design and implementation of a randomized controlled trial of two models of delivering education about treatment-focused genetic testing to younger women newly diagnosed with breast cancer. BMC Cancer. 2012, 12: 320-

Chivers Seymour K, Addington-Hall J, Lucassen AM, Foster CL: What facilitates or impedes family communication following genetic testing for cancer risk? A systematic review and meta-synthesis of primary qualitative research. J Genet Couns. 2010, 19: 330-342.

Mireskandari S, Sherman KA, Meiser B, Taylor AJ, Gleeson M, Andrews L, Tucker KM: Psychological adjustment among partners of women at high risk of developing breast/ovarian cancer. Genet Med. 2007, 9: 311-320.

Amir E, Freedman OC, Seruga B, Evans DG: Assessing women at high risk of breast cancer: a review of risk assessment models. J Natl Cancer Inst. 2010, 102: 680-691.

Dite GS, Mahmoodi M, Bickerstaffe A, Hammet F, Macinnis RJ, Tsimiklis H, Dowty JG, Apicella C, Phillips KA, Giles GG, Southey MC, Hopper JL: Using SNP genotypes to improve the discrimination of a simple breast cancer risk prediction model. Breast Cancer Res Treat. 2013, 139: 887-896.

Eriksson L, Hall P, Czene K, Dos Santos SI, McCormack V, Bergh J, Bjohle J, Ploner A: Mammographic density and molecular subtypes of breast cancer. Br J Cancer. 2012, 107: 18-23.

Swerdlow AJ, Cooke R, Bates A, Cunningham D, Falk SJ, Gilson D, Hancock BW, Harris SJ, Horwich A, Hoskin PJ, Linch DC, Lister TA, Lucraft HH, Radford JA, Stevens AM, Syndikus I, Williams MV: Breast cancer risk after supradiaphragmatic radiotherapy for Hodgkin’s lymphoma in England and Wales: a National Cohort Study. J Clin Oncol. 2012, 30: 2745-2752.

Aupperlee MD, Leipprandt JR, Bennett JM, Schwartz RC, Haslam SZ: Amphiregulin mediates progesterone-induced mammary ductal development during puberty. Breast Cancer Res BCR. 2013, 15: R44-

Denkert C, Bucher E, Hilvo M, Salek R, Oresic M, Griffin J, Brockmoller S, Klauschen F, Loibl S, Barupal DK, Budczies J, Iljin K, Nekljudova V, Fiehn O: Metabolomics of human breast cancer: new approaches for tumor typing and biomarker discovery. Genome Med. 2012, 4: 37-

CAS   PubMed   PubMed Central   Google Scholar  

Santen RJ, Boyd NF, Chlebowski RT, Cummings S, Cuzick J, Dowsett M, Easton D, Forbes JF, Key T, Hankinson SE, Howell A, Ingle J, Breast Cancer Prevention Collaborative Group: Critical assessment of new risk factors for breast cancer: considerations for development of an improved risk prediction model. Endocr Relat Cancer. 2007, 14: 169-187.

Cuzick J, Sestak I, Bonanni B, Costantino JP, Cummings S, DeCensi A, Dowsett M, Forbes JF, Ford L, LaCroix AZ, Mershon J, Mitlak BH, Powles T, Veronesi U, Vogel V, Wickerham DL, SERM Chemoprevention of Breast Cancer Overview Group: Selective oestrogen receptor modulators in prevention of breast cancer: an updated meta-analysis of individual participant data. Lancet. 2013, 381: 1827-1834.

LaCroix AZ, Powles T, Osborne CK, Wolter K, Thompson JR, Thompson DD, Allred DC, Armstrong R, Cummings SR, Eastell R, Ensrud KE, Goss P, Lee A, Neven P, Reid DM, Curto M, Vukicevic S, PEARL Investigators: Breast cancer incidence in the randomized PEARL trial of lasofoxifene in postmenopausal osteoporotic women. J Natl Cancer Inst. 2010, 102: 1706-1715.

Goss PE, Ingle JN, Ales-Martinez JE, Cheung AM, Chlebowski RT, Wactawski-Wende J, McTiernan A, Robbins J, Johnson KC, Martin LW, Winquist E, Sarto GE, Garber JE, Fabian CJ, Pujol P, Maunsell E, Farmer P, Gelmon KA, Tu D, Richardson H, NCIC CTG MAP.3 Study Investigators: Exemestane for breast-cancer prevention in postmenopausal women. N Engl J Med. 2011, 364: 2381-2391.

Decensi A, Gandini S, Serrano D, Cazzaniga M, Pizzamiglio M, Maffini F, Pelosi G, Daldoss C, Omodei U, Johansson H, Macis D, Lazzeroni M, Penotti M, Sironi L, Moroni S, Bianco V, Rondanina G, Gjerde J, Guerrieri-Gonzaga A, Bonanni B: Randomized dose-ranging trial of tamoxifen at low doses in hormone replacement therapy users. J Clin Oncol. 2007, 25: 4201-4209.

Rosner B, Glynn RJ, Tamimi RM, Chen WY, Colditz GA, Willett WC, Hankinson SE: Breast cancer risk prediction with heterogeneous risk profiles according to breast cancer tumor markers. Am J Epidemiol. 2013, 178: 296-308.

Uray IP, Brown PH: Chemoprevention of hormone receptor-negative breast cancer: new approaches needed. Recent Results Cancer Res. 2011, 188: 147-162.

Chlebowski RT, Anderson GL, Gass M, Lane DS, Aragaki AK, Kuller LH, Manson JE, Stefanick ML, Ockene J, Sarto GE, Johnson KC, Wactawski-Wende J, Ravdin PM, Schenken R, Hendrix SL, Rajkovic A, Rohan TE, Yasmeen S, Prentice RL, WHI Investigators: Estrogen plus progestin and breast cancer incidence and mortality in postmenopausal women. JAMA. 2010, 304: 1684-1692.

Anderson GL, Chlebowski RT, Aragaki AK, Kuller LH, Manson JE, Gass M, Bluhm E, Connelly S, Hubbell FA, Lane D, Martin L, Ockene J, Rohan T, Schenken R, Wactawski-Wende J: Conjugated equine oestrogen and breast cancer incidence and mortality in postmenopausal women with hysterectomy: extended follow-up of the Women’s Health Initiative randomised placebo-controlled trial. Lancet Oncol. 2012, 13: 476-486.

Wiseman M: The second World Cancer Research Fund/American Institute for Cancer Research expert report. Food, nutrition, physical activity, and the prevention of cancer: a global perspective. Proc Nutr Soc. 2008, 67: 253-256.

Parkin DM, Boyd L, Walker LC: 16. The fraction of cancer attributable to lifestyle and environmental factors in the UK in 2010. Br J Cancer. 2011, 105: S77-S81.

Li CI, Chlebowski RT, Freiberg M, Johnson KC, Kuller L, Lane D, Lessin L, O’Sullivan MJ, Wactawski-Wende J, Yasmeen S, Prentice R: Alcohol consumption and risk of postmenopausal breast cancer by subtype: the women’s health initiative observational study. J Natl Cancer Inst. 2010, 102: 1422-1431.

Hansen J, Stevens RG: Case–control study of shift-work and breast cancer risk in Danish nurses: impact of shift systems. Eur J Cancer. 2012, 48: 1722-1729.

Anderson AS, Mackison D, Boath C, Steele R: Promoting changes in diet and physical activity in breast and colorectal cancer screening settings: an unexplored opportunity for endorsing healthy behaviors. Cancer Prev Res. 2013, 6: 165-172.

Huang Z, Hankinson SE, Colditz GA, Stampfer MJ, Hunter DJ, Manson JE, Hennekens CH, Rosner B, Speizer FE, Willett WC: Dual effects of weight and weight gain on breast cancer risk. JAMA. 1997, 278: 1407-1411.

Harvie M, Howell A, Vierkant RA, Kumar N, Cerhan JR, Kelemen LE, Folsom AR, Sellers TA: Association of gain and loss of weight before and after menopause with risk of postmenopausal breast cancer in the Iowa women’s health study. Cancer Epidemiol Biomarkers Prev. 2005, 14: 656-661.

Eliassen AH, Colditz GA, Rosner B, Willett WC, Hankinson SE: Adult weight change and risk of postmenopausal breast cancer. JAMA. 2006, 296: 193-201.

Teras LR, Goodman M, Patel AV, Diver WR, Flanders WD, Feigelson HS: Weight loss and postmenopausal breast cancer in a prospective cohort of overweight and obese US women. CCC. 2011, 22: 573-579.

Niraula S, Ocana A, Ennis M, Goodwin PJ: Body size and breast cancer prognosis in relation to hormone receptor and menopausal status: a meta-analysis. Breast Cancer Res Treat. 2012, 134: 769-781.

Jung S, Spiegelman D, Baglietto L, Bernstein L, Boggs DA, van den Brandt PA, Buring JE, Cerhan JR, Gaudet MM, Giles GG, Goodman G, Hakansson N, Hankinson SE, Helzlsouer K, Horn-Ross PL, Inoue M, Krogh V, Lof M, McCullough ML, Miller AB, Neuhouser ML, Palmer JR, Park Y, Robien K, Rohan TE, Scarmo S, Schairer C, Schouten LJ, Shikany JM, Sieri S, et al: Fruit and vegetable intake and risk of breast cancer by hormone receptor status. J Natl Cancer Inst. 2013, 105: 219-236.

Prentice RL, Caan B, Chlebowski RT, Patterson R, Kuller LH, Ockene JK, Margolis KL, Limacher MC, Manson JE, Parker LM, Paskett E, Phillips L, Robbins J, Rossouw JE, Sarto GE, Shikany JM, Stefanick ML, Thomson CA, Van Horn L, Vitolins MZ, Wactawski-Wende J, Wallace RB, Wassertheil-Smoller S, Whitlock E, Yano K, Adams-Campbell L, Anderson GL, Assaf AR, Beresford SA, et al: Low-fat dietary pattern and risk of invasive breast cancer: the Women’s Health Initiative Randomized Controlled Dietary Modification Trial. JAMA. 2006, 295: 629-642.

Chlebowski RT, Rose D, Buzzard IM, Blackburn GL, Insull W, Grosvenor M, Elashoff R, Wynder EL: Adjuvant dietary fat intake reduction in postmenopausal breast cancer patient management. The Women’s Intervention Nutrition Study (WINS). Breast Cancer Res Treat. 1992, 20: 73-84.

Pierce JP, Natarajan L, Caan BJ, Parker BA, Greenberg ER, Flatt SW, Rock CL, Kealey S, Al-Delaimy WK, Bardwell WA, Carlson RW, Emond JA, Faerber S, Gold EB, Hajek RA, Hollenbach K, Jones LA, Karanja N, Madlensky L, Marshall J, Newman VA, Ritenbaugh C, Thomson CA, Wasserman L, Stefanick ML: Influence of a diet very high in vegetables, fruit, and fiber and low in fat on prognosis following treatment for breast cancer: the Women’s Healthy Eating and Living (WHEL) randomized trial. JAMA. 2007, 298: 289-298.

Friedenreich CM: Physical activity and breast cancer: review of the epidemiologic evidence and biologic mechanisms. Recent Results Cancer Res. 2011, 188: 125-139.

Fontein DB, de Glas NA, Duijm M, Bastiaannet E, Portielje JE, Van de Velde CJ, Liefers GJ: Age and the effect of physical activity on breast cancer survival: A systematic review. Cancer Treat Rev. 2013, 39: 958-965.

Key TJ: Endogenous oestrogens and breast cancer risk in premenopausal and postmenopausal women. Steroids. 2011, 76: 812-815.

Farhat GN, Cummings SR, Chlebowski RT, Parimi N, Cauley JA, Rohan TE, Huang AJ, Vitolins M, Hubbell FA, Manson JE, Cochrane BB, Lane DS, Lee JS: Sex hormone levels and risks of estrogen receptor-negative and estrogen receptor-positive breast cancers. J Natl Cancer Inst. 2011, 103: 562-570.

Evans DG, Warwick J, Astley SM, Stavrinos P, Sahin S, Ingham S, McBurney H, Eckersley B, Harvie M, Wilson M, Beetles U, Warren R, Hufton A, Sergeant JC, Newman WG, Buchan I, Cuzick J, Howell A: Assessing individual breast cancer risk within the U.K. National Health Service Breast Screening Program: a new paradigm for cancer prevention. Cancer Prev Res. 2012, 5: 943-951.

Darabi H, Czene K, Zhao W, Liu J, Hall P, Humphreys K: Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement. BCR. 2012, 14: R25-

Brower V: Homing in on mechanisms linking breast density to breast cancer risk. J Natl Cancer Inst. 2010, 102: 843-845.

Martin LJ, Boyd NF: Mammographic density. Potential mechanisms of breast cancer risk associated with mammographic density: hypotheses based on epidemiological evidence. BCR. 2008, 10: 201-

Article   PubMed   PubMed Central   CAS   Google Scholar  

Cuzick J, Warwick J, Pinney E, Duffy SW, Cawthorn S, Howell A, Forbes JF, Warren RM: Tamoxifen-induced reduction in mammographic density and breast cancer risk reduction: a nested case–control study. J Natl Cancer Inst. 2011, 103: 744-752.

Courneya KS, Karvinen KH, McNeely ML, Campbell KL, Brar S, Woolcott CG, McTiernan A, Ballard-Barbash R, Friedenreich CM: Predictors of adherence to supervised and unsupervised exercise in the Alberta Physical Activity and Breast Cancer Prevention Trial. J Phys Act Health. 2012, 9: 857-866.

Rack B, Andergassen U, Neugebauer J, Salmen J, Hepp P, Sommer H, Lichtenegger W, Friese K, Beckmann MW, Hauner D, Hauner H, Janni W: The German SUCCESS C Study - the first European lifestyle study on breast cancer. Breast Care (Basel). 2010, 5: 395-400.

Villarini A, Pasanisi P, Traina A, Mano MP, Bonanni B, Panico S, Scipioni C, Galasso R, Paduos A, Simeoni M, Bellotti E, Barbero M, Macellari G, Venturelli E, Raimondi M, Bruno E, Gargano G, Fornaciari G, Morelli D, Seregni E, Krogh V, Berrino F: Lifestyle and breast cancer recurrences: the DIANA-5 trial. Tumori. 2012, 98: 1-18.

CAS   PubMed   Google Scholar  

Baselga J, Campone M, Piccart M, Burris HA, Rugo HS, Sahmoud T, Noguchi S, Gnant M, Pritchard KI, Lebrun F, Beck JT, Ito Y, Yardley D, Deleu I, Perez A, Bachelot T, Vittori L, Xu Z, Mukhopadhyay P, Lebwohl D, Hortobagyi GN: Everolimus in postmenopausal hormone-receptor-positive advanced breast cancer. N Engl J Med. 2012, 366: 520-529.

Anisimov VN, Zabezhinski MA, Popovich IG, Piskunova TS, Semenchenko AV, Tyndyk ML, Yurova MN, Rosenfeld SV, Blagosklonny MV: Rapamycin increases lifespan and inhibits spontaneous tumorigenesis in inbred female mice. Cell Cycle. 2011, 10: 4230-4236.

Longo VD, Fontana L: Intermittent supplementation with rapamycin as a dietary restriction mimetic. Aging. 2011, 3: 1039-1040.

Goodwin PJ, Thompson AM, Stambolic V: Diabetes, metformin, and breast cancer: lilac time?. J Clin Oncol. 2012, 30: 2812-2814.

Reis-Filho JS, Pusztai L: Gene expression profiling in breast cancer: classification, prognostication, and prediction. Lancet. 2011, 378: 1812-1823.

Baird RD, Caldas C: Genetic heterogeneity in breast cancer: the road to personalized medicine?. BMC Med. 2013, 11: 151-

Lawrence MS, Stojanov P, Polak P, Kryukov GV, Cibulskis K, Sivachenko A, Carter SL, Stewart C, Mermel CH, Roberts SA, Kiezun A, Hammerman PS, McKenna A, Drier Y, Zou L, Ramos AH, Pugh TJ, Stransky N, Helman E, Kim J, Sougnez C, Ambrogio L, Nickerson E, Shefler E, Cortés ML, Auclair D, Saksena G, Voet D, Noble M, DiCara D, et al: Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature. 2013, 499: 214-218.

Dawson SJ, Rueda OM, Aparicio S, Caldas C: A new genome-driven integrated classification of breast cancer and its implications. EMBO J. 2013, 32: 617-628.

Metzger-Filho O, Tutt A, de Azambuja E, Saini KS, Viale G, Loi S, Bradbury I, Bliss JM, Azim HA, Ellis P, Di Leo A, Baselga J, Sotiriou C, Piccart-Gebhart M: Dissecting the heterogeneity of triple-negative breast cancer. J Clin Oncol. 2012, 30: 1879-1887.

Russnes HG, Navin N, Hicks J, Borresen-Dale AL: Insight into the heterogeneity of breast cancer through next-generation sequencing. J Clin Invest. 2011, 121: 3810-3818.

Samuel N, Hudson TJ: Translating genomics to the clinic: implications of cancer heterogeneity. Clin Chem. 2013, 59: 127-137.

Jansson MD, Lund AH: MicroRNA and cancer. Mol Oncol. 2012, 6: 590-610.

Akhtar N, Streuli CH: An integrin-ILK-microtubule network orients cell polarity and lumen formation in glandular epithelium. Nat Cell Biol. 2013, 15: 17-27.

Bazzoun D, Lelievre S, Talhouk R: Polarity proteins as regulators of cell junction complexes: Implications for breast cancer. Pharmacol Ther. 2013, 138: 418-427.

Lelievre SA: Tissue polarity-dependent control of mammary epithelial homeostasis and cancer development: an epigenetic perspective. J Mammary Gland Biol Neoplasia. 2010, 15: 49-63.

Xue B, Krishnamurthy K, Allred DC, Muthuswamy SK: Loss of Par3 promotes breast cancer metastasis by compromising cell-cell cohesion. Nat Cell Biol. 2013, 15: 189-200.

Martin FT, Dwyer RM, Kelly J, Khan S, Murphy JM, Curran C, Miller N, Hennessy E, Dockery P, Barry FP, O'Brien T, Kerin MJ: Potential role of mesenchymal stem cells (MSCs) in the breast tumour microenvironment: stimulation of epithelial to mesenchymal transition (EMT). Breast Cancer Res Treat. 2010, 124: 317-326.

Weigelt B, Lo AT, Park CC, Gray JW, Bissell MJ: HER2 signaling pathway activation and response of breast cancer cells to HER2-targeting agents is dependent strongly on the 3D microenvironment. Breast Cancer Res Treat. 2010, 122: 35-43.

Pontiggia O, Sampayo R, Raffo D, Motter A, Xu R, Bissell MJ, Joffe EB, Simian M: The tumor microenvironment modulates tamoxifen resistance in breast cancer: a role for soluble stromal factors and fibronectin through beta1 integrin. Breast Cancer Res Treat. 2012, 133: 459-471.

Martinez-Outschoorn UE, Goldberg A, Lin Z, Ko YH, Flomenberg N, Wang C, Pavlides S, Pestell RG, Howell A, Sotgia F, Lisanti MP: Anti-estrogen resistance in breast cancer is induced by the tumor microenvironment and can be overcome by inhibiting mitochondrial function in epithelial cancer cells. Cancer Biol Ther. 2011, 12: 924-938.

Hanahan D, Coussens LM: Accessories to the crime: functions of cells recruited to the tumor microenvironment. Cancer Cell. 2012, 21: 309-322.

He WS, Dai XF, Jin M, Liu CW, Rent JH: Hypoxia-induced autophagy confers resistance of breast cancer cells to ionizing radiation. Oncol Res. 2012, 20: 251-258.

Article   PubMed   CAS   Google Scholar  

Tan EY, Yan M, Campo L, Han C, Takano E, Turley H, Candiloro I, Pezzella F, Gatter KC, Millar EK, O'Toole SA, McNeil CM, Crea P, Segara D, Sutherland RL, Harris AL, Fox SB: The key hypoxia regulated gene CAIX is upregulated in basal-like breast tumours and is associated with resistance to chemotherapy. Br J Cancer. 2009, 100: 405-411.

Milas L, Hittelman WN: Cancer stem cells and tumor response to therapy: current problems and future prospects. Semin Radiat Oncol. 2009, 19: 96-105.

Mimeault M, Batra SK: Hypoxia-inducing factors as master regulators of stemness properties and altered metabolism of cancer- and metastasis-initiating cells. J Cell Mol Med. 2013, 17: 30-54.

Rundqvist H, Johnson RS: Hypoxia and metastasis in breast cancer. Curr Top Microbiol Immunol. 2010, 345: 121-139.

Postovit LM, Abbott DE, Payne SL, Wheaton WW, Margaryan NV, Sullivan R, Jansen MK, Csiszar K, Hendrix MJ, Kirschmann DA: Hypoxia/reoxygenation: a dynamic regulator of lysyl oxidase-facilitated breast cancer migration. J Cell Biochem. 2008, 103: 1369-1378.

Obeid E, Nanda R, Fu YX, Olopade OI: The role of tumor-associated macrophages in breast cancer progression (Review). Int J Oncol. 2013, 43: 5-12.

Lewis CE, Hughes R: Inflammation and breast cancer. Microenvironmental factors regulating macrophage function in breast tumours: hypoxia and angiopoietin-2. BCR. 2007, 9: 209-

Louie E, Nik S, Chen JS, Schmidt M, Song B, Pacson C, Chen XF, Park S, Ju J, Chen EI: Identification of a stem-like cell population by exposing metastatic breast cancer cell lines to repetitive cycles of hypoxia and reoxygenation. BCR. 2010, 12: R94-

Dittmer J, Rody A: Cancer stem cells in breast cancer. Histol Histopathol. 2013, 28: 827-838.

Mao Q, Zhang Y, Fu X, Xue J, Guo W, Meng M, Zhou Z, Mo X, Lu Y: A tumor hypoxic niche protects human colon cancer stem cells from chemotherapy. J Cancer Res Clin Oncol. 2013, 139: 211-222.

Van Keymeulen A, Rocha AS, Ousset M, Beck B, Bouvencourt G, Rock J, Sharma N, Dekoninck S, Blanpain C: Distinct stem cells contribute to mammary gland development and maintenance. Nature. 2011, 479: 189-193.

van Amerongen R, Bowman AN, Nusse R: Developmental stage and time dictate the fate of Wnt/beta-catenin-responsive stem cells in the mammary gland. Cell Stem Cell. 2012, 11: 387-400.

de Visser KE, Ciampricotti M, Michalak EM, Tan DW, Speksnijder EN, Hau CS, Clevers H, Barker N, Jonkers J: Developmental stage-specific contribution of LGR5(+) cells to basal and luminal epithelial lineages in the postnatal mammary gland. J Pathol. 2012, 228: 300-309.

Smalley M, Piggott L, Clarkson R: Breast cancer stem cells: Obstacles to therapy. Cancer Lett. 2012, 338: 57-62.

Iliopoulos D, Hirsch HA, Wang G, Struhl K: Inducible formation of breast cancer stem cells and their dynamic equilibrium with non-stem cancer cells via IL6 secretion. Proc Natl Acad Sci U S A. 2011, 108: 1397-1402.

Sarrio D, Franklin CK, Mackay A, Reis-Filho JS, Isacke CM: Epithelial and mesenchymal subpopulations within normal basal breast cell lines exhibit distinct stem cell/progenitor properties. Stem Cells. 2012, 30: 292-303.

Chaffer CL, Marjanovic ND, Lee T, Bell G, Kleer CG, Reinhardt F, D’Alessio AC, Young RA, Weinberg RA: Poised chromatin at the ZEB1 promoter enables breast cancer cell plasticity and enhances tumorigenicity. Cell. 2013, 154: 61-74.

Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS, Thorsen T, Quist H, Matese JC, Brown PO, Botstein D, Lønning PE, Børresen-Dale AL: Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A. 2001, 98: 10869-10874.

Banerji S, Cibulskis K, Rangel-Escareno C, Brown KK, Carter SL, Frederick AM, Lawrence MS, Sivachenko AY, Sougnez C, Zou L, Cortes ML, Fernandez-Lopez JC, Peng S, Ardlie KG, Auclair D, Bautista-Piña V, Duke F, Francis J, Jung J, Maffuz-Aziz A, Onofrio RC, Parkin M, Pho NH, Quintanar-Jurado V, Ramos AH, Rebollar-Vega R, Rodriguez-Cuevas S, Romero-Cordoba SL, Schumacher SE, Stransky N: Sequence analysis of mutations and translocations across breast cancer subtypes. Nature. 2012, 486: 405-409.

Shah SP, Roth A, Goya R, Oloumi A, Ha G, Zhao Y, Turashvili G, Ding J, Tse K, Haffari G, Bashashati A, Prentice LM, Khattra J, Burleigh A, Yap D, Bernard V, McPherson A, Shumansky K, Crisan A, Giuliany R, Heravi-Moussavi A, Rosner J, Lai D, Birol I, Varhol R, Tam A, Dhalla N, Zeng T, Ma K, Chan SK, et al: The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature. 2012, 486: 395-399.

Cancer Genome Atlas N: Comprehensive molecular portraits of human breast tumours. Nature. 2012, 490: 61-70.

Solin LJ, Gray R, Baehner FL, Butler SM, Hughes LL, Yoshizawa C, Cherbavaz DB, Shak S, Page DL, Sledge GW, Davidson NE, Ingle JN, Perez EA, Wood WC, Sparano JA, Badve S: A multigene expression assay to predict local recurrence risk for ductal carcinoma in situ of the breast. J Natl Cancer Inst. 2013, 105: 701-710.

Naba A, Clauser KR, Hoersch S, Liu H, Carr SA, Hynes RO: The matrisome: in silico definition and in vivo characterization by proteomics of normal and tumor extracellular matrices. MCP. 2012, 11: M111.014647-

Glukhova MA, Streuli CH: How integrins control breast biology. Curr Opin Cell Biol. 2013, 25: 633-641.

Ito Y, Iwase T, Hatake K: Eradication of breast cancer cells in patients with distant metastasis: the finishing touches?. Breast Cancer. 2012, 19: 206-211.

Sampieri K, Fodde R: Cancer stem cells and metastasis. Semin Cancer Biol. 2012, 22: 187-193.

Takebe N, Warren RQ, Ivy SP: Breast cancer growth and metastasis: interplay between cancer stem cells, embryonic signaling pathways and epithelial-to-mesenchymal transition. BCR. 2011, 13: 211-

Finak G, Bertos N, Pepin F, Sadekova S, Souleimanova M, Zhao H, Chen H, Omeroglu G, Meterissian S, Omeroglu A, Hallett M, Park M: Stromal gene expression predicts clinical outcome in breast cancer. Nat Med. 2008, 14: 518-527.

Kalluri R, Zeisberg M: Fibroblasts in cancer. Nat Rev Cancer. 2006, 6: 392-401.

Barker HE, Cox TR, Erler JT: The rationale for targeting the LOX family in cancer. Nat Rev Cancer. 2012, 12: 540-552.

Favaro E, Lord S, Harris AL, Buffa FM: Gene expression and hypoxia in breast cancer. Genome Med. 2011, 3: 55-

Milani M, Harris AL: Targeting tumour hypoxia in breast cancer. Eur J Cancer. 2008, 44: 2766-2773.

Lundgren K, Holm C, Landberg G: Hypoxia and breast cancer: prognostic and therapeutic implications. CMLS. 2007, 64: 3233-3247.

Ward C, Langdon SP, Mullen P, Harris AL, Harrison DJ, Supuran CT, Kunkler IH: New strategies for targeting the hypoxic tumour microenvironment in breast cancer. Cancer Treat Rev. 2013, 39: 171-179.

Bailey KM, Wojtkowiak JW, Hashim AI, Gillies RJ: Targeting the metabolic microenvironment of tumors. Adv Pharmacol. 2012, 65: 63-107.

Dos Santos CO, Rebbeck C, Rozhkova E, Valentine A, Samuels A, Kadiri LR, Osten P, Harris EY, Uren PJ, Smith AD, Hannon GJ: Molecular hierarchy of mammary differentiation yields refined markers of mammary stem cells. Proc Natl Acad Sci U S A. 2013, 110: 7123-7130.

Makarem M, Spike BT, Dravis C, Kannan N, Wahl GM, Eaves CJ: Stem cells and the developing mammary gland. J Mammary Gland Biol Neoplasia. 2013, 18: 209-219.

Visvader JE: Keeping abreast of the mammary epithelial hierarchy and breast tumorigenesis. Genes Dev. 2009, 23: 2563-2577.

Ablett MP, Singh JK, Clarke RB: Stem cells in breast tumours: are they ready for the clinic?. Eur J Cancer. 2012, 48: 2104-2116.

Badve S, Nakshatri H: Breast-cancer stem cells-beyond semantics. Lancet Oncol. 2012, 13: e43-e48.

Kaimala S, Bisana S, Kumar S: Mammary gland stem cells: more puzzles than explanations. J Biosci. 2012, 37: 349-358.

La Porta CA: Thoughts about cancer stem cells in solid tumors. World J Stem Cells. 2012, 4: 17-20.

Mani SA, Guo W, Liao MJ, Eaton EN, Ayyanan A, Zhou AY, Brooks M, Reinhard F, Zhang CC, Shipitsin M, Campbell LL, Polyak K, Brisken C, Yang J, Weinberg RA: The epithelial-mesenchymal transition generates cells with properties of stem cells. Cell. 2008, 133: 704-715.

Polyak K, Weinberg RA: Transitions between epithelial and mesenchymal states: acquisition of malignant and stem cell traits. Nat Rev Cancer. 2009, 9: 265-273.

Scheel C, Weinberg RA: Phenotypic plasticity and epithelial-mesenchymal transitions in cancer and normal stem cells?. Int J Cancer. 2011, 129: 2310-2314.

Al-Hajj M, Wicha MS, Benito-Hernandez A, Morrison SJ, Clarke MF: Prospective identification of tumorigenic breast cancer cells. Proc Natl Acad Sci U S A. 2003, 100: 3983-3988.

Harrison H, Farnie G, Howell SJ, Rock RE, Stylianou S, Brennan KR, Bundred NJ, Clarke RB: Regulation of breast cancer stem cell activity by signaling through the Notch4 receptor. Cancer Res. 2010, 70: 709-718.

Muller V, Riethdorf S, Rack B, Janni W, Fasching P, Solomayer E, Aktas B, Kasimir-Bauer S, Pantel K, Fehm T, DETECT study group: Prognostic impact of circulating tumor cells assessed with the Cell Search AssayTM and AdnaTest BreastTM in metastatic breast cancer patients: the DETECT study. BCR. 2012, 14: R118-

Giordano A, Gao H, Cohen EN, Anfossi S, Khoury J, Hess K, Krishnamurthy S, Tin S, Cristofanilli M, Hortobagyi GN, Woodward WA, Lucci A, Reuben JM: Clinical of cancer stem cells in bone marrow of early breast cancer patients. Ann Oncol. 2013, [Epud ahead of print]

Baccelli I, Schneeweiss A, Riethdorf S, Stenzinger A, Schillert A, Vogel V, Klein C, Saini M, Bauerle T, Wallwiener M, Holland-Letz T, Höfner T, Sprick M, Scharpff M, Marmé F, Sinn HP, Pantel K, Weichert W, Trumpp A: Identification of a population of blood circulating tumor cells from breast cancer patients that initiates metastasis in a xenograft assay. Nat Biotechnol. 2013, 31: 539-544.

Willis L, Graham TA, Alarcon T, Alison MR, Tomlinson IP, Page KM: What can be learnt about disease progression in breast cancer dormancy from relapse data?. PloS one. 2013, 8: e62320-

Balic M, Lin H, Williams A, Datar RH, Cote RJ: Progress in circulating tumor cell capture and analysis: implications for cancer management. Expert Rev Mol Diagn. 2012, 12: 303-312.

Barriere G, Riouallon A, Renaudie J, Tartary M, Rigaud M: Mesenchymal and stemness circulating tumor cells in early breast cancer diagnosis. BMC Cancer. 2012, 12: 114-

Sceneay J, Smyth MJ, Moller A: The pre-metastatic niche: finding common ground. Cancer Metastasis Rev. 2013, [Epud ahead of print]

Peinado H, Lavotshkin S, Lyden D: The secreted factors responsible for pre-metastatic niche formation: old sayings and new thoughts. Semin Cancer Biol. 2011, 21: 139-146.

Nguyen DX, Bos PD, Massague J: Metastasis: from dissemination to organ-specific colonization. Nat Rev Cancer. 2009, 9: 274-284.

Hu G, Kang Y, Wang XF: From breast to the brain: unraveling the puzzle of metastasis organotropism. J Mole Cell Biol. 2009, 1: 3-5.

Hsieh SM, Look MP, Sieuwerts AM, Foekens JA, Hunter KW: Distinct inherited metastasis susceptibility exists for different breast cancer subtypes: a prognosis study. BCR. 2009, 11: R75-

Scheel C, Weinberg RA: Cancer stem cells and epithelial-mesenchymal transition: concepts and molecular links. Semin Cancer Biol. 2012, 22: 396-403.

Dave B, Mittal V, Tan NM, Chang JC: Epithelial-mesenchymal transition, cancer stem cells and treatment resistance. BCR. 2012, 14: 202-

Drasin DJ, Robin TP, Ford HL: Breast cancer epithelial-to-mesenchymal transition: examining the functional consequences of plasticity. BCR. 2011, 13: 226-

Giordano A, Gao H, Anfossi S, Cohen E, Mego M, Lee BN, Tin S, De Laurentiis M, Parker CA, Alvarez RH, Valero V, Ueno NT, De Placido S, Mani SA, Esteva FJ, Cristofanilli M, Reuben JM: Epithelial-mesenchymal transition and stem cell markers in patients with HER2-positive metastatic breast cancer. Mol Cancer Ther. 2012, 11: 2526-2534.

Kasimir-Bauer S, Hoffmann O, Wallwiener D, Kimmig R, Fehm T: Expression of stem cell and epithelial-mesenchymal transition markers in primary breast cancer patients with circulating tumor cells. BCR. 2012, 14: R15-

Chui MH: Insights into cancer metastasis from a clinicopathologic perspective: Epithelial-Mesenchymal Transition is not a necessary step. Int J Cancer. 2013, 132: 1487-1495.

Marchini C, Montani M, Konstantinidou G, Orru R, Mannucci S, Ramadori G, Gabrielli F, Baruzzi A, Berton G, Merigo F, Fin S, Iezzi M, Bisaro B, Sbarbati A, Zerani M, Galiè M, Amici A: Mesenchymal/stromal gene expression signature relates to basal-like breast cancers, identifies bone metastasis and predicts resistance to therapies. PloS one. 2010, 5: e14131-

Kim MY, Oskarsson T, Acharyya S, Nguyen DX, Zhang XH, Norton L, Massague J: Tumor self-seeding by circulating cancer cells. Cell. 2009, 139: 1315-1326.

Comen E, Norton L: Self-seeding in cancer. Recent Res Cancer Res. 2012, 195: 13-23.

Gorges TM, Tinhofer I, Drosch M, Rose L, Zollner TM, Krahn T, von Ahsen O: Circulating tumour cells escape from EpCAM-based detection due to epithelial-to-mesenchymal transition. BMC Cancer. 2012, 12: 178-

Kallergi G, Papadaki MA, Politaki E, Mavroudis D, Georgoulias V, Agelaki S: Epithelial to mesenchymal transition markers expressed in circulating tumour cells of early and metastatic breast cancer patients. BCR. 2011, 13: R59-

Yu M, Bardia A, Wittner BS, Stott SL, Smas ME, Ting DT, Isakoff SJ, Ciciliano JC, Wells MN, Shah AM, Concannon KF, Donaldson MC, Sequist LV, Brachtel E, Sgroi D, Baselga J, Ramaswamy S, Toner M, Haber DA, Maheswaran S: Circulating breast tumor cells exhibit dynamic changes in epithelial and mesenchymal composition. Science. 2013, 339: 580-584.

De Mattos-Arruda L, Cortes J, Santarpia L, Vivancos A, Tabernero J, Reis-Filho JS, Seoane J: Circulating tumour cells and cell-free DNA as tools for managing breast cancer. Nat Rev Clin Oncol. 2013, 10: 377-389.

Murtaza M, Dawson SJ, Tsui DW, Gale D, Forshew T, Piskorz AM, Parkinson C, Chin SF, Kingsbury Z, Wong AS, Marass F, Humphray S, Hadfield J, Bentley D, Chin TM, Brenton JD, Caldas C, Rosenfeld N: Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA. Nature. 2013, 497: 108-112.

Zhang F, Chen JY: Breast cancer subtyping from plasma proteins. BMC Med Genomics. 2013, 6: S6-

Corcoran C, Friel AM, Duffy MJ, Crown J, O’Driscoll L: Intracellular and extracellular microRNAs in breast cancer. Clin Chem. 2011, 57: 18-32.

Hendrix A, Hume AN: Exosome signaling in mammary gland development and cancer. Int J Dev Biol. 2011, 55: 879-887.

Marleau AM, Chen CS, Joyce JA, Tullis RH: Exosome removal as a therapeutic adjuvant in cancer. J Transl Med. 2012, 10: 134-

Eccles SA, Paon L: Breast cancer metastasis: when, where, how?. Lancet. 2005, 365: 1006-1007.

Eccles: Growth regulatory pathways contributing to organ selectivity of metastasis. Cancer Metastasis: Biologic Basis and Therapeutics. 2011, Cambridge: Cambridge University Press, 204-214.

Chapter   Google Scholar  

Mina LA, Sledge GW: Rethinking the metastatic cascade as a therapeutic target. Nat Rev Clin Oncol. 2011, 8: 325-332.

Wilson C, Holen I, Coleman RE: Seed, soil and secreted hormones: potential interactions of breast cancer cells with their endocrine/paracrine microenvironment and implications for treatment with bisphosphonates. Cancer Treat Rev. 2012, 38: 877-889.

Fidler IJ: The role of the organ microenvironment in brain metastasis. Semin Cancer Biol. 2011, 21: 107-112.

Peto R, Davies C, Godwin J, Gray R, Pan HC, Clarke M, Cutter D, Darby S, McGale P, Taylor C, Wang YC, Bergh J, Di Leo A, Albain K, Swain S, Piccart M, Pritchard K, Early Breast Cancer Trialists’ Collaborative G: Comparisons between different polychemotherapy regimens for early breast cancer: meta-analyses of long-term outcome among 100,000 women in 123 randomised trials. Lancet. 2012, 379: 432-444.

Darby S, McGale P, Correa C, Taylor C, Arriagada R, Clarke M, Cutter D, Davies C, Ewertz M, Godwin J, Gray R, Pierce L, Whelan T, Wang Y, Peto R, Early Breast Cancer Trialists’ Collaborative G: Effect of radiotherapy after breast-conserving surgery on 10-year recurrence and 15-year breast cancer death: meta-analysis of individual patient data for 10,801 women in 17 randomised trials. Lancet. 2011, 378: 1707-1716.

Davies C, Godwin J, Gray R, Clarke M, Cutter D, Darby S, McGale P, Pan HC, Taylor C, Wang YC, Dowsett M, Ingle J, Peto R, Early Breast Cancer Trialists’ Collaborative G: Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: patient-level meta-analysis of randomised trials. Lancet. 2011, 378: 771-784.

Senkus EKS, Penault-Llorca F, Poortmans P, Thompson A, Zackrisson S, Cardoso F: ESMO Guidelines Working Group. Ann Oncol. 2013, doi: 10.1093/annonc/mdt284

Khoury T: Delay to formalin fixation alters morphology and immunohistochemistry for breast carcinoma. Appl Immunohistochem Mol Morphol. 2012, 20: 531-542.

Dale DC: Poor prognosis in elderly patients with cancer: the role of bias and undertreatment. J Support Oncol. 2003, 1: 11-17.

Seah MD, Chan PM: Rethinking undertreatment in elderly breast cancer patients. Asian J Surg. 2009, 32: 71-75.

Harder H, Ballinger R, Langridge C, Ring A, Fallowfield LJ: Adjuvant chemotherapy in elderly women with breast cancer: patients’ perspectives on information giving and decision making. Psychooncology. 2013, doi: 10.1002/pon.3338

Ring A, Harder H, Langridge C, Ballinger RS, Fallowfield LJ: Adjuvant chemotherapy in elderly women with breast cancer (AChEW): an observational study identifying MDT perceptions and barriers to decision making. Ann Oncol. 2013, 24: 1211-1219.

Armes J, Crowe M, Colbourne L, Morgan H, Murrells T, Oakley C, Palmer N, Ream E, Young A, Richardson A: Patients’ supportive care needs beyond the end of cancer treatment: a prospective, longitudinal survey. J Clin Oncol. 2009, 27: 6172-6179.

Maguire P: Psychological aspects. ABC of Breast Diseases. 2002, London: BMJ Books, 150-153. 2nd Edition. Edited by Dixon M.

Hulbert-Williams N, Neal R, Morrison V, Hood K, Wilkinson C: Anxiety, depression and quality of life after cancer diagnosis: what psychosocial variables best predict how patients adjust?. Psychooncology. 2011, doi: 10.1002/pon.1980

Jacobsen PB: Screening for psychological distress in cancer patients: challenges and opportunities. J Clin Oncol. 2007, 25: 4526-4527.

The International. Psycho-Oncology Society. http://www.ipos-society.org/about/news/standards_news.aspx ,

Thompson AM, Moulder-Thompson SL: Neoadjuvant treatment of breast cancer. Ann Oncol. 2012, 23: x231-x236.

Bartelink H, Horiot JC, Poortmans PM, Struikmans H, Van den Bogaert W, Fourquet A, Jager JJ, Hoogenraad WJ, Oei SB, Warlam-Rodenhuis CC, Pierart M, Collette L: Impact of a higher radiation dose on local control and survival in breast-conserving therapy of early breast cancer: 10-year results of the randomized boost versus no boost EORTC 22881–10882 trial. J Clin Oncol. 2007, 25: 3259-3265.

Whelan TJ, Pignol JP, Levine MN, Julian JA, MacKenzie R, Parpia S, Shelley W, Grimard L, Bowen J, Lukka H, Perera F, Fyles A, Schneider K, Gulavita S, Freeman C: Long-term results of hypofractionated radiation therapy for breast cancer. N Engl J Med. 2010, 362: 513-520.

Group ST, Bentzen SM, Agrawal RK, Aird EG, Barrett JM, Barrett-Lee PJ, Bliss JM, Brown J, Dewar JA, Dobbs HJ, Haviland JS, Hoskin PJ, Hopwood P, Lawton PA, Magee BJ, Mills J, Morgan DA, Owen JR, Simmons S, Sumo G, Sydenham MA, Venables K, Yarnold JR: The UK Standardisation of Breast Radiotherapy (START) Trial A of radiotherapy hypofractionation for treatment of early breast cancer: a randomised trial. Lancet Oncol. 2008, 9: 331-341.

Group ST, Bentzen SM, Agrawal RK, Aird EG, Barrett JM, Barrett-Lee PJ, Bentzen SM, Bliss JM, Brown J, Dewar JA, Dobbs HJ, Haviland JS, Hoskin PJ, Hopwood P, Lawton PA, Magee BJ, Mills J, Morgan DA, Owen JR, Simmons S, Sumo G, Sydenham MA, Venables K, Yarnold JR: The UK Standardisation of Breast Radiotherapy (START) Trial B of radiotherapy hypofractionation for treatment of early breast cancer: a randomised trial. Lancet. 2008, 371: 1098-1107.

Vaidya JS, Joseph DJ, Tobias JS, Bulsara M, Wenz F, Saunders C, Alvarado M, Flyger HL, Massarut S, Eiermann W, Keshtgar M, Dewar J, Kraus-Tiefenbacher U, Sütterlin M, Esserman L, Holtveg HM, Roncadin M, Pigorsch S, Metaxas M, Falzon M, Matthews A, Corica T, Williams NR, Baum M: Targeted intraoperative radiotherapy versus whole breast radiotherapy for breast cancer (TARGIT-A trial): an international, prospective, randomised, non-inferiority phase 3 trial. Lancet. 2010, 376: 91-102.

Rampinelli C, Bellomi M, Ivaldi GB, Intra M, Raimondi S, Meroni S, Orecchia R, Veronesi U: Assessment of pulmonary fibrosis after radiotherapy (RT) in breast conserving surgery: comparison between conventional external beam RT (EBRT) and intraoperative RT with electrons (ELIOT). Technol Cancer Res Treat. 2011, 10: 323-329.

Hannoun-Levi JM, Resch A, Gal J, Kauer-Dorner D, Strnad V, Niehoff P, Loessl K, Kovacs G, Van Limbergen E, Polgar C, On behalf of the GEC-ESTRO Breast Cancer Working Group: Accelerated partial breast irradiation with interstitial brachytherapy as second conservative treatment for ipsilateral breast tumour recurrence: Multicentric study of the GEC-ESTRO Breast Cancer Working Group. Radiother Oncol. 2013, doi: 1016/j.radonc.2013.03.026

Smith BD, Arthur DW, Buchholz TA, Haffty BG, Hahn CA, Hardenbergh PH, Julian TB, Marks LB, Todor DA, Vicini FA, Whelan TJ, White J, Wo JY, Harris JR: Accelerated partial breast irradiation consensus statement from the American Society for Radiation Oncology (ASTRO). Int J Radiat Oncol Biol Phys. 2009, 74: 987-1001.

Polgar C, Van Limbergen E, Potter R, Kovacs G, Polo A, Lyczek J, Hildebrandt G, Niehoff P, Guinot JL, Guedea F, Johansson B, Ott OJ, Major T, Strnad V, GEC-ESTRO Breast Cancer Working Group: Patient selection for accelerated partial-breast irradiation (APBI) after breast-conserving surgery: recommendations of the Groupe Europeen de Curietherapie-European Society for Therapeutic Radiology and Oncology (GEC-ESTRO) breast cancer working group based on clinical evidence (2009). Radiother Oncol. 2010, 94: 264-273.

Tinterri C, Gatzemeier W, Zanini V, Regolo L, Pedrazzoli C, Rondini E, Amanti C, Gentile G, Taffurelli M, Fenaroli P, Tondini C, Saccetto G, Sismondi P, Murgo R, Orlandi M, Cianchetti E, Andreoli C: Conservative surgery with and without radiotherapy in elderly patients with early-stage breast cancer: a prospective randomised multicentre trial. Breast. 2009, 18: 373-377.

Hughes KS, Schnaper LA, Cirrincione C, Berry DA, McCormick B, Muss HB, Shank B, Hudis C, Winer EP, Smith BL: ASCO Annual Meeting 2010. 2010, Lumpectomy plus tamoxifen with or without irradiation in women age 70 or older with early breast cancer, Journal of Clinical Oncology,

Lipkus IM, Peters E, Kimmick G, Liotcheva V, Marcom P: Breast cancer patients’ treatment expectations after exposure to the decision aid program adjuvant online: the influence of numeracy. Med Decis Making. 2010, 30: 464-473.

Fallowfield L, Jenkins V, Farewell V, Saul J, Duffy A, Eves R: Efficacy of a Cancer Research UK communication skills training model for oncologists: a randomised controlled trial. Lancet. 2002, 359: 650-656.

El Turabi A, Abel GA, Roland M, Lyratzopoulos G: Variation in reported experience of involvement in cancer treatment decision making: evidence from the National Cancer Patient Experience Survey. Br J Cancer. 2013, 109: 780-787.

Fleissig A, Fallowfield LJ, Langridge CI, Johnson L, Newcombe RG, Dixon JM, Kissin M, Mansel RE: Post-operative arm morbidity and quality of life. Results of the ALMANAC randomised trial comparing sentinel node biopsy with standard axillary treatment in the management of patients with early breast cancer. Breast Cancer Res Treat. 2006, 95: 279-293.

Giuliano AE, Hunt KK, Ballman KV, Beitsch PD, Whitworth PW, Blumencranz PW, Leitch AM, Saha S, McCall LM, Morrow M: Axillary dissection vs no axillary dissection in women with invasive breast cancer and sentinel node metastasis: a randomized clinical trial. JAMA. 2011, 305: 569-575.

Rutgers EJ, Donker M, Straver ME, Meijnen P, Van De Velde CJ, Mansel RE, Westenberg H, Orzales L, Bouma WH, van der Mijle H, Nieuwenhuijzen P, Sanne C, Veltkamp LS, Messina CGM, Duez NJ, Hurkmans C, Bogaerts J, van Tienhoven G: ASCO Annual Meeting. 2013, Radiotherapy or surgery of the axilla after a positive sentinel node in breast cancer patients: final analysis of the EORTC AMAROS trial (10981/22023), Journal of Clinical Oncology,

Smith BD: Using chemotherapy response to personalize choices regarding locoregional therapy: a new era in breast cancer treatment?. J Clin Oncol. 2012, 30: 3913-3915.

Azim HA, Michiels S, Zagouri F, Delaloge S, Filipits M, Namer M, Neven P, Symmans WF, Thompson A, Andre F, Loi S, Swanton C: Utility of prognostic genomic tests in breast cancer practice: The IMPAKT 2012 Working Group Consensus Statement. Ann Oncol. 2013, 24: 647-654.

Wei S, Liu L, Zhang J, Bowers J, Gowda GA, Seeger H, Fehm T, Neubauer HJ, Vogel U, Clare SE, Raferty D: Metabolomics approach for predicting response to neoadjuvant chemotherapy for breast cancer. Mol Oncol. 2013, 7: 297-307.

Dowsett M, Cuzick J, Wale C, Forbes J, Mallon EA, Salter J, Quinn E, Dunbier A, Baum M, Buzdar A, Howell A, Bugarini R, Baehner FL, Shak S: Prediction of risk of distant recurrence using the 21-gene recurrence score in node-negative and node-positive postmenopausal patients with breast cancer treated with anastrozole or tamoxifen: a TransATAC study. J Clin Oncol. 2010, 28: 1829-1834.

Albain KS, Barlow WE, Shak S, Hortobagyi GN, Livingston RB, Yeh IT, Ravdin P, Bugarini R, Baehner FL, Davidson NE, Sledge GW, Winer EP, Hudis C, Ingle JN, Perez EA, Pritchard KI, Shepherd L, Gralow JR, Yoshizawa C, Allred DC, Osborne CK, Hayes DF, Breast Cancer Intergroup of North America: Prognostic and predictive value of the 21-gene recurrence score assay in postmenopausal women with node-positive, oestrogen-receptor-positive breast cancer on chemotherapy: a retrospective analysis of a randomised trial. Lancet Oncol. 2010, 11: 55-65.

Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, Baehner FL, Walker MG, Watson D, Park T, Hiller W, Fisher ER, Wickerham DL, Bryant J, Wolmark N: A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004, 351: 2817-2826.

Coates PJ, Appleyard MV, Murray K, Ackland C, Gardner J, Brown DC, Adamson DJ, Jordan LB, Purdie CA, Munro AJ, Wright EG, Dewar JA, Thompson AM: Differential contextual responses of normal human breast epithelium to ionizing radiation in a mouse xenograft model. Can Res. 2010, 70: 9808-9815.

Arslan UY, Oksuzoglu B, Aksoy S, Harputluoglu H, Turker I, Ozisik Y, Dizdar O, Altundag K, Alkis N, Zengin N: Breast cancer subtypes and outcomes of central nervous system metastases. Breast. 2011, 20: 562-567.

Rennert G, Pinchev M, Rennert HS: Use of bisphosphonates and risk of postmenopausal breast cancer. J Clin Oncol. 2010, 28: 3577-3581.

Chlebowski RT, Col N: Bisphosphonates and breast cancer prevention. Anticancer Agents Med Chem. 2012, 12: 144-150.

Coleman RE: Adjuvant bone-targeted therapy to prevent metastasis: lessons from the AZURE study. Curr Opin Support Palliat Care. 2012, 6: 322-329.

Paterson AH, Anderson SJ, Lembersky BC, Fehrenbacher L, Falkson CI, King KM, Weir LM, Brufsky AM, Dakhil S, Lad T, Baez-Diaz L, Gralow JR, Robidoux A, Perez EA, Zheng P, Geyer CE, Swain S, Costantino JP, Mamounas EP, Wolmark N: Oral clodronate for adjuvant treatment of operable breast cancer (National Surgical Adjuvant Breast and Bowel Project protocol B-34): a multicentre, placebo-controlled, randomised trial. Lancet Oncol. 2012, 13: 734-742.

Gnant M, Dubsky P, Hadji P: Bisphosphonates: prevention of bone metastases in breast cancer. Recent Results Cancer Res. 2012, 192: 65-91.

Comen E, Norton L, Massague J: Clinical implications of cancer self-seeding. Nat Rev Clin Oncol. 2011, 8: 369-377.

Azim H, Azim HA: Targeting RANKL in breast cancer: bone metastasis and beyond. Expert Rev Anticancer Ther. 2013, 13: 195-201.

Drooger JC, van der Padt A, Sleijfer S, Jager A: Denosumab in breast cancer treatment. Eur J Pharmacol. 2013, doi: 10.1016/j.ejphar.2013.03.034

Formenti SC, Demaria S: Radiation therapy to convert the tumor into an in situ vaccine. Int J Radiat Oncol Biol Phys. 2012, 84: 879-880.

Liauw SL, Connell PP, Weichselbaum RR: New paradigms and future challenges in radiation oncology: an update of biological targets and technology. Sci Transl Med. 2013, 5: 173sr172-

Coles CE, Brunt AM, Wheatley D, Mukesh MB, Yarnold JR: Breast radiotherapy: less is more?. Clin Oncol (R Coll Radiol). 2013, 25: 127-134.

Yarnold J, Bentzen SM, Coles C, Haviland J: Hypofractionated whole-breast radiotherapy for women with early breast cancer: myths and realities. Int J Radiat Oncol Biol Phys. 2011, 79: 1-9.

Mannino M, Yarnold JR: Local relapse rates are falling after breast conserving surgery and systemic therapy for early breast cancer: can radiotherapy ever be safely withheld?. Radiother Oncol. 2009, 90: 14-22.

Blamey RW, Bates T, Chetty U, Duffy SW, Ellis IO, George D, Mallon E, Mitchell MJ, Monypenny I, Morgan DA, Macmillan RD, Patnick J, Pinder SE: Radiotherapy or tamoxifen after conserving surgery for breast cancers of excellent prognosis: British Association of Surgical Oncology (BASO) II trial. Eur J Cancer. 2013, 49: 2294-2302.

Kunkler I: Adjuvant chest wall radiotherapy for breast cancer: black, white and shades of grey. Eur J Surg Oncol. 2010, 36: 331-334.

Critchley AC, Thompson AM, Chan HY, Reed MW: Current controversies in breast cancer surgery. Clin Oncol (R Coll Radiol). 2013, 25: 101-108.

Riou O, Lemanski C, Guillaumon V, Lauche O, Fenoglietto P, Dubois JB, Azria D: Role of the radiotherapy boost on local control in ductal carcinoma in situ. Int J Surg Oncol. 2012, 2012: 748196-

PubMed   PubMed Central   Google Scholar  

Kirkbride P, Hoskin PJ: Implementation of stereotactic ablative radiotherapy (stereotactic body radiotherapy). Clin Oncol (R Coll Radiol). 2012, 24: 627-628.

Somaiah N, Yarnold J, Lagerqvist A, Rothkamm K, Helleday T: Homologous recombination mediates cellular resistance and fraction size sensitivity to radiation therapy. Radiother Oncol. 2013, 1008: 155-1561.

Dowsett M, Nielsen TO, A’Hern R, Bartlett J, Coombes RC, Cuzick J, Ellis M, Henry NL, Hugh JC, Lively T, McShane L, Paik S, Penault-Llorca E, Prudkin L, Regan M, Salter J, Sotiriou C, Smith IE, Viale G, Zujewski JA, Hayes DF, International Ki-67 in Breast Cancer Working Group: Assessment of Ki67 in breast cancer: recommendations from the International Ki67 in Breast Cancer working group. J Natl Cancer Inst. 2011, 103: 1656-1664.

Paik S, Tang G, Shak S, Kim C, Baker J, Kim W, Cronin M, Baehner FL, Watson D, Bryant J, Costantino JP, Geyer CE JR, Wickerham DL, Wolmark N: Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol. 2006, 24: 3726-3734.

van de Vijver MJ, He YD, van’t Veer LJ, Dai H, Hart AA, Voskuil DW, Schreiber GJ, Peterse JL, Roberts C, Marton MJ, Parrish M, Atsma D, Witteveen A, Glas A, Delahaye L, van der Velds T, Bartelink H, Rodenhuis S, Rutgers ET, Friend SH, Bernards R: A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002, 347: 1999-2009.

Goss PE, Ingle JN, Martino S, Robert NJ, Muss HB, Piccart MJ, Castiglione M, Tu D, Shepherd LE, Pritchard KI, Livingston RB, Davidson NE, Norton L, Peres ES, Abrams JS, Cameron DA, Palmer MJ, Pater JL, et al: Randomized trial of letrozole following tamoxifen as extended adjuvant therapy in receptor-positive breast cancer: updated findings from NCIC CTG MA.17. J Natl Cancer Inst. 2005, 97: 1262-1271.

Davies C, Pan H, Godwin J, Gray R, Arriagada R, Raina V, Abraham M, Medeiros Alencar VH, Badran A, Bonfill X, Bradbury J, Clarke M, Collins R, Davis SR, Delmestri A, Fores JF, Haddad P, Hou MF, Inbar M, Khaled H, Kielanowska J, Kwan WH, Mathew BS, Mittra I, Muller B, Nicolucci A, Peralta O, Pernas F, Petruzelka L, Pienkowski T, et al: Long-term effects of continuing adjuvant tamoxifen to 10 years versus stopping at 5 years after diagnosis of oestrogen receptor-positive breast cancer: ATLAS, a randomised trial. Lancet. 2013, 381: 805-816.

Jakesz R, Jonat W, Gnant M, Mittlboeck M, Greil R, Tausch C, Hilfrich J, Kwasny W, Menzel C, Samonigg H, Seifert M, Gademann G, Kaufmann M, Woldgang J, ABCSG and the GABG: Switching of postmenopausal women with endocrine-responsive early breast cancer to anastrozole after 2 years’ adjuvant tamoxifen: combined results of ABCSG trial 8 and ARNO 95 trial. Lancet. 2005, 366: 455-462.

Goldhirsch A, Ingle JN, Gelber RD, Coates AS, Thurlimann B, Senn HJ: Panel m: Thresholds for therapies: highlights of the St Gallen International Expert Consensus on the primary therapy of early breast cancer 2009. Ann Oncol. 2009, 20: 1319-1329.

Osborne CK, Neven P, Dirix LY, Mackey JR, Robert J, Underhill C, Schiff R, Gutierrez C, Migliaccio I, Anagnostou VK, Rimm DL, Magill P, Sellers M: Gefitinib or placebo in combination with tamoxifen in patients with hormone receptor-positive metastatic breast cancer: a randomized phase II study. Clin Cancer Res. 2011, 17: 1147-1159.

Carlson RW, O’Neill A, Vidaurre T, Gomez HL, Badve SS, Sledge GW: A randomized trial of combination anastrozole plus gefitinib and of combination fulvestrant plus gefitinib in the treatment of postmenopausal women with hormone receptor positive metastatic breast cancer. Breast Cancer Res Treat. 2012, 133: 1049-1056.

Baselga J, Bradbury I, Eidtmann H, Di Cosimo S, de Azambuja E, Aura C, Gomez H, Dinh P, Fauria K, Van Dooren V, Aktan G, Goldkirsch A, Chang TW, Horvath Z, Coccia-Portugal M, Dormont J, Tseng LM, Kunz G, Sohn JH, Semiglazov V, Lerzo G, Palacova M, Probachai V, Pusztai L, Untch M, Gelber RD, Piccart-Gebhart M, NeoALTTO Study Team: Lapatinib with trastuzumab for HER2-positive early breast cancer (NeoALTTO): a randomised, open-label, multicentre, phase 3 trial. Lancet. 2012, 379: 633-640.

Hamilton-Burke W, Coleman L, Cummings M, Green CA, Holliday DL, Horgan K, Maraqa L, Peter MB, Pollock S, Shaaban AM, Smith L, Speirs V: Phosphorylation of estrogen receptor beta at serine 105 is associated with good prognosis in breast cancer. Am J Pathol. 2010, 177: 1079-1086.

O’Hara J, Vareslija D, McBryan J, Bane F, Tibbitts P, Byrne C, Conroy RM, Hao Y, Gaora PO, Hill AD, McIlroy M, Young LS: AIB1:ERalpha transcriptional activity is selectively enhanced in aromatase inhibitor-resistant breast cancer cells. Clin Cancer Res. 2012, 18: 3305-3315.

Santen RJ, Fan P, Zhang Z, Bao Y, Song RX, Yue W: Estrogen signals via an extra-nuclear pathway involving IGF-1R and EGFR in tamoxifen-sensitive and -resistant breast cancer cells. Steroids. 2009, 74: 586-594.

Ross-Innes CS, Stark R, Teschendorff AE, Holmes KA, Ali HR, Dunning MJ, Brown GD, Gojis O, Ellis IO, Green AR, Ali S, Chin SF, Palmieri C, Caldas C, Carroll JS: Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature. 2012, 481: 389-393.

Di Leva G, Gasparini P, Piovan C, Ngankeu A, Garofalo M, Taccioli C, Iorio MV, Li M, Volinia S, Alder H, Nakamura T, Nuovo G, Liu Y, Nephew KP, Croce CM: MicroRNA cluster 221–222 and estrogen receptor alpha interactions in breast cancer. J Natl Cancer Inst. 2010, 102: 706-721.

Dunn BK, Jegalian K, Greenwald P: Biomarkers for early detection and as surrogate endpoints in cancer prevention trials: issues and opportunities. Recent Results Cancer Res. 2011, 188: 21-47.

Pece S, Tosoni D, Confalonieri S, Mazzarol G, Vecchi M, Ronzoni S, Bernard L, Viale G, Pelicci PG, Di Fiore PP: Biological and molecular heterogeneity of breast cancers correlates with their cancer stem cell content. Cell. 2010, 140: 62-73.

Giamas G, Filipovic A, Jacob J, Messier W, Zhang H, Yang D, Zhang W, Shifa BA, Photiou A, Tralau-Stewart C, Castellano L, Green AR, Coombes RC, Ellis IO, Ali S, Lenz HJ, Stebbing J: Kinome screening for regulators of the estrogen receptor identifies LMTK3 as a new therapeutic target in breast cancer. Nat Med. 2011, 17: 715-719.

Johnston S, Pippen J, Pivot X, Lichinitser M, Sadeghi S, Dieras V, Gomez HL, Romieu G, Manikhas A, Kennedy MJ, Press MF, Maltzman J, Florance A, O’Rourke L, Oliva C, Stein S, Pegram M: Lapatinib combined with letrozole versus letrozole and placebo as first-line therapy for postmenopausal hormone receptor-positive metastatic breast cancer. J Clin Oncol. 2009, 27: 5538-5546.

Elsberger B, Paravasthu DM, Tovey SM, Edwards J: Shorter disease-specific survival of ER-positive breast cancer patients with high cytoplasmic Src kinase expression after tamoxifen treatment. J Cancer Res Clin Oncol. 2012, 138: 327-332.

Iorns E, Turner NC, Elliott R, Syed N, Garrone O, Gasco M, Tutt AN, Crook T, Lord CJ, Ashworth A: Identification of CDK10 as an important determinant of resistance to endocrine therapy for breast cancer. Cancer Cell. 2008, 13: 91-104.

Turner N, Pearson A, Sharpe R, Lambros M, Geyer F, Lopez-Garcia MA, Natrajan R, Marchio C, Iorns E, Mackay A, Gillett C, Grigoriadis A, Tutt A, Reis-Filho JS: FGFR1 amplification drives endocrine therapy resistance and is a therapeutic target in breast cancer. Cancer Res. 2010, 70: 2085-2094.

Higgins MJ, Baselga J: Targeted therapies for breast cancer. J Clin Invest. 2011, 121: 3797-3803.

Gnant M: Overcoming endocrine resistance in breast cancer: importance of mTOR inhibition. Expert Rev Anticancer Ther. 2012, 12: 1579-1589.

Zardavas D, Baselga J, Piccart M: Emerging targeted agents in metastatic breast cancer. Nat Rev Clin Oncol. 2013, 10: 191-210.

Moulder S, Moroney J, Helgason T, Wheler J, Booser D, Albarracin C, Morrow PK, Koenig K, Kurzrock R: Responses to liposomal Doxorubicin, bevacizumab, and temsirolimus in metaplastic carcinoma of the breast: biologic rationale and implications for stem-cell research in breast cancer. J Clin Oncol. 2011, 29: e572-e575.

Hoelder S, Clarke PA, Workman P: Discovery of small molecule cancer drugs: successes, challenges and opportunities. Mol Oncol. 2012, 6: 155-176.

Kauselmann G, Dopazo A, Link W: Identification of disease-relevant genes for molecularly-targeted drug discovery. Curr Cancer Drug Targets. 2012, 12: 1-13.

Swain SM, Kim SB, Cortes J, Ro J, Semiglazov V, Campone M, Ciruelos E, Ferrero JM, Schneeweiss A, Knott A, Clark E, Ross G, Benyunes MC, Baselga J: Pertuzumab, trastuzumab, and docetaxel for HER2-positive metastatic breast cancer (CLEOPATRA study): overall survival results from a randomised, double-blind, placebo-controlled, phase 3 study. Lancet Oncol. 2013, 14: 461-471.

Criscitiello C, Azim HA, Agbor-Tarh D, de Azambuja E, Piccart M, Baselga J, Eidtmann H, Di Cosimo S, Bradbury I, Rubio IT: Factors associated with surgical management following neoadjuvant therapy in patients with primary HER2-positive breast cancer: results from the NeoALTTO phase III trial. Ann Oncol. 2013, 24: 1980-1985.

Goldhirsch A, Piccart-Gebhart MJ, Procter M, Azambuja E de, Weber HA, Untch M, Smith I, Gianni L, Jackisch C, Cameron D, Bell R, Dowsett M, Gelber RD, Leyland-Jones B, Baselga J: The HERA Study Team HERA TRIAL: 2 years versus 1 year of trastuzumab after adjuvant chemotherapy in women with HER2-positive early breast cancer at 8 years of median follow up. Cancer Research. 72 (24): December 15, 2012 Supplement 3;

Pivot X, Romieu G, Debled M, Pierga JY, Kerbrat P, Bachelot T, Lortholary A, Espie M, Fumoleau P, Serin D, Jacquin JP, Jouannaud C, Rios M, Abadie-Lacourtoisie S, Tubiana-Mathieu N, Cany L, Catala S, Khayat D, Pauporte I, Kramar A, PHARE trial investigators: 6 months versus 12 months of adjuvant trastuzumab for patients with HER2-positive early breast cancer (PHARE): a randomised phase 3 trial. Lancet Oncol. 2013, 14: 741-748.

Tenori L, Oakman C, Claudino WM, Bernini P, Cappadona S, Nepi S, Biganzoli L, Arbushites MC, Luchinat C, Bertini I, Di Leo A: Exploration of serum metabolomic profiles and outcomes in women with metastatic breast cancer: a pilot study. Mol Oncol. 2012, 6: 437-444.

Duncan JS, Whittle MC, Nakamura K, Abell AN, Midland AA, Zawistowski JS, Johnson NL, Granger DA, Jordan NV, Darr DB, Usary J, Kuan PF, Smalley DM, Major B, He X, Hoadley KA, Zhou B, Sharpless NE, Perou C, Kim WY, Gomez SM, Chen X, Jin J, Frye SV, Earp HS, Graves LM, Johnson GL: Dynamic reprogramming of the kinome in response to targeted MEK inhibition in triple-negative breast cancer. Cell. 2012, 149: 307-321.

Heiser LM, Sadanandam A, Kuo WL, Benz SC, Goldstein TC, Ng S, Gibb WJ, Wang NJ, Ziyad S, Tong F, Bayani N, Hu Z, Billig JI, Dueregger A, Lewis S, Jakkula L, Korkola JE, Durinck S, Pepin F, Guan Y, Purdom E, Neuvial P, Bengtsson H, Wood KW, Smith PG, Vassiley LT, Hennessy BT, Greshock J, Bachman KE, Hardwicke MA, et al: Subtype and pathway specific responses to anticancer compounds in breast cancer. Proc Natl Acad Sci U S A. 2012, 109: 2724-2729.

Kelly CM, Buzdar AU: Using multiple targeted therapies in oncology: considerations for use, and progress to date in breast cancer. Drugs. 2013, 73: 505-515.

Sultana R, Abdel-Fatah T, Abbotts R, Hawkes C, Albarakati N, Seedhouse C, Ball G, Chan S, Rakha EA, Ellis IO, Madhusudan S: Targeting XRCC1 deficiency in breast cancer for personalized therapy. Cancer Res. 2013, 73: 1621-1634.

Miller WR, Larionov A, Anderson TJ, Evans DB, Dixon JM: Sequential changes in gene expression profiles in breast cancers during treatment with the aromatase inhibitor, letrozole. Pharmacogenomics J. 2012, 12: 10-21.

Larionov AFD, Caldwell H, Sims A, Fawkes A, Murphy L, Renshaw L, Dixon J: Gene expression profiles of endocrine resistant breast tumours. Cancer Res. 2009, 69: 809-810.

Bartlett JM, Brookes CL, Robson T, van de Velde CJ, Billingham LJ, Campbell FM, Grant M, Hasenburg A, Hille ET, Kay C, Kieback DG, Putter H, Markopoulos C, Kranenbarg E, Mallon EA, Dirix L, Seynaeve C, Rea D: Estrogen receptor and progesterone receptor as predictive biomarkers of response to endocrine therapy: a prospectively powered pathology study in the Tamoxifen and Exemestane Adjuvant Multinational trial. J Clin Oncol. 2011, 29: 1531-1538.

Honma N, Horii R, Iwase T, Saji S, Younes M, Takubo K, Matsuura M, Ito Y, Akiyama F, Sakamoto G: Clinical importance of estrogen receptor-beta evaluation in breast cancer patients treated with adjuvant tamoxifen therapy. J Clin Oncol. 2008, 26: 3727-3734.

Yan Y, Li X, Blanchard A, Bramwell VH, Pritchard KI, Tu D, Shepherd L, Myal Y, Penner C, Watson PH, Leygue E, Murphy LC: Expression of both estrogen receptor-beta 1 (ER-beta1) and its co-regulator steroid receptor RNA activator protein (SRAP) are predictive for benefit from tamoxifen therapy in patients with estrogen receptor-alpha (ER-alpha)-negative early breast cancer (EBC). Ann Oncol. 2013, 24: 1986-1993.

De Amicis F, Thirugnansampanthan J, Cui Y, Selever J, Beyer A, Parra I, Weigel NL, Herynk MH, Tsimelzon A, Lewis MT, Chamness GC, Hilsenbeck SG, Ando S, Fuqua SA: Androgen receptor overexpression induces tamoxifen resistance in human breast cancer cells. Breast Cancer Res Treat. 2010, 121: 1-11.

Garay JP, Park BH: Androgen receptor as a targeted therapy for breast cancer. Am J Cancer Res. 2012, 2: 434-445.

Fan P, Yue W, Wang JP, Aiyar S, Li Y, Kim TH, Santen RJ: Mechanisms of resistance to structurally diverse antiestrogens differ under premenopausal and postmenopausal conditions: evidence from in vitro breast cancer cell models. Endocrinology. 2009, 150: 2036-2045.

Thompson AM, Jordan LB, Quinlan P, Anderson E, Skene A, Dewar JA, Purdie CA: Prospective comparison of switches in biomarker status between primary and recurrent breast cancer: the Breast Recurrence In Tissues Study (BRITS). Breast Cancer Res. 2010, 12: R92-

Amir E, Clemons M, Purdie CA, Miller N, Quinlan P, Geddie W, Coleman RE, Freedman OC, Jordan LB, Thompson AM: Tissue confirmation of disease recurrence in breast cancer patients: pooled analysis of multi-centre, multi-disciplinary prospective studies. Cancer Treat Rev. 2012, 38: 708-714.

Moussa O, Purdie C, Vinnicombe S, Thompson AM: Biomarker discordance: prospective and retrospective evidence that biopsy of recurrent disease is of clinical utility. Cancer Biomark. 2012, 12: 231-239.

Makubate B, Donnan PT, Dewar JA, Thompson AM, McCowan C: Cohort study of adherence to adjuvant endocrine therapy, breast cancer recurrence and mortality. Br J Cancer. 2013, 108: 1515-1524.

Thompson AM, Johnson A, Quinlan P, Hillman G, Fontecha M, Bray SE, Purdie CA, Jordan LB, Ferraldeschi R, Latif A, Hadfield KD, Clarke RB, Ashcroft L, Evans DG, Howell A, Nikoloff M, Lawrence J, Newman WG: Comprehensive CYP2D6 genotype and adherence affect outcome in breast cancer patients treated with tamoxifen monotherapy. Breast Cancer Res Treat. 2011, 125: 279-287.

Loi S, Sirtaine N, Piette F, Salgado R, Viale G, Van Eenoo F, Rouas G, Francis P, Crown JP, Hitre E, de Azambuja E, Quinaux E, Di Leo A, Michiels S, Piccart MJ, Sotiriou C: Prognostic and predictive value of tumor-infiltrating lymphocytes in a phase III randomized adjuvant breast cancer trial in node-positive breast cancer comparing the addition of docetaxel to doxorubicin with doxorubicin-based chemotherapy: BIG 02–98. J Clin Oncol. 2013, 31: 860-867.

Group BIGC, Mouridsen H, Giobbie-Hurder A, Goldhirsch A, Thurlimann B, Paridaens R, Smith I, Mauriac L, Forbes J, Price KN, Regan MM, Gelber RD, Coates AS: Letrozole therapy alone or in sequence with tamoxifen in women with breast cancer. N Engl J Med. 2009, 361: 766-776.

Coombes RC, Kilburn LS, Snowdon CF, Paridaens R, Coleman RE, Jones SE, Jassem J, Van de Velde CJ, Delozier T, Alvarez I, Del Mastro L, Ortmann O, Diedrich K, Coates AS, Bajetta E, Homberg SB, Dodwell D, Mickiewicz E, Anderson J, Lonning PE, Cocconi G, Forbes J, Castiglione M, Stuart N, Stewart A, Fallowfield LJ, Bertelli G, Hall E, Bogle RG, Carpentieri M, et al: Survival and safety of exemestane versus tamoxifen after 2–3 years’ tamoxifen treatment (Intergroup Exemestane Study): a randomised controlled trial. Lancet. 2007, 369: 559-570.

Palmieri C, Shah D, Krell J, Gojis O, Hogben K, Riddle P, Ahmad R, Tat T, Fox K, Porter A, Mahmoud S, Kirschke S, Shousha S, Gudi M, Coombes RC, Leonard R, Cleator S: Management and outcome of HER2-positive early breast cancer treated with or without trastuzumab in the adjuvant trastuzumab era. Clin Breast Cancer. 2011, 11: 93-102.

Fontein DB, Seynaeve C, Hadji P, Hille ET, van de Water W, Putter H, Kranenbarg EM, Hasenburg A, Paridaens RJ, Vannetzel JM, Markopoulos C, Hoxumi Y, Bartlett JM, Jones SE, Rea DW, Nortier JW, van de Velde CJ: Specific adverse events predict survival benefit in patients treated with tamoxifen or aromatase inhibitors: an international tamoxifen exemestane adjuvant multinational trial analysis. J Clin Oncol. 2013, 31: 2257-2264.

Blackwell KL, Burstein HJ, Storniolo AM, Rugo HS, Sledge G, Aktan G, Ellis C, Florance A, Vukelja S, Bischoff J, Baselga J, O’Shaughnessy J: Overall survival benefit with lapatinib in combination with trastuzumab for patients with human epidermal growth factor receptor 2-positive metastatic breast cancer: final results from the EGF104900 Study. J Clin Oncol. 2012, 30: 2585-2592.

Gianni L, Pienkowski T, Im YH, Roman L, Tseng LM, Liu MC, Lluch A, Staroslawska E, de la Haba-Rodriguez J, Im SA, Pedrini JL, Poirier B, Pedrini JL, Poirier B, Morandi P, Semiglazov V, Srimuninnimi V, Bianchi G, Szado T, Ratnayake J, Ross G, Valagussa P: Efficacy and safety of neoadjuvant pertuzumab and trastuzumab in women with locally advanced, inflammatory, or early HER2-positive breast cancer (NeoSphere): a randomised multicentre, open-label, phase 2 trial. Lancet Oncol. 2012, 13: 25-32.

Baselga J, Bradbury I, Eidtmann H, Di Cosimo S, de Azambuja E, Aura C, Gomez H, Dinh P, Fauria K, Van Dooren V, Aktan G, Goldhirsch A, Chang TW, Horvath Z, Coccia-Portugal M, Domant J, Tseng LM, Kunz G, Sohn JH, Semiglazov V, Lerzo G, Palacova M, Probachai V, Pusztai L, Untch M, Gelber RD, Piccart-Gebhart M, NeoALTTO Study Team: Lapatinib with trastuzumab for HER2-positive early breast cancer (NeoALTTO): a randomised, open-label, multicentre, phase 3 trial. Lancet. 2012, 379: 633-640.

Baselga J, Cortes J, Kim SB, Im SA, Hegg R, Im YH, Roman L, Pedrini JL, Pienkowski T, Knott A, Clark E, Benyunes MC, Ross G, Swain SM, CLEOPATRA Study Group: Pertuzumab plus trastuzumab plus docetaxel for metastatic breast cancer. N Engl J Med. 2012, 366: 109-119.

Gelmon KA, Tischkowitz M, Mackay H, Swenerton K, Robidoux A, Tonkin K, Hirte H, Huntsman D, Clemons M, Gilks B, Yerushalmi R, Macpherson E, Carmichael J, Oza A: Olaparib in patients with recurrent high-grade serous or poorly differentiated ovarian carcinoma or triple-negative breast cancer: a phase 2, multicentre, open-label, non-randomised study. Lancet Oncol. 2011, 12: 852-861.

Cleator S, Heller W, Coombes RC: Triple-negative breast cancer: therapeutic options. Lancet Oncol. 2007, 8: 235-244.

Molyneux G, Smalley MJ: The cell of origin of BRCA1 mutation-associated breast cancer: a cautionary tale of gene expression profiling. J Mammary Gland Biol Neoplasia. 2011, 16: 51-55.

Michalak EM, Jonkers J: Studying therapy response and resistance in mouse models for BRCA1-deficient breast cancer. J Mammary Gland Biol Neoplasia. 2011, 16: 41-50.

Ran S, Volk L, Hall K, Flister MJ: Lymphangiogenesis and lymphatic metastasis in breast cancer. Pathophysiology. 2010, 17: 229-251.

Ferris RL, Lotze MT, Leong SP, Hoon DS, Morton DL: Lymphatics, lymph nodes and the immune system: barriers and gateways for cancer spread. Clin Exp Metastasis. 2012, 29: 729-736.

Gomes FG, Nedel F, Alves AM, Nor JE, Tarquinio SB: Tumor angiogenesis and lymphangiogenesis: tumor/endothelial crosstalk and cellular/microenvironmental signaling mechanisms. Life Sci. 2013, 92: 101-107.

Lenzer J: FDA committee votes to withdraw bevacizumab for breast cancer. BMJ. 2011, 343: d4244-

D’Agostino RB: Changing end points in breast-cancer drug approval–the Avastin story. N Engl J Med. 2011, 365: e2-

Shojaei F: Anti-angiogenesis therapy in cancer: current challenges and future perspectives. Cancer Lett. 2012, 320: 130-137.

Nagy JA, Benjamin L, Zeng H, Dvorak AM, Dvorak HF: Vascular permeability, vascular hyperpermeability and angiogenesis. Angiogenesis. 2008, 11: 109-119.

Kerbel RS: Strategies for improving the clinical benefit of antiangiogenic drug based therapies for breast cancer. J Mammary Gland Biol Neoplasia. 2012, 17: 229-239.

Sitohy B, Nagy JA, Dvorak HF: Anti-VEGF/VEGFR therapy for cancer: reassessing the target. Cancer Res. 2012, 72: 1909-1914.

Chew V, Toh HC, Abastado JP: Immune microenvironment in tumor progression: characteristics and challenges for therapy. J Oncol. 2012, 2012: 608406-

Andre F, Dieci MV, Dubsky P, Sotiriou C, Curigliano G, Denkert C, Loi S: Molecular pathways: involvement of immune pathways in the therapeutic response and outcome in breast cancer. Clin Cancer Res. 2013, 19: 28-33.

Reisfeld RA: The tumor microenvironment: a target for combination therapy of breast cancer. Crit Rev Oncog. 2013, 18: 115-133.

Chen YT, Ross DS, Chiu R, Zhou XK, Chen YY, Lee P, Hoda SA, Simpson AJ, Old LJ, Caballero O, Neville A: Multiple cancer/testis antigens are preferentially expressed in hormone-receptor negative and high-grade breast cancers. PloS one. 2011, 6: e17876-

Adams S, Greeder L, Reich E, Shao Y, Fosina D, Hanson N, Tassello J, Singh B, Spagnoli GC, Demaria S, Jungbluth AA: Expression of cancer testis antigens in human BRCA-associated breast cancers: potential targets for immunoprevention?. Cancer Immunol Immunother. 2011, 60: 999-1007.

Corner J, Wright D, Hopkinson J, Gunaratnam Y, McDonald JW, Foster C: The research priorities of patients attending UK cancer treatment centres: findings from a modified nominal group study. Br J Cancer. 2007, 96: 875-881.

Hewitt M, Rowland JH, Yancik R: Cancer survivors in the United States: age, health, and disability. J Geront A, Biol Sci Med Sci. 2003, 58: 82-91.

Foster C, Wright D, Hill H, Hopkinson J, Roffe L: Psychosocial implications of living 5 years or more following a cancer diagnosis: a systematic review of the research evidence. Eur J Cancer Care (Engl). 2009, 18: 223-247.

Hubbard G, Menzies S, Flynn P, Adams S, Haseen F, Thomas I, Scanlon K, Reed L, Forbat L: Relational mechanisms and psychological outcomes in couples affected by breast cancer: a systematic review of the literature. BMJ, Supportive and Palliative Care. 2013, 3: 1-7.

Foster C, Fenlon D: Recovery and self-management support following primary cancer treatment. Br J Cancer. 2011, 105: S21-S28.

Cimprich B, Janz NK, Northouse L, Wren PA, Given B, Given CW: Taking CHARGE: A self-management program for women following breast cancer treatment. Psychooncology. 2005, 14: 704-717.

Bloom JR, Stewart SL, D’Onofrio CN, Luce J, Banks PJ: Addressing the needs of young breast cancer survivors at the 5 year milestone: can a short-term, low intensity intervention produce change?. J Cancer Surviv. 2008, 2: 190-204.

Reed E, Simmonds P, Haviland J, Corner J: Quality of life and experience of care in women with metastatic breast cancer: a cross-sectional survey. J Pain Symptom Manage. 2012, 43: 747-758.

Aranda S, Schofield P, Weih L, Yates P, Milne D, Faulkner R, Voudouris N: Mapping the quality of life and unmet needs of urban women with metastatic breast cancer. Eur J Cancer Care (Engl). 2005, 14: 211-222.

Hopwood P, Howell A, Maguire P: Psychiatric morbidity in patients with advanced cancer of the breast: prevalence measured by two self-rating questionnaires. Br J Cancer. 1991, 64: 349-352.

Pinder KL, Ramirez AJ, Black ME, Richards MA, Gregory WM, Rubens RD: Psychiatric disorder in patients with advanced breast cancer: prevalence and associated factors. Eur J Cancer. 1993, 29A: 524-527.

Kissane DW, Grabsch B, Love A, Clarke DM, Bloch S, Smith GC: Psychiatric disorder in women with early stage and advanced breast cancer: a comparative analysis. Aust N Z J Psychiatry. 2004, 38: 320-326.

Grunfeld EA, Maher EJ, Browne S, Ward P, Young T, Vivat B, Walker G, Wilson C, Potts HW, Westcombe AM, Richards MA, Ramirez AJ: Advanced breast cancer patients’ perceptions of decision making for palliative chemotherapy. J Clin Oncol. 2006, 24: 1090-1098.

Karamouzis MV, Ioannidis G, Rigatos G: Quality of life in metastatic breast cancer patients under chemotherapy or supportive care: a single-institution comparative study. Eur J Cancer Care. 2007, 16: 433-438.

Cheville AL, Troxel AB, Basford JR, Kornblith AB: Prevalence and treatment patterns of physical impairments in patients with metastatic breast cancer. J Clin Oncol. 2008, 26: 2621-2629.

Headley JA, Ownby KK, John LD: The effect of seated exercise on fatigue and quality of life in women with advanced breast cancer. Oncol Nurs forum. 2004, 31: 977-983.

Asola R, Huhtala H, Holli K: Intensity of diagnostic and treatment activities during the end of life of patients with advanced breast cancer. Breast Cancer Res Treat. 2006, 100: 77-82.

Gagnon B, Mayo NE, Hanley J, MacDonald N: Pattern of care at the end of life: does age make a difference in what happens to women with breast cancer?. J Clin Oncol. 2004, 22: 3458-3465.

Richardson A, Addington-Hall J, Amir Z, Foster C, Stark D, Armes J, Brearley SG, Hodges L, Hook J, Jarrett N, Stamataki Z, Scott I, Walker J, Ziegler L, Sharpe MS: Knowledge, ignorance and priorities for research in key areas of cancer survivorship: findings from a scoping review. Br J Cancer. 2011, 105: S82-S94.

Stanton AL, Luecken LJ, MacKinnon DP, Thompson EH: Mechanisms in psychosocial interventions for adults living with cancer: opportunity for integration of theory, research, and practice. J Consult Clin Psychol. 2013, 81: 318-335.

Fenlon DR, Corner JL, Haviland JS: A randomized controlled trial of relaxation training to reduce hot flashes in women with primary breast cancer. J Pain Symptom Manage. 2008, 35: 397-405.

Osborn RL, Demoncada AC, Feuerstein M: Psychosocial interventions for depression, anxiety, and quality of life in cancer survivors: meta-analyses. Int J Psychiatry Med. 2006, 36: 13-34.

Spiegel D, Bloom JR, Kraemer HC, Gottheil E: Effect of psychosocial treatment on survival of patients with metastatic breast cancer. Lancet. 1989, 2: 888-891.

Edwards AG, Hulbert-Williams N, Neal RD: Psychological interventions for women with metastatic breast cancer. Cochrane Database Syst Rev. 2008, 3: CD004253

Emilsson S, Svensk AC, Tavelin B, Lindh J: Support group participation during the post-operative radiotherapy period increases levels of coping resources among women with breast cancer. Eur J Cancer Care (Engl). 2012, 21: 591-598.

Hoey LM, Ieropoli SC, White VM, Jefford M: Systematic review of peer-support programs for people with cancer. Patient Educ Couns. 2008, 70: 315-337.

Ganz PA, Kwan L, Stanton AL, Bower JE, Belin TR: Physical and psychosocial recovery in the year after primary treatment of breast cancer. J Clin Oncol. 2011, 29: 1101-1109.

Capozzo MA, Martinis E, Pellis G, Giraldi T: An early structured psychoeducational intervention in patients with breast cancer: results from a feasibility study. Cancer Nurs. 2010, 33: 228-234.

Gielissen MF, Verhagen CA, Bleijenberg G: Cognitive behaviour therapy for fatigued cancer survivors: long-term follow-up. Br J Cancer. 2007, 97: 612-618.

Ritterband LM, Bailey ET, Thorndike FP, Lord HR, Farrell-Carnahan L, Baum LD: Initial evaluation of an Internet intervention to improve the sleep of cancer survivors with insomnia. Psychooncology. 2012, 21: 695-705.

Armes J, Chalder T, Addington-Hall J, Richardson A, Hotopf M: A randomized controlled trial to evaluate the effectiveness of a brief, behaviorally oriented intervention for cancer-related fatigue. Cancer. 2007, 110: 1385-1395.

Mann E, Smith M, Hellier J, Hunter MS: A randomised controlled trial of a cognitive behavioural intervention for women who have menopausal symptoms following breast cancer treatment (MENOS 1): trial protocol. BMC Cancer. 2011, 11: 44-

Duijts SF, van Beurden M, Oldenburg HS, Hunter MS, Kieffer JM, Stuiver MM, Gerritsma MA, Menke-Pluymers MB, Plaisier PW, Rijna H, Lopes Cardozo AM, Timmers G, van der Meij S, van der Veen H, Bijker N, de Widt-Levert LN, Geenen MM, Heuff G, van Dulken EJ, Aaronson NK BE: Efficacy of cognitive behavioral therapy and physical exercise in alleviating treatment-induced menopausal symptoms in patients with breast cancer: results of a randomized, controlled, multicenter trial. J Clin Oncol. 2012, 30: 4124-4133.

Thompson J, Cocker H, Coleman RE, Colwell B, Freeman JV, Holmes K, Reed MW, Anthony C, Greenfield D: Breast cancer aftercare; preparing patients for discharge from routine hospital follow-up (PREP). Proceedings of the British Psychosocial Oncology Society Conference: 3–4 December 2009. 2009, Cardiff, Wales: Psycho-Oncology, 19(Suppl. 3):S1–S20 (2010)

Shennan C, Payne S, Fenlon D: What is the evidence for the use of mindfulness-based interventions in cancer care? A review. Psychooncology. 2011, 20: 681-697.

Campbell KL, Neil SE, Winters-Stone KM: Review of exercise studies in breast cancer survivors: attention to principles of exercise training. Br J Sports Med. 2011, 46: 909-916.

Speck RM, Courneya KS, Masse LC, Duval S, Schmitz KH: An update of controlled physical activity trials in cancer survivors: a systematic review and meta-analysis. J Cancer Surviv. 2010, 4: 87-100.

Fong DY, Ho JW, Hui BP, Lee AM, Macfarlane DJ, Leung SS, Cerin E, Chan WY, Leung IP, Lam SH, Taylor AJ, Cheng KK: Physical activity for cancer survivors: meta-analysis of randomised controlled trials. BMJ. 2012, 344: e70-

Mutrie N, Campbell A, Barry S, Hefferon K, McConnachie A, Ritchie D, Tovey S: Five-year follow-up of participants in a randomised controlled trial showing benefits from exercise for breast cancer survivors during adjuvant treatment. Are there lasting effects?. J Cancer Surviv. 2012, 6: 420-430.

Classen C, Butler LD, Koopman C, Miller E, DiMiceli S, Giese-Davis J, Fobair P, Carlson RW, Kraemer HC, Spiegel D: Supportive-expressive group therapy and distress in patients with metastatic breast cancer: a randomized clinical intervention trial. Arch Gen Psychiatry. 2001, 58: 494-501.

Watson EK, Rose PW, Neal RD, Hulbert-Williams N, Donnelly P, Hubbard G, Elliott J, Campbell C, Weller D, Wilkinson C: Personalised cancer follow-up: risk stratification, needs assessment or both?. Br J Cancer. 2012, 106: 1-5.

Fenlon D, Frankland J, Foster CL, Brooks C, Coleman P, Payne S, Seymour J, Simmonds P, Stephens R, Walsh B, Addington-Hall JM: Living into old age with the consequences of breast cancer. Eur J Oncol Nurs. 2013, 17: 311-316.

Watts K, Meiser B, Conlon H, Rovelli S, Tiller K, Zorbas H, Lewis C, Neil G, Friedlander M: A specialist breast care nurse role for women with metastatic breast cancer: enhancing supportive care. Oncol Nurs Forum. 2011, 38: 627-631.

Absolom K, Eiser C, Michel G, Walters SJ, Hancock BW, Coleman RE, Snowden JA, Greenfield DM: Follow-up care for cancer survivors: views of the younger adult. Br J Cancer. 2009, 101: 561-567.

Fenlon DR, Corner JL, Haviland J: Menopausal hot flushes after breast cancer. Eur J Cancer Care (Engl). 2009, 18: 140-148.

Mann E, Smith MJ, Hellier J, Balabanovic JA, Hamed H, Grunfeld EA, Hunter MS: Cognitive behavioural treatment for women who have menopausal symptoms after breast cancer treatment (MENOS 1): a randomised controlled trial. Lancet Oncol. 2012, 13: 309-318.

Castellon SA, Ganz PA, Bower JE, Petersen L, Abraham L, Greendale GA: Neurocognitive performance in breast cancer survivors exposed to adjuvant chemotherapy and tamoxifen. J Clin Exp Neuropsychol. 2004, 26: 955-969.

Rausch R, Kraemer S, Pietras CJ, Le M, Vickrey BG, Passaro EA: Early and late cognitive changes following temporal lobe surgery for epilepsy. Neurology. 2003, 60: 951-959.

Oliveri JM, Day JM, Alfano CM, Herndon JE, Katz ML, Bittoni MA, Donohue K, Paskett ED: Arm/hand swelling and perceived functioning among breast cancer survivors 12 years post-diagnosis: CALGB 79804. J Cancer Surviv. 2008, 2: 233-242.

Fourie WJ, Robb KA: Physiotherapy management of axillary web syndrome following breast cancer treatment: discussing the use of soft tissue techniques. Physiotherapy. 2009, 95: 314-320.

Holliday DL, Speirs V: Choosing the right cell line for breast cancer research. Breast Cancer Res. 2011, 13: 215-

Lacroix M, Leclercq G: Relevance of breast cancer cell lines as models for breast tumours: an update. Breast Cancer Res Treat. 2004, 83: 249-289.

Liu X, Ory V, Chapman S, Yuan H, Albanese C, Kallakury B, Timofeeva OA, Nealon C, Dakic A, Simic V, Haddad BR, Rhim JS, Dritschilo A, Riegel A, McBride A, Schlegel R: ROCK inhibitor and feeder cells induce the conditional reprogramming of epithelial cells. Am J Pathol. 2012, 180: 599-607.

Yuan H, Myers S, Wang J, Zhou D, Woo JA, Kallakury B, Ju A, Bazylewicz M, Carter YM, Albanese C, Grant N, Shad A, Dritschilo A, Liu X, Schlegel R: Use of reprogrammed cells to identify therapy for respiratory papillomatosis. N Engl J Med. 2012, 367: 1220-1227.

Lee GY, Kenny PA, Lee EH, Bissell MJ: Three-dimensional culture models of normal and malignant breast epithelial cells. Nat Methods. 2007, 4: 359-365.

Calvo F, Sahai E: Cell communication networks in cancer invasion. Curr Opin Cell Biol. 2011, 23: 621-629.

Vinci M, Gowan S, Boxall F, Patterson L, Zimmermann M, Court W, Lomas C, Mendiola M, Hardisson D, Eccles SA: Advances in establishment and analysis of three-dimensional tumor spheroid-based functional assays for target validation and drug evaluation. BMC Biol. 2012, 10: 29-

Krishnan V, Shuman LA, Sosnoski DM, Dhurjati R, Vogler EA, Mastro AM: Dynamic interaction between breast cancer cells and osteoblastic tissue: comparison of two- and three-dimensional cultures. J Cell Physiol. 2011, 226: 2150-2158.

Quail DF, Maciel TJ, Rogers K, Postovit LM: A unique 3D in vitro cellular invasion assay. J Biomol Screen. 2012, 17: 1088-1095.

Ho KS, Poon PC, Owen SC, Shoichet MS: Blood vessel hyperpermeability and pathophysiology in human tumour xenograft models of breast cancer: a comparison of ectopic and orthotopic tumours. BMC Cancer. 2012, 12: 579-

DeRose YS, Gligorich KM, Wang G, Georgelas A, Bowman P, Courdy SJ, Welm AL, Welm BE, et al: Patient-derived models of human breast cancer: protocols for in vitro and in vivo applications in tumor biology and translational medicine. Current protocols in pharmacology. Edited by: Enna SJ, John Wiley & Sons . 2013, Chapter 14:Unit14 23

Kabos P, Finlay-Schultz J, Li C, Kline E, Finlayson C, Wisell J, Manuel CA, Edgerton SM, Harrell JC, Elias A, Sartorius CA: Patient-derived luminal breast cancer xenografts retain hormone receptor heterogeneity and help define unique estrogen-dependent gene signatures. Breast Cancer Res Treat. 2012, 135: 415-432.

Rottenberg S, Jaspers JE, Kersbergen A, van der Burg E, Nygren AO, Zander SA, Derksen PW, de Bruin M, Zevenhoven J, Lau A, Boulter R, Cranston A, O’Conner MJ, Martin NM, Borst P, Jonkers J: High sensitivity of BRCA1-deficient mammary tumors to the PARP inhibitor AZD2281 alone and in combination with platinum drugs. Proc Natl Acad Sci U S A. 2008, 105: 17079-17084.

Mollard S, Mousseau Y, Baaj Y, Richard L, Cook-Moreau J, Monteil J, Funalot B, Sturtz FG: How can grafted breast cancer models be optimized?. Cancer Biol Ther. 2011, 12: 855-864.

Zhang X, Claerhout S, Prat A, Dobrolecki LE, Petrovic I, Lai Q, Landis MD, Wiechmann L, Schiff R, Giuliano M, Wong H, Fuqua SW, Contreras A, Gutierrez C, Huang J, Mao S, Pavlick AC, Froehlich AM, Wu MF, Tsimelzon A, Hilsenbeck SG, Chen ES, Zuloaga P, Shaw CA, Rimawi MF, Perou CM, Mills GB, Chang JC, Lewis MT: A renewable tissue resource of phenotypically stable, biologically and ethnically diverse, patient-derived human breast cancer xenograft models. Cancer Res. 2013, 73: 4885-4897.

Borowsky AD: Choosing a mouse model: experimental biology in context–the utility and limitations of mouse models of breast cancer. Cold Spring Harb Perspect Biol. 2011, 3: a009670-

Andrechek ER, Nevins JR: Mouse models of cancers: opportunities to address heterogeneity of human cancer and evaluate therapeutic strategies. J Mol Med. 2010, 88: 1095-1100.

Caligiuri I, Rizzolio F, Boffo S, Giordano A, Toffoli G: Critical choices for modeling breast cancer in transgenic mouse models. J Cell Physiol. 2012, 227: 2988-2991.

Kirma NB, Tekmal RR: Transgenic mouse models of hormonal mammary carcinogenesis: advantages and limitations. J Steroid Biochem Mol Biol. 2012, 131: 76-82.

Uhr JW, Pantel K: Controversies in clinical cancer dormancy. Proc Natl Acad Sci U S A. 2011, 108: 12396-12400.

Giampieri S, Manning C, Hooper S, Jones L, Hill CS, Sahai E: Localized and reversible TGFbeta signalling switches breast cancer cells from cohesive to single cell motility. Nat Cell Biol. 2009, 11: 1287-1296.

Eccles SA, Welch DR: Metastasis: recent discoveries and novel treatment strategies. Lancet. 2007, 369: 1742-1757.

Francia G, Cruz-Munoz W, Man S, Xu P, Kerbel RS: Mouse models of advanced spontaneous metastasis for experimental therapeutics. Nat Rev Cancer. 2011, 11: 135-141.

Eckhardt BL, Francis PA, Parker BS, Anderson RL: Strategies for the discovery and development of therapies for metastatic breast cancer. Nature Rev Drug Dis. 2012, 11: 479-497.

Guerin E, Man S, Xu P, Kerbel RS: A model of postsurgical advanced metastatic breast cancer more accurately replicates the clinical efficacy of antiangiogenic drugs. Cancer Res. 2013, 73: 2743-2748.

Kievit FM, Stephen ZR, Veiseh O, Arami H, Wang T, Lai VP, Park JO, Ellenbogen RG, Disis ML, Zhang M: Targeting of primary breast cancers and metastases in a transgenic mouse model using rationally designed multifunctional SPIONs. ACS Nano. 2012, 6: 2591-2601.

Fang Y, Chen Y, Yu L, Zheng C, Qi Y, Li Z, Yang Z, Zhang Y, Shi T, Luo J, Liu M: Inhibition of breast cancer metastases by a novel inhibitor of TGFbeta receptor 1. J Natl Cancer Inst. 2013, 105: 47-58.

Palmieri D, Lockman PR, Thomas FC, Hua E, Herring J, Hargrave E, Johnson M, Flores N, Qian Y, Vega-Valle E, Tasker KS, Rudraraju V, Mittapalli RK, Gaasch JA, Bohn KA, Thorsheim HR, Liewehr DJ, Davis S, Reilly JF, Walker R, Bronder JL, Feigenbaum L, Steinberg S, Camphausen K, Meltzer PS, Richon VM, Smith QR, Steeq PS: Vorinostat inhibits brain metastatic colonization in a model of triple-negative breast cancer and induces DNA double-strand breaks. Clin Cancer Res. 2009, 15: 6148-6157.

Xia TS, Wang J, Yin H, Ding Q, Zhang YF, Yang HW, Liu XA, Dong M, Du Q, Ling LJ, Zha XM, Fu W, Wang S: Human tissue-specific microenvironment: an essential requirement for mouse models of breast cancer. Oncol Rep. 2010, 24: 203-211.

Steeg PS: Perspective: the right trials. Nature. 2012, 485: S58-S59.

Wong AL, Lee SC: Mechanisms of resistance to trastuzumab and novel therapeutic strategies in HER2-positive breast cancer. Int J Breast Cancer. 2012, 2012: 415170-

Polyak K: Heterogeneity in breast cancer. J Clin Invest. 2011, 121: 3786-3788.

Lindell KO, Erlen JA, Kaminski N: Lessons from our patients: development of a warm autopsy program. PLoS medicine. 2006, 3: e234-

Hadad S, Iwamoto T, Jordan L, Purdie C, Bray S, Baker L, Jellema G, Deharo S, Hardie DG, Pusztai L, Moulder-Thompson S, Dewar JA, Thompson AM: Evidence for biological effects of metformin in operable breast cancer: a pre-operative, window-of-opportunity, randomized trial. Breast Cancer Res Treat. 2011, 128: 783-794.

Leary AF, Hanna WM, van de Vijver MJ, Penault-Llorca F, Ruschoff J, Osamura RY, Bilous M, Dowsett M: Value and limitations of measuring HER-2 extracellular domain in the serum of breast cancer patients. J Clin Oncol. 2009, 27: 1694-1705.

Witzel I, Loibl S, von Minckwitz G, Mundhenke C, Huober J, Hanusch C, Henschen S, Hauschild M, Lantzsch T, Tesch H, Latos K, Just M, Hilfrich J, Barinoff J, Eulenburg CZ, Roller M, Untch M, Muller V: Monitoring serum HER2 levels during neoadjuvant trastuzumab treatment within the GeparQuattro trial. Breast Cancer Res Treat. 2010, 123: 437-445.

Thureau S, Clatot F, Laberge-Le-Couteulx S, Baron M, Basuyau JP, Blot E: Elevated HER2 extracellular domain level in primary breast cancer with HER2 overexpression predicts early failure of adjuvant trastuzumab. Anticancer Res. 2012, 32: 1429-1433.

Molina R, Escudero JM, Munoz M, Auge JM, Filella X: Circulating levels of HER-2/neu oncoprotein in breast cancer. Clin Chem Lab Med. 2012, 50: 5-21.

Dietel M, Johrens K, Laffert M, Hummel M, Blaker H, Muller BM, Lehmann A, Denkert C, Heppner FL, Koch A, Sers C, Anagnostopoulos I: Predictive molecular pathology and its role in targeted cancer therapy: a review focussing on clinical relevance. Cancer Gene Ther. 2013, 20: 211-221.

Modur V, Hailman E, Barrett JC: Evidence-based laboratory medicine in oncology drug development: from biomarkers to diagnostics. Clin Chem. 2013, 59: 102-109.

Knowles SM, Wu AM: Advances in immuno-positron emission tomography: antibodies for molecular imaging in oncology. J Clin Oncol. 2012, 30: 3884-3892.

Capala J, Bouchelouche K: Molecular imaging of HER2-positive breast cancer: a step toward an individualized ‘image and treat’ strategy. Curr Opin Oncol. 2010, 22: 559-566.

Asselin MC, O’Connor JP, Boellaard R, Thacker NA, Jackson A: Quantifying heterogeneity in human tumours using MRI and PET. Eur J Cancer. 2012, 48: 447-455.

Waterton JC, Pylkkanen L: Qualification of imaging biomarkers for oncology drug development. Eur J Cancer. 2012, 48: 409-415.

Segal E, Sirlin CB, Ooi C, Adler AS, Gollub J, Chen X, Chan BK, Matcuk GR, Barry CT, Chang HY, Kuo MD: Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol. 2007, 25: 675-680.

Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RGPM, Granton P, Zegers CML, Gillies R, Boellard R, Dekker A, Aerts HJ: Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012, 48: 441-446.

Macaskill EJ, Bartlett JM, Sabine VS, Faratian D, Renshaw L, White S, Campbell FM, Young O, Williams L, Thomas JS, Barber MD, Dixon JM: The mammalian target of rapamycin inhibitor everolimus (RAD001) in early breast cancer: results of a pre-operative study. Breast Cancer Res Treat. 2011, 128: 725-734.

Basch E, Jia X, Heller G, Barz A, Sit L, Fruscione M, Appawu M, Iasonos A, Atkinson T, Goldfarb S, Culkin A, Kris MG, Schrag D: Adverse symptom event reporting by patients vs clinicians: relationships with clinical outcomes. J Natl Cancer Inst. 2009, 101: 1624-1632.

Dietary Fish and Omega 3 Fatty Acids for Breast Cancer Prevention. [ http://clinicaltrials.gov/show/NCT01282580 ]

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Acknowledgements

We would like to acknowledge the helpful contributions to the final manuscript from the Executive Advisory Board: Kevin Brindle, Robert E Coleman, Charles Coombes, Jack Cuzick, Mitchell Dowsett, Lesley Fallowfield, Christine Friedenreich, William J Gullick, Barry Gusterson, Craig Jordan, Sunil Lakhani, Bettina Meiser, Emma Pennery, Rebecca Riggins and Stephen Johnston. We would also like to acknowledge the contributions of the patient advocate representatives Mairead McKenzie and Marion Lewis from Breast Cancer Care’s Service User Research Panel.

SAE acknowledges support from the NIHR RM/ICR Biomedical Research Centre, ICR and Cancer Research UK.

AMT acknowledges support from Breast Cancer Campaign, Breakthrough Breast Cancer and CR-UK.

Breast Cancer Campaign staff Lisa Wilde, Phyllis Quinn and Stuart Griffiths assisted in the design and implementation of the gap analysis initiative and acted as facilitators throughout the process. Geraldine Byrne was responsible for co-ordinating and delivering the logistics and acted as a facilitator at the nine gap analysis workshops that were held at the Breast Cancer Campaign offices.

We thank Dr Alexis Willet who provided editorial assistance on behalf of Punch Consulting.

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Eric O Aboagye, Simak Ali, James M Flanagan & David J Mann

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Correspondence to Suzanne A Eccles or Alastair M Thompson .

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Competing interests.

Dr Galina Velikova: Chair of a working group of the National Cancer Survivorship Initiative led by Macmillan Cancer Support.

Drs Helen Bryant and Dr Nicola Curtin: hold patents for PARP inhibitors.

Professor William Gallagher: co-Founder and part-time Chief Scientific Officer of OncoMark, a molecular diagnostics company.

Dr Martin Leach: director of Specialty Scanners plc, developing MRI-based diagnosis and treatment systems.

Dr Sacha Howell: Advisory Board honoraria from AstraZeneca, Roche, Novartis, Genomic Health and Celgene.

Dr Robert Stein: shareholder in GlaxoSmithKline and chief investigator of the OPTIMA study; travel funds received from Celgene, Roche, BristolMeyersSquibb, SanofiAventis and Novartis; Advisory Board fees from Novartis, Amgen, GSK, Roche and AstraZeneca.

Dr Nigel Bundred has received paid honoraria from Genomic Health.

The remaining authors declare that they have no competing interests.

Authors’ contributions

*denotes recipient of Breast Cancer Campaign funding in the last five years. ≠ denotes current Breast Cancer Campaign Scientific Advisory Board membership. # denotes current Breast Cancer Campaign Board of Trustees membership. Chairs: SAE # and AMT # conceived the overall strategy, designed the workshop formats and authored the manuscript on the basis of the final reports submitted by the nine working groups. Group Leaders: RBC, IDSS, DGE* ≠ , CF ≠ ,WMG ≠ , AH ≠ , IH* ≠ , LJJ*, SPL, SPR ≠ , PS* ≠ , and VS* led their respective groups with the help of the Deputy Group Leaders, co-ordinated responses from a pre-circulated questionnaire, and wrote and submitted final reports. Deputy Group Leaders: EOA, NJB a , JMF* ≠ , JMWG*, AJH*, MH, AK, JRM*, PM* ≠ , ES, MJS* ≠ , ER, and RN* supported the activities of the Group Leaders in contributing to collating workshop presentations and discussions and producing the final reports from each group. Working group members: SA*, ASA , JA*, FB*, JPB*, KB* ≠ , NJB b , HEB ≠ , JMB, AMC*, JSC*, CEC*, GJRC*, AC, NJC, LVD* ≠ , SWD, DFE, DME, DRE*, JE, DFF*, MGC, AJG, VG, AMG, BTH, SH, SJH ≠ , GH, NHW, MSH, BJ, TJK, CCK, IHK*, MOL, DJM, JFM* ≠ , LAM, SGM ≠ , JEM, DWM, WRM, JRM, SMM*, JPBOC, ROC*, CP, PDPP*, EAR ≠ , JMS*, RS ≠ , JS, CHS, ANJT, GV, RAW*, CJW, KJW ≠ and LSY all participated in/contributed to the gap analysis workshops, discussions and in generating the respective reports. NJB a Nigel J Bundred. NJB b Nicola J Brown. All authors read and approved the final manuscript.

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Eccles, S.A., Aboagye, E.O., Ali, S. et al. Critical research gaps and translational priorities for the successful prevention and treatment of breast cancer. Breast Cancer Res 15 , R92 (2013). https://doi.org/10.1186/bcr3493

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Received : 08 August 2013

Accepted : 12 September 2013

Published : 01 October 2013

DOI : https://doi.org/10.1186/bcr3493

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• Breast Cancer
• Circulating Tumor Cells (CTCs)
• Current Cancer Stem Cell (CSC)
• Mammographic Density
• Triple-negative Breast Cancer (TNBC)

Breast Cancer Research

ISSN: 1465-542X

• Submission enquiries: [email protected]

• Introduction
• Conclusions
• Article Information

Arrows show potential event transitions.

QALY indicates quality-adjusted life-year. To convert pounds sterling to US dollars, multiply by 1.28.

eMethods 1. Detailed Explanation of Method of Assigning Breast Density and Mirai Scores

eTable 1. Summary of Breast Density-Based Values of the Diagnostic Accuracy of Mammography and Annual Change in Breast Density

eTable 2. Summary of Screening Costs and Sources

eTable 3. Health Utility and QALY Parameters

eTable 4. Hazard Ratios for Invasive Cancer Survival

eTable 5. Costs and Invasive Cancer Care by Age, Mode of Detection, and Duration Since Detection

eTable 6. Costs of DCIS Cancer Care by Age, Mode of Detection, and Duration Since Detection

eMethods 2. External Validation of the Model

eTable 7. Validation Results and Actual NHS Screening Outcomes Compared With Model Predictions

eTable 8. PSA Parameter Table

eTable 9. Disutility Related to Screening Increased by 20%

eTable 10. Disutility Related to Screening Decreased by 20%

eTable 11. Mammographic Screen Sensitivity Increased by 20%

eTable 12. Mammographic Screen Sensitivity Decreased by 20%

eTable 13. Cancer Treatment Costs Increased by 20%

eTable 14. Cancer Treatment Costs Decreased by 20%

eTable 15. Mammogram and Further Assessment Costs Increased by 20%

eTable 16. Mammogram and Further Assessment Costs Decreased by 20%

eTable 17. Health-Related Quality of Life Loss From Cancer Increased by 20%

eTable 18. Health-Related Quality of Life Loss From Cancer Decreased by 20%

eTable 19. Screen Detection Has No Independent Effect on Cancer Mortality

eTable 20. Breakdown of NHS Incurred

eFigure. Incremental Population Net Monetary Benefit

eReferences

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Hill H , Roadevin C , Duffy S , Mandrik O , Brentnall A. Cost-Effectiveness of AI for Risk-Stratified Breast Cancer Screening. JAMA Netw Open. 2024;7(9):e2431715. doi:10.1001/jamanetworkopen.2024.31715

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Cost-Effectiveness of AI for Risk-Stratified Breast Cancer Screening

• 1 School of Medicine and Population Health, University of Sheffield, Sheffield, United Kingdom
• 2 Nottingham Clinical Trials Unit, University of Nottingham, Nottingham, United Kingdom
• 3 Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom

Question   What is the cost-effectiveness of artificial intelligence (AI)–guided risk-stratified mammography screening intervals in the United Kingdom National Breast Cancer Screening Program?

Findings   This decision analytical model evaluated 4 AI-based risk-stratified screening interval regimens in comparison with the current United Kingdom screening program. The net monetary benefit of introducing the optimal regimen ranged from approximately £60.4 million (US $77.3 million) to £85.3 million (US $109.2 million).

Meaning   These results suggest that AI-based risk stratified breast cancer screening programs may be more cost-effective screening programs, providing additional health benefits with fewer resources than universal screening intervals for all.

Importance   Previous research has shown good discrimination of short-term risk using an artificial intelligence (AI) risk prediction model (Mirai). However, no studies have been undertaken to evaluate whether this might translate into economic gains.

Objective   To assess the cost-effectiveness of incorporating risk-stratified screening using a breast cancer AI model into the United Kingdom (UK) National Breast Cancer Screening Program.

Design, Setting, and Participants   This study, conducted from January 1, 2023, to January 31, 2024, involved the development of a decision analytical model to estimate health-related quality of life, cancer survival rates, and costs over the lifetime of the female population eligible for screening. The analysis took a UK payer perspective, and the simulated cohort consisted of women aged 50 to 70 years at screening.

Exposures   Mammography screening at 1 to 6 yearly screening intervals based on breast cancer risk and standard care (screening every 3 years).

Main Outcomes and Measures   Incremental net monetary benefit based on quality-adjusted life-years (QALYs) and National Health Service (NHS) costs (given in pounds sterling; to convert to US dollars, multiply by 1.28).

Results   Artificial intelligence–based risk-stratified programs were estimated to be cost-saving and increase QALYs compared with the current screening program. A screening schedule of every 6 years for lowest-risk individuals, biannually and triennially for those below and above average risk, respectively, and annually for those at highest risk was estimated to give yearly net monetary benefits within the NHS of approximately £60.4 (US $77.3) million and £85.3 (US $109.2) million, with QALY values set at £20 000 (US $25 600) and £30 000 (US $38 400), respectively. Even in scenarios where decision-makers hesitate to allocate additional NHS resources toward screening, implementing the proposed strategies at a QALY value of £1 (US $1.28) was estimated to generate a yearly monetary benefit of approximately £10.6 (US $13.6) million.

Conclusions and Relevance   In this decision analytical model study of integrating risk-stratified screening with a breast cancer AI model into the UK National Breast Cancer Screening Program, risk-stratified screening was likely to be cost-effective, yielding added health benefits at reduced costs. These results are particularly relevant for health care settings where resources are under pressure. New studies to prospectively evaluate AI-guided screening appear warranted.

Early detection of breast cancer is a top priority for the United Kingdom (UK) National Health Service (NHS). 1 The NHS largely uses an age-based screening strategy, 2 inviting women aged 50 to 70 years for mammography screening triennially; most other countries with a breast screening program adopt a biennial program. 3 This one-size-fits-all approach might be improved by tailoring screening so that those at highest risk receive the greatest intensity of screening (risk-based screening).

The NHS is considering the integration of artificial intelligence (AI) and machine learning into mammogram interpretation for breast screening in the future. 1 , 4 AI is currently not used in NHS breast screening appointments in the UK due to a lack of high-quality prospective studies, 5 but emerging prospective data are promising. 6 , 7

In parallel with AI for cancer detection, AI for risk prediction has been proposed. One such model (Mirai) interprets data automatically generated from mammogram screenings, without the need for data collection through questionnaires. 8 , 9 The AI model offers an immediate estimation of an individual’s short-term risk of cancer incidence following a mammogram with negative findings. The AI model is open source and freely available for research and is arguably the best (retrospectively) validated AI model for short-term breast cancer risk assessment. One analysis included an external validation across 7 hospitals spanning 3 continents, 8 and overall, evidence suggests that in the short term, it attains a higher area under the receiving operating characteristics curve than classical risk models or breast density. 9 - 11

While the performance of the AI model for risk assessment is highly promising, one area that has not been rigorously evaluated is whether using the model to guide screening could offer value for money. Our aim was therefore to assess the potential cost-effectiveness of integrating risk-stratified screening using the model into the UK National Breast Cancer Screening Program, through a health economic model. We considered risk-based screening strategies that would be expected to require the same number of screens as the current triennial program, assuming perfect adherence. 12 In practice, strategies that involve extended screening intervals for a larger proportion of the population than those who receive more frequent screening might require fewer screens overall.

Specific risk-stratified breast cancer screening regimens (RSBCRs), determined by risk thresholds, were based on recent work that used a simplified deterministic model to evaluate potential effectiveness. 12 These RSBCRs involve screening intervals aligned with corresponding risk thresholds tailored for the AI model chosen for this research. In this report, we developed an economic model to assess the cost-effectiveness of 4 AI-based screening strategies in comparison with the current screening program. The economic model was then used to estimate health-related quality of life, survival, and NHS costs (reported in pounds sterling; to convert to US dollars, multiply by 1.28) over the lifetime of the female population eligible for screening in the UK. Results from our analysis might inform future prospective evaluations of AI-guided screening.

This study was conducted from January 1, 2023, to January 31, 2024. We followed the Consolidated Health Economic Evaluation Reporting Standards ( CHEERS ) reporting guideline and the National Institute for Health and Care Excellence methods of technology appraisal manual. 13 We developed a discrete event simulation 14 model in R, version 4.2.2, 15 using the simmer’ package (R Project for Statistical Computing) 16 to accommodate individual attributes that evolve over time within the simulation such as breast cancer risk. 14 The work was funded by Cancer Research UK and the policy research unit in Economic Evaluation of Health and Care Interventions. Because the data are drawn from publicly available sources, ethics approval is not required for decision modeling in the UK.

The following 4 strategies 12 were distinguished by screening intervals in years for groups categorized by a 3-year risk score (RS):

One year (RS, ≥2.57%), 2 years (RS, 1.32%-2.57%), 3 years (RS, 1.22%-1.32%), or 6 years (RS, ≤1.22%)

One year (RS, ≥2.72%), 3 years (RS, 1.23%-2.72%), or 6 years (RS, ≤1.23%)

Two years (RS, ≥2.79%), 3 years (RS, 1.35%-2.79%), or 6 years (RS, ≤1.35%)

One year (RS, ≥2.20%), 3 years (RS, 1.23%-2.20%), or 4 years (RS, ≤1.23%).

The model focused on 5 dynamic processes: (1) Women transition between risk groups based on screen attendance and AI-model–assessed risk scores. (2) Mammogram accuracy is based on breast density, which changes as women age. (3) Attendance patterns to screening appointments are influenced by age and attendance history. (4) Attendance history and time intervals between screenings impact cancer stage. (5) Cancer prognosis is influenced by age, where it was identified (at regular screening or not), and attendance history, with worse outcomes for women who have not attended breast screenings.

Figure 1 depicts the model structure, illustrating the sequence of clinical events and potential pathways from initiation to screening, cancer detection, and mortality. Women enter the model at the first screening invitation at 50 years of age, with subsequent invitations occurring at regular intervals until 70 years of age. Each screening appointment includes an updated AI risk assessment, determining the timing of the next screen invitation. If a woman misses her initial screening, invitations are sent every 3 years thereafter. The model ends when individuals reach cancer-related mortality or mortality from all other causes.

At entry, women are assigned predetermined ages of noncancer mortality and have a chance of developing breast cancer up to 74 years of age, sourced from Office for National Statistics life expectancy tables (2018-2020) 17 and NHS breast cancer registry data. 18 The age at which symptomatic cancer is detected in a primary care setting is sampled from 2020 incidence data, 18 adjusted for age-related lead time in the UK screening program using overdiagnosis estimates. 19 Screening may identify cancer at an earlier age than this determined age of symptomatic cancer. Data on attendance from 2018 to 2019, 20 stratified by age and screening history, were used to calculate attendance probability to a screening invitation. The NHS incurs a cost of £14.52 (US $18.59) 21 for each screening invitation (details of screening costs are in eTable 2 in Supplement 1 ). Screening invitations end at 70 years of age or the nearest subsequent year if screening is not scheduled at 70 years of age. For instance, in simulating the current program with screenings every 3 years, a woman attending screening at 68 years of age would receive her last invitation at 71 years of age, after which any developing cancers would be detected symptomatically.

Attending women undergo a mammogram; if cancer is detected, further assessments are conducted. Breast density and AI model scores are from UK women aged 46 to 74 years and applied based on age and cancer presence. 11 Details are available in eMethods 1 in Supplement 1 .

In the model, a woman’s age for potential tumor detection is set before her first screening and remains constant throughout her life. Screening detection ages are calculated by subtracting the tumor presence period from the age at which symptomatic cancer is detected. 22 The tumor presence period is derived from national breast screening program data and is based on age, with mean durations ranging from approximately 6 years at 35 years of age to 8 years at 85 years of age. 22

Each mammography session has a cost of £54.32 (US $69.53) 23 and results in a quality-adjusted life-year (QALY) loss of 0.0014 due to associated discomfort. 24 A summary table of the diagnostic accuracy of mammography is presented in eTable 1 in Supplement 1 . Sensitivity estimates are sourced from recent estimates in a population-based screening program and vary based on breast density, ranging from 62% (dense) to 90% (not dense). 25 False-positive results occur when no underlying tumor is present during a screening, with chances ranging from 1.5% (not dense) to 2.9% (dense), 25 and they cause a QALY loss of 0.0771. 26 Mammographic results indicating cancer leads to further assessments, which verify whether the cancer is present, and involve mammography, ultrasonography, and biopsy totaling £484.90 (US $620.67). 27 Detailed breakdowns of mammography’s diagnostic accuracy, screening and further assessment costs, and screen-related QALY losses are found in eTables 2 and 3 in Supplement 1 .

Between screening appointments, cancers are detected immediately in primary care on reaching the age of symptomatic detection, with the primary care appointment costing £37.00 (US $47.36), 28 and further assessment totaling £484.90 (US $620.67) NHS cost. 27 Mortality due to other causes than breast cancer can occur and aging results in a health utility loss based on a published formula. 29

After cancer detection, cancer is classified as ductal carcinoma in situ (DCIS) or invasive tumor TNM stages I to IV, determined by age and mode of detection (at a screening appointment or symptomatically in primary care) using UK population screening data. 30 The stage distribution is adjusted based on the time since the last screen, using US data comparing stage distribution by screening frequency. 31 Detailed methodology and sources can be found in Hill et al. 32

Treatment-related health losses 33 and NHS costs 34 , 35 vary by age, stage, mode of detection (screen or symptomatic), and time since detection (for follow-up costs and health recovery). Health utility losses after cancer are taken from multivariate regression, using the utility decrements on age, stage, and mode of detection. 33 Recovery times are 11 years for screen-detected and interval cancers and 12 years for symptomatic cancers, based on peak health-related quality of life values post cancer detection. 33 Stage 4 cancers are assumed to show no improvement in quality of life. 26

Cancer survival estimates come from Office for National Statistics mortality statistics 17 , 36 and multivariable regressions. 37 - 39 Mortality hazard ratios by stage 37 and detection mode 38 , 39 are applied to age-based mortality 17 to determine life expectancy. Cancer treatment-related utility losses, costs, and survival estimates are available in eTables 4 to 6 in Supplement 1 .

Costs were assessed from a UK payer perspective 13 and reported in 2022 pounds sterling. 27 , 28 Quality-adjusted life-years and costs were discounted at 3.5%. 13 The main economic outcome is incremental net monetary benefit (INMB) per woman, which quantifies in monetary terms the net benefit of interventions by reflecting the potential alternative use of intervention resources for other health care treatments. 40 Incremental net monetary benefit is established from differences in patient costs, and from assigning a monetary value to the difference in QALYs, which we assume to be £20 000 (US $25 600), £30 000 (US $38 400), and £1 (US $1.28) per QALY. The latter represents a scenario where a decision-maker is reluctant to spend additional NHS resources to increase population health. Population-wide INMB is derived by multiplying the per-woman INMB by the population size of women at 50 years of age eligible for screening invitations, which is 174 523, sourced from the 2022 national breast screening program data. 20 Clinical outcomes include tumor stage at detection, cancers detected during screening, breast cancer deaths prevented by RSBCRs in the population (174 523 women), and the number of screens conducted.

All analyses were performed using R, version 4.2.2 (R Program for Statistical Computing). 15 We conducted external validation of the model against targets derived from the 2022 national breast screening program data (eMethods 2 and eTable 7 in Supplement 1 ). 30 , 41 To identify where savings occur, cost results are divided into screening-related costs and those for treating DCIS and invasive cancers. Cancer treatment costs are broken down by stage (DCIS, stages I-II, and stages III-IV) and mode of detection (screen-detected and interval cancers). Deterministic sensitivity analysis is conducted by adjusting the cancer treatment and screening costs, health-related quality of life losses from cancer and screening, and mammography sensitivity. Probabilistic sensitivity analysis is performed using 250 Monte Carlo simulations 42 using probabilistic sensitivity analysis parameter distributions reported in eTable 8 in Supplement 1 . The probability that each RSBCR is cost-effective is illustrated on a cost-effectiveness acceptability curve. 40 Population-wide INMB estimates from the 250 Monte Carlo simulations are calculated across cost per QALY values ranging from £1 (US $1.28) to £100 000 (US $128 000).

Table 1 shows base-case economic results and the annual impact of the economic results in the entire population. All the AI-based regimens were associated with reduced NHS costs and increased QALYs compared with the current screening program. The strategy of conducting screening every 6 years for low risk, every 2 to 3 years for medium risk, and annually for high risk had the highest additional net monetary gain per woman invited for screening. This amounts to £346 (US $442.88) and £489 (US $625.92) under the assumption of QALY values being £20 000 (US $25 600) and £30 000 (US $38 400), respectively. Consequently, this leads to an annual net monetary benefit within the NHS screening program totaling £10.6 million (US $13.6 million) for QALY values of £1, £60.4 million (US $77.3 million) for QALY values of £20 000, and £85.3 million (US $109.2 million) for QALY values of £30 000. The 3 alternative approaches had comparable figures for the net monetary benefit gained. For instance, the screening strategy of 6 yearly, triannual, and biannual screening has the smallest incremental net monetary benefit, amounting to £188 (US $240.64) and £236 (US $302.08) per person, assuming QALY values of £20 000 and £30 000, respectively. This resulted in an annual population incremental net benefit of £32.7 million (US $41.9 million), assuming £20 000 per QALY and £41.2 million (US $52.7 million), assuming £30 000 per QALY.

Table 2 shows the clinical and screening program results. The clinical outcomes of all regimens showed an improvement compared with the current screening program. Compared with the current screening program, conducting screening every 6 years for low risk, every 2 to 3 years for medium risk, and annually for high risk resulted in a higher number of screen-detected cancers (10 549 vs 8943), a greater percentage of DCIS cancers at detection (17.1% vs 13.6%), a reduction in the number of screens (mean [SD], 3.22 [0.02] vs 4.68 [0.05]), and prevention of 834 deaths due to breast cancer.

The deterministic sensitivity analysis results (eTables 9-19 in Supplement 1 ) align with the cost-effectiveness of screening regimens found in the base-case model. Screening every 6 years for low risk, every 2 to 3 years for medium risk, and annually for high risk was likely to be most beneficial. Risk-stratified breast cancer screening regimens maintained their cost-effectiveness compared with the current program and each other. The breakdown of NHS costs incurred by women (eTable 20 in Supplement 1 ) show screening costs are lower in the RSBCR than in the current program. Cancer treatment costs incurred are larger for RSBCRs due to life extension for patients with cancer from increased screen detection of cancers and early cancer detection (see Table 2 ).

The probabilistic sensitivity analysis results in Table 3 demonstrate an improvement in the cost-effectiveness of screening regimens compared with the base-case model, without a shift in the ranking of programs from most to least cost-effective. The regimen with screening intervals of 1, 2, 3, or 6 years had the highest probability of being cost-effective (59% at £20 000 [US $25 600] per QALY) and had the largest net monetary benefit for all cost per QALY thresholds (eFigure in Supplement 1 ). The cost-effectiveness acceptability curve ( Figure 2 ) shows the regimen of 2, 3, or 6 years was likely to be the least cost-effective alternative option for RSBCRs, and the current screening program has a negligible probability of being cost-effective across all cost per QALY thresholds.

Under our analytic model, AI-based risk-stratified screening is likely to be cost-effective compared with the current one-size-fits-all screening program. Our findings are consistent with previous modeling studies demonstrating the cost-effectiveness of risk-stratified screening, whether based on traditional risk factors like family history or newer methods such as polygenic risk scores. 26 , 43 - 45 However, our model is the first to suggest that health care resources might be reduced with RSBCRs, while attaining at least the same effectiveness for the population. 43 - 45 This study is also the first to assess the cost-effectiveness of AI interpretation of breast images for risk assessment during routine screening. 43 - 45 This might be more feasible at scale than the other risk assessment methods.

While the estimated incremental economic benefits per individual invited to breast screening may seem modest in the base-case model, ranging from £188 (US $240.64) to £346 (US $442.88), the annual monetary gains for the NHS might be substantial, estimated to range from £32.7 million (US $ 41.9 million) to £60.4 million (US $77.3 million). In the probabilistic model, these savings are around 3 times larger. The health economic model suggests that RSBCRs can reduce screening utilization, NHS costs, and invasive cancer in the population and increase QALYs. The reduction in the number of screenings with an RSBCR could free up resources to address screening backlogs and reduce wait times where those problems exist in the UK, 46 potentially further improving breast cancer outcomes.

Annual screening RSBCRs lead to higher percentages of screen-detected cancers and greater percentages of DCIS cancers at detection, along with higher QALYs. However, biennial screening for the highest-risk group, with corresponding 3 and 6 yearly screening for other risk groups, incurs lower costs due to fewer screenings. Screen utilization is not the only explanation of the difference in cost-effectiveness among the regimens. Other contributing factors are the proportion of individuals at medium and high risk within the population undergoing intensified screening surveillance, their uptake to screening appointments, and the accuracy of mammography in women with denser breast tissue that makes cancers harder to detect. Although cost-effectiveness is important, it is not the only factor to consider when comparing the benefits of the regimen. 47 For example, a population accustomed to the UK’s triennial screening program may view a 6-year gap between screenings as unacceptable, favoring a 4-year interval instead. This change to an RSBCR screening program with a 4- year screening interval for patients at low risk would generate an annual monetary benefit of £50 million (US $64 million), assuming a QALY is valued at £20 000 (US $25 600). Significant variations in screening frequency within programs can be accepted by patients. In the UK, colonoscopy surveillance guidelines for cancer vary by risk level, with no surveillance for patients at low risk, every 3 years for patients at intermediate risk, and annually for patients at high risk. 48

This study has some limitations. First, the model is parametrized for the UK. Findings may not be directly generalizable to alternative geographical populations, such as the US or elsewhere in Europe. Clinical guidelines in these regions often recommend universal annual or biennial screening, while a triennial screening program is in place in the UK, and typically screening also begins at earlier ages in these regions. 49 Therefore, the comparator screen detection rates based on triennial screening, on which the model depends, are likely to differ across populations with varied screening frequencies and onset ages. For example, in a setting where annual screening is the norm, all the regimens considered would reduce costs but also effectiveness. Further analysis using the model could usefully contribute to decisions on the economic value of decreasing intensity of screening based on breast cancer AI in such settings.

In this decision analytical model study of risk-based screening with AI-based risk assessment, risk-based screening delivered health benefits while using fewer NHS resources compared with the current UK breast screening program. New studies to prospectively evaluate AI-guided screening appear warranted.

Accepted for Publication: July 10, 2024.

Published: September 5, 2024. doi:10.1001/jamanetworkopen.2024.31715

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Hill H et al. JAMA Network Open .

Corresponding Author: Harry Hill, PhD, School of Medicine and Population Health, Regent Court, University of Sheffield, Sheffield S1 4 DA, United Kingdom ( [email protected] ).

Author Contributions: Dr Hill had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Hill, Roadevin, Duffy, Brentnall.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Hill, Roadevin.

Critical review of the manuscript for important intellectual content: Hill, Duffy, Mandrik, Brentnall.

Statistical analysis: All authors.

Obtained funding: Hill, Brentnall.

Administrative, technical, or material support: Hill, Roadevin.

Supervision: Hill, Duffy.

Conflict of Interest Disclosures: Dr Brentnall reported receiving royalties from Cancer Research UK arising from commercial use of the Tyrer-Cuzick breast cancer risk evaluation tool (IBIS) outside the submitted work. No other disclosures were reported.

Funding/Support: This research was funded by grant 2019DecPR1395 from Breast Cancer Now (Dr Brentnall); grant C49757/A28689 from Cancer Research UK (Dr Brentnall); grant PR-PRU-1217-20401 from the National Institute of Health Research (NIHR) Policy Research Programme, conducted through the Policy Research Unit in Economic Methods of Evaluation in Health and Social Care Interventions (Dr Hill);. and grant PR-PRU-1217-21601 from the NIHR Policy Research Programme, conducted through the Policy Research Unit in Cancer Awareness, Screening and Early Diagnosis (Dr Duffy).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

Data Sharing Statement: See Supplement 2 .

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• Published: 02 September 2024

Lipidomics and metabolomics as potential biomarkers for breast cancer progression

• Alanis Carmona 1 ,
• Samir Mitri 2 ,
• Ted A. James 2 &
• Jessalyn M. Ubellacker 1  

npj Metabolic Health and Disease volume  2 , Article number:  24 ( 2024 ) Cite this article

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• Cancer metabolism
• Metabolomics

Breast cancer is the most prevalent cancer among women in the United States, representing ~30% of all new female cancer cases annually. For the year 2024, it is estimated that 310,720 new instances of invasive breast cancer will be diagnosed, and breast cancer will be responsible for over 42,000 deaths among women. Today, despite the availability of numerous treatments for breast cancer and its symptoms, most cancer-related deaths result from metastasis for which there is no treatment. This emphasizes the importance of early detection and treatment of breast cancer before it spreads. For initial detection and staging of breast cancer, clinicians routinely employ mammography and ultrasonography, which, while effective for broad screening, have limitations in sensitivity and specificity. Advanced biomarkers could significantly enhance the precision of early detection, enable more accurate monitoring of disease evolution, and facilitate the development of personalized treatment plans tailored to the specific molecular profile of each tumor. This would not only improve therapeutic outcomes, but also help in avoiding overtreatment and the associated side effects, thereby improving the quality of life for patients. Thus, the pursuit of novel biomarkers, potentially encompassing metabolomic and lipidomic signatures, is essential for advancing breast cancer diagnosis and treatment. In this brief review, we will provide an overview of the current translational potential of metabolic and lipidomic biomarkers for predicting breast cancer prognosis and response to therapy.

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Introduction.

Breast cancer is the most prevalent cancer among women in the United States, representing ~30% of all new female cancer cases annually. For the year 2024, it is estimated that 310,720 new instances of invasive breast cancer will be diagnosed, and breast cancer will be responsible for over 42,000 deaths among women 1 . Today, despite the availability of numerous treatments for breast cancer and its symptoms, most cancer-related deaths are still predominantly due to metastasis 2 . While there are treatments available that can manage metastatic breast cancer and improve quality of life, it remains incurable, and the focus is often on extending life rather than achieving a cure 3 . This fact emphasizes the necessity for comprehensive management strategies that prioritize prevention, early detection, and vigilant monitoring throughout the course of breast cancer. Despite clinical advances in these areas, not all cases of breast cancer can be intercepted before progressing to stage IV, whether at initial diagnosis or through recurrence 4 . This limitation highlights the complexity of breast cancer and current gaps in management. It underscores the need for continued research into effective therapies to reduce the incidence of advanced disease.

The primary breast tissue biomarkers currently used in clinical practice to guide treatment decisions include hormone receptor status (estrogen receptor and progesterone receptor) and human epidermal growth factor receptor 2 (HER2) 5 . These receptors are identified through pathological analysis and play an important role in determining the most appropriate treatment approach for patients with breast cancer 5 . Breast cancer is further classified into subtypes (Luminal A, Luminal B, HER2-enriched, and Basal-like), each with distinct biological characteristics. This classification informs prognosis and guides treatment decisions tailored to the unique features of each cancer 6 , but are not comprehensive in predicting disease progression or response to therapy across all patient subtypes 7 .

Breast cancers can also vary widely in terms of their genetic and molecular characteristics, their response to treatments, and their potential for aggressiveness and metastasis 8 . Due to this heterogeneity, there is a need for more robust and sensitive biomarkers capable of predicting the risk of breast cancer progression as well as response to therapies. Advanced biomarkers could significantly enhance the precision of early detection, enable more accurate disease monitoring, and facilitate the development of personalized treatment plans tailored to the specific molecular profile of each tumor. This would not only improve therapeutic outcomes but also help avoid overtreatment and its associated adverse effects, thereby improving patient outcomes and quality of life. Thus, pursuing novel biomarkers, potentially encompassing metabolomic and lipidomic signatures, is essential for advancing breast cancer diagnosis and treatment. The utility of metabolomics is emphasized by its proven capability to detect early metabolic signatures indicative of disease states in longitudinal studies, often well before clinical symptoms become evident. Notably, early metabolic markers have been identified for diseases such as multiple sclerosis, pancreatic cancer, type 2 diabetes, and cognitive decline 4 , 5 , 6 , 7 , 8 , 9 , 10 . Recently, it has been demonstrated that lipids can be used as biomarkers with health-to-disease transitions as observed through comprehensive lipidomic profiling of longitudinally collected plasma samples, thus highlighting the role of lipidomic changes in diabetes, ageing, inflammation, and immune homeostasis 11 . The demonstrated ability of metabolomics and lipidomics to predict and correlate with various disease states, and the heightened metabolic and lipid adaptations that take place in cancer cells compared to non-cancer cells suggests predicting cancer progression with metabolic and lipidomic biomarkers is feasible 12 . For example, metabolomics and lipidomics have recently been employed to map out and elucidate intratumor metabolic heterogeneity in the context of gastric cancer 13 .

Recent studies have significantly advanced our understanding of the metabolic landscape in breast cancer, highlighting the complexity and clinical relevance of metabolic stratification. Studies have identified distinct metabolic subtypes of human breast tumors, underscoring their potential therapeutic implications and clinical relevance 14 . This stratification could guide personalized treatment strategies, improving patient outcomes. A recent study further explored the metabolic heterogeneity within cancers, emphasizing the diverse metabolic adaptations that tumors undergo to survive and proliferate 15 . Another study utilized spatially resolved multiomics to reveal cell-specific metabolic remodeling in gastric cancer, demonstrating the intricate metabolic interactions at the cellular level, which may also be applicable to breast cancer research 13 . Recent work has highlighted the burgeoning field of spatial metabolomics, suggesting that it could drive innovation in cancer research by providing more detailed metabolic maps of tumors 16 . A recent study investigated metastatic triple-negative breast cancer, showing how these tumors adapt their metabolism to different tissues while retaining essential metabolic signatures, which is crucial for understanding metastasis 17 . Finally, the role of metabolites in the tumor microenvironment has recently been explored, detailing how metabolic interactions between cancer cells and their surroundings can influence systemic metabolism, potentially offering new avenues for therapeutic intervention 18 . Together, these studies underscore the importance of metabolic profiling in cancer.

In this brief review, we will provide an overview of the current translational potential of metabolic and lipidomic biomarkers for predicting breast cancer prognosis and response to therapy.

Metabolomic biomarkers of breast cancer progression

Metabolic processes shape the functional dynamics of cancer cells and their progression 19 . Metabolomics, the study of these processes, offers a precise method to understand how physiological conditions relate to both external influences and diseases. Metabolites are particularly useful in this context because they act as immediate indicators of disease processes and can serve as direct biomarkers reflecting pathogenic activities. Metabolomics provides an intricate and comprehensive view of biological processes and metabolic pathways, positioning it as a potentially pivotal tool in precision medicine for offering a precise and objective molecular-level analysis 20 . This omic technique further provides direct readouts, through metabolic maps and profiles, that have the potential to predict metabolic states ranging from the cellular level to the organ of interest 16 .

Recent innovations in highly sensitive, low-input assays allow for detailed profiling of the metabolome and lipidome from minimal amounts of biological samples, which is revolutionizing the use of lipidomics and metabolomics as biomarkers for tracking the progression of diseases such as breast cancer 21 . In cancer, fundamental shifts in metabolic pathways have been demonstrated to promote cancer progression in mouse and patient samples 22 . For instance, cancer cells have been shown to capitalize on glucose uptake and downstream shifts in metabolic processes to provide maximal cellular fuel to maintain cancer cell growth and proliferation 23 . Furthermore, tumors are dependent on metabolites to sustain growth and survival, and are able to adapt nutrient usage in response to the nutrient availability 18 .

Amino acid metabolism has also been shown to be altered in cancer cells compared to non-cancer cells 24 . Cancer cells require and exploit glutamate as it is a precursor molecule for various growth signaling pathways and thus is critical for cell proliferation 24 . Proline catabolism, by the enzyme proline dehydrogenase (PRODH), has been shown to play a crucial role in supporting the growth and spread of metastatic breast cancer cells, as demonstrated in both 3D culture systems and in vivo mouse models 25 . Elevated PRODH expression and proline catabolic activity were observed in metastatic tumors compared to primary tumors, indicating its potential as a biomarker and/or target for breast cancer progression 25 .

Additional metabolite markers, including lactate, β-hydroxybutyrate, acetoacetate, glycoproteins, pyruvate, glutamate, and mannose, have been shown to be increased in early cases of breast cancer 26 , 27 , 28 . The presence of altered metabolic pathways for early breast cancer detection has also been noted in patient samples, notably those involving taurine, hypotaurine, and the metabolism of amino acids such as alanine, aspartate, and glutamate, thus highlighting these metabolites as potential biomarkers of breast cancer progression 28 . Redox imbalance is also a hallmark of metabolic adaptations as cancer cells tend to experience higher levels of oxidative stress and mitochondrial dysfunction 29 , 30 . Studies have shown that breast cancer cells upregulate antioxidant pathways to maintain redox balance and sustain cell survival 31 .

In the PAM50 classification of breast cancer, Luminal A, Luminal B, HER2-enriched, and basal-subtypes subtypes have defined metabolic and lipidomic signatures that have been determined to have clinical relevance. In this classification system, tumors classified as Luminal A are characterized by the presence of ER and/or PR and the lack of HER2; clinically, these tumors are typically low-grade and slow-growing as they rely more on oxidative phosphorylation making the cancer type less aggressive 32 . This tumor subtype has moderate lipid synthesis as it affects cholesterol metabolism which can be controlled, further reflecting the subtype’s less aggressive nature 33 . Like Luminal A, Luminal B tumors are ER-positive and HER2-positive, yet they are PR-negative 34 . This tumor type has higher glycolytic activity and elevated lipid synthesis, which supports cell growth, making the subtype more aggressive in nature 35 . The HER2-enriched subtype has an overexpression of the HER2 protein. Similar to the Luminal B subtype, HER-enriched has increased glycolytic activity as well as glutamine metabolism and increased lipid biosynthesis which contributes to the overall aggressiveness of the HER2 positive subtype 36 . Specifically, the overexpression of HER2 can modulate lipid metabolism by promoting an upregulation in gene expression of fatty acid synthesis and uptake, including CD36, which is involved in the uptake of fatty acids and oxidized low-density lipoproteins, and the expression of which has been linked to an overall poor prognosis 37 . Finally, the Basal-like subtype, unlike the aforementioned types, lacks the ER, PR, and HER2 receptors 38 . This subtype is also able to readily exploit glycolytic pathways to promote cell proliferation even in normoxic conditions making it one of the most aggressive subtypes 39 . To meet high energy demands, these cancer cells have high lipid uptake and fatty acid oxidation 39 . Luminal A, Luminal B, HER2-enriched, and basal-subtypes have unique clinical signatures that influence prognosis and treatment triaging for breast cancer.

Distinct metabolic changes may occur in the different tissue environments to which breast cancer metastasizes 17 . These specific changes in metabolism could provide valuable insights, potentially serving as indicators for the prediction of breast cancer spread to specific distant metastatic sites such as lymph nodes. Furthermore, previous work has shown that patients with breast cancer undergoing neoadjuvant chemotherapy may achieve a pathologic complete response in the breast, yet not replicate the same response in the axillary lymph nodes, revealing a discordant treatment outcome depending on metastatic site 40 . The unique behavior of cancer within lymph nodes, as opposed to the primary tumor site, can be linked to the specific microenvironment within the lymphatic system. This environment not only facilitates the transportation and dissemination of cancer cells but also shapes their growth, survival, and responsiveness to treatment, setting it apart from other tissue environments 41 , 42 .

Studies have demonstrated that metabolites in the lymph node correlate with cancer progression to lymph nodes; cancer cells expressing arachidonate 15-lipoxygenase-1 can not only cause damage to the lymph endothelium, but also facilitate the entrance of tumor cells into the vessel; making it a key candidate metabolite in the progression of cancer 43 . Similarly, studies have shown breast metastatic spread through the lymph nodes has shown an upregulation in PRODH as well as asparagine synthetase, thus proline and asparagine are metabolites of interest in progression of lymph node metastatic breast cancer 25 . Because cancer cells in the lymph node tumor microenvironment undergo metabolomic reprograming, the altered nutrient environment presents an opportunity for the identification of metabolites as biomarkers in cancer progression 17 , 44 . Nevertheless, the lymphatic tumor microenvironment represents an under-researched area for biomarkers pertinent to breast cancer progression. Furthermore, breast cancer cells in other distant metastatic sites (including lung, liver, brain, and bone) likely undergo metabolic alterations that could be detected by unique metabolomic/lipidomic profiling of tissues from different metastatic locations.

Currently, changes in specific metabolites during disease progression have led to the identification of biomarkers for some, but not all cancers 44 . however, no definitive metabolic biomarkers have yet been identified in breast cancer that can predict disease progression in patients 45 . Prior work has shown that glucose, threonine, and beta-hydroxybutyrate are upregulated in the blood and correlate with nonspecific symptoms of breast cancer like weight loss and fatigue 46 . Further studies in patient samples have shown metabolites such as glycine, taurine, lactate, and succinate are increased, and glucose and inositol are decreased in breast cancer tissues compared to non-tumorous tissue 47 . A recent study has shown that metabolomics can provide a prognostic framework to identify distinct metabolomic profiles in serum samples that differentiate early from metastatic breast cancer, thereby enhancing the precision of risk stratification and therapeutic decision-making of conventional clinical methodologies 48 . Another study using patient samples explored the metabolic landscape of breast cancer and subsequently identified three distinct breast cancer subtypes that correlated with tumor aggressiveness and patient outcomes; these subtypes showed pronounced dysregulation in bile-acid biosynthesis, methionine pathway, fatty acid metabolism, and glucose metabolism 14 .

Given that cancer is metabolically active disease, the identification of metabolites that serve not only as biomarkers but as necessary agents of cancer progression could provide the opportunity to intervene with antimetabolite therapeutics 49 , such as those reviewed in Fig. 1 and Table 1 . For instance, if a breast cancer patient’s lymph node metabolomic analysis indicates upregulation in folate metabolism, clinicians could tailor a treatment plan that includes methotrexate, an antimetabolite that inhibits dihydrofolate reductase, thus depleting folate, to potentially enhance the efficacy of standard chemotherapy and mitigate cancer progression 50 .

A Carbon metabolism has revealed various targets of interest in the folate synthesis pathway. Metabolomic techniques have led to the development of drugs like methotrexate and capecitabine to target thymidylate synthase 102 and methotrexate to target key enzyme dihydrofolate reductase (DHFR) 103 . B Glucose metabolism modulators target glucose transporters phloretin 104 and WZB117 105 as well as key enzymes in the glycolytic pathway like inhibition of hexokinase 2 via Genistein-27 106 and Resveratrol 107 , 108 . Within the same pathway, metabolomic techniques have further pioneered anti-cancer drugs targeting glucose 6 phosphate dehydrogenase (G6PD), like Polydatin 109 which inhibits glycolysis, as well drugs targeting the conversion of pyruvate to lactate via the key enzyme lactate dehydrogenase (LDH) with Oxamate 110 and FX11 111 . C Fatty acid metabolism targets include Betulin which inhibits sterol regulatory element binding protein 1 (SREBP-1). At a synthesis level, key enzymes acetyl-CoA carboxylase is inhibited via Soraphen A 112 and fatty-acid synthase by TVB-2640 113 , 114 . Figure generated with BioRender.com.

Lipidomic biomarkers of breast cancer progression

Lipids, such as sterols and various forms of glycerides and phospholipids, are critical hydrophobic compounds that form the structure of cell membranes and are integral to processes like energy storage and cell signaling within the cell 51 . In cancer, notably in breast cancer, there is a profound remodeling of lipid metabolism, characterized by increased de novo lipogenesis and alterations in lipidomic profiles due to the rapid proliferation and metabolic demands of cancer cells, which is heightened during tumor progression 44 , 52 . Prior work has shown that cancer cells can enhance lipolytic pathways to metabolize stored triglycerides and fatty acids, which are crucial for maintaining rapid cell division and invasion 53 . These modifications are not merely a consequence of cancer but also contribute to tumor progression and metastasis by providing necessary components for cell membrane formation and energy production.

Furthermore, lipid metabolism in cancer is influenced by the need for various biological processes, such as the production of steroid hormones, vitamins, bile acids, and eicosanoids. This requirement is met through a combination of dietary intake and endogenous synthesis, with most cancer cells depending more heavily on exogenous lipids due to their increased metabolic requirements 54 , 55 , 56 , 57 , 58 . Altered lipid metabolism in cancer not only supports the energy and structural needs of proliferating cells but also mediates stress responses, such as endoplasmic reticulum stress and ferroptosis, further exacerbating cancer phenotypes 53 , 59 , 60 . Cancer cells also often upregulate lipolytic pathways where lipolysis and β-oxidation, may be activated in cancer cells 61 . This ensures that cells can then breakdown stored triglycerides and fatty acids to sustain proliferation 61 . Lipidomic studies in patient breast cancer samples also have revealed a correlation between lipid profiles and both the type of cancer tissue and the tumor grade. Another significant shift observed with tumor growth is the change in the balance of choline-containing compounds 62 , 63 .

Lipid metabolism plays a critical role in breast cancer as lipid remodeling can shape the tumor microenvironment by altering immune cell responses 64 . Specifically, tumor cells can alter the tumor microenvironment by secreting signaling molecules and can exacerbate cancer progression as cancer-associated fibroblasts and immune cell function become compromised 65 . The direct effect on cancer cells can disrupt many processes and specifically alter lipid metabolism in cancer cells by enhancing lipid biosynthesis by promoting an upregulation in fatty acid, cholesterol, and phospholipid synthesis to promote cell growth 53 . The accumulation of lipid droplets in cancer cells can subsequently inactivate immune signaling molecules to foster a hospitable environment by providing the necessary energy reserves and redox balance 66 .

Lipid metabolism remodeling further hinders cancer phenotypes through its direct association with myeloid-derived suppressor cells (MDSCs) and tumor-associated macrophages (TAMs) as tumor-infiltrated MDSC upregulates fatty acid uptake and oxidation 67 . Myeloid cells have been deemed regulators of metastasis and cancer/tumor progression as they negatively regulate immune responses; regulation is dependent on the type of myeloid cell involved which can include and range from polymorphonuclear neutrophils, eosinophils, dendritic cells, macrophages, megakaryocytes, and basophils 67 . TAMs also support tumor growth as lipid metabolism can reprogram cells to a pro-tumorigenic state 67 . The lipid tumor microenvironment can also reshape how cancer cells behave and facilitate metabolic reprogramming, as lipids are a source of energy. The readily available abundance of energy provided by lipids is able to sustain cancer cell growth and proliferation as cancer cells can use fatty acids undergo beta oxidation to generate adenosine triphosphate 68 . Lipids, like prostaglandins, within the TME, can further modulate an immune response due to their ability to readily suppress and alter immune function; they can further promote the formation of new blood vessels or angiogenesis to further supply nutrients necessary for tumor growth 69 .

Given these significant changes in lipid metabolism, lipidomics emerges as a promising field for developing biomarkers for breast cancer progression. By analyzing the lipid profiles of cancer cells, particularly the patterns of lipid synthesis, remodeling, and breakdown, lipidomics can offer valuable insights into the metabolic state of tumors. This understanding could lead to novel therapeutic strategies targeting lipid biosynthesis and metabolism, potentially decreasing cancer progression.

Influence of environmental and lifestyle factors on metabolome and lipidome

Metabolomics and lipidomics have facilitated new insights into the connections between environmental and lifestyle factors (such as dietary factors) and disease states 70 . For example, branched-chain amino acids have been associated with obesity and insulin resistance 71 , and physical activity and dietary-associated changes are known to induce extensive alterations to the plasma metabolome and lipidome 72 . Plasma levels are directly influenced by dietary intake as consumption of food and drinks provides the body with nutrients, fats, vitamins, mineral and proteins that can be absorbed into the bloodstream 73 , 74 . Depending on the content of the diet, metabolic pathways can become enriched as substrates necessary to carryout processes become readily available. For example, a diet high in carbohydrates will raise plasma glucose levels as the carbohydrates provide the substrates necessary to fuel glycolytic pathways 45 . This will most typically result in a higher presence of glucose related metabolites being enriched from metabolomic analysis. To a similar degree, a diet high in fat content would most likely reveal an elevated lipid and cholesterol panel as the substrates necessary for the metabolic pathways associated would be available 75 . Because cancer cells can readily rewire their metabolism in cancer progression, this further contributes to the overall metabolic heterogeneity and aggressiveness of the cancer 15 .

It has been shown that certain metabolites, like trimethylamine N -oxide, are upregulated in patients whose diet consists of meat, fish, and eggs as these foods enrich the gut microbiome to show an upregulation in this metabolite 76 . Additionally, analysis of metabolite profiles from the plasma of patients with breast cancer from the Nurses’ Health Study demonstrate many associations between metabolite subclasses and breast cancer risk, many of which could be influenced by dietary consumption 77 . In summary, dietary and environmental factors are critical additional elements that can substantially affect the lipidome and metabolome, potentially influencing the progression of breast cancer. It is important to acknowledge the influence of environmental and lifestyle (including dietary) factors on metabolome and lipidome within the context of analyzing -omics profiles as an additional variable for breast cancer progression.

Patient specimens for metabolomics/lipidomics profiling

Metabolomic and lipidomic profiling techniques can analyze small quantities and volumes of both solid and liquid tissue types. This flexibility means a variety of tissue sources can be used for profiling including plasma, serum, saliva, primary tumor or metastatic site biopsies (including lymph node biopsies), and urine 78 , 79 , among others. One study using saliva identified that patients with breast cancer had a distinct metabolite profile of volatile metabolites (including acetic, propanoic, and benzoic acids) that could potentially be used as a prognostic biomarker 80 . Another study using metabolomics of salivary samples of patients with breast cancer determined that the ratios of polyamines correlated with cancer stage in patients and increased with worsened health status 81 . A dried blood spot technique for analyzing amino acids and acylcarnitines identified piperamide, asparagine, proline, tetradecenoylcarnitine/palmitoylcarnitine, phenylalanine/tyrosine, and the glycine/alanine ratio as potential biomarkers for breast cancer diagnosis 82 .

Plasma sampling enables the detection of various indicators of cancer, including circulating tumor cells, serum-based disease markers, circulating tumor DNA, and plasma DNA methylation patterns 83 , 84 . These indicators can be analyzed together with metabolomic and lipidomic profiles derived from the same plasma samples for a comprehensive understanding of breast cancer progression at a molecular-level. Fine-needle aspiration (FNA) biopsies also provide sufficient sample to be able to obtain a metabolomic/lipidomic profile, and FNA techniques have been successfully applied to other -omic profilings in cancer including proteomics 85 . Since FNA is commonly performed on primary breast tumors and lymph node biopsies, it represents a novel sampling method that could be used to determine the metabolic and lipidomic profile of the tissues for comparison of profiles between primary tumor and progression to lymph nodes 85 , 86 , 87 (Fig. 2 ).

As fine-needle aspirations are commonly performed on primary breast tumors and lymph node biopsies, it represents a novel sampling method that could be used to determine the metabolic and lipidomic profile of the tissues for comparison of profiles between primary tumor and progression to lymph nodes. Figure generated with BioRender.com.

Benefits and challenges of lipidomics/metabolomics profiling in breast cancer

Lipidomics and metabolomics profiling offer potential benefits in breast cancer management, specifically by opening new opportunities for personalized treatments. In the context of lipidomics and metabolomics profiling for breast cancer, several types of specimens can be considered for clinical analysis. Primary tumor samples are the most direct source of metabolic information specific to the malignancy 88 . Lymph node samples, often obtained during staging procedures, provide critical insights into metastatic processes 89 . Plasma represents a highly feasible option due to its non-invasive collection and potential to reflect systemic metabolic alterations associated with breast cancer. Moreover, plasma can be longitudinally sampled over time, allowing for dynamic monitoring of disease progression and response to treatment 66 , 81 , 82 . Other potential sample types include urine, which offers ease of collection and potential for reflecting systemic metabolic changes, and fine-needle aspiration samples of primary tumors, lymph nodes, and larger metastases at distant sites (including liver biopsies). While each specimen type presents unique advantages and challenges, in the future routine clinical implementation will likely favor those that balance diagnostic value with practicality and patient comfort, particularly plasma and minimally invasive biopsy techniques 83 , 84 .

By discovering the unique lipid and metabolite alterations associated with an individual’s breast cancer progression, these tools have the potential to inform tailored treatment regimens that integrate with standard oncologic care, including targeted therapy and conventional treatments like chemotherapy and immunotherapy. Despite their promise, the use of lipidomic and metabolomic profiling as prognostic and diagnostic tools in breast cancer also faces technical and methodological challenges. Immediate freezing of patient samples on dry ice is imperative to preserve the integrity of the metabolome and lipidome, and plasma must be processed without delay to prevent coagulation—prerequisites that demand meticulous handling and rapid processing 90 , 91 , 92 , 93 , 94 . Temperature fluctuations can impair the integrity and overall accuracy of the sample analysis 91 , 92 , 93 , 94 . The shortcomings of these tools further include the conditions at which samples must be prepared.

Variability in metabolites due to diet, medication, gut microbiome influences that differ from patient-to-patient, as well as timing of sample collection present significant confounders for analysis necessitating careful consideration of these factors in study designs 95 . Diet can also influence the lipidome not only from patient-to-patient but within an individual patient as lipid composition can change based on dietary consumption. These techniques also rely on advanced instrumentation, such as high-resolution mass spectrometry, and may require collaborative efforts across multiple institutions to ensure consistency and validity of data 11 .

Furthermore, the heterogeneity and structural complexity of metabolites, present substantial challenges in the specific detection and analysis of metabolic alterations tied to cancer progression 96 . This complexity is particularly problematic in translational research involving cellular samples from patients, which exhibit significant variability, thereby complicating the application of standard metabolic methodologies. One of the most challenging aspects of lipidomic and metabolic profiling from plasma or tissue samples is the ability to differentiate between metabolites derived from immune versus cancer cells within a sample—a challenge that current and future technological advancements must address to refine metabolomics and lipidomics as diagnostic and prognostic tools in breast cancer progression. Furthermore, while lipidomic and metabolomic profiling can identify potential biomarkers, we currently lack a broad range of drugs to target these metabolic changes, underscoring the gap between biomarker discovery and therapeutic application.

Ongoing clinical trials for metabolomic and lipidomic profiling in the identification of biomarkers have become a powerful tool for metabolic diseases. Specifically in colorectal cancer, -omic techniques have been previously utilized to identify predictive biomarkers for the prediction of colorectal cancer, thus demonstrating the feasibility of conducting such clinical trials in breast cancer 12 . Clinical trials in the identification of metabolomic and lipidomic biomarkers are technically feasible as low cell number input is possible and able to offer insightful and key information. Recent advancements in metabolomic/lipidomic technologies such as spatial metabolomics, single-cell metabolic profiling (scMEP) 97 , assessing metabolism by flow cytometry (Met-Flow), and nutrient uptake assays (QUAS-R) 98 , along with single-cell metabolomics and SpaceM 99 , have greatly improved the precision and throughput of metabolite quantification in breast cancer research 100 , 101 . As these technologies advance, they are increasingly being combined with multiomics and imaging techniques, forming an integrated approach that deepens our understanding of metabolic functions and of identifying predictive biomarkers in breast cancer progression.

Outstanding questions

Key outstanding questions in the use of lipidomics and metabolomics as potential biomarkers for breast cancer progression include:

What is the anticipated specificity and sensitivity of lipidomic and metabolomic biomarkers for breast cancer progression?

Can metabolomic and lipidomic profiles be used for the early detection of breast cancer?

What are the underlying mechanisms by which changes in the metabolome and lipidome contribute to breast cancer progression?

How do environmental and lifestyle (including dietary) factors influence the metabolome and lipidome in the context of breast cancer?

How can lipidomic and metabolomic biomarkers be used to inform treatment decisions in breast cancer?

Given the heterogeneity of breast cancer, what is the extent to which personalized versus generalizable lipidomic and metabolomic biomarkers can be used to predict disease progression in diverse patient populations?

How can lipidomic and metabolomic data be integrated with genomic, transcriptomic, and proteomic data to provide a more comprehensive understanding of breast cancer progression?

Are there dynamic changes in the lipidome and metabolome during breast cancer treatment, and can these changes predict therapeutic response or relapse?

How can advances in cell isolation techniques and mass spectrometry technology improve the accuracy, speed, ability to analyze low cell number metabolomics, ability to distinguish between cancer versus non-cancer (including immune cell) metabolomic/lipidomic profiles, and the cost-effectiveness of lipidomic and metabolomic profiling?

What are the challenges in translating metabolomic and lipidomic findings from the lab to the clinic?

Siegel, R. L., Giaquinto, A. N. & Jemal, A. Cancer statistics, 2024. CA Cancer J. Clin. 74 , 12–49 (2024).

Article   PubMed   Google Scholar  

Park, M. et al. Breast cancer metastasis: mechanisms and therapeutic implications. Int. J. Mol. Sci. 23 , 6806 (2022).

Harbeck, N. et al. Breast cancer. Nat. Rev. Dis. Prim. 5 , 66 (2019).

Feng, Y. et al. Breast cancer development and progression: risk factors, cancer stem cells, signaling pathways, genomics, and molecular pathogenesis. Genes Dis. 5 , 77–106 (2018).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Onitilo, A. A., Engel, J. M., Greenlee, R. T. & Mukesh, B. N. Breast cancer subtypes based on ER/PR and Her2 expression: comparison of clinicopathologic features and survival. Clin. Med. Res. 7 , 4–13 (2009).

Veerla, S., Hohmann, L., Nacer, D. F., Vallon-Christersson, J. & Staaf, J. Perturbation and stability of PAM50 subtyping in population-based primary invasive breast cancer. npj Breast Cancer 9 , 83 (2023).

Lindström, L. S. et al. Clinically used breast cancer markers such as estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 are unstable throughout tumor progression. J. Clin. Oncol. 30 , 2601–2608 (2012).

Perou, C. M. et al. Molecular portraits of human breast tumours. Nature 406 , 747–752 (2000).

Article   CAS   PubMed   Google Scholar  

Mapstone, M. et al. Plasma phospholipids identify antecedent memory impairment in older adults. Nat. Med. 20 , 415–418 (2014).

Mayers, J. R. et al. Elevation of circulating branched-chain amino acids is an early event in human pancreatic adenocarcinoma development. Nat. Med. 20 , 1193–1198 (2014).

Hornburg, D. et al. Dynamic lipidome alterations associated with human health, disease and ageing. Nat. Metab. 5 , 1578–1594 (2023).

Rossi, C. et al. Breast cancer in the era of integrating “Omics” approaches. Oncogenesis 11 , 17 (2022).

Article   PubMed   PubMed Central   Google Scholar  

Sun, C. et al. Spatially resolved multi-omics highlights cell-specific metabolic remodeling and interactions in gastric cancer. Nat. Commun. 14 , 2692 (2023).

Iqbal, M. A. et al. Metabolic stratification of human breast tumors reveal subtypes of clinical and therapeutic relevance. iScience 26 , 108059 (2023).

Demicco, M., Liu, X.-Z., Leithner, K. & Fendt, S.-M. Metabolic heterogeneity in cancer. Nat. Metab. 6 , 18–38 (2024).

Alexandrov, T. Spatial metabolomics: from a niche field towards a driver of innovation. Nat. Metab. 5 , 1443–1445 (2023).

Roshanzamir, F., Robinson, J. L., Cook, D., Karimi-Jafari, M. H. & Nielsen, J. Metastatic triple negative breast cancer adapts its metabolism to destination tissues while retaining key metabolic signatures. Proc. Natl Acad. Sci. USA 119 , e2205456119 (2022).

Elia, I. & Haigis, M. C. Metabolites and the tumour microenvironment: from cellular mechanisms to systemic metabolism. Nat. Metab. 3 , 21–32 (2021).

Griffin, J. L. & Shockcor, J. P. Metabolic profiles of cancer cells. Nat. Rev. Cancer 4 , 551–561 (2004).

Clish, C. B. Metabolomics: an emerging but powerful tool for precision medicine. Cold Spring Harb. Mol. Case Stud. 1 , a000588 (2015).

Cosgrove, J. et al. A call for accessible tools to unlock single-cell immunometabolism research. Nat. Metab. 6 , 779–782 (2024).

Jang, M., Kim, S. S. & Lee, J. Cancer cell metabolism: implications for therapeutic targets. Exp. Mol. Med. 45 , e45 (2013).

Boroughs, L. K. & DeBerardinis, R. J. Metabolic pathways promoting cancer cell survival and growth. Nat. Cell Biol. 17 , 351–359 (2015).

Gyamfi, J., Kim, J. & Choi, J. Cancer as a metabolic disorder. Int. J. Mol. Sci. 23 , 1155 (2022).

Elia, I. et al. Proline metabolism supports metastasis formation and could be inhibited to selectively target metastasizing cancer cells. Nat. Commun. 8 , 15267 (2017).

Asiago, V. M. et al. Early detection of recurrent breast cancer using metabolite profiling. Cancer Res. 70 , 8309–8318 (2010).

Budczies, J. et al. Remodeling of central metabolism in invasive breast cancer compared to normal breast tissue - a GC-TOFMS based metabolomics study. BMC Genomics 13 , 334 (2012).

Jové, M. et al. A plasma metabolomic signature discloses human breast cancer. Oncotarget 8 , 19522–19533 (2017).

Piskounova, E. et al. Oxidative stress inhibits distant metastasis by human melanoma cells. Nature 527 , 186–191 (2015).

Le Gal, K. et al. Antioxidants can increase melanoma metastasis in mice. Sci. Transl. Med. 7 , 308re308 (2015).

Google Scholar  

Harris, I. S. & DeNicola, G. M. The complex interplay between antioxidants and ROS in cancer. Trends Cell Biol. 30 , 440–451 (2020).

Cappelletti, V. et al. Metabolic footprints and molecular subtypes in breast cancer. Dis. Markers 2017 , 7687851 (2017).

Albi, E. et al. The effect of cholesterol in MCF7 human breast cancer cells. Int. J. Mol. Sci. 24 , 3007–30013 (2023).

Das, C. et al. A prismatic view of the epigenetic-metabolic regulatory axis in breast cancer therapy resistance. Oncogene 43 , 1727–1741 (2024).

Li, W. et al. Comprehensive analysis of the association between tumor glycolysis and immune/inflammation function in breast cancer. J. Transl. Med. 18 , 92 (2020).

Bartlome, S. & Berry, C. C. Recent insights into the effects of metabolism on breast cancer cell dormancy. Br. J. Cancer 127 , 1385–1393 (2022).

Taborda Ribas, H., Sogayar, M. C., Dolga, A. M., Winnischofer, S. M. B. & Trombetta-Lima, M. Lipid profile in breast cancer: from signaling pathways to treatment strategies. Biochimie 219 , 118–129 (2024).

Pazaiti, A. & Fentiman, I. S. Basal phenotype breast cancer: implications for treatment and prognosis. Women’s. Health 7 , 181–202 (2011).

PubMed   Google Scholar  

Liu, S., Li, Y., Yuan, M., Song, Q. & Liu, M. Correlation between the Warburg effect and progression of triple-negative breast cancer. Front. Oncol. 12 , 1060495 (2022).

Flores, R. et al. Discordant breast and axillary pathologic response to neoadjuvant chemotherapy. Ann. Surg. Oncol. 30 , 8302–8307 (2023).

Ubellacker, J. M. et al. Lymph protects metastasizing melanoma cells from ferroptosis. Nature 585 , 113–118 (2020).

Reticker-Flynn, N. E. et al. Lymph node colonization induces tumor-immune tolerance to promote distant metastasis. Cell 185 , 1924–1942.e1923 (2022).

Kerjaschki, D. et al. Lipoxygenase mediates invasion of intrametastatic lymphatic vessels and propagates lymph node metastasis of human mammary carcinoma xenografts in mouse. J. Clin. Invest. 121 , 2000–2012 (2011).

Suri, G. S., Kaur, G., Carbone, G. M. & Shinde, D. Metabolomics in oncology. Cancer Rep. 6 , e1795 (2023).

Article   Google Scholar  

Silva, M. E., Pupo, A. A. & Ursich, M. J. Effects of a high-carbohydrate diet on blood glucose, insulin and triglyceride levels in normal and obese subjects and in obese subjects with impaired glucose tolerance. Braz. J. Med. Biol. Res. 20 , 339–350 (1987).

CAS   PubMed   Google Scholar  

Larkin, J. R. et al. Metabolomic biomarkers in blood samples identify cancers in a mixed population of patients with nonspecific symptoms. Clin. Cancer Res. 28 , 1651–1661 (2022).

Giskeødegård, G. F. et al. Lactate and glycine-potential MR biomarkers of prognosis in estrogen receptor-positive breast cancers. NMR Biomed. 25 , 1271–1279 (2012).

Oakman, C. et al. Identification of a serum-detectable metabolomic fingerprint potentially correlated with the presence of micrometastatic disease in early breast cancer patients at varying risks of disease relapse by traditional prognostic methods. Ann. Oncol. 22 , 1295–1301 (2011).

Luengo, A., Gui, D. Y. & Vander Heiden, M. G. Targeting metabolism for cancer therapy. Cell Chem. Biol. 24 , 1161–1180 (2017).

Gonen, N. & Assaraf, Y. G. Antifolates in cancer therapy: structure, activity and mechanisms of drug resistance. Drug Resist. Updat. 15 , 183–210 (2012).

Horn, A. & Jaiswal, J. K. Structural and signaling role of lipids in plasma membrane repair. Curr. Top. Membr. 84 , 67–98 (2019).

Hilvo, M. et al. Novel theranostic opportunities offered by characterization of altered membrane lipid metabolism in breast cancer progression. Cancer Res. 71 , 3236–3245 (2011).

Fu, Y. et al. Lipid metabolism in cancer progression and therapeutic strategies. MedComm 2 , 27–59 (2021).

Ackerman, D. & Simon, M. C. Hypoxia, lipids, and cancer: surviving the harsh tumor microenvironment. Trends Cell Biol. 24 , 472–478 (2014).

Ackerman, D. et al. Triglycerides promote lipid homeostasis during hypoxic stress by balancing fatty acid saturation. Cell Rep. 24 , 2596–2605.e2595 (2018).

Kamphorst, J. J. et al. Hypoxic and Ras-transformed cells support growth by scavenging unsaturated fatty acids from lysophospholipids. Proc. Natl Acad. Sci. USA 110 , 8882–8887 (2013).

Qiu, B. et al. HIF2α-dependent lipid storage promotes endoplasmic reticulum homeostasis in clear-cell renal cell carcinoma. Cancer Discov. 5 , 652–667 (2015).

Young, R. M. et al. Dysregulated mTORC1 renders cells critically dependent on desaturated lipids for survival under tumor-like stress. Genes Dev. 27 , 1115–1131 (2013).

Williams, K. J. et al. An essential requirement for the SCAP/SREBP signaling axis to protect cancer cells from lipotoxicity. Cancer Res. 73 , 2850–2862 (2013).

Snaebjornsson, M. T., Janaki-Raman, S. & Schulze, A. Greasing the wheels of the cancer machine: the role of lipid metabolism in cancer. Cell Metab. 31 , 62–76 (2020).

Zaidi, N. et al. Lipogenesis and lipolysis: the pathways exploited by the cancer cells to acquire fatty acids. Prog. Lipid Res. 52 , 585–589 (2013).

Bathen, T. F. et al. Feasibility of MR metabolomics for immediate analysis of resection margins during breast cancer surgery. PLoS One 8 , e61578 (2013).

Mimmi, M. C. et al. High-performance metabolic marker assessment in breast cancer tissue by mass spectrometry. Clin. Chem. Lab Med. 49 , 317–324 (2011).

Jin, H. R. et al. Lipid metabolic reprogramming in tumor microenvironment: from mechanisms to therapeutics. J. Hematol. Oncol. 16 , 103 (2023).

Liu, Y. & Cao, X. Characteristics and significance of the pre-metastatic niche. Cancer Cell 30 , 668–681 (2016).

Petan, T. Lipid droplets in cancer. Rev. Physiol. Biochem Pharm. 185 , 53–86 (2023).

Hicks, K. C., Tyurina, Y. Y., Kagan, V. E. & Gabrilovich, D. I. Myeloid cell-derived oxidized lipids and regulation of the tumor microenvironment. Cancer Res. 82 , 187–194 (2022).

Garcia, C., Andersen, C. J. & Blesso, C. N. The role of lipids in the regulation of immune responses. Nutrients 15 , 3899 (2023).

Nie, J. Z., Wang, M. T. & Nie, D. Regulations of tumor microenvironment by prostaglandins. Cancers (Basel) 15 , 3090 (2023).

Belhaj, M. R., Lawler, N. G. & Hoffman, N. J. Metabolomics and lipidomics: expanding the molecular landscape of exercise biology. Metabolites 11 , 151 (2021).

Newgard, C. B. et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 9 , 311–326 (2009).

Ding, M. et al. Metabolome-wide association study of the relationship between habitual physical activity and plasma metabolite levels. Am. J. Epidemiol. 188 , 1932–1943 (2019).

Padron-Monedero, A., Rodríguez-Artalejo, F. & Lopez-Garcia, E. Dietary micronutrients intake and plasma fibrinogen levels in the general adult population. Sci. Rep. 11 , 3843 (2021).

Smilowitz, J. T. et al. Nutritional lipidomics: molecular metabolism, analytics, and diagnostics. Mol. Nutr. Food Res. 57 , 1319–1335 (2013).

Moholdt, T., Parr, E. B., Devlin, B. L., Giskeødegård, G. F. & Hawley, J. A. Effect of high-fat diet and morning or evening exercise on lipoprotein subfraction profiles: secondary analysis of a randomised trial. Sci. Rep. 13 , 4008 (2023).

Rafiq, T. et al. Nutritional metabolomics and the classification of dietary biomarker candidates: a critical review. Adv. Nutr. 12 , 2333–2357 (2021).

Romanos-Nanclares, A. et al. Consumption of olive oil and risk of breast cancer in U.S. women: results from the Nurses’ health studies. Br. J. Cancer 129 , 416–425 (2023).

Nam, H., Chung, B. C., Kim, Y., Lee, K. & Lee, D. Combining tissue transcriptomics and urine metabolomics for breast cancer biomarker identification. Bioinformatics 25 , 3151–3157 (2009).

Woo, H. M. et al. Mass spectrometry based metabolomic approaches in urinary biomarker study of women’s cancers. Clin. Chim. Acta 400 , 63–69 (2009).

Cavaco, C. et al. Screening of salivary volatiles for putative breast cancer discrimination: an exploratory study involving geographically distant populations. Anal. Bioanal. Chem. 410 , 4459–4468 (2018).

Takayama, T. et al. Diagnostic approach to breast cancer patients based on target metabolomics in saliva by liquid chromatography with tandem mass spectrometry. Clin. Chim. Acta 452 , 18–26 (2016).

Wang, Q. et al. A dried blood spot mass spectrometry metabolomic approach for rapid breast cancer detection. Onco Targets Ther. 9 , 1389–1398 (2016).

PubMed   PubMed Central   Google Scholar  

Tang, Q., Cheng, J., Cao, X., Surowy, H. & Burwinkel, B. Blood-based DNA methylation as biomarker for breast cancer: a systematic review. Clin. Epigenetics 8 , 115 (2016).

Rajkumar, T. et al. Identification and validation of plasma biomarkers for diagnosis of breast cancer in South Asian women. Sci. Rep. 12 , 100 (2022).

Lin, P. et al. Deciphering novel biomarkers of lymph node metastasis of thyroid papillary microcarcinoma using proteomic analysis of ultrasound-guided fine-needle aspiration biopsy samples. J. Proteom. 204 , 103414 (2019).

Article   CAS   Google Scholar  

Sigmon, D. F. & Fatima, S. in StatPearls (StatPearls Publishing Copyright © 2024, StatPearls Publishing LLC, 2024).

Roskell, D. E. & Buley, I. D. Fine needle aspiration cytology in cancer diagnosis. BMJ 329 , 244–245 (2004).

Gaude, E. & Frezza, C. Tissue-specific and convergent metabolic transformation of cancer correlates with metastatic potential and patient survival. Nat. Commun. 7 , 13041 (2016).

Ji, H. et al. Lymph node metastasis in cancer progression: molecular mechanisms, clinical significance and therapeutic interventions. Signal Transduct. Target Ther. 8 , 367 (2023).

Johnson, C. H. & Gonzalez, F. J. Challenges and opportunities of metabolomics. J. Cell Physiol. 227 , 2975–2981 (2012).

Ulmer, C. Z. et al. A review of efforts to improve lipid stability during sample preparation and standardization efforts to ensure accuracy in the reporting of lipid measurements. Lipids 56 , 3–16 (2021).

Fomenko, M. V., Yanshole, L. V. & Tsentalovich, Y. P. Stability of metabolomic content during sample preparation: blood and brain tissues. Metabolites 12 , 811 (2022).

Reis, G. B. et al. Stability of lipids in plasma and serum: effects of temperature-related storage conditions on the human lipidome. J. Mass Spectrom. Adv. Clin. Lab 22 , 34–42 (2021).

Haid, M. et al. Long-term stability of human plasma metabolites during storage at −80 °C. J. Proteome Res. 17 , 203–211 (2018).

Hong, B. V. et al. A single 36-h water-only fast vastly remodels the plasma lipidome. Front. Cardiovasc. Med. 10 , 1251122 (2023).

Kim, J. & DeBerardinis, R. J. Mechanisms and implications of metabolic heterogeneity in cancer. Cell Metab. 30 , 434–446 (2019).

Hartmann, F. J. et al. Single-cell metabolic profiling of human cytotoxic T cells. Nat. Biotechnol. 39 , 186–197 (2021).

Ahl, P. J. et al. Met-Flow, a strategy for single-cell metabolic analysis highlights dynamic changes in immune subpopulations. Commun. Biol. 3 , 305 (2020).

Rappez, L. et al. SpaceM reveals metabolic states of single cells. Nat. Methods 18 , 799–805 (2021).

Schönberger, K. et al. LC-MS-based targeted metabolomics for FACS-purified rare cells. Anal. Chem. 95 , 4325–4334 (2023).

DeVilbiss, A. W. et al. Metabolomic profiling of rare cell populations isolated by flow cytometry from tissues. eLife 10 , e61980 (2021).

Lluch, A. et al. Phase III trial of adjuvant capecitabine after standard neo-/adjuvant chemotherapy in patients with early triple-negative breast cancer (GEICAM/2003-11_CIBOMA/2004-01). J. Clin. Oncol. 38 , 203–213 (2020).

Rana, R. M. et al. In silico study identified methotrexate analog as potential inhibitor of drug resistant human dihydrofolate reductase for cancer therapeutics. Molecules 25 , 3510 (2020).

Wu, K. H. et al. The apple polyphenol phloretin inhibits breast cancer cell migration and proliferation via inhibition of signals by type 2 glucose transporter. J. Food Drug Anal. 26 , 221–231 (2018).

Zhao, Y., Butler, E. B. & Tan, M. Targeting cellular metabolism to improve cancer therapeutics. Cell Death Dis. 4 , e532 (2013).

Tao, L. et al. Gen-27, a newly synthesized flavonoid, inhibits glycolysis and induces cell apoptosis via suppression of hexokinase II in human breast cancer cells. Biochem. Pharm. 125 , 12–25 (2017).

Dai, W. et al. By reducing hexokinase 2, resveratrol induces apoptosis in HCC cells addicted to aerobic glycolysis and inhibits tumor growth in mice. Oncotarget 6 , 13703–13717 (2015).

Boocock, D. J. et al. Phase I dose escalation pharmacokinetic study in healthy volunteers of resveratrol, a potential cancer chemopreventive agent. Cancer Epidemiol. Biomark. Prev. 16 , 1246–1252 (2007).

Mele, L. et al. A new inhibitor of glucose-6-phosphate dehydrogenase blocks pentose phosphate pathway and suppresses malignant proliferation and metastasis in vivo. Cell Death Dis. 9 , 572 (2018).

Le, A. et al. Inhibition of lactate dehydrogenase A induces oxidative stress and inhibits tumor progression. Proc. Natl. Acad. Sci. USA 107 , 2037–2042 (2010).

Zhou, M. et al. Warburg effect in chemosensitivity: targeting lactate dehydrogenase-A re-sensitizes taxol-resistant cancer cells to taxol. Mol. Cancer 9 , 33 (2010).

Corominas-Faja, B. et al. Chemical inhibition of acetyl-CoA carboxylase suppresses self-renewal growth of cancer stem cells. Oncotarget 5 , 8306–8316 (2014).

Falchook, G. et al. First-in-human study of the safety, pharmacokinetics, and pharmacodynamics of first-in-class fatty acid synthase inhibitor TVB-2640 alone and with a taxane in advanced tumors. EClinicalMedicine 34 , 100797 (2021).

Król, S. K., Kiełbus, M., Rivero-Müller, A. & Stepulak, A. Comprehensive review on betulin as a potent anticancer agent. Biomed. Res. Int. 2015 , 584189 (2015).

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Acknowledgements

We thank all members of the Ubellacker Lab for their helpful suggestions for this review. We are grateful for the support from the Breast Cancer Research Alliance (J.M.U.), Landry Cancer Biology Research Fellowship (A.C.), and the Ludwig Center at Harvard who have provided funding to make this review possible.

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A.C. and J.M.U. wrote the review with input and clinical insights from S.M. and T.A.J. All authors reviewed the manuscript.

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Carmona, A., Mitri, S., James, T.A. et al. Lipidomics and metabolomics as potential biomarkers for breast cancer progression. npj Metab Health Dis 2 , 24 (2024). https://doi.org/10.1038/s44324-024-00027-0

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Concise review: breast cancer stems cells and their role in metastases

Mohammad kamalabadi farahani.

a Department of Medical Laboratory Sciences, School of Paramedical

Mohammad Farjadmehr

c Student Research Committee

Amir Atashi

b Department of Tissue Engineering, School of Medicine, Shahroud University of Medical Sciences

Alireza Momeni

d Department of hematology and Oncology, School of Medicine

Mahin Behzadifard

e Department of Laboratory Sciences, School of Allied Medical Sciences, Dezful University of Medical Sciences, Dezful, Iran

Associated Data

Not applicable.

Background:

Breast cancer stem cells (BCSCs) have been suggested to be responsible for the development of Breast cancer (BC). The aim of this study was to evaluate BCSCs and the target organs microenvironment immunophenotyping markers in common BC metastases, and therapeutic targets regarding to the mentioned criteria.

Material and methods:

This narrative review involved searching international databases; PubMed, Google Scholar using predetermined keywords including breast cancer, breast cancer stem cells, breast cancer metastases, immunophenotyping, immunohistochemistry and metastases. The search results were assessed based on the title, abstract, and full text of the articles, and relevant findings were included in the review.

BCSCs express high amounts of aldehyde dehydrogenase 1 (ALDH1), Ganglioside 2 (GD2), CD44 and CD133 but are negative for CD24 marker. CXCR4 and OPN have high expression in the cells and may contribute in BC metastasis to the bone. Nestin, CK5, prominin-1 (CD133) markers in BCSCs have been reported to correlate with brain metastasis. High expression of CD44 in BCSCs and CXCL12 expression in the liver microenvironment may contribute to BC metastasis to the liver. Aberrantly expressed vascular cell adhesion molecule-1 (VCAM-1) that binds to collagen and elastin fibers on pulmonary parenchyma, and CXCR4 of BCSCs and CXCL12 in lung microenvironment may promote the cells homing and metastasis to lung.

Conclusion:

As in various types of BC metastases different markers that expressed by the cells and target organ microenvironment are responsible, BCSCs immunophenotyping can be used as target markers to predict the disease prognosis and treatment.

Introduction

• Breast cancer (BC) is known to be one of the most common types of cancer and the main cause of cancer-related mortality in women, which is categorized at least into five subtypes: luminal A, luminal B, human epithelial growth factor receptor type 2 (HER2), basal-like, and claudin-low
• Cancer stem cells (CSCs) have been suggested to be responsible for the development of various malignancies such as BC. BCSCs (Breast CSCs) are a small subpopulation of breast cancer cells that play a critical role in this metastasis to other organs of the body.
• BCSCs express high amounts of aldehyde dehydrogenase 1 (ALDH1), Ganglioside 2 (GD2), CD44 and CD133 but are negative for CD24 marker. CXCR4 and OPN have high expression in the cells and may contribute in BC metastasis to the bone. Nestin, CK5, prominin-1 (CD133) markers in BCSCs have been reported to correlate with brain metastasis.
• High expression of CD44 in BCSCs and CXCL12 expression in the liver microenvironment may contribute to BC metastasis to the liver. Aberrantly expressed vascular cell adhesion molecule-1 (VCAM-1) that binds to collagen and elastin fibers on pulmonary parenchyma, and CXCR4 of BCSCs and CXCL12 in lung microenvironment may promote the cells homing and metastasis to lung.

Breast cancer (BC) is known to be one of the most common types of cancer and the main cause of cancer-related mortality in women, which is categorized at least into five subtypes: luminal A, luminal B, human epithelial growth factor receptor type 2 (HER2), basal-like, and claudin-low 1 , 2 . Advancements in BC diagnosis and new treatment strategies that employ target therapies in combination with apoptotic ligands and chemotherapy have led to a significant decrease in the rate of the patient’s mortality 3 . Different treatment modalities have been used according to the cancer subtypes and gene expression profiles including hormonal therapies for hormone receptor-positive (HR+) ( subtypes luminal A and luminal B) 3 , inhibitors therapy for Her2-enriched BC 4 , and inhibitors of poly ADP-ribose polymerase (PARP) for triple-negative BC (TNBC) and BRCA1-mutant tumors 5 .

Despite the availability of various approaches for BC treatment, drug resistance, tumor relapse, and metastasis may occur. In such conditions, the survival rate of the patients will be very low 6 . Due to the emergence of CSCs subpopulation, drug resistance, cancer aggressiveness, and metastasis may occur because of the high tumorgenicity potential, self-renewal ability, and high invasion and migration capacity of the cells 7 . The cells are characterized by the high expression of Aldehyde dehydrogenase 1 (ALDH1), Ganglioside 2 (GD2), CD44 and CD133 8 . Additionally, Notch, Hedgehog, Wnt, Hippo, etc., signaling pathways support their stemness features 9 . In Her2-dependent BC, dissemination of certain stem cells may occur very early and even in the pre-malignant phase and the metastatic tumor cells can remain dormant in the target tissue for a long time 10 , 11 .

Metastases account for 90% of human cancer-related deaths 12 . BC metastasizes mainly to bone (50–65%), lungs (17%), brain (16%), liver (6%), but other organs like kidney, spleen, or uterus are the relatively rare locations 13 , 14 . During metastasis, certain tumor cells detach from the primary tumor, circulate in the blood, lymphatic and/or primo vascular system (PVS), and finally exit from the circulation and form a new tumor in an appropriate tissue 15 . Recurrence, metastasis, and chemo-resistant are major problems in BC patients. Metastatic tumor cells gain resistant potential that ultimately leads to failure in common therapeutic approaches including chemotherapy and radiotherapy. Several studies have focused on the molecular characteristics of the metastasis process that could help develop new therapies 16 , 17 . This research aims to evaluate the BCSCs and metastatic site markers that are important in BC metastases to the main site of this cancer.

Material and methods

This narrative review involved searching PubMed and Google Scholar, using predetermined keywords, including. We conducted the PUBMED search using the following search terms: (“cancer stem cells” or “breast cancer stem cells” (“breast cancer metastases”) (“immunophenotyping” or “immunohistochemistry”) and (“microenvironment”). The search results were assessed to choose the relevant articles based on the titles and the abstracts that had including criteria. The criteria used for this review were: Breast cancer metastases; brain, bone, liver, lung, breast cancer stem cells, microenvironment, immunophenotyping, and original publications. The used exclusion criteria were articles that had not included breast cancer metastases; BCSCs; breast cancer stem cells; or microenvironment and did not assess immunophenotyping or immunohistochemistry.

Circulating tumor cells (CTCs)

Only asmall fraction of heterogeneous CTCs have stem cell-like and survival features and disseminate to distant organs to induce the formation of secondary tumors. These cells are known as circulating tumor stem cells (CTSCs) and usually, the detection of CTCs is associated with poor prognosis. In some patients, the cells are present with no detectable metastasis, and not all CTCs have the potency to induce metastasis. In tumor cells, reactivation of an embryonic program known as epithelial to mesenchymal transition (EMT) may occur. EMT is an important step in cancer progression that gives epithelial cells, as non-motile cells, the ability to invasion into adjacent tissues. Various cytokines originated from surrounding stroma support EMT 18 , 19 . CSCs are one of the actors in the EMT process and transformation into CTCs. In metastatic BC, CTCs have been identified as possible markers of metastasis correlated with worse prognosis. It is possible to distinguish mesenchymal CTCs, epithelial CTCs, and CTCs with phenotype of stemness that could have both epithelial-mesenchymal (EM) and mesenchymal-epithelial (ME) potentials. In this regard, CTCs are quite heterogeneous, and merely quantitative evaluation of t these cells cannot distinguish between prognostic values of different CTCs subpopulations (Fig. ​ (Fig.1). 1 ). Analysis of CTCs subpopulations can provide a better insight into the characteristics of CSCs. For instance, identification of EMT and stemness markers on CTCs could help identify the presence of CSCs and target the population more precisely, which are primarily responsible for disease dissemination, resistance to therapies, and ultimately worse prognosis 20 , 21 . ALDH1, CD44, and CD24 are known main markers associated with circulating CSCs that give the cells higher metastatic potential. Breast cancer stem cells (BCSCs) can easily switch between the ME phenotype EpCAM+CD49f+ ALDH1+, and EM phenotypeEpCAM− CD49f+ are positive for CD44/CD24. Identification of EM phenotype of circulating CSCs is based on EpCAM systems but distinguishing them from pure epithelial CTCs without further analysis is impossible 22 – 24 . Invasion assays for circulating subpopulations of EM-CSCs and ME-CSCs revealed that EM-CSCs had a greater invasive capacity than ME phenotype. This finding can support the notion that detected CSCs from the primary tumor undergo EMT and then enter the circulation and spread to distant organs. Before reverting to a mesenchymal/epithelial phenotype, micro-metastases are quiescent, and then CSCs could form a new bulk tumor 22 , 25 .

Schematic diagram depicting metastatic Breast cancer stem cells separation from initial presentation as an in-situ tumor mass at the primary site, into circulating tumor cells and induce macroscopically metastatic lesions at secondary sites.

CSCs in blood circulation

CTCs collected from metastatic BC patients usually exhibit overexpression of stem cell markers. This finding suggests that a subpopulation of CTCs that express cancer stem cell markers is responsible for induction of metastasis 25 , 26 . A specific subtype of CTCs can express stem cell markers CD133 and CD44 giving the cells characteristics of CSCs and then circulating cancer stem-like cells (cCSCs). The cells express CD133 and EpCAM following CD45 depletion. Notably, tumor cells expressing CD133 display CSCs features and could induce tumors in animal models 27 – 30 .

CSCs in lymphatic circulation

In 80% of solid tumors, metastasis via the lymphatic system precedes metastasis via the vascular system 31 . However, the molecular characteristics of tumor cells that end up in sentinel lymph nodes (SLNs) are not fully elucidated. In a recent study using an innovative technique, lymph and lymph-circulating tumor cells (LCTCs) en route to the SLN were collected in an immunocompetent animal model of BC metastasis. Results indicated that the gene and protein expression profiles of LCTCs and blood-circulating tumor cells (BCTCs) as they exit the primary tumor were similar but distinct from those of primary tumors and lymph node metastases (LNMs) 32 . LCTCs, but not BCTCs, exist in clusters, display a hybrid EMT /CSCs-like phenotype, and are efficient metastatic precursors. These results demonstrate that tumor cells metastasizing through the lymphatic system are different from those spread by blood circulation. Understanding the relative contribution of these cells to overall peripheral blood-circulating tumor cells is essential for cancer therapy 32 , 33 .

Origin of BCSCs

Current experimental evidence proposed two different but closely related theories about the origin of BCSCs and tumor heterogeneity 34 . Stochastic model or clonal evolution and Hierarchical or CSC model. The first hypothesis suggests that the tumorigenic potential of the cells residing in the tumor site is similar; however; sequential mutations in intratumoral clones can lead to tumor heterogeneity. Additionally, the origin of CSCs can be from differentiated mammary cells due to mutations that occur in the course of the disease. In this regard, the de-differentiation process leads to the generation of de novo CSCs because of exposure to radiation and/or chemotherapy agents that induce genetic alterations in non-malignant somatic cells 35 .

Hierarchical theory suggests that BCSCs arise from either mammary stem cells or progenitor cells 36 , 37 . This hypothesis seems more plausible because the cell surface markers such as CD44 and CD24, which are expressed on mammary differentiated progenitor cells are also expressed on BCSCs population. BCSCs display a range of abilities including self-renewal, differentiation, tumor initiation, invasion, and resistance to conventional therapy 38 .

BCSCs are characterized by a specific immunophenotype included; CD44+/CD24−, ALDH1 high, CD133+, Ganglioside 2+ (GD2+) 8 , 38 . CD44+CD24−/low cells have a higher ability to generate tumors upon transplantation into immune-deficient mice 39 . In ductal and inflammatory BC, ALDH1+, CD44+, and CD24− fraction enriches tumor-propagating cells and mediates metastasis and ALDH1 expression being associated with poor outcomes 40 .

It was reported that BCSCs displayed high heterogeneity among BC patients, which played a significant role in BC recurrence and metastasis, consisting of location in the tumor, biological characteristics, tumor-initiating capacity, genetic differences, and so on. Recently BCSCs have been categorized into different types, mainly according to their biomarker status, epithelial or mesenchymal status, and other biological factors. Most recently, many researches revealed that there was a potential association between BCSCs and the metastatic organotropism of breast cancer and response to treatment 41 .

CSCs in metastatic locations

Under the physiological situation, low cell-to-matrix interaction causes anoikis that acts as a barrier and prevents cancer cells migration and metastasis. However, CSCs are resistant to anoikis and enter the circulation and grow in distant organs 42 , 43 . The ability to transition between mesenchymal-like (EMT) and epithelial-like (MET) cell phenotypes determines metastasis features and permits the CSCs to adapt to the changes in metastatic locations 7 , 44 . BC metastases may occur after years or decades of remission, and the lymph node is the preferential route of metastasis. The expression of specific molecules such as CXCR4 and CD44 on BCSCs and the presence of their ligands CXCL12, hyaluronan and osteopontin (OPN), respectively, may signify BC metastases 45 , 46 . OPN is involved in cell proliferation, migration, inflammation, and tumor progression in various tissues. OPN induces stemness by interacting with CD44 BCSCs express higher level of CXCR4, stimulation of CXCR4 signaling by SDF-1 induces mammosphere forming capacity and anoikis-resistance in breast cancer cells 47 . Wnt activation is also shown to be higher in the anoikis-resistant cell population 48 . Downregulation of metalloprotease-disintegrin ADAM12 reduces cell migration, invasion and anoikis-resistance in claudin-low breast cancer cells by suppressing the activation of EGFR signaling pathway 49 .

BC metastasis to bone

Breast-to-bone metastasis (BBM) is the most frequent metastasis of BC that is predominantly diagnosed in the luminal type 50 , 51 . White race, young age, HR+ status, advanced tumor stage, and higher tumor grade are reported as the risk factors of BBM 52 . BCSCs reach the bone through feeder vessels of the marrow. Comparing BBM and non-bone sites, a clinical study demonstrated that 92% and 17% of cases showed high score PTHrP secretion, respectively. PTHrP leads to increased expression of the membrane protein receptor activator of nuclear factor κB (RANK) ligand (RANKL) and decreased osteoprotegerin (OPG) expression on osteoblasts membrane. RANKL promotes the differentiation of osteoclast precursors and bone matrix degradation. Osteoclasts activity leads to the release of transforming growth factor-b (TGF-b), insulin-like growth-factor-1 (IGF-1), calcium, bone morphogenetic proteins (BMPs), fibroblast growth factors (FGF) in bone environment enabling cancer cell proliferation and survival. In BBM osteolysis, hypercalcemia, severe pain, reduced mobility, and bone fractures are common, spinal cord compression and bone marrow aplasia can also occur. The enrichment of bone with OPG, TGF-b, platelet-derived growth factor (PDGF) and CXCL12 act as molecular mediators in BBM. CXCR4-CXCL12 axis has a critical role in BBM, and experimentally blocking this axis by AMD3100 (Plerixafor) led to a decrease in the BCSCs metastasis to the bone 46 , 51 , 53 . BCSCs expression of CD44, CXCR4 are high and the markers might have a role in the metastasis of BC cells to the bone (Fig. ​ (Fig.3 3 ) 54 .

Signaling pathways and surface markers, which are involved in the maintenance of stemness, self-renewal and drug resistance can be targets in breast cancer stem cells therapy. CD44, CD133, GD2, and ALDH1 are helpful candidates for this purpose. EGFR, epidermal growth factor receptor; GSI, gamma-secretase inhibitors; HA, hyaluronic acid.

BC metastasis to lung

BC metastasis to the lung as the second most frequent site culminates in a 5-year overall survival of 16.8% 55 . In animal experiments, it was shown that following intravenous injection of cancer cells into lung, two factors will hamper entry of cells into the lung microenvironment: first, a continuous layer of endothelial cells formed through tight cell-cell junctions, and second, killing cancer cells by leukocytes 56 . Metadherin (MTDH) is a cell surface molecule expressed by the lung endothelium and mediates BCSCs transmigration and homing to lung 57 . MTDH mediates adhesion of CSCs to the lung endothelium, thus MTDH inhibitors (the antibodies against MTDH), tyrosine kinase inhibitor (TKI); SU6668, and DNA vaccines have been suggested as inhibitors of BC metastasis to lung 56 , 58 . CCL2 overexpression promotes BC metastasis to both lung and bone and blocking CCL2 function with a neutralizing antibody can reduce such metastases 59 . A strong correlation of PDGF and TGF-b overexpression in BC metastasis to bone and lung was described previously 60 .

BCSCs are not associated with luminal BC and HER2- positive subtypes, while basal-like BC (BLBC) shows a lung tropism, the primary mechanism underlying this tropism is unclear 61 . In a previous study on 4T1 mouse mammary carcinoma model, CD44vC cells showed much higher potential to promote lung colonization than CD44v cells 62 . In lung metastasis, another related study demonstrated the heterogeneity of BCSCs in clinical samples and showed that BCSCs expressed CD44v by interacting with osteopontin in the lung microenvironment promoting lung metastasis 63 . CD44-negative human BCSCs also promote lung metastasis implying that CD44 is not considered a good marker for lung invasion. BCSCs express a higher level of CXCR4 compared to normal breast tissue. Additionally, a high level of CXCR4 ligand (CXCL12) is expressed in the lung where BC cells prefer to metastasize. Increased activation of Wnt/ β-catenin signaling in BCSCs compared to normal stem-like cells, Tenascin-C (TNC), Periostin (POSTN) and Versican (VCAN) are involved in EMT and play a critical role in the BC metastasis. BCSCs aberrantly expressed vascular cell adhesion molecule-1 (VCAM-1) that bind to collagen and elastin fibers on pulmonary parenchyma and may have a role in homing and metastasis (Fig. ​ (Fig.2 2 ) 61 , 64 , 65 .

Breast cancer stem cells (BCSCs) and target organs markers in the main site of metastases, including lung (A), brain (B), liver (C) and bone (D). (A) MTDH on the surface of pulmonary vascular endothelial cells facilitates the metastasis of BCSC to the lung parenchyma. In the microenvironment of the lung, BCSCs change to CCL2 through CCR2, through VCAM-1 to collagen and elastin, through CXCR4 to CXCL12, through PDGFR to PDGF, and Through the TGF-βR on their surface, they connect to the TGF-β present in this microenvironment. (B) The expression of nestin, α-SMA, and CK5 in BCSC, as well as CD133 and HER2 on the surface of these cells, cause metastasis to the brain. (C) In the liver, BCSCs connect to CXCL12 via CCR4, to HA via CD44, and to collagen IV and fibronectin in the liver microenvironment via claudin-2. Also, the expression of HER2 on the surface of BCSCs plays a role in their metastasis to the liver. (D) In the bone, BCSCs are converted to FGF through the FGFR present on their surface, and IGF-1 through the IGF-1R on their surface, BMPR to BMP, CCR2 on their surface to CCL2, PDGFR on their surface to PDGF, and TGF-βR on their surface binds to TGF-β present in this microenvironment. α-SMA, alpha-smooth muscle actin; BMPR, bone morphogenetic protein receptor; CK5, cytokeratin 5; FGFR, fibroblast growth factor receptor; HER2, human epidermal growth factor receptor 2; IGF-1, insulin-like growth factor-1; IGF-1R, insulin-like growth factor-1 receptor; HA, hyaluronic acid; MTDH, metadherin; PDGFR, platelet-derived growth factor receptor; TGF-β, transforming growth factor-β; TGF-βR, transforming growth factor-β receptor; VCAM-1, vascular cell adhesion molecule-1.

BC metastasis to brain

Brain is one of the target sites of BC metastases. Expression of BC brain metastases (BCBM) have a poor prognosis of around 6–18 months after the diagnosis. BC subtypes including human epidermal growth factor receptor 2 (HER2/neu)-enriched (20–30%) and TNBC (45–60%) commonly have the highest potential to induce brain metastases. Three types of BCBM, depending on the anatomic features of the brain, include parenchymal metastasis as the most common type (78%), leptomeningeal metastasis (8%), and choroid plexus are rare metastasis 66 . Compared to other metastasis sites, BCBM takes a longer time after initial BC diagnosis. This delay can be related to blood-brain barrier (BBB) with a specific compartment, including tight junctions, endothelial cells, microglia, pericytes, and astrocytes. Additionally, BBB induces a selective permeability of the brain to macromolecules. Thus, regulatory molecules and biological pathways in BBB are vital in preventing BCBM. For instance, reactive astrocytes secret miR-19a-containng exosomes which increase the aggressive metastasis of cancer cells into the brain 67 . Contribution of BBB disruption to the brain metastasis has been demonstrated not to be the case because therapeutic levels of drug conjugates were detected only in 15% of tumor lesions (Fig. ​ (Fig.2 2 ) 68 – 70 .

BC metastasis to liver

In BC liver metastasis (BCLM) the survival time is only 4–8 months. Luminal A and HER2 positive subtypes promote predominant liver metastasis compared to other BC subtypes. The liver microenvironment and sinusoidal structure have a crucial role in the initiation of homing and progression of BCLM 71 . Claudins form the backbone of the tight junction between epithelial cells and control junction permeability. Claudin-2 is weakly expressed in primary human BC. Decreased Claudin-2 mediates BCLM by enhancing adhesion to fibronectin and collagen type-IV as extracellular matrix proteins that are abundant in the liver microenvironment. It has been reported that high expression of CD44 in CSCs and CXCL12 expression in the liver microenvironment may contribute to BC metastasis to the liver (Fig. ​ (Fig.2 2 ) 72 .

Therapeutic targeting of breast cancer stem cell markers

Signaling pathways and surface markers that are involved in the maintenance of stemness, self-renewal and drug resistance have been proposed as therapeutic targets in BCSCs therapy. CD44, CD133, GD2, ALDH1, the main markers for BCSCs isolation, are important in BCSCs phenotypic features and maintenance 39 . Targeting the markers as potential therapeutic approaches to eradicate CSCs can be helpful. CD44 is a cell surface receptor that interacts with hyaluronic acid (HA) and is a critical molecule in the maintenance of stemness property in BCSCs. Knocking down the CD44 marker in CSCs leads to the induction of differentiation in the cells and renders BCSCs more susceptible to doxorubicin 73 . In support of the above, some studies showed that coating two anti-cancer drugs Paclitaxel and rapamycin with HA enhanced their efficacy 74 , 75 .

CD133 is a well-known BCSCs surface glycoprotein important in stemness maintenance, and anti-CD133-immunotoxin conjugates directly targeting CD133 molecule can be used as immunotoxin therapy for BCSCs 76 . AC-133 (a monoclonal anti-CD133 antibody) and Saponin (a known toxin) are mostly used to generate immunotoxins. AC-133 conjugation with Saponin induces cell proliferation arrest and death in CD133+ve cells. Receptor-mediated endocytosis is essential for immunotoxins delivery into the tumor cells, but since the rate of drug penetration through endocytosis and lysosomal degradation is low, immunotoxins therapy has limited efficiency 77 .

Glycosphingolipid GD2 is another BCSCs surface marker also related to stemness maintenance. Inhibition of GD2 synthesis through shRNA or small molecule triptolide can reduce CSCs population and it may be used as a therapeutic target for BCSCs eradication 78 .

Last but not least is ALDH1. This enzyme is a phenotypic marker associated with maintaining stemness of BCSCs, and targeting ALDH1 can be used as a therapeutic approach to eradicate ALDH1+ve CSCs. Withaferin A can target ALDH1 leading to loss of BCSCs stemness 79 . In a novel method using crystallized iron oxide nanoparticles, ALDH1+ve BCSCs can be targeted and eliminated through photothermal therapy (Fig. ​ (Fig.3 3 ) 80 .

Targeting self-renewal pathways of breast cancer stem cells

The distinct self-renewal property of CSCs differentiates them within a heterogeneous tumor cell population. Different signaling pathways such as Notch Wnt/β-catenin, Hedgehog, Hippo, NF-κB and RTK regulate self-renewal capacity of the BCSCs 81 – 83 . Gamma-secretase inhibitors (GSI) such as MK-0752 and PF-03084014 can target gamma-secretase that is an important part of the notch signaling pathway in BCSCs and make them more sensitive to docetaxel 84 , 85 . In addition, Capsaicin could induce apoptosis in BCSCs 86 . A recent study has revealed that vitamin D compounds and Gemini analog of vitamin D, BXL0124, specifically inhibit Notch signaling pathway leading to BCSCs differentiation 87 . LGK974 88 and Tankyrase inhibitors like XAV939 and IWR-1 have been reported to inhibit Wnt pathway, which in turn could reduce BCSC population 89 . Celecoxib is a NSAID drug that reduces BCSCs population by inhibiting the Wnt/β-catenin pathway 90 . Hedgehog pathway (HH) pathway has an important role in the regulation of stemness in BCSCs and targeting this pathway could be a helpful therapeutic approach for the eradication of CSCs population in BC. In line with this, GANT61 an inhibitor of HH can reduce the CSC population in TNBCs 91 . Trametesro biniophila murr (Huaier extract) has been shown to inhibit stemness characteristic and could reduce the BCSC population 92 . Genistein has also been used to reduce the population of BCSCs 93 . Activation of NFκB pathway has a role in the BCSCs maintenance. Different anti-inflammatory molecules such as aspirin has been shown to have a significant anti-BCSC feature 94 . Various modulators of estrogen receptor like tamoxifen or raloxifene as adjuvant therapy for hormone responsive types of BC patients could be helpful 95 . Recently EGFR has been suggested as a target for anti-BCSCs therapy, since EGFR is frequently mutated or overexpressed in different types of BC. Several anti-EGFR therapeutic agents including small molecular inhibitors and monoclonal antibodies are available (Fig. ​ (Fig.3 3 ) 81 .

BCSCs chemo- and radiation resistance

Radiotherapy as a standard treatment for BC patients poses complications of DNA damage due to high-energy radiation 96 , 97 . Furthermore, radiation resistance due to residual BCSCs has become the main challenge in BC therapy. Such resistance occurs through deranged DNA repair pathways and enhanced activity of the free radical scavenging system 98 . In breast cancer, the term inflammatory-senescence is used to describe the aging-related increase in systemic pro-inflammatory conditions in human. Inflammatory aging is a breakdown of the multi-layered cytokine network, where stem cells and stromal fibroblasts become pro-inflammatory cytokine-overexpressing cells due to the accumulation of DNA damage. Inflammatory aging is self-perpetuating because pro-inflammatory cytokines can ignite a DNA damage response in other cells surrounding DNA-damaged cells. Pro-inflammatory signals are sent by macrophages, which are key factors in the aging process of inflammation, both locally and systemically. Based on this, we hypothesize that inflamm-aging is an important factor in the increased incidence and progression of cancer in the elderly. Breast cancer is presented as a paradigmatic example of this association 99 .

The free radical scavenging capacity of BCSCs is higher than that of non-CSCs because BCSCs show higher expression of the components of the free radical scavenging system, which may decrease DNA damage and cell death mediated by reactive oxygen species (ROS) 100 , 101 . Radiotherapy induces the activation of NFκB 21 , 102 , an important transcription factor in various physiological and pathological situations 22 . NFκB activates anti-apoptotic genes including MKP1 and manganese superoxide dismutase (MnSOD), which are DNA damage scavengers and down-regulators of apoptotic signaling. 103 In this context, the resistance of BCSCs to DNA-damaging radiotherapy may give the chance of BC aggressiveness 104 , 105 . Her2 as a tyrosine kinase receptor has been known as a reliable biomarker for CSCs. Her2 overexpression in CSCs is associated with tumor relapse and aggressiveness and poor prognosis 106 , 107 . The expression of Her2 induced by radiotherapy in BCSCs might be responsible for resistance to this therapy and increased BC aggressiveness and relapse 108 . BCSCs residing at primary and metastatic sites are responsible for intrinsic ( de novo ) drug resistance, whilst acquired ( secondary ) resistance may develop in the course of treatment 109 , 110 . BCSCs highly express drug efflux proteins including P-glycoprotein (ABC1), multidrug resistance-associated proteins (MRP), and breast cancer resistance protein (BCRP) that play roles in anti-cancer chemotherapy resistance 111 , 112 . Anti-estrogen therapy is one of the therapeutic choices for estrogen receptor-positive (ER+ve) breast cancer; however; 20–40% of ER+ve tumors acquired resistance to anti-estrogen therapy through multiple mechanisms 113 . In acquired resistance to anti-estrogen therapy, BCSCs and tumor heterogeneity play critical roles 114 . BCSCs also express a high level of ALDH1 as a detoxifying enzyme. Selective ALDH3A1 inhibition by benzimidazole analogs increases mafosfamide sensitivity in cancer cells 115 . Taken all together, targeting BCSCs drug resistance features will be helpful in controlling tumor progression and increasing patients’ disease-free survival.

Cancer invasion to the target organs is a complex and selective process. The proportion of CSCs among metastatic tumor cells is significantly higher than that among primary tumor cells 116 . This is partly explained by the intrinsic resistance of CSCs to anoikis, a regulated cell process that is responsible for induction of death in cells detached from the substratum. Most of the cancer cells die in the circulation whereas CSCs survive and establish metastatic lesions at distant sites 117 , 118 . Less than 1% of cancer cells have the chance to induce metastasis. After organ infiltration, the dormancy of CSCs determines the time of macro-metastasis. Cancer cells may not be competent to form a colony at the infiltration site. In this case, they either die or enter the dormancy phase. Dormancy is a critical potential of CSCs that adapts them to the new microenvironment and induces colonization successfully. In the dormancy stage, the cells are in the G0 phase of the cell cycle but can enter cell division in response to mitotic signals. Additionally, dormancy causes tumor cells to resist chemotherapy and the tumors relapse. CD44, CD133, GD2, ALDH1, the main markers for BCSCs isolation and are important in BCSCs phenotypic features and maintenance 39 . In this review, we aimed to summarize the current biomarkers for Cancer stem cells (CSCs) as a subpopulation of tumor cells that can drive tumor initiation and cause relapses in the metastatic process of breast cancer. We provide an overview of the most frequently used CSC markers and their implementation as biomarkers. Due to their importance, several biomarkers that characterize CSCs have been identified and correlated to diagnosis, therapy, and prognosis. However, CSCs have been shown to display high plasticity, which changes their phenotypic and functional appearance. These CSC markers might be influenced by therapeutics, such as chemo- and radiotherapy, and the tumor microenvironment. It points out, that it is crucial to identify and monitor residual CSCs. As a future perspective, a targeted immune-mediated strategy for the removal of CSCs and targeting the markers of BCSCs that are involved in maintenance of stemness, self-renewal and drug resistance have been proposed as therapeutic strategy in BC.

Limitations of the study

We mentioned only some target therapy for BC in this review.

Ethics approval

This study was approved by the Research Ethics Committee of Dezful University of Medical Sciences (ethical approval code: IR.DUMS.REC.1402.069).

Source of funding

Author contribution.

M.K.F. and M.B. designed the project and collected the data. A.M. and A.A. and M.F. wrote the manuscript. All authors read the final manuscript and approved it. The authors read and approved the final manuscript.

Conflicts of interest disclosure

The authors declare no conflict of interest.

Research registration unique identifying number (UIN)

Mahin Behzadifard.

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Provenance and peer review.

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