Magnetic resonance imaging (MRI) is the most sensitive exam for detecting breast cancer. The American College of Radiology recommends women with 20% or greater lifetime risk of developing breast cancer be screened annually with MRI. However, other high-risk populations would also benefit. Hartmann et al. reported women with atypical hyperplasia have nearly a 30% incidence of breast cancer at 25-year follow-up. Women with dense breast tissue have up to a 4-fold increased risk of breast cancer when compared to average-risk women; their cancers are more likely to be mammographically occult. Because multiple cohorts of women are at high risk for developing breast cancer, there has been a movement to develop an abbreviated MRI (abMRI) protocol to expand the availability of MRI screening. Studies on abMRI effectiveness have been promising, with Weinstein et al. demonstrating a cancer detection rate of 27.4/1000 in women with dense breasts after a negative digital breast tomosynthesis. Breast MRI is also used to evaluate the extent of disease as part of preoperative assessment in women with newly diagnosed breast cancer, and to assess a patient’s response to neoadjuvant chemotherapy. This paper aims to explore the current uses of MRI and propose future indications and directions. Breast cancer is the most common cancer in women globally and the second leading cause of cancer deaths. In the United States, there are over 250,000 new cases of breast cancer annually with over 46,000 women dying of the disease . Over thInitially, breast cancer screening was performed with mammography alone. However, as we increasingly understand breast cancer risk, we have been increasingly implementing risk-based screening, that is, screening based on an individual woman’s risk of developing breast cancer. The use of mammography alone is no longer be the optimal approach for the detection of earlier, more curable breast cancer. Depending on a woman’s individualized risk, optimal screening is tailored and may include mammography, ultrasound and MRI as well as exciting, emerging technologies such as ultrasound tomography  or abbreBRCA 1 or 2 mutations, untested first-degree relatives of BRCA-positive patients, history of chest radiation for Hodgkin’s lymphoma between the ages of 10 and 30 years, and women with PalB2 mutations and other genetic syndromes associated with elevated breast cancer risk [Specific recommendations for mammographic screening vary widely. The American Society of Breast Surgeons and American College of Radiology recommend annual screening beginning at age 40 for all women at average risk of breast cancer ,4, the Acer risk . As the most sensitive exam, breast MRI has many clinical uses in addition to screening. This paper aims to outline and explore the current indications for breast MRI, including as a tool for screening high-risk populations, evaluating extent of disease as part of preoperative assessment in women with newly diagnosed breast cancer, and assessing a patient’s response to neoadjuvant chemotherapy. We will also propose future indications for breast MRI, such as using MRI as a biomarker for breast cancer risk and treatment response, as well as implementing abbreviated protocol breast MRI programs to expand access of screening breast MRI to those who would benefit from it.BRCA mutations, the sensitivity of mammograms is decreased and has been reported to be 33% [Mammography, the mainstay of breast cancer screening, is an excellent examination that has been critical in reducing breast cancer mortality; screening mammography has been proven to reduce mortality by 20–35% . Yet, mao be 33% , signifio be 33% . BecauseIn women with dense breasts, screening breast ultrasound has been utilized to detect mammographically occult breast cancers. Importantly, studies have shown that the mammographically occult cancers detected with screening breast ultrasound are most frequently small, invasive, node-negative cancers ,13. ThesBRCA mutation have a 60–85% cumulative lifetime risk of developing breast cancer [BRCA-positive women, 44.7% of cancers are not detectable with mammography and are detected with MRI, whereas 2.1% of cancers are detected by mammogram and not identified with MRI [BRCA-positive women have a better prognosis than those detected with mammography, in that they are more frequently node -negative [BRCA mutations, at 26 per 1000 women screened [Women with a t cancer . The sent cancer . Studieswith MRI . Furthernegative . A studynegative . Lo et anegative . Sippo escreened . The robBRCA mutation or history of chest radiation  [It is important to note that the population of women at increased risk of breast cancer, for whom MRI is critically important, has been expanding. Initially, the recommendation for annual screening with MRI was only for those with a genetic mutation, where 27 cancers per 1000 women screened with MRI were detected . FurtherWomen with dense breast tissue are at moderately increased risk of breast cancer (15–20% lifetime risk). Forty percent of women in the US have dense breasts . Women wMRI screening has political implications as well. In the United States, screening mammography is covered without a co-pay or deductible as part of the Affordable Care Act (ACA); screening MRI is not. It is not uncommon for insurance to reject MRI screening, especially in women who do not have a deleterious genetic mutation. Efforts are underway to facilitate the approval of MRI screening. With the robust evidence demonstrating the significant benefit of screening MRI in women with a personal history of breast cancer, a history of high-risk lesions, and even in women with dense breast tissue, Pennsylvania Governor Tom Wolf signed Senate Bill 595 in June 2020 which requires insurance coverage of supplemental breast cancer screening with MRI in women with extremely dense breasts (BIRADS D) and in women with heterogeneously dense breast tissue (BIRADS C) and one other high-risk factor . It is hIn women with newly diagnosed breast cancer, preoperative staging is required to assess disease extent to optimize treatment strategy. Assessment of locoregional metastases into the axillary, internal mammary and supraclavicular nodal basins is critical information for surgical and treatment planning. Preoperative breast MRI plays an important role in delineating the extent of the primary tumor and diagnosing multicentric and contralateral disease, as well as in the evaluation of local nodal metastases and chest wall involvement. Studies have demonstrated that when women with newly diagnosed breast cancer are evaluated with MRI, additional foci of disease are identified in 10% of women: 7% in the ipsilateral breast and 3% in the contralateral breast . Of the A meta-analysis by Plana et al.  demonstrGiven its sensitivity, MRI is the most powerful tool to assess breast cancer and a significant number of women are found to have disease that was occult on mammography and ultrasound. This information aids in surgical planning with the goal of decreasing positive margins and re-excision rates. This is particularly true in women with pure DCIS or known invasive cancer with an extensive DCIS component . In a coThe use of preoperative MRI in all newly diagnosed patients is still controversial and guidelines differ among institutions. Although there is solid evidence that MRI demonstrates disease that is not seen by conventional modalities, it is still unclear if that translates to better patient outcomes, specifically disease-free survival and overall survival. Moreover, studies have shown that women with newly diagnosed breast cancer who undergo MRI have a higher rate of mastectomies. Preoperative MRI may be most beneficial in patients with triple-negative breast cancer (TNBC), invasive lobular cancers, or patients who undergo breast conservation without radiation therapy. Wang et al. demonstrated that preoperative MRI improved breast cancer survival in women who underwent breast-conserving without radiation therapy . Given tAlthough many trials have demonstrated improved accuracy as well as margin assessment with MRI, studies have also shown that preoperative MRI results in an increased rate of mastectomies. A meta-analysis of 19 studies by Houssami et al., reported that preoperative MRI was associated with a 1.4 increased odds ratio for mastectomy, and MRI was also associated with increased odds ratio (1.91) of a contralateral prophylactic mastectomy . The incNeoadjuvant chemotherapy (NACT) is the preferable approach in patients with locally advanced (stage II or III) and TNBC. Long-term outcomes for patients receiving NACT versus adjuvant chemotherapy were demonstrated in a patient-level meta-analysis conducted by the Early Breast Cancer Trialists’ Collaborative Group . When coIt is important to assess response to NACT as early as possible during therapy, as a poor response can trigger a change in the chemotherapeutic regimen. There are multiple methods for evaluating response to neoadjuvant chemotherapy. Initially, response to neoadjuvant chemotherapy relied on physical examination along with conventional breast imaging, i.e., mammogram, and US. Studies have demonstrated a moderate correlation between pCR and physical examination, mammography and ultrasound reported at 57%, 74% and 79%, respectively . More reThe ability of MRI to assess response to NACT is based on a number of factors, including tumor molecular subtype  , as wellRecently, a novel approach to assess response to NACT has been reported. Duric and colleagues reported accurate assessment of early response to neoadjuvant chemotherapy utilizing ultrasound tomography . This apBreast cancer can be identified on MRI by its pattern of enhancement after the administration of intravenous contrast. Because malignancies undergo uncontrolled angiogenesis in patterns different from benign breast tissue, breast cancer generally appears as a relatively bright focus of enhancement on MRI. Some benign as well as high-risk lesions also enhance, and kinetic information of MRI contrast uptake and excretion from tissues can help differentiate benign and malignant lesions. It is important to note that normal fibroglandular breast tissue enhances, a phenomenon known as background parenchymal enhancement (BPE), albeit generally at a slower rate than cancer or high-risk lesions. BPE is thought to reflect the background physiologic activity of a woman’s breast tissue and is affected by each woman’s unique vascular and hormonal factors, exposure to endogenous and exogenous hormones, and use of endocrine therapy . MenopauBackground parenchymal enhancement is characterized in the breast imaging (BIRADS) lexicon as minimal, mild, moderate, and marked . ModeratIncreased estrogen exposure is a known risk for breast cancer due to tumorigenesis via estrogen receptors. BPE is positively affected by both endogenous and exogenous estrogen. Studies have demonstrated a positive correlation between BPE and breast cancer risk ,60. ModeTamoxifen and other estrogen receptor modulators are used both as adjuvant therapy for estrogen receptor / progesterone receptor (ER/PR) positive breast cancer and as a risk reduction strategy in women at increased risk of breast cancer. Studies have demonstrated up to a 50% decrease in incidence of cancer when tamoxifen is used for risk reduction . When MR99mTc-sestamibi, a nuclear radiotracer, to physiologically differentiate between benign and malignant breast tissue. MBI/BSGI detects cancer with a sensitivity and specificity of up to 96% and 80%, respectively [Molecular breast imaging (MBI), also known as breast-specific gamma-imaging (BSGI), is another sensitive physiologic imaging modality used to detect breast cancer . This exectively  and can ectively . Patient99mTc-sestamibi uptake within normal fibroglandular tissue. Studies have shown elevated BPU is associated with increased breast cancer risk [Background parenchymal uptake (BPU) is the MBI/BSGI equivalent of BPE in MRI, and is defined as the cer risk ; BPU canGiven that MRI is the most sensitive tool to detect cancer, there has been a movement to expand access to MRI screening. Due to its cost, full-protocol, dynamic contrast-enhanced breast MRI (fpMRI) has traditionally been offered as a screening tool only to women at the highest risk for breast cancer . An fpMRAbbreviated breast MRI (abMRI) has been developed to increase access and decrease the cost of screening breast MRI. AbMRI consists of fewer sequences requiring a shorter time and lower cost for the examination. Multiple studies have investigated whether abMRI can replace a fpMRI in the screening setting. A prospective study by Kuhl et al.  demonstrA major barrier to implementation of a population-wide abMRI screening program has been cost. The average insurance reimbursement for a fpMRI is $1084. Studies have demonstrated that 8% of screening MRIs detect a suspicious lesion (BIRADS 4 or 5) requiring additional workup and possible biopsy . By way Given that the cost of abMRI can be as low as $299, which is lower than the insurance copay for a fpMRI, some institutions have developed robust MRI high-risk screening programs using a self-pay model. Grimm et al.  recommenIn order to implement wider use of abMRI, a standardized abMRI protocol is needed. Future steps will likely include development of universal abMRI protocols as well as quality measures and guidelines. Subsequent studies will undoubtedly further define the role of abMRI in screening guidelines .MRI is the most sensitive, currently available method for the detection of breast cancer. Its indications include screening high-risk populations, evaluating women with newly diagnosed breast cancer for extent of disease and planning surgical management, and assessing response to neoadjuvant chemotherapy. Currently developing indications include using abMRI to expand the availability of screening MRI to other high-risk populations. Breast MRI may also be used as a biomarker for breast cancer risk and efficacy of tamoxifen therapy as hormonal treatment for women with breast cancer, and as a risk reduction strategy for women at increased risk of breast cancer.


BRCA mutation carriers are also hypomethylated in cervical samples of women with poor prognostic breast cancer. Our data indicate that a systemic epigenetic programming defect is highly prevalent in women who develop breast cancer. Further studies validating the WID-BC-index may enable clinical implementation for monitoring breast cancer risk.Genetic and non-genetic factors contribute to breast cancer development. An epigenome-based signature capturing these components in easily accessible samples could identify women at risk. Here, we analyse the DNA methylome in 2,818 cervical, 357 and 227 matched buccal and blood samples respectively, and 42 breast tissue samples from women with and without breast cancer. Utilising cervical liquid-based cytology samples, we develop the DNA methylation-based Women’s risk IDentification for Breast Cancer index (WID-BC-index) that identifies women with breast cancer with an AUROC (Area Under the Receiver Operator Characteristic) of 0.84 (95% CI: 0.80–0.88) and 0.81 (95% CI: 0.76–0.86) in internal and external validation sets, respectively. CpGs at progesterone receptor binding sites hypomethylated in normal breast tissue of women with breast cancer or in  Breast cancer is most commonly diagnosed via a needle biopsy. In this study, the authors show that cervical samples from women with breast cancer have a methylation signature different to that of healthy controls. To date, the identification of individuals with primary cancer is achieved by assessing evidence directly from the tumour (e.g. imaging2 or detection of cancer cell products released into the system4). Currently, available early detection strategies, such as mammography screening, suffer from low performance in young women, over-diagnosis, and decreasing attendance rates, and its benefit on mortality reduction has recently been questioned5. High-risk prevention strategies and risk-stratified screening require high discriminatory accuracy, that can only be achieved with predictors much stronger than those yet discovered6. Yet the best predictive models combining non-genetic factors and a polygenic risk score have only led to a Receiver Operator Characteristic (ROC) Area Under the Curve (AUC) of 0.687. In contrast, cervical cancer screening  that offers simple non-invasive access to precancerous cells has reduced the incidence and mortality from cervical cancer by more than 50%8.Breast cancer is by far the most common cancer in females in general, and a leading cause of death in young women9 and could potentially serve as a surrogate for both genetic and non-genetic factors including lifestyle, reproductive and environmental exposures contributing to breast cancer development10. A number of proof of principle studies, so far exclusively performed in blood, have demonstrated that certain DNAme changes are associated with breast cancer predisposition16. Steroid hormones are essential drivers of breast cancer formation and hence it is a requirement that surrogate tissue is sufficiently hormone sensitive. For instance, prolonged17 or higher18 exposure to progesterone is strongly associated with the formation of poor prognostic breast cancer22 and the progesterone receptor antagonist mifepristone effectively prevents breast cancer formation in mice23.Epigenetic (i.e. DNAme) changes have been identified in normal breast tissue adjacent to breast cancers24. Thus, we aimed to assess whether DNAme profiles derived from cervical liquid-based cytology samples , are able to identify women with primary breast cancer.Sample heterogeneity and the choice of surrogate tissue are deemed to be among the most important factors for impending clinical implementationBRCA mutation carriers and in archived samples from a population cohort. In addition, we specifically assess DNAme at progesterone receptor binding sites (PR-BS) in breast and cervical samples to evaluate whether systemic factors driving carcinogenesis in the breast can be captured by assessment of the cervical epigenome.Here, we perform an epigenome-wide DNAme analysis in cervical liquid-based cytology samples from women who had recently been diagnosed with breast cancer and in matched controls. We establish the WID-BC test (Women’s risk IDentification for Breast Cancer index) and validate it in independent sets of cervical samples. We also carry out further validation in samples from women with ovarian cancer and a set of matched cervical, buccal, and blood samples from 25.For the discovery set, we collected samples from 329 women with primary breast cancer with poor prognosis features (defined by >2 cm cancers and/or lymph-node positive and/or hormone-receptor negative and/or grade 3) from 14 European centres at the time of diagnosis and before treatment commenced, and 869 women without breast cancer   in the discovery dataset  and the remaining one-third was used as an internal validation set  .Predictive performance was evaluated as a function of set Fig.  and optiset Fig. . The WIDIn samples with an immune cell proportion fraction ≤0.5 and immune cell proportion >0.5 the AUC was 0.85 (95% CI: 0.78–0.91) and 0.86 (95% CI: 0.80–0.91), respectively, suggesting that discriminatory signals are present in both epithelial and immune cells. We used a statistical model to estimate epithelial- and immune-specific variability at each of the 29,000 CpGs and divided them into “epithelial” , “immune”  or “shared”  subsets as shown in Fig. Since the WID-BC-index is defined as a weighted sum of 29,000 beta-values the index can be split into three subcomponents by taking the weighted sums corresponding to each of the three subsets of CpGs  can be detected in the cervix, we developed a breast cancer-specific epigenetic signature based on data from the TCGA-BRCA programme by applying lasso regression on the top differentially methylated CpGs between normal tissue and primary tumour tissue. This signature, the TCGA-Breast Cancer index (TCGA-BC-index), consisted of 31 CpGs and was able to discriminate non-cancerous from breast cancer tissue perfectly in a validation set, but did not discriminate controls from breast cancer cases in cervical samples Fig. . This inA separate independent external validation dataset consisting of 225 controls and 113 breast cancer cases was used to validate the index performance Fig.  resultinBRCA1 (n = 57) and BRCA2 (n = 53) mutation carriers (i.e. women who have not yet developed breast and/or ovarian cancer) and 114 healthy controls. In mutation carriers of BRCA1 and BRCA2 (whose breast cancer risk is slightly lower and starts to increase later than that of BRCA1 carriers27) we observed AUCs of 0.61 (95% CI: 0.52–0.69) and 0.55 (95% CI: 0.46–0.64), respectively  after the cervical sample collection. To assess the impact of the storage protocol on DNAme , we developed a statistical technique to infer the signal-to-noise ratio of each sample (Supplementary Methods). The biobanked samples suffered from a lower signal-to-noise ratio compared to the discovery set Fig. .Despite this degradation, in 131 poor prognostic breast cancer cases  we estimated an AUC > 0.7 in samples with the highest signal-to-noise levels Fig. . In 154 These findings suggest that the biobanked samples suffered substantial degradation and that the discriminatory signal may have been masked by background noise in the lowest quality samples. We conclude that it is not possible to fully validate the WID-BC-index in currently available cohort-based long-term archived samples stored in methanol-based fluid at >−30 °C.29. The index decomposition in Fig. BRCA dataset above  in buccal samples, and 0.64 (95% CI: 0.56–0.72) in blood  in buccal samples and 0.60 (95% CI: 0.51–0.69) in the blood  between the WID-BC-index computed in matched cervical and buccal samples. The immune and shared subcomponents showed the greatest discriminatory performance . We found that the discriminatory signal derived using cervical samples was also present in these matched buccal samples Fig. , yieldin30: the risk to develop either ovarian and endometrial cancer is higher in women who develop breast cancer at an early and later age, respectively30.Women with breast cancer are also at higher risk for ovarian and endometrial cancerHence, we additionally analysed 242 and 217 cervical samples from women with ovarian and endometrial cancer, respectively Fig. . We obtaFor each of the validation datasets we computed odds ratios corresponding to quartiles of the WID-BC-index Table .Table 1Op < 10−9 and 0.33, p < 10−6 in controls and cases respectively). The Illumina 650k Infinium Global Screening Array was used to genotype matched blood samples from a subset of 314 cases and 816 controls in the discovery set. We computed a recently published polygenic risk score  for breast cancer prediction. In the 107 cases and 280 controls from the internal validation set we found a modest but significant correlation of 0.13 (p = 0.03) between the PRS and the WID-BC-index . In all samples of the discovery set for which PRS data were available  the AUC was 0.67 ; however removal of these 78 mQTLs from the WID-BC-index did not result in a significant drop in performance  and women who had not undergone hormone replacement therapy . A statistically significant association was found between the WID-BC-index and age  between the WID-BC-index and the fat content of the sample irrespective of whether the sample was taken from an epithelial or nonepithelial organ. These findings indicate that the WID-BC-index is reflective of a fat cell program.In order to assess whether the WID-BC-index is reflective of a cell-specific program, we analysed all ENCODE samples 37 in at-risk breast tissue is mirrored in cervical cells. We assessed the 19,258 PR-BS CpGs which are available on the EPIC array and computed the differences in mean methylation at PR-BS CpGs in 14 normal breast samples from healthy women and 14 normal breast samples from women adjacent to their triple-negative breast cancer. We compared this to the differences in mean cervical PR-BS methylation between healthy controls and breast cancers from our discovery set cervical samples and observed a substantial skew towards PR-BS sites which are hypomethylated in both cervical and breast tissue in at-risk women with a highly significant overlap of 2,623 hypo-methylated CpGs (defined as a methylation difference < −0.02) in both breast and cervical samples  levels of progesterone in women at risk lead to a reduction of methylation in both surrogate  and at-risk  which eventually triggers breast cancer Fig. .We have identified a cervical DNAme signature, the WID-BC-index, which provides an opportunity to identify women with a poor prognostic primary breast cancer based on a sample, which has no direct anatomical link to the diseased organ. Women in the top quartile of the WID-BC-index have a 15.7 fold increased risk for breast cancer independent of any other risk factors (Table BRCA mutation)  Fig. . ProgestBRCA1 mutation)—likely at least in part due to cell-nonautonomous factors38. The fact that the WID-BC-index is able to identify women who have a BRCA1 mutation is quite unexpected because the inclusion criteria that we have chosen for this set (i.e. women without risk-reducing surgery and who had not developed a cancer yet) were biased against those carriers with the highest risk.The notion of predisposition is further supported by the fact that the index also identifies women with ovarian  cancer and those women who have a very high risk of developing both breast and ovarian cancer , this effect was not essential for the discriminatory capacity of the WID-BC-index, evidenced by the fact that in the validation set no cell-type difference existed between cases and controls. Moreover, by decomposing the index into tissue-specific components we were able to show that the dominant signal is present in epithelial cells, and is also partly present in immune cells. The validation in matched buccal and blood samples confirm the systemic nature of the DNAme signal, although further prospective studies will be required to confirm whether the WID-BC-index can be used as a biomarker of breast cancer risk. Due to the current study design, disease effects cannot entirely be ruled out.Although in the discovery set the proportion of immune cells in the cervical samples of the breast cancer cases was significantly lower and specifically pronounced for monocytes  and hence for practical reasons it is impossible to quantify progesterone exposure over time using conventional technologies ; DNAme at those DNA sites which are bound by the progesterone receptor might act as a good surrogate marker for overall exposure and thereby reflects breast cancer predisposition.Our findings described here are consistent with data published more than 30 years ago showing that patients with hereditary breast cancer and their first-degree relatives harbour a differentiation defect6. Due to the inherently fixed nature of the genome and the plastic nature of the epigenome, it is not possible at this time to state whether DNAme in a cervical liquid-based cytology sample truly outperforms the currently available (largely SNP-based) risk-predicting algorithms and how long in advance of breast cancer diagnosis the cancer risk can accurately be predicted.Considerable effort in the past has shown that by combining SNPs, mammographic density, and epidemiologic risk factors the AUC corresponding to breast cancer prediction can be increased up to 0.68We have demonstrated that long-term storage of cervical samples in the methanol-based fluid is associated with a substantial decrease in sample quality and thereby a dramatic decrease in the signal-to-noise ratio. Nevertheless, we have demonstrated that despite this technical limitation some of the signal is retained in the higher quality samples and does not seem to vary depending on the time between sample donation and cancer diagnosis.Side-by-side comparisons assessing various predictors of risk using the same population will be required in the future. Studying sequential population-based cervical liquid-based cytology samples (in which the DNA has been isolated or the cell pellet stored at −80 °C within a few weeks after sample collection) from women who develop a breast cancer several years later will (i) be a prerequisite for assessing whether the actual risk-predictive nature of the WID-BC-index will truly outperform current breast cancer predictive algorithms and (ii) determine whether the index should be implemented in a clinical setting in order to guide preventive and early detective measures. The WID-BC-index has been developed and validated in the European population; the test also needs to be further validated in non-European populations. Whether DNAme profiles assessed in cervical cytology samples, and/or in breast tissue samples, can also act as surrogates for monitoring breast cancer preventive measures will need to be assessed in large-scale prospective clinical trials.BRCA mutation carriers or healthy volunteers from the general population (control) were approached via outreach campaigns, public engagement in the UK.The case-control study presented in this manuscript is a sub-study of the FORECEE (4 C) programme which was conducted as part of a multicentre study involving several recruitment sites in 5 European countries  . Interference at the level of steroid hormones  leads to a substantial change in DNAmeAfter signing an informed consent, participants completed an epidemiological questionnaire, as well as a feedback form after their participation.The epidemiological survey was administered via the Qualtrics application on dedicated iPads. The survey contained questions relating to health habits, relevant risk factors, and also made enquiries as to historical health habits, as well as obtaining a thorough medical and obstetric history. Cervical samples were collected at appropriate clinical venues by trained staff and the collection of cervical liquid-based cytology were carried out by a small group of research midwives or physicians with a view to establishing a standard practice. Buccal samples were collected using Copan 4N6FLOQ Swabs, Thermofisher Scientific. Participants were not compensated for participation.Biological samples were given an anonymous Participant ID Number, which was assigned to the person’s name in a securely stored link file. Women with a current diagnosis of (a) primary breast cancer with poor prognosis features (Grade III and/or T2/3 and/or N1/2 and/or hormone receptor positive) or (b) malignant invasive epithelial ovarian or endometrial cancer and recruited prior to receiving any systemic treatment  or surgery or radiotherapy were eligible as breast, ovarian or endometrial cancer cases respectively. Clinical and epidemiological characteristics of the discovery and external validation sets are presented in Supplementary Tables For the FORECEE discovery set controls were initially matched one-to-one with cases based on menopausal status, age (5 year age ranges where possible), and recruitment centre/country. However, due to an imbalance in the recruitment of cases and controls at some centres, a number of cases were matched on age and menopausal status alone. Cancer histological data was collected post-recruitment either by clinicians directly involved in the diagnosis/treatment of the cancer cases or by a nominated data manager with access to the in-house hospital systems.Cervical samples were taken at collaborating hospitals and recruitment centres using the ThinPrep system . Cervical cells were sampled from the cervix using a cervix brush  which was rotated 5 times through 360 degrees whilst in contact with the cervix to maximise cell sampling. The brush was removed from the vagina and immersed in a ThinPrep vial containing Preserve-cyt fluid and then pushed against the bottom of the vial 10 times to facilitate the release of the cells from the brush into the solution. The sample vial was sealed and stored locally at room temperature. Buccal cells were collected using two Copan 4N6FLOQ Buccal Swabs  by firmly brushing the swab head 5–6 times against the buccal mucosa of each cheek. The swabs were re-capped and left to dry out at room temperature within the sampling tube which contains a drying desiccant. 2.5 ml of venous whole blood was collected in PAX gene blood DNA tubes (BD Biosciences #761165) and stored locally at 4 ˚C. All samples were shipped to UCL at ambient temperature.43. This biobank has ethical approval from the Regionala etikprövingsnämnden i Stockholm. The biobank also stores an aliquot from all samples taken on clinical indication in the region’s gynaecological clinics and the catchment area constitutes ~20% of the entire nation. Women who participate in screening are informed in the invitation letter that their sample will be stored, and potentially used for ethically approved research. Information is given on how to opt out of this procedure, should the woman wish not to have her sample stored. Very few women opt out. We linked all LBC samples in the biobank from years 2011 to 2015 to the Swedish National Cancer Registry, to identify all cases of breast cancer that had occurred among women who had at least one sample stored in the biobank (n = 325). We used the same register to identify all women who had not been diagnosed with breast cancer within the study period, and from these sampled a 1:1 control set of healthy women frequency-matched on age and sample year . From each vial, 100 µL of biobanked ThinPrep material was retrieved and submitted to UCL for extraction and methylation analyses. Ultimately, 599 samples passed QC and had sufficient phenotypic information available and were used as a further external validation set.The Clinical Cytology biobank at the Karolinska University Laboratory, Karolinska University Hospital, systematically stores a compacted aliquot of all ThinPrep liquid-based cytology samples taken as part of the organised cervical screening programme in the Stockholm-Gotland capital regionBRCA1 (n = 9) or a BRCA2 (n = 5) mutation : normal breast tissue from 14 women who underwent cosmetic breast operations, normal breast tissue from women who underwent prophylactic mastectomies due to a When preparing for sample storage in the laboratory, cervical samples were poured into 50 ml Falcon tubes and left to sediment at room temperature for 2 h. One mL wide bore tips were then used to transfer the enriched cellular sediment into a 2 mL vial. The cervical sediments were washed twice with PBS, lysed, and stored temporarily at −20 ˚C ahead of extraction. The Copan 4N6FLOQ Buccal Swabs were cut and lysed sequentially in the same aliquot of lysis buffer prior to temporary storage at −20 ˚C ahead of extraction. Whole blood samples were simply held transiently at −20 ˚C until DNA extraction. DNA was extracted from whole blood, cervical and buccal tissue lysates on a Hamilton Star liquid handling platform using the Nucleo-Mag Blood 200ul kit  with prior modifications for optimal lysis of cervical cell pellets and paired buccal swabs. For breast tissues, DNA was extracted from up to 40 mg of tissue using the Lipid Tissue kit from Macherey Nagel (cat # 740471.50), and the manufacturer’s instructions were followed. DNA concentration and quality absorbance ratios were measured using Nanodrop-8000, Thermoscientific Inc. Extracted DNA was stored at −80 ˚C until further analysis.Cervical, buccal and breast tissue DNA was normalised to 25 ng/µL and 500 ng total DNA was bisulfite modified using the EZ-96 DNA Methylation-Lightning kit  on the Hamilton Star Liquid handling platform. Eight µL of modified DNA was subjected to methylation analysis on the Illumina InfiniumMethylation EPIC BeadChip  at UCL Genomics according to the manufacturer’s standard protocol.p value > 0.01 were regarded as failed. Any samples with >10% failed probes, and any probes with >10% failure rate were removed from the dataset. Beta values from failed probes (approximately 0.001% of the dataset) were imputed using the impute.knn function as part of the impute R package, version 1.62.0.All methylation microarray data were processed through the same standardised pipeline. Raw data was loaded using the R package minfi, version 1.36.0. Any samples with median methylated and unmethylated intensities <9.5 were removed. Any probes with a detection 44 , and chrY probes were removed from the dataset. An additional 6102 previously identified probes that followed a trimodal methylation pattern characteristic of an underlying SNP were removed.Non-CpG probes (2932), SNP-related probes as identified by Zhou et al.Background intensity correction and dye bias correction was performed using the minfi single sample preprocessNoob function. Probe bias correction was performed using the beta mixture quantile normalisation (BMIQ) algorithm in the ChAMP package, version 2.18.3.The fraction of immune cell contamination, and the relative proportions of different immune cell subtypes in each sample, were estimated using the EpiDISH package, version 2.6.1, using the epithelial, fibroblast and immune cell reference dataset. The top 1000 most variable probes (ranked by standard deviation) were used in a principal component analysis. Statistical tests were performed in order to identify any anomalous associations between plate, sentrix position, date of array processing, date of DNA creation, study centre, immune contamination fraction, age, type (case versus control) and the top ten principal components. Finally, two-thirds of the discovery dataset was randomly selected for use as the training dataset and the remaining third was allocated to the internal validation dataset. This split was carried out once, and the same training and validation sets were used in all subsequent analyses.https://www.encodeproject.org/; see Supplementary Table A total of 113 samples were downloaded from the ENCODE database  and corresponding 95% confidence intervals were calculated using the pROC R package, version 1.18.0.Contamination by immune cells presented a challenge with respect to the identification of differentially methylated positions (DMPs) as differential methylation that occurred solely in epithelial cells was diminished in samples with high immune cell proportion and vice versa. In order to overcome this, we linearly regressed the beta values on immune cell proportion for each CpG site, the linear models being fitted to cases and controls separately. The intercept points at immune cell proportion = 0 were used as estimates of mean beta values in cases and controls in a pure epithelial cell population. The difference between these intercept points provided a delta-beta estimate in epithelial cells. The difference between intercept points at immune cell proportion = 1 provided immune cell delta-beta estimates. A list of ranked CpGs was produced according to delta-beta estimates in epithelial cells. Age was included as an additional variable when linearly regressing on immune cell proportion.The R package glmnet, version 2.0–18, was used to train classifiers with a mixing parameter value of alpha = 0  and alpha = 1  with binomial response type. Data from the training dataset were used to fit the classifiers. A ranked list of CpGs was generated by taking the CpG with the largest epithelial delta-beta, followed by the CpG with the largest immune delta-beta, followed by the next largest epithelial delta-beta and so forth (any duplicates were removed). The top n CpGs from the list of ranked CpGs were used as inputs to the classifier. Ten-fold cross-validation was used inside the training set by the cv.glmnet function in order to determine the optimal value of the regularisation parameter lambda. The AUC was used as a metric of classifier performance which was evaluated on the internal validation dataset as a function of n, the number of CpGs used as inputs during training. The maximum value of n was 30,000.The optimal classifier was selected based on the highest AUC obtained in the internal validation dataset. Once the optimal number of inputs was determined, the training and internal validation datasets were combined and the classifier was refitted using the entire discovery dataset with alpha and lambda fixed to their optimal values. This finalised classifier was then applied to the external validation dataset and the corresponding AUC was computed.Denoting the top n CpGs as 2 (using the dev.ratio quantity). The Brier score was computed using the brier.score function in the iterativeBMA R package, version 1.42.The cv.glmnet function in the glmnet R function was used to compute an estimate of the pseudo-RY = α + βindex, where 45.A calibration curve was generated using the val.prob function in the rms R package, version 6.1-1 (Supplementary Note n = 791) and normal tissue (n = 96) was downloaded from TCGA using the TCGAbiolinks R package, version 2.16.4, extracting all primary tumour and normal tissue samples in the TCGA-BRCA project [https://portal.gdc.cancer.gov/projects/TCGA-BRCA] for which Illumina 450k Methylation Data was available. The data were randomly split into a training and a testing set (70−30%). The TCGA-BC-index was developed by identifying the top differentially methylated CpGs and classifier development as outlined above. The final TCGA-BC-index consisted of 31 CpGs and was assessed in the testing set as well as the 3 C discovery and validation set.Illumina 450 K Array Methylation data from primary tumours  was numerically optimised with respect to i and the quantities μ and σ are the same as defined above.We aimed to estimate how much variability across the 29,000 CpGs in the WID-BC-index could be attributed to epithelial cells or immune cells. An example of a CpG with high variability in epithelial cells and low variability in immune cells is given in Fig. 37. Filtered progesterone binding site (GSE40724 [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40724])37 or raw data (GSE68355 [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE68355])36 were accessed from GEO using the GEOquery R package, version 2.58.0. Raw data were preprocessed to filter for fold change enrichment >10 and FDR < 5% (using the “fold_enrichment” and “FDR (%)” variables in the raw data files), and consensus binding sites were defined as occurring in two out of three replicates. CpGs in these ChIP-Seq peaks were identified using the Illumina EPIC array Probe manifest. PR-BS CpGs were defined as CpGs detected in both PR ChIP-Seq datasets .Progesterone receptor binding site (PR-BS) CpGs were defined based on published progesterone receptor ChIP-Seq datasets identified as high quality from the Cistrome DatabaseFor each of the 19,258 PR-BS CpGs we defined ∆β as the difference in mean beta value between breast cancer cases and controls  and normal-adjacent and normal tissue (in breast tissue). We then counted the number of observed CpGs with ∆β < -0.02 in both breast and cervical datasets  and similarly the number with ∆β>0.02 in both datasets . The threshold of 0.02 was prespecified based on typical observed differences in cervical samples, although the main focus of this analysis was to assess the common directionality of methylation in cervical samples and breast tissue rather than compare absolute methylation differences. To test whether we observed significantly more overlapping CpGs than expected, a contingency table of PR-BS CpGs either hypo- (defined as ∆β < −0.02) or hypermethylated (defined as ∆β > 0.02) in one or both datasets was created and p-values were generated using the Chi-Squared test. The expected proportion of overlapping CpGs was calculated as the proportion of PR-BS CpGs hypomethylated in cervical samples multiplied by the proportion of PR-BS CpGs hypomethylated in breast samples. The same approach was applied to hypermethylated PR-BS CpGs.BRCA mutation carriers. Statistical analysis was carried out using Wilcoxon tests (paired for before-after comparison). Figure We calculated the mean beta values of the 2,623 overlapping hypomethylated CpGs in 14 normal breast tissue samples from healthy controls and 14 32. The 822 probes at loci associated with breast cancer risk were extracted and those present in the WID-BC-index (n = 78) were removed from the WID-BC-index for assessment of their influence on the performance.mQTL data was obtained from the supplementary information of a recent publication by Ho et al.In total, 318 breast cancer case subjects and 850 controls from the discovery set .46. Three breast cancer cases and eight controls with a call rate less than 95% were excluded. One breast cancer case and three controls were further removed due to genetically inferred sex not being female. Genetic variants with a missing genotype rate greater than 5%, minor allele frequency (MAF) less than 1% or a significant departure from Hardy-Weinberg equilibrium  were excluded.General subject and single nucleotide polymorphism (SNP) quality control (QC) was performed using PLINK version 1.947, a relatedness inference algorithm, was used to identify duplicate/monozygotic twin or first-degree relative pairs. One control subject pair was identified as being a duplicate/monozygotic twin pair, and nine control pairs were inferred to be first-degree relatives. The subject within each related pair with the lowest call rate was excluded. After performing QC, 314 breast cancer case subjects, 816 controls and 479,105 variants were retained in the SNP discovery sample.KINGhttp://zzz.bwh.harvard.edu/plink/res.shtml. Subjects found not to cluster around HapMap European samples were excluded from further analyses. After excluding non-European subjects, 305 breast cancer cases and 754 controls were retained in the SNP discovery sample.Non-European subjects were identified by plotting the top two principal components, generated using GCTA version 1.26.0, for the SNP discovery samples and 270 HapMap phase II release 23 samples  downloaded in PLINK-formatted binary files from 48 and 1000 Genomes Phase 3 reference panel, the SNP discovery dataset went through further QC before being phased (Eagle2) and imputed. Variants where strand, allele, genetic position, or allele frequencies were not concordant with the 1000 Genomes Phase 3 reference panel were removed before phasing and imputation using Strand Tools.Using the Michigan Imputation ServerR2 < 0.5 and removal of variants observed to have three or more alleles, 303 of the 313 SNPs used by Mavaddat et al.31 to develop a 313 SNP breast cancer polygenic risk score (PRS) were successfully imputed. We constructed a breast cancer PRS for each subject in the discovery set, such that the PRS is equal to (3):i-th SNP taken from publicly available Oncoarray summary association results49  and xij is the number of copies of the effect allele present in each discovery set subject subjected to genotyping. Scores were generated using PLINK version 1.9.After imputation, exclusion of variants with imputation Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


In addition to impacting incidence, risk factors for breast cancer may also influence recurrence and survival from the disease. However, it is unclear how these factors affect combinatorial biomarkers for aiding treatment decision-making in breast cancer.Patients were 8179 women with histologically confirmed invasive breast cancer, diagnosed and treated in a large cancer hospital in Beijing, China. Individual clinicopathological  and immunohistochemical  markers were used to define clinically relevant combinatorial prognostic biomarkers, including the Nottingham Prognostic Index  and IHC4 score . Odds ratios (ORs) and 95% confidence intervals (CIs) for associations between breast cancer risk factors and quartiles (Q1–Q4) of NPI and IHC4 were assessed in multivariable polytomous logistic regression models.trend(95% CI) = 1.20(1.05–1.37);Ptrend = 0.007), overweight (OR(95% CI)vs normal = 1.60(1.29–1.98)), and obesity (OR(95% CI) vs normal = 2.12(1.43–3.14)) were associated with higher likelihood of developing tumors with high (Q4) versus low (Q1) NPI score. Conversely, increasing age (ORtrend(95% CI) = 0.75(0.66–0.84);Ptrend < 0.001) and positive family history of breast cancer (FHBC) (OR(95% CI) = 0.66(0.45–0.95)) were inversely associated with NPI. Only body mass index (BMI) was associated with IHC4, with overweight (OR(95% CI) vs normal = 0.82(0.66–1.02)) and obese (OR(95% CI) vs normal = 0.52(0.36–0.76)) women less likely to develop high IHC4 tumors. Notably, elevated BMI was associated with higher NPI irrespective of hormone receptor-expression status.Overall, increasing parity (OROur findings indicate that factors affecting breast cancer incidence, particularly age, parity, FHBC, and BMI, may impact clinically relevant prognostic biomarkers with implications for surveillance, prognostication, and counseling.The online version contains supplementary material available at 10.1007/s10549-021-06294-5. Breast cancer is a clinically heterogeneous disease and differences in tumor behavior can influence treatment recommendations and clinical outcomes, including recurrence and survival, among breast cancer patients , 2. In aIn general, most previous studies examining the relationships between breast cancer risk factors and tumor behavior have focused on the individual tumor characteristics, and not their constellation. Higher BMI, for instance, is reportedly associated with higher grade or larger size tumors; nulliparity and menopausal hormone therapy use (MHT) with highly proliferating and lobular carcinomas, respectively; and higher parity with P53 expressing tumors –12. The To aid prognostication in breast cancer, several tumor characteristics including histologic grade, tumor size, lymph nodal involvement, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and KI67, a marker of proliferation, have been combined into clinically useful prognostic algorithms –16. The Despite their prognostic relevance, it remains unclear whether breast cancer risk factors influence these combinatorial prognostic markers. Our main aim in this study was, therefore, to investigate the associations between breast cancer risk factors and breast tumor behavior defined by NPI and IHC4 score among a cohort of Chinese breast cancer patients. Key findings from the primary analysis were then re-evaluated in an independent cohort of Polish patients.n = 2386) at five participating hospitals in Warsaw and Lodz over a three-year period between 2000 and 2003 }We calculated the IHC4 score based on the published equation combining ER, PR, HER2 and KI67 :IHC4 sco2: underweight (<18.5), normal , overweight (25–30), and obese (>30); and FHBC in a first degree relative: yes (if present) or no . NPI and IHC4 score were categorized into quartiles as follows; Q1 (<25th percentile); Q2 (25th–50th percentile); Q3 (50th–75th percentile); and Q4 (>75th percentile). Associations between breast cancer risk factors and tumor-related prognostic indicators were assessed in polytomous logistic regression models with quartiles of NPI and IHC4 score as outcomes (Q1 = baseline category) and breast cancer risk factors as predictors. Analyses were performed overall and following stratification by age (≤50 and >50), as surrogate for menopausal status, and by tumor hormone receptor (HR) expression status (i.e. HR + and HR−). In sensitivity analysis, we used PREDICT[2) when the factor was removed from the fully adjusted model. For those factors that were significantly related to NPI and/or IHC4 in the Chinese population, we repeated the analysis in an independent sample of 972 breast cancer cases from PBCS [The following breast cancer risk factors were examined: age: <35 (reference), 35–45, 45–55, >55 years; parity: nulliparous (reference), 1, 2, ≥3 children; age at menarche: ≤12 (reference), 13, 14, ≥15 years; BMI, in kg/mrom PBCS . MissingTable trend (95% CI) = 0.75 (0.66–0.84), Ptrend < 0.001). Conversely, higher parity was significantly associated with higher NPI (ORtrend (95% CI) = 1.20 (1.05–1.37), Ptrend = 0.007). Furthermore, overweight (OR (95% CI) = 1.60 (1.29–1.98)) and obese (OR (95% CI) = 2.12 (1.43–3.14) women were substantially more likely than normal weight women to have high (Q4 vs Q1) NPI values. There was a significant trend with increasing BMI and NPI (ORtrend (95% CI) = 1.53 (1.30–1.79), Ptrend < 0.001). A positive FHBC in a first degree relative was inversely associated with higher NPI values (OR (95% CI) Q4 vs Q1 = 0.66 (0.45–0.95); P = 0.027). Other factors, including breastfeeding and age at menarche were not associated with the NPI. In sensitivity analysis using PREDICT, we found strikingly similar associations between age, parity, BMI, and family history with PREDICT as we did in relation to the NPI (Supplementary Table 2). The associations between parity, BMI, and FHBC with NPI were similar in women with HR + and HR− breast cancer  = 0.82 (0.66–1.02)) and obese (OR (95% CI) = 0.52 (0.36–0.76)) women were less likely to have tumors with high IHC4 score  = 1.34 (0.82–2.19) and obese (OR (95% CI) = 2.27 (1.32–3.89) women were more likely to have high NPI when compared to normal weight women (Table % CI = 1.2), which was compared with the nested model using LR tests. In general, age was most relevant for the estimation of models for BMI, parity, and FHBC. This was followed by tumor size, ER, HER2 and grade for obesity; node status, grade, tumor size and ER for parity; and node status, HER2, and grade for FHBC  and IHC  parameters to compute the NPI and IHC4 score and investigated relationships with breast cancer risk factors. We found younger age, higher parity, being overweight or obese, and not having a FHBC to be associated with more clinically advanced breast cancer, represented by high NPI. Unlike what we observed in relation to the NPI, however, being overweight or obese was associated with lower IHC4 score, indicating less aggressive breast cancer phenotype. Nevertheless, accounting for HR expression, women with elevated BMI were more likely to have clinically advanced HR + and HR− breast cancers. Taken together, these results suggest that breast cancer risk factors may influence the biology and clinical presentation of breast tumors, with implications for improved surveillance and the development of prognostic tools incorporating risk factors.In addition to tumor-related factors, the age at breast cancer diagnosis has long been recognized to be associated with worse clinical outcomes –25. Our Our findings of associations between increasing parity and clinically advanced breast cancer is in line with previously reported associations between parity and aggressive breast cancer phenotypes –28. GiveResults from epidemiological studies indicate that elevated BMI more strongly predisposes to HR− than HR + breast cancers among premenopausal women, with the converse predominating among postmenopausal women , 34. GivThe literature on the relationship between FHBC and clinical outcomes in breast cancer is not consistent –44. In tWe assessed the external generalizability of findings from the Chinese population by investigating the key results in an independent population of Polish breast cancer patients. With the exception of parity, the associations between risk factors and NPI were in the same direction in the Polish and Chinese populations. In particular, the associations between BMI and NPI were significant in both populations. Unlike Chinese patients, for whom increasing parity was statistically significantly associated with higher NPI, parity was not associated with higher NPI among the Polish patients. Given the documented associations between pregnancy-associated breast cancer, which could occur up to 10 years after pregnancy, and aggressive phenotypes of breast cancer , it is pAn important consideration for the development of targeted prevention strategies in breast cancer is the identification of women at high risk of developing the most aggressive subtypes of breast cancer, for whom interventions to prevent invasive disease can be instituted. Based on our findings, higher parity as well as being overweight or obese were associated with poor prognostic indices, suggesting a potential beneficial effect for improved surveillance among such women. Relatedly, these findings may be suggestive of the importance of including breast cancer risk factors as part of breast cancer prognostic models. To date, only PREDICT has included breast cancer risk factors, i.e. age at diagnosis and mode of detection, in its calculations and has been shown to outperform the IHC4 score, IHC subtyping, and C-score in terms of prognostic power . It is pThe assembly of several clinicopathological and epidemiological factors for >8000 patients is an important strength of this study. In terms of limitations, however, this population is largely unscreened, as such, some of the reported associations may be subject to detection bias. For example, smaller tumors may be less likely to be palpated in obese than normal weight women. Accordingly, overweight and/or obese women may be more likely to have larger tumors on the basis of detection alone. Nevertheless, other tumor characteristics were independently associated with higher BMI in this study, suggesting some biological underpinning for our findings. Moreover, detection bias is itself a clinically important problem that needs to be addressed. Also, we did not have clinical outcome data such as recurrence or survival for these patients, which precluded our ability to perform mediation analyses or to directly examine associations between breast cancer risk factors and clinical outcomes. Nonetheless, NPI and IHC4 score strongly predict clinical outcomes in breast cancer. Together, they reflect the spectrum of clinicopathological characteristics that are used in routine clinical practice to inform breast cancer management.In conclusion, our results support associations between breast cancer risk factors and two clinically useful prognostic factors in breast cancer, i.e. NPI and IHC4. Specifically, we found younger age at onset, higher parity, being overweight/obese and absent FHBC to be associated with poor prognostic indices, indicated by higher NPI score, irrespective of HR expression. Although elevated BMI was associated with low IHC4 score, connoting clinically less aggressive HR + tumors, NPI-related findings were supportive of poor prognosis among overweight/obese patients irrespective of tumor HR status. These findings highlight important relationships between breast cancer risk factors and clinically relevant prognostic factors, with potential implications for the development of surveillance, prognostication, and counseling strategies that take host factors into account.Supplementary file1 (DOCX 72 kb)Below is the link to the electronic supplementary material.


Advancements in cancer therapeutics have resulted in increases in cancer-related survival; however, there is a growing clinical dilemma. The current balancing of survival benefits and future cardiotoxic harms of oncotherapies has resulted in an increased burden of cardiovascular disease in breast cancer survivors. Risk stratification may help address this clinical dilemma. This study is the first to assess the association between a coronary artery disease-specific polygenic risk score and incident coronary artery events in female breast cancer survivors.We utilized the Studies in Epidemiology and Research in Cancer Heredity prospective cohort involving 12,413 women with breast cancer with genotype information and without a baseline history of cardiovascular disease. Cause-specific hazard ratios for association of the polygenic risk score and incident coronary artery disease (CAD) were obtained using left-truncated Cox regression adjusting for age, genotype array, conventional risk factors such as smoking and body mass index, as well as other sociodemographic, lifestyle, and medical variables.Over a median follow-up of 10.3 years (IQR: 16.8) years, 750 incident fatal or non-fatal coronary artery events were recorded. A 1 standard deviation higher polygenic risk score was associated with an adjusted hazard ratio of 1.33  for incident CAD.This study provides evidence that a coronary artery disease-specific polygenic risk score can risk-stratify breast cancer survivors independently of other established cardiovascular risk factors.The online version contains supplementary material available at 10.1186/s13058-021-01465-0. There were approximately 2.1 million new cases of incident female breast cancer in 2018 globally, accounting for 25% of cancer cases in women . This isInherited genetic variation is also known to affect risk: genome-wide association studies (GWAS) have identified many common genetic variants associated with CAD, and polygenic risk scores (PRS) have been shown to provide useful CAD risk discrimination –11. Polyn = 12413) for this study. The final analytic sample contained 8946 participants after removing those of non-European ancestries (n = 15) and those who experienced an event before diagnosis (n = 3452).The Studies in Epidemiology and Research in Cancer Heredity (SEARCH) cohort is a population-based prospective study based in the Eastern Region of England, which was served by the East Anglian Cancer registry until 2002 and the Eastern Region Cancer Intelligence Unit from 2002 to 2016. Recruitment of patients was conducted from June 1996 to December 2016. Incident breast cancer cases were all cases diagnosed under the age of 70 years from July 1996 to December 2016. Patients completed a self-administered questionnaire upon recruitment, which included questions about personal information, reproductive history, and other medical history. Tumor characteristics were obtained from the national cancer registry. Follow-up was ascertained through death registration with the most recent update provided by Public Health England on May 31st, 2020. This provides the causes of death recorded on parts 1 and 2 of the death certificate. The SEARCH dataset was restricted to female breast cancer cases who had complete genotype information  data was used to identify incident CAD events. HES data comprises a record for each finished consultant episode (FCE), which is a period of care for a patient under a single consultant at a single hospital . Diagnosn = 8404) and batch II on the Illumina Infinium OncoArray (n = 4009). Both chips provide genome-wide coverage of common variants with 211115 SNPs on the iCOGS array  time from diagnosis to entry into the study was 1.8 years , and the median follow-up time was 10.3 years . A total of 750 individuals experienced a CAD event during follow-up. Out of the genotyped participants, 9496 (77%) received adjuvant hormonal therapy, 8773 (71%) received radiotherapy, and 4735 (38%) received adjuvant chemotherapy. A summary of other lifestyle, medical treatment, breast cancer, and medical history characteristics is presented in Table Age-adjusted models were initially assessed to understand individual associations of each variable with incident CAD survival Table . There wmodel2 = 1.34; 95% CI 1.21–1.49). Adjusting for other sociodemographic, lifestyle, and medical variables did not substantially change the HR of PRS . A similar magnitude HR was found for another polygenic risk score, GRS49K [model5 = 1.31; 95% CI 1.19–1.44). Density distributions for both standardized PRS and GRS49K can be found in Figure S2 in Additional file The sample size for the multivariate model was 8946 with a total of 432 events after including only participants who experienced an event after entry into the study and had European ancestry Fig. . The haz, Model 5). The interaction effect between PRS and log(BMI) scaled to the mean was not significant at the 95% confidence level. Addition of an interaction term between PRS and smoking status slightly attenuated the effect of PRS, and the interaction effect between being a past smoker and the PRS approached nominal significance (P = 0.069). The joint hazard ratios are presented in Table Interactions between PRS and established cardiovascular risk factors, log(BMI) and smoking, were added separately to a model containing genotype array, eight genetic PCs, log(BMI), smoking, education level, drinking, parity, hormone replacement therapy Table . The bas3The hazard ratios of the interaction terms between PRS and radiotherapy, PRS and chemotherapy, and PRS and anti-hormone therapy were 1.15 , 0.93 , and 1.15  respectively in a model containing genotype array, eight genetic PCs, log(BMI), smoking, education level, drinking, parity, and hormone replacement therapy  after adjusting for age, genotype array, and genetic PCs . The total proportion of CAD and non-CAD cases that was reclassified was 22% and 11% respectively. The net proportion of CAD and non-CAD cases assigned to a higher risk category was 12% and 5.6% respectively for a 10-year CAD incidence risk , independent of established cardiovascular risk factors , oncotherapies and other variables associated with cardiovascular risk (education level) in a cohort of British women with breast cancer. Our results support previous evidence that the association of PRS and CAD risk may operate through molecular pathways that do not overlap with those of traditional risk factors such as smoking and BMI. This is consistent with the original PRS analysis which found only a modest attenuation for PRS when adjusting for BMI, smoking status, as well as diabetes, hypertension, family history of heart disease, and cholesterol levels  . SeveralHowever, we note that there was an almost significant interactive effect between being a past smoker and the PRS. Since PRS is known to be correlated to certain conventional CAD risk factors , 30, it The PRS improves risk discrimination in breast cancer survivors. For instance, we found an over twofold HR for CAD in a comparison of individuals in the top versus bottom one-fifth of the risk score distribution. Furthermore, when considering 10-year risk of incident CAD following breast cancer diagnosis, we found that 5.6% of lower risk participants who did not have a recorded CAD event were reclassified to a higher risk group with the addition of PRS to the baseline model  was coded as dichotomous variable (whether or not a patient received treatment). The loss of information about other treatment aspects  may have contributed to measurement error that resulted in the associations reported in our paper. Furthermore, the association of the interaction of PRS and chemotherapy is likely explained by selection bias, where healthy patients are more likely to undergo chemotherapy. More granular data will be required to further assess these associations.The role that PRS may play in breast cancer clinical care is currently unclear, but fundamentally, PRS may be used to help estimate the lifetime risk of cardiovascular disease in a breast cancer survivor. This may have two clinically useful benefits: (1) facilitate earlier detection of cardiovascular risk in breast cancer survivors to help them more effectively manage cardiovascular risk factors earlier to reduce future cardiovascular risk and (2) aid in treatment decision-making when considering the negative cardiotoxic effects of their treatment regiments.This is especially important in breast cancer patients who face the unique challenge of needing to maximize gains from cancer treatment while also minimizing its cardiotoxic effects. More women are surviving breast cancer with an increase in 5-year survival for early stage breast cancer from 79% in 1990 to 88% in 2012  (there wThere are some limitations in interpreting the current findings. The association between PRS and incident CAD could not be adjusted for important risk factors such as diabetes, hyperlipidaemia, family history of cardiovascular disease, and hypertension because these data were not collected. It is worth noting; however, that the PRS used in this study has been shown in other cohorts to provide additional predictive benefit over standard cardiovascular risk prediction algorithms such as the Framingham risk score, which include such metabolic risk factors . TreatmeCardiovascular disease is an important long-term risk among women who survive breast cancer. This risk is increased by some breast cancer therapies. Comprehensive risk models for cardiovascular disease have the potential to help in the clinical management of this risk and may improve long-term outcomes for these women.Additional file 1. Supplementary appendix containing additional description of methods and tables and figures for sensitivity analyses described in the main text.