There are no studies done to evaluate the distribution of mammographic breast density and factors associated with it among Pakistani women.Participants included 477 women, who had received either diagnostic or screening mammography at two hospitals in Karachi Pakistan. Mammographic breast density was assessed using the Breast Imaging Reporting and Data System. In person interviews were conducted using a detailed questionnaire, to assess risk factors of interest, and venous blood was collected to measure serum vitamin D level at the end of the interview. To determine the association of potential factors with mammographic breast density, multivariable polytomous logistic regression was used.High-density mammographic breast density (heterogeneously and dense categories) was high and found in 62.4% of women. There was a significant association of both heterogeneously dense and dense breasts with women of a younger age group < 45 years  and  respectively. Women with heterogeneously dense and dense breasts versus fatty and fibroglandular breasts had a higher history of benign breast disease  and  respectively. There was an inverse relationship between breast density and body mass index. Women with dense breasts and heterogeneously dense breasts had lower body mass index (OR 0.94 95% CI 0.90–0.99) and  respectively. There was no association of mammographic breast density with serum vitamin D levels, diet, and breast cancer.The findings of a positive association of higher mammographic density with younger age and benign breast disease and a negative association between body mass index and breast density are important findings that need to be considered in developing screening guidelines for the Pakistani population. Mammography (MMG) is the baseline investigation used for screening and diagnosis of breast cancer, which remains responsible for more than 500,000 deaths each year worldwide . MammogrSeveral factors can affect MBD like age, heredity, parity, ethnicity, diet, hormonal replacement therapy (HRT), and the molecular subtypes of breast cancer , 7. An iThere is no organized (service) screening program in Pakistan and most of the mammography done is opportunistic  screening . Due to The source population of this study is from a multicenter hospital-based case-control study which was conducted at two large tertiary care hospitals of Karachi, the details of the study and the questionnaire used are described in the previously published article . BrieflyTwo view mammography was performed for all patients comprising of medio-lateral oblique (MLO) and cranio-caudal (CC) views on a computed radiography (CR) system. In the CR system, the X- Rays passing through the breast, grid, and cassette cover are absorbed by the plate reader system that comprises photostimulable storage phosphor (PSP). An electronic latent image is produced on the PSP due to the local absorption of X-ray energy that varies with the anatomical variation of breast parenchyma. The cassette is subsequently placed in the reader that captures the information and converts it to a digital signal that is finally displayed at the workstation . The sofUsing the American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS 5th edition), the mammographic breast density on mammograms was categorized as; Category 1, Predominantly fatty (less than 25% glandular); category 2 for scattered fibroglandular (25–50% glandular); category 3 for heterogeneously dense (51–75% glandular) and category 4 for dense breast parenchyma (more than 75% glandular) . CategorDietary intake assessment was done by using the validated food frequency questionnaire FFQ . Intake After the interview, blood samples were collected from the study participants and serum 25 (OH)D level was measured using ELISA.Histopathology and estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor (HER2/neu) status of breast cancer cases were retrieved from medical records .The ethical approval was obtained by the Human Research Ethics Committee of the University of Adelaide and the Ethical Review Committees of two hospitals in Karachi Pakistan: Aga Khan University Hospital AKUH and Karachi Institute of Radiation and Nuclear Medicine Hospital KIRAN. Patients who were literate read and signed the informed consent form and informed consent was obtained verbally from those who could not read or write.Multinomial logistic regression models were applied to compute odds ratios (ORs) and 95% confidence intervals (CIs) for the MBD categories. The fatty and scattered fibroglandular tissue categories were merged and used as a reference. Multivariate models were adjusted for variables found to be significantly associated with breast density and known risk factors for breast cancer such as age, BMI, age at menarche and menopause in the postmenopausal group, parity, and family history of breast cancer among first-degree relatives, BMI, and the food categories. SPSS 22.0 software  was used for the analysis.The mean age of the study participants was 46.2 years (SD 11.7). Hundred and eighty 37.3%) women had breast biopsy-proven breast cancer and 297 (62.7%) of the women had normal or benign breast disease. Figure 7.3% womeTable There was no significant difference observed in the tumor characteristics among different MBD categories Table .Table 2TThere was a positive association of MBD with young age, nulliparity and low parity, family history of breast cancer and history of benign breast disease BBD. Significant protective association of MBD was observed for women of younger age, menopausal status, younger age at first birth (<20 years AFB) and BMI Table .Table 3AThere was no association reported between different MBD and food categories of grains, fruits, vegetables, plant proteins, dairy products, white and red meat as shown in Table Multivariable analysis using multinomial logistic regression Table  shows thThe study findings of significant association of younger age and BBD with higher MBD and protective association of higher BMI are consistent with other studies. Younger age as an important determinant of a high MBD is similar to other studies done in different populations –27. In aIn our study, the inverse relationship between MBD and higher BMI remained the same in both menopausal and premenopausal women. The inverse relationship between BMI and MBD has been reported in previous studies among both menopausal and premenopausal women , 33, 34.Similar to our study findings, a study among Japanese women reported that younger age and BMI were inversely related to high MBD . In anotHowever, this study observed no association between MBD and breast cancer, and this lack of association has also been reported in a study done in the USA  and one The high percentage of high MBD (62.4%) in Pakistani women is important information and will be helpful in planning or advising for an organized screening mammography program in Pakistan in future. This is higher than the reported percentage among women in the USA  and loweSignificant association of younger age with high MBD in our study is an important finding because ACR guidelines are followed here in the absence of screening guidelines established for Pakistani women where mean age at the time of breast cancer diagnosis reported is also much younger compared to the women in America (46.1 years ± SD 10.1) . MammogrStudy limitations include small sample size and limited generalizability of the study population due to lack of any organized screening mammography program in Pakistan. Moreover, there was no availability of digital radiography (DR) which was installed in 2019, after the completion of the data collection period of the study. We could not calculate percentage density and absolute dense area due to the lack of availability of the software. There is still a lack of standardization in MBD assessment with high density definitions varying widely from 25 to 75% of dense tissues on mammograms in different studies. Though we tried our best to evaluate MBD with standard mammographic procedures, breast density classification as well as a standardized definition of MBD. Still, some misclassification of MBD could have affected our results since we used visual classification using BI RADS by two experienced radiologists. However, a study reported very good agreement between automatic assessment software of breast density based on artificial intelligence (AI) and visual assessment by a senior and a junior radiologist . The strTo our knowledge, this study is the first to report unique distribution of MBD and identify factors associated with MBD in Pakistani women. The findings of positive association of higher mammographic density younger age and BBD and negative association between BMI and breast density are consistent with predictors of mammographic density observed in other populations; however, certain risk factors were not significantly associated with BMD. These are important findings which may be helpful to develop screening guidelines for Pakistani population. Given the current role of breast density in determining breast cancer screening protocols, public health policy, and future research directions, it is important to validate our findings in a larger scale investigation with the advanced technology of the DR system and assess if it is better than screen-film mammography in women with dense breasts.


PALB2 pathogenic gene variants  and the CHEK2 c.1100delC (CHEK2_1100delC) PGV to familial breast and ovarian cancer, and PALB2_PGV associated breast cancer pathology.To investigate the contribution of PALB2_PGV and CHEK2_1100delC testing were recorded in 3,127 women with histologically confirmed diagnoses of invasive breast cancer, carcinoma in situ, or epithelial nonmucinous ovarian cancer, and 1,567 female controls. Breast cancer pathology was recorded in PALB2_PGV cases from extended families.Outcomes of germline PALB2 and 44 CHEK2_1100delC PGVs were detected in patients . Grade 3 ER-positive HER2-negative, grade 3 and triple negative (TN) tumors were enriched in cases with PALB2 PGVs compared with all breast cancers known to our service . PALB2_PGV likelihood increased with increasing Manchester score (MS)  but not for CHEK2_1100delC . PALB2 PGVs showed perfect segregation in 20/20 first-degree relatives with breast cancer, compared with 7/13 for CHEK2_1100delC (P = 0.002).Thirty-five PALB2 PGVs and CHEK2_1100delC together account for ~2.5% of familial breast/ovarian cancer risk. PALB2 PGVs are associated with grade 3, TN, and grade 3 ER-positive HER2-negative breast tumors. Therefore, the originally calculated relative risk was based on study of the families rather than the odds ratio (OR) generated from case–control analysis because this is usually inflated by using familial risk probands. Given the low predicted lifetime risk of only 20–30%, PALB2 testing was not widely utilised until it became included in gene panel testing. It was not until more than 7 years later in 2014 that penetrance analysis in 158 families suggested risks approaching high-risk with a 35% (95% CI: 26–46%) estimated risk by age 70 years.7 This risk was higher in the context of a close relative with breast cancer. This analysis was updated recently in 524 affected families with estimated risks to age 80 years of 53% (95% CI: 44–63%) for female breast cancer and 5% (95% CI: 2–10%) for ovarian cancer.8Breast cancer is the most common form of cancer in women.PALB2, the original assessment of CHEK2 as a moderate risk gene has been maintained from its discovery in 2002.11 Nonetheless, like PALB2, the risk increases when there is a positive family history of breast cancer.11 In the UK, while initially consensus was reached to include both CHEK2 and PALB2 in breast cancer gene panels in the UK,12 mainstreaming of tests to oncologists and other specialties in both breast and ovarian cancer now only includes PALB2 in addition to BRCA1 and BRCA2.13In contrast to PALB2, but not CHEK2, to UK genetic testing guidance, we report our single-center experience to date of germline PALB2 and CHEK2 c.1100delC; p.(Thr367MetfsTer15) (CHEK2_1100delC) testing in breast/ovarian cancer; these data will help provide evidence for future testing guidance.In view of these changes in recommendations, namely the recent addition of BRCA1, BRCA2, PALB2, and CHEK2_c.1100delC for PGVs. Women were either referred for genetic testing to the Manchester Center for Genomic Medicine (MCGM) or the Family History Risk and Prevention Clinic (FHRPC) at the Nightingale Center, Wythenshawe Hospital .14 In addition, 524 women were tested as part of the population based Predicting the Risk Of Cancer At Screening (PROCAS) study, in Greater Manchester.15 Demographic details of the study population are outlined in Table n = 1,567, aged 46–73 years), who were also recruited to the PROCAS study, were included as controls.Women were eligible for this study if they had a histologically confirmed diagnosis of invasive or in situ breast cancer or an epithelial nonmucinous ovarian cancer and had undergone germline testing of PALB2 associated breast cancer in the extended families.For pathology comparisons we included 1,843 women known to the FHRPC/MCGM who had developed breast cancer but did not have a PGV in the known breast cancer predisposition genes. We also included women with a Clinical or research consent was given for extended testing of breast cancer associated genes .BRCA1/2 and CHEK2_c.1100delC  by a combination of next-generation sequencing and multiplex ligation-dependent probe amplification (MLPA). Testing of DNA in the cohort and controls all patients was performed by a combination of targeted sequencing, panel test, and exomes  had DNA extracted from saliva. Those attending MCGM had DNA initially analyzed for PGVs in 16 Variants that were classified as likely pathogenic and pathogenic only are reported here and combined along with gene rearrangements as PGVs.Variants were classified according to the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) guidelines.17 The probability of a BRCA1/2 PGV was determined using the Manchester score (MS) for each affected individual.18 This adds scores for each breast and ovarian cancer in a direct lineage with increasing scores for earlier age at onset and pathologies in the proband suggestive of BRCA1 such as high-grade serous ovarian cancer and triple negative breast cancer (TNT). A MS of 15–19 and 20–24 roughly equate to a 10% and 20% likelihood threshold for BRCA1/2 respectively with a score of ≥40 equivalent to a likelihood of >75%.18Tumor pathology information was obtained for each case when available through hospital records, and cancer registries as previously described.19Statistical analysis was undertaken using GraphPad Prism version 9.0.1 for Windows  and GraphPad QuickCalcs.BRCA1, BRCA2, PALB2, and CHEK2_c.1100delC (Table PALB2 and 44 (1.41%) with CHEK2_c.1100delC. This compared to rates of 3/1,567  and 5/1,567  for PALB2 and CHEK2_c.1100delC respectively in the PROCAS controls, generating ORs of 5.90 (95% CI = 1.92–18.36) and 44.46 (95% CI = 1.86–10.46) respectively.A total of 3,127 index cases with breast and/or ovarian cancer have undergone testing for PGVs of PALB2 PGVs,  , and one with CHEK2_c.1100delC . Considering the breast cancer only index cases, there were 33 PALB2 PGVs and 43 CHEK2_c.1100delC PGVs . Testing in the population based PROCAS breast cancer study identified PALB2 PGVs in 4/524 (0.76%)  and the CHEK2_c.1100delC PGV in 9/524 (1.71%) , whereas testing of breast cancer index cases in the context of family history/early onset breast cancer only, detected PALB2 PGVs in 29/2,301 (1.26%)  and the CHEK2 c.1100delC PGV in 34/2,301 (1.48%) .Testing of 302 cases with ovarian cancer detected two PALB2 PGV was 49.8 years  and, for the CHEK2_c.1100delCPGV, 49.3 years .The mean age at diagnosis of first breast cancer for those with a PALB2. There were ten instances of c.3113G>A; p.(Trp1038Ter) in cases and one in controls, and four each of c.3116del; p.(Asn1039llefsTer2) and c.3549C>G; p.(Tyr1183Ter), with neither occurring in controls , of PALB2 were detected in the patient cohort.Overall, there were three common PGVs in PALB2 PGV was detected. Where pathology grade and receptor status were known, 8/25 breast tumors were triple negative . This was similar to the numbers and age at diagnosis of TNTs  known to our service with a BRCA2 PGV (n = 9) , but significantly fewer than for a BRCA1 PGV (P  < 0.0001). While there was a trend toward increasing age of TNT diagnoses with a PALB2 PGV as compared with BRCA1 PGV associated TNTs , this was not significant (P = 0.060).Table PALB2 PGV further, we then considered all the patients with PALB2 associated breast cancers known to our service where a full histological record was available (n = 43), some of whom had genetic testing through other sources and so are not included in the 33 presented in Table PALB2 PGVs, as compared with 11.1% (204/1843) of all breast cancers cases known to our service where full histology was known .To investigate the breast pathology where there is a ble n = 4, some ofPALB2 PGV. A grade 3 ER-positive HER2-negative phenotype occurred in 15/43 (34.9%) PALB2 PGVs, while accounting for 254/1843 (13.8%) breast cancers .We then investigated other breast cancer subtypes and whether they were also enriched in heterozygotes for a PALB2 PGVs as compared with 562/1381 (40.7%) of all invasive ductal carcinomas known to our service and testing negative for BRCA1, BRCA2, and PALB2 PGVs .For a grade 3 phenotype, regardless of receptor status, these again were overrepresented occurring in 28/37 (75.7%) individuals with a BRCA1/2, MS was determined for all affected women  compared with 4/520 with a MS = 20–39, 0.8%, P = NS) (Table PALB2 there was a significantly higher likelihood of a PGV for MS of 20–39 than for MS < 9 and MS < 15 (P < 0.05)  with breast cancer who were available for testing; many FDRs with breast cancer were deceased and unavailable for testing and not all living FDRs opted for testing. This compared to only 7/13 for CHEK2_1100delC (P = 0.002) families  with breast cancer risk.The timely importance of our study reflects the recent publication and implementation of the National Health Service England (NHSE) national test directory.BRCA1 and BRCA2 PGVs with high-risk breast cancer predisposition has been well recognized and clinical diagnostic testing of these genes has been offered for the past 23 years in Manchester. While PALB2 was identified as a cause of hereditary breast cancer in 2007,6 it is only more recently that PALB2 PGVs have been shown to be associated with a >50% lifetime breast cancer risk in females.8 Routine diagnostic testing of PALB2 has been offered in MCGM since 2016, with some samples having been analyzed previously through research studies with diagnostic laboratory confirmation of any PGVs identified.The association of germline CHEK2_1100delC and PALB2 PGVs in our patients and this was significant across all subgroups, except for PALB2 PGVs in the population based PROCAS subgroup. This is in keeping with PGVs of PALB2 being relatively rare and associated with high-risk breast cancer predisposition, and so would be less likely to be detected in excess in our non-high-risk breast cancer cases.We detected an excess of PALB2 PGV showed that a higher MS (20–29 and 20–39) is strongly correlated with the presence of a germline PALB2 PGV as compared with a lower MS (<15), the current NHSE threshold of diagnostic BRCA1/2 and PALB2 testing.21 Interestingly at very high MS, in our data set, the likelihood of a PALB2 PGV appears to tail off as compared with BRCA1/2; further data will be needed to see if this finding is replicated. These data confirm the validity and clinical utility of the MS in the prediction of high-risk single gene breast cancer predisposition. Although the MS was designed for BRCA1/2 PGV likelihood, as PALB2 is a high-risk gene and associated with increasing frequency as MS rises, MS is also a useful marker for PALB2 PGVs except at very high scores. The lack of association of MS with the CHEK2_1100delC PGV likely reflects the associated moderate risk breast cancer predisposition of CHEK2_1100delC. When working up families for breast cancer gene panel testing, our data does suggest that MS is not suitable for determining likelihood of the presence of the CHEK2_1100delC PGV.Our analysis of MS and likelihood of a PALB2 PGVs in individuals with high-grade serous ovarian cancer. PALB2 PGVs are associated with a 5% lifetime risk of ovarian cancer8 and our data would suggest that the additional weighting within the MS for ovarian cancer (and not counting mucinous subtypes) is appropriate.We also detected two PALB2 PGVs with a tendency to the development of TNTs,22 accounting for 28% of breast tumors occurring in female PALB2 PGV heterozygotes known to our service. While this tumor phenotype is more typical of BRCA1 PGVs, TNTs  occur in 16% of BRCA2 associated breast cancers and at increasing ages as compared with BRCA1.23 In our data set, the mean age of the TNTs associated with a PALB2 PGV (46.6 years) was similar to that for TNTs associated with a BRCA2 PGV (46.8 years) and, while tending toward an older age than for BRCA1 PGVs (40.4 years), this was not significant.We confirmed the recently reported association of PALB2 PGV, comprising 35% of breast cancers in our PALB2 PGV cohort, which, to our knowledge, has not been previously demonstrated. While Hu et al.22 noted an OR of 5.2 for ER-positive HER2-negative breast tumors for PALB2 PGVs, their study did not account for tumor grade. Considering grade in particular, we also found a marked excess of grade 3 tumors occurring in 76% of all PALB2 PGV associated breast cancers. While our data may be biased toward referrals received where a woman has developed a high-grade breast cancer with or without a relevant family history, it is likely that such an association is real, although further data collection is needed to tease out these tumor phenotype correlations.More striking was the association of grade 3 ER-positive HER2-negative tumors with a PALB2_PGVs where BRCA1/2 testing was negative.Our data would suggest that, for women who had a breast cancer where the histology is of a TNT or grade 3 ER-positive HER2-negative phenotype in association with a high MS, there may be merit it considering testing for germline BRCA1/2 and PALB2 PGVs, and strong association with MS, likely reflect the combined BRCA1-PALB2-BRCA2 functional unit that facilitates the repair of double stranded DNA breaks using the high-fidelity homologous recombination repair pathway.24The similarities of the phenotypes associated with BRCA1/2 PGVs and their investigation in clinical trials of advanced breast cancer. Given the functional interaction of PALB2 with BRCA1 and BRCA2, and the phenotypic similarities of the associated cancers presented within and by others,8 it is likely these agents will also be of utility in the treatment of PALB2 deficient cancers. In fact, a recent phase II trial investigating Olaparib in metastatic breast cancer showed an objective response rate of 82% where there was a germline PALB2 PGV; furthermore, 85% of the breast cancers were ER-positive HER2-negative (grade not given).25Exploitation of this HR pathway has enabled the utilization of poly (ADP-ribose) polymerase (PARP) inhibitors in the drug treatment of ovarian cancers associated with germline and/or somatic BRCA1 and BRCA2 PGVs are much more common than those affecting PALB2, with BRCA1/2 PGVs detected in 7% of our patient cohort and PALB2 PGVs in 1%. While the reasons for these differing frequencies may not be clear, influencing factors may include the smaller size of PALB2 and, until recently, PALB2 PGVs have not been routinely screened for in either patient or population cohorts. Furthermore, BRCA1/2 PGVs are thought to be as frequent as 1 in 250–300 of the general population.26What is less clear is, despite BRCA1, BRCA2, and PALB2 forming a functional unit, why PALB2 in our cohort, c.3113G>A; p.(Trp1038Ter), c.3116del; p.(Asn1039llefsTer2) and c.3549; p.(Tyr1183Ter), together accounting for 50% of the individuals with PALB2 PGVs detected. These PGVs lie within the C-terminal WD40 domain of PALB2, which interacts with BRCA2. We note that for PALB2 PGVs reported by Yang et al.,8 while PGVs were distributed throughout PALB2, c.3113G>A was the most common PGV identified in breast/ovarian cancer detected in 61 families, with c.3549C>G detected in 19 families, and c.3116del in 9. Given our much smaller data set we cannot prove or disprove the possibility of a founder effect in our local population in the absence of haplotype studies; however, it may be that these loci represent PALB2 mutation hotspots, especially as the PALB2 founder PGVs reported are not localized to the 3’ end.28Of note we detected three recurrent PGVs of PALB2 del copy-number variants (CNVs) detected, both in this study and that reported by Yang et al.,8 again these all reside within the C-terminal WD40 domain that interacts with BRCA2. Thus, these data together may, in part, explain the similarities between the phenotypes seen with BRCA2 and PALB2 PGVs. Furthermore, it is likely that the genomic architecture toward the 3’ end of PALB2 predisposes to recombination events given this is where the CNVs and the balanced translocation we detected have been described. We note that our PALB2 testing strategy did not include CNVs for all our patients and so it is possible that further PALB2 CNVs remain to be identified in this cohort. Based on the data we report, we would recommend that any clinical testing strategy for PALB2 PGVs includes CNV analyses.Regarding the CHEK2 PGVs with moderate risk breast cancer predisposition is well recognized. CHEK2 has a role in DNA repair, although at an earlier stage whereby it detects then determines the cellular response to DNA damage.29 We detected the CHEK2_1100delC PGV in 44/3,127 cases compared with just 5/1,567 controls reflecting the also increased OR attained from the much larger BRIDGES study.20 There has been considerable debate regarding the utility of including the moderate risk penetrance genes, CHEK2 and ATM, in breast cancer diagnostic genetic testing panels. The recent BRIDGES data would support their inclusion, given that, along with the high-risk breast cancer genes, BRCA1, BRCA2, and PALB2, truncating PGVs are associated with a significant increased breast cancer risk, all with OR > 2.20 Similar findings were reflected in the CARRIERS study30 although with an ATM OR < 2; possibly reflecting the classification of all variants identified, including missense, as being PGV only where classified as (likely) pathogenic in ClinVar.31 We do not have a comprehensive data set for ATM PGVs and therefore were not included in our analyses; but data from BRIDGES20 would suggest ATM ought to also be included in breast cancer diagnostic panels. For our future studies, we would seek to attain data for germline ATM PGVs and include in our comparisons.The association of CHEK2 PGVs have been associated with a relative risk of breast cancer of 2.2,9 a high risk of contralateral disease,32 and are enriched in this cohort and others, this would substantiate inclusion of testing for CHEK2 PGVs in clinical diagnostic breast cancer genetic testing panels.Given truncating PALB2 PGVs and CHEK2_1100delC within a family, we detected perfect concordance for PALB2 in FDR with breast cancer but not for CHEK2 with only 7/13 testing positive for the familial variant. This likely reflects the moderate risk predisposition of CHEK2_1100delC and that breast cancer risk arises from the combined contribution of single PGVs and polygenic risk score (PRS) with PRS having a greater contribution where the single PGV effect is lower. Recently it has been shown that the combination of a high-risk breast cancer PRS and CHEK2_1100delC equated to a breast cancer lifetime risk equivalent to that of a PALB2 PGV alone.33 These data suggest that CHEK2_1100delC should be incorporated into diagnostic breast cancer genetic testing panels as this knowledge of moderate risk PGVs, combined with a PRS, will become part of routine practice in breast cancer risk assessment.Considering segregation of PALB2 PGVs and the CHEK2_1100delC PGV in breast/ovarian cancer, we show a 1% detection rate for PALB2 PGVs, 1.4% for CHEK2_1100delC, and ~7% for BRCA1/2 PGVs. Breast cancers associated with PALB2 PGVs tended toward phenotypes seen with a BRCA2 PGV, namely both triple negative and high-grade ER-positive HER2-negative tumors. The detection of PALB2 and CHEK2 PGVs, in addition to PGVs detected in BRCA1/2, in breast/ovarian cancer is important for accurate risk assessment and the activation of subsequent cancer prevention and early detection strategies in the individual and their family.In this single-center comprehensive study of germline Supplementary Information


This study aimed to evaluate the association between thyroid dysfunction and breast cancer risk. We included 239,436 females of the UK Biobank cohort. Information on thyroid dysfunction, personal and family medical history, medications, reproductive factors, lifestyle, and socioeconomic characteristics was retrieved from baseline self‐reported data and hospital inpatient databases. Breast cancer diagnoses were identified through population‐based registries. We computed Cox models to estimate hazard ratios (HRs) of breast cancer incidence for thyroid dysfunction diagnosis and treatments, and examined potential confounding and effect modification by comorbidities and breast cancer risk factors. In our study, 3,227 (1.3%) and 20,762 (8.7%) women had hyper‐ and hypothyroidism prior to the baseline. During a median follow‐up of 7.1 years, 5,326 (2.2%) women developed breast cancer. Compared to no thyroid dysfunction, there was no association between hypothyroidism and breast cancer risk overall : 0.84–1.02, 442 cases), but we found a decreased risk more than 10 years after hypothyroidism diagnosis . There was no association with hyperthyroidism overall  but breast cancer risk was elevated among women with treated hyperthyroidism  or aged 60 years or more at hyperthyroidism diagnosis , and 5–10 years after hyperthyroidism diagnosis . In conclusion, breast cancer risk was reduced long after hypothyroidism diagnosis, but increased among women with treated hyperthyroidism. Future studies are needed to determine whether the higher breast cancer risk observed among treated hyperthyroidism could be explained by hyperthyroidism severity, type of treatment or aetiology. In this study, we included 239,436 women in the general population‐based UK Biobank cohort to investigate the association between thyroid dysfunction and breast cancer risk. Our study suggested there was no association between hyper‐ and hypothyroidism in overall and breast cancer risk. However, among treated hyperthyroidism, we found a higher breast cancer risk, which could be explained by hyperthyroidism severity or aetiology. Participants were volunteers aged from 39 to 71 years, and residing in England, Wales, or Scotland, who gave their written informed consents.We included participants without cancer diagnosis of any type (except non‐melanoma skin cancer) that was self‐reported or recorded in cancer registries prior to baseline, i.e. the first visit at a UKB centre for study enrollment. We excluded women who underwent a mastectomy prior to baseline, or had less than one year of follow‐up. After exclusions, our study population included 239,436 women Figure . Follow‐2.2In the primary analyses, we used information on baseline thyroid dysfunction diagnosis  and treatments  that was self‐reported during the baseline interview or recorded (at least once) in a hospital inpatient database prior to baseline Table . HyperthWe investigated the following exposure variables: ever diagnosis of hyper‐/hypothyroidism, thyroid dysfunction treatment modalities, time since diagnosis, time since treatment onset, age at diagnosis and calendar year of diagnosis.2.3Breast cancer cases were defined as diagnoses of invasive  or in situ cancers (n=874) recorded in the cancer registries . We considered only first cancer occurrences. Women diagnosed with cancer of any type during follow‐up (except non‐melanoma skin cancer) were censored on diagnosis date.2.4We considered comorbidities and breast cancer risk factors, and healthcare‐related factors at baseline as potential confounders or effect modifiers Appendix .The comorbidities of interest were overweight/obesity, hypertension, diabetes, depression, and autoimmune conditions. Type 1 and 2 diabetes were identified using a modified version of a published algorithm that was developed using the UKB data and validated against external primary and secondary care databases Appendix .17 SinWe considered well‐established breast cancer risk factors as potential confounders or effect modifiers: menopausal status, age at menarche, parity, age at first birth, family history of breast cancer, use of menopausal hormone therapy (MHT), use of oral contraception, and level of physical activities. Baseline age at menopause was defined as age at bilateral oophorectomy or reported menopause whichever occurred first. If unknown, it was defined in order of priority as age at MHT initiation, or 51 years otherwise. The age threshold corresponds to the median value of age at menopause in the study population.Other factors suggested to be possibly associated with breast cancer risk in the literature were considered as potential confounders: race, alcohol consumption, and smoking status. Townsend deprivation score of residence, educational attainment, occupation, and adherence to breast and cervical cancer screening programs, which might reflect different levels of health care access and cancer surveillance, were also analysed as potential confounders.For all the above‐mentioned factors, missing data were infrequent , and handled either by defining an “unknown” category  or imputing the median value in the study population (for continuous variables).2.5We used Cox proportional hazards models to compute hazards ratios (HRs) and 95% confidence intervals (CIs) of breast cancer incidence according to thyroid dysfunction diagnosis and treatments. Time since baseline (i.e. UKB inclusion) was considered as the time scale. Models were adjusted for age at baseline, menopausal status, family history of breast cancer, parity and age at first birth, and level of physical activity. Proportional hazards assumptions were graphically evaluated based on plots of scaled Schoenfeld residuals against time, and tested by introducing an interaction term between thyroid dysfunction and follow‐up time. No evidence of non‐proportionality was found.Potential confounding effect was assessed by evaluating the age‐adjusted associations with thyroid dysfunction, and changes in adjusted HRs for breast cancer risk exceeding 10%.Several sensitivity analyses were conducted. While hyperthyroid patients are usually treated, the proportion of hyperthyroid women without information on treatment was too high to be considered as untreated individuals. Since ATDs are used as the first‐line treatment for Graves’ disease (the most common cause of hyperthyroidism) in the UK 33.1The prevalence of hyper‐ and hypothyroidism at baseline was 1.3% and 8.7%, respectively. Compared to women with no thyroid dysfunction, hyper‐ and hypothyroid women were likely to be older, postmenopausal, MHT and oral contraception ever user, obese/overweight, to have had a child at an earlier age, to have a lower level of physical activity, and more comorbidities at baseline Table . During 3.2We found no statistically significant association between breast cancer risk and hyperthyroidism in overall Table , but an For treated hyperthyroidism, there was a higher breast cancer risk among women menopaused at ages >51 years  compared to women who had earlier menopause  or were premenopausal at baseline  (p‐heterogeneity=0.09) Table . We foun3.3We found no statistically significant association between hypothyroidism and breast cancer risk, overall , or after stratification by calendar year at diagnosis or treatment Table .We found no confounding or modifying effect by comorbidities, and breast cancer risk factors Figure , except 4In this study, there was no association between thyroid dysfunction and breast cancer risk overall. However, breast cancer risk varied according to hyperthyroidism treatment status, with a 38% higher breast cancer risk in women treated for hyperthyroidism compared to women with no thyroid dysfunction and no increased risk among hyperthyroid women without information on treatment. The risk was particularly elevated at 5–10 years after hyperthyroidism diagnosis and among women diagnosed for hyperthyroidism at the age of 60 years or more. Women with a history of hypothyroidism for 10 years or more or diagnosed before the age of 40 years were at a lower risk of breast cancer. Menopausal status and age at menopause modified the association of both treated hyper‐ and hypothyroidism.Accumulated evidence in recent years has not provided a clear understanding of the role of hypothyroidism on breast cancer risk. Some In the current study, we reported an increased risk of breast cancer among women with treated hyperthyroidism while the meta‐analysis In our study, baseline characteristics did not differ substantially between hyperthyroidism with/without information on treatment Table . IncreasTreatments are generally not recommended in subclinical hyperthyroidism when overt conditions are often treated as soon as diagnosed.Most,Biological mechanisms underlying the association between breast cancer risk and TSH remains unclear, but a number of explanation for thyroid hormones have been explored in vitro and in vivo. T4 and T3 activate MPAK pathways and phosphorylate ER α, inducing cell proliferation.In this study, the breast cancer risk estimates for thyroid dysfunction were not affected by a wide range of potential confounders and effect modifiers, except menopausal status and late age at menopause irrespective of other factors. Few studies have investigated a potential effect modification by menopausal status and reproductive factors. A recent study showed a positive association between hyperthyroidism and reproductive risk factors of breast cancer.Late age at menopause has been confirmed risk factor of breast cancer, which lengthens the cumulative exposure to cycling reproductive hormones among women.The current study has major strengths, including a large population size, a high level of follow‐up completeness and outcome ascertainment through regional registries and hospital databases, and its wide range of available information. The crossover among inpatient data and self‐reported data on personal medical history allowed us to capture a broad range of health conditions. The UKB also includes detailed information on reproductive factors, lifestyle, socioeconomic status and family medical history with low levels of missing data, which helped us to study essential risk factors.However, several limitations can be flagged. Details on cancer stage, grade, and receptor status were unavailable, and we could not investigate whether the risk estimates varied according to tumour characteristics. Lack of information on laboratory measurements of thyroid hormones, aetiology, and clinical symptoms of thyroid dysfunction prevented us from determining the severity, the exposure window of thyroid dysfunction (as overt conditions are often treated as soon as diagnosed) and disentangling the independent role of severity, and aetiology. We were unable to account for thyroid dysfunction diagnosis/treatments for the whole study population during follow‐up or to study the independent effects of thyroid dysfunction treatments, and evaluate the impact of different treatment‐related factors: RAI dosage, partial versus total thyroidectomy, duration of use and adherence to ATDs prescription. In the hyperthyroidism analyses, given that the higher risks were consistent across different types of treatment, accounting for treatment‐related factors is unlikely to change our risk estimates. However, considering the intertwined relationship between hyperthyroidism aetiology, severity and treatment for further research is needed to confirm our finding. The data on comorbidities were also quite limited with no information on severity, age at onset, and duration of conditions, so it is possible that we did not account for all the possible effects of comorbidities on the association between thyroid dysfunction and breast cancer risk.In conclusion, our study suggested that higher breast cancer risk among hyperthyroid women could be explained by hyperthyroidism severity or aetiology, while there was a lower risk among women with 10 years or more of hypothyroidism. The association between thyroid dysfunction and breast cancer risk was modified by menopausal status and age at menopause, suggesting that the positive association between increased blood levels of thyroid hormones and breast cancer risk was even stronger with late age at menopause.The authors declare no conflict of interest.This study was performed under generic ethical approval obtained by UK Biobank from the National Health Service National Research Ethics Service .Supplementary MaterialClick here for additional data file.


This proof-of-concept study suggests that models combining genetic data with classical risk factors may be used in the future for the prediction of breast-cancer risk and, therefore, supports their potential clinical utility for targeted preventative strategies in Cypriot women.Breast cancer is the most commonly diagnosed type of cancer in women worldwide. Stratification of women based on their individual breast cancer risk could guide targeted preventative strategies and population screening. Integrated models that combine the effects of a polygenic risk score (PRS) with classical breast cancer risk factors could provide an individualized breast-cancer risk estimation. Although various studies have extensively evaluated the performance of such integrated models in populations of European ancestry, no previous studies have included individuals of Greek-Cypriot origin. To this end, we have assessed the predictive performance of a 15-SNP PRS  1.66 (1.25–2.19). The integrated risk model obtained an AUC (95% CI) 0.70 (0.67–0.72) and had the ability to stratify women according to their disease status at the extreme deciles. These results provide evidence that the combination of PRS with classical risk factors may be used in the future for the stratification of Cypriot women based on their disease risk, and support its potential clinical utility for targeted preventative actions and population screening.The PRS combines multiplicatively the effects of common low-risk single nucleotide polymorphisms (SNPs) and has the potential to be used for the estimation of an individual’s risk for a trait or disease. PRS has been successfully implemented for the prediction of breast cancer risk. The combination of PRS with classical breast cancer risk factors provides a more comprehensive risk estimation and could, thus, improve risk stratification and personalized preventative strategies. In this study, we assessed the predictive performance of the combined effect of PRS Breast cancer is the most commonly diagnosed type of cancer in women around the world . Disease313 risk distribution, females in the 1st and 99th percentiles had 0.27-times and 4-times increased overall breast cancer risk, respectively  compared to controls [mean (SD) = 0.595 (0.316)]  1.66 (1.25–2.19) and p-value = 0.0004. The AUC (95% CI) of PRS15 was 0.55 (0.52–0.57) and was well-calibrated . As illustrated in 15 risk distribution, the estimated OR (95% CI) for those in the first quartile was 0.98 (0.76–1.27), p-value = 0.88, whereas for those in the fourth quartile was 1.51 (1.19–1.94), p-value = 0.0009 ] a. The PR= 0.0009 . The est history .15. 15 and any of the classical breast cancer risk factors (15 was constructed. The integrated risk model achieved a risk discrimination score of AUC (95% CI) 0.70 (0.67–0.72) and was well calibrated   . Sensitip-value = 1.55 × 10−5, and included 15.4% of controls, and 4.4% of cases. Similarly, the estimated OR (95% CI) in the 2nd quartile was 0.48 (0.31–0.75), with p-value = 0.001 and included 15% of controls and 5.8% of cases. In contrast, the estimated OR (95% CI) in the 9th decile was 3.22 (2.04–5.13) with p-value = 6.46 × 10−7 and included 5% of controls and 13% of cases, and in the last decile the estimated OR (95% CI) was 4.58 (2.88–7.4), with p-value = 2.44 × 10−10 and included 4.2% of controls and 15.6% of cases  being in the same direction, as previously described in the iCOGS study [15 was significantly associated with an increased breast cancer risk in Greek-Cypriot women, with OR (95% CI) of 1.66 (1.25–2.19), which falls within the range of ORs of the published PRSs constructed and evaluated in women of European descent, according to the Polygenic Score (PGS) Catalog [15 was 0.55 (0.52–0.57), which was slightly lower compared with previous studies in European populations where the AUC ranged between 0.58–0.65 [15, women in the highest quartile of the PRS15 risk distribution had a statistically significant 1.5-times greater breast cancer risk compared to the average. To date, more than 200 common, low-risk SNPs have been discovered through GWAS to be associated with breast cancer predisposition in women of European ancestry, and a PRSan women ,14. In oGS study ,8. Based Catalog . The AUC.58–0.65 . Incorpo15 and all the risk factors was constructed. Combination of PRS15 with classical risk factors achieved a discrimination score of AUC (95% CI) 0.70 (0.67–0.72), and had the ability to stratify women based on their disease status at the extreme deciles which are the most important for risk-stratified preventative strategies . Our results, provide evidence that such integrated risk models have the capacity to stratify Greek-Cypriot women based on their personal breast cancer risk. Similar to other studies, we did not identify any significant interactions between the PRS and the other classical breast cancer risk factors  20,24,4,424,46; 92 and 5 phenotypic risk factors in a cohort of Spanish women. Similarly to our results, the integrated risk model had the ability to stratify women according to their disease risk at the extreme deciles, and had a slightly higher predictive power compared to our study, AUC (95% CI) 0.80 (0.77–0.83) [18 within the Tyrer–Cuzick model in combination with mammographic density, could substantially improve risk prediction accuracy, with AUC (95% CI) 0.67 (0.62–0.71) [These results are consistent with previously published studies, demonstrating that integrated risk models, including a PRS, and classical breast cancer risk factors have the ability to stratify women according to their disease risk ,20,24,2677–0.83) . AdditioThe main limitation of our study is the small number of SNPs included in the PRS. The inclusion of the PRS into a model with only including the classical risk factors did not make a substantial increase in the predictive performance of the model. In the future, larger studies, and incorporation of additional SNPs in the PRS, as well as additional risk factors in the integrated risk model, such as mammographic density, are needed for defining the best tool to be used in our population. In this study, we evaluated the predictive performance of the combined effect of a PRS with classical risk factors for the prediction of breast cancer risk in Greek-Cypriot women. Despite the limited number of SNPs included in the risk prediction model, our study highlights that it is worth assessing further the clinical utility of PRS for Greek-Cypriot women. In this study, we evaluated the predictive performance of a polygenic risk score consisting of 15 previously identified SNPs, in combination with classical breast cancer risk factors in women of Greek-Cypriot origin. Although the PRS and risk prediction models are extensively evaluated in individuals of European ancestry, no previous study included women from our population. This study demonstrates that polygenic information in combination with classical risk factors has the power to stratify Greek-Cypriot women based on their disease risk. These results suggest the potential clinical utility for the use of a combined model for the prediction of breast cancer risk in Cyprus.


Background—In 2019, the IARC concluded that “night shift work is probably carcinogenic to humans (Group 2A), based on limited evidence from human epidemiological studies and sufficient evidence of cancer and strong mechanistic evidence in experimental Animals.” The negative health consequences of night shift work may depend on how the night shifts are scheduled. The aim of this study was to investigate how the characteristics of night work affect the risk of developing breast cancer. Methods—A case–control study was conducted in 2015–2019 in the Lodz region. The case group included 494 women with breast cancer, while the control group included 515 healthy women. Results—Night work was found to be the third most important factor regarding breast cancer after a high BMI and a short or no breastfeeding period and before factors such as early menstruation, late menopause, no pregnancy, and smoking. The harmful effects of night work were influenced by its intensity, frequency, rotation, and the number of night shift years worked. Night work increases the breast cancer risk by 2.34 times, and high-intensity night work increases the breast cancer risk by 2.66 times. Conclusions—Appropriate ergonomic recommendations for night shift work for employers should be considered. Working at night is inconsistent with the biological rhythm of human life. The body adapts to its circadian rhythm thanks to light stimuli. The master biological clock, which is located in the suprachiasmatic nuclei (SCN) , helps the body get used to 24 h environmental cycles. The cells of the biological clock, which are present in all organs of the human body, have the ability to regulate endogenous biological rhythms ,2. WorkiIn 2007, the International Agency for Research on Cancer (IARC) concluded that “shift work is probably carcinogenic to humans (Group 2A), based on limited evidence of carcinogenicity for breast cancer from eight human studies, in addition to sufficient evidence from studies in experimental animals” . In 2019The negative health consequences of night work may depend on how the night shifts are scheduled. Most epidemiological studies have found that higher night work intensity (number hours or night shifts per day/week/month) is associated with a higher risk of breast cancer ,14,15,16Breast cancer is the most frequently diagnosed cancer among women . It is also a leading cause of cancer-related deaths in women (15.0%) . In PolaSince in our previous article , we coulThe case–control study was conducted in 2015–2019 in the Lodz region. The case group included 494 women over 35 years old, who had been diagnosed with malignant breast cancer and had a tumor resection or mastectomy. The control group included 515 women without breast cancer. The women from the case group were patients of the Oncological Surgery Department and the Second Department of Oncological Surgery, Oncological Surgery Clinics of the Provincial Specialist Hospital M. Kopernik in Lodz; the surgery department of Poddębice Health Center; the Provincial Specialist Hospital M. Skłodowska-Curie in Zgierz. The anthropometric data of the research participants were assessed using a self-assessment questionnaire. A detailed description of the study design and study population, including the selection of women for the case/control groups and a description of the research tool, can be found in our previous article: “Night Shift Work—A Risk Factor for Breast Cancer” . The stuThe outcome variable was breast cancer. The criteria for including women in the case group were histopathologically confirmed breast cancer or/and mastectomy and no history of other cancers. The criterion for including women in the control group was no history of breast cancer.3 days or less/more than 3 days of consecutive night shifts (one after another);Forward/backward rotation of shift work;Less/more than 10 years of night work before illness developed;Number of night work years.Respondents were asked whether they ever worked for at least 6 months. We were interested in all types of employment: full-time or part-time work, self-employment, work in a family business, and work in the army or on a farm. If respondents ever worked for at least 6 months, they were asked if they ever worked a night shift, with the possible answer options being “Yes” and “No.” Respondents working night shifts were asked whether they had worked three nights or less on a specific shift or more than three consecutive night shifts, and then they were transferred to the next shift. We also asked about the order of the shifts. If the order of shifts was I (morning)→II (afternoon)→III (night), we named it the “forward rotation.” If the order of shifts was III→II→I, we named it the “backward rotation.” The respondents were also asked how many times a month they worked at night. Finally, we also asked about the starting point for shift work, with possible answers being “Within 12 months before breast cancer,” “1–5 years before breast cancer,” “5–10 years before breast cancer,” “Over 10 years before breast cancer.” Finally, from the group of night shift employees, we derived four variables measuring the intensity of such work:The respondents reported their birth date ), place of living , medium-sized town , large-sized town ), marital status , and education level .Breast cancer risk factors that were assessed included the age of first menstrual period and age of menopause, pregnancy history , smoking status, and body mass index.p < 0.05). Exponentiating the estimators from Gretl package produced the odds and the direct estimators from logistic regression in Statistica. Each of the A–G columns of We use contingency tables and multiple logistic (logit) regression to estimate the crude  and adjuConsidering the aim of the study , the analysis covered 973 women who answered the questions regarding night shift work (out of 1009 women). Among these women, 478 patients had been diagnosed with breast cancer (case group) and 495 women without a breast cancer diagnosis (control group). As we showed in our previous study , the casIn Finally, we estimated logit models, which initially included all ten disease predictors listed in The estimators from the first (OR) column of p < 0.008) and meant that every additional year of night shifts increased the risk by 3.1%. Finally, if we combined the (D) and (E) factors and selected people who worked nights for more than ten years and more than three consecutive nights, the OR increased to 2.66 and meant that such people have odds of developing breast cancer that was 2.66 times higher than all the others.From the last row of Some epidemiological studies found that a longer duration  of rotating and night shift work increases breast cancer risk ,15,16,28p = 0.73) or after (p = 0.90) first pregnancy, or by time since they last worked night shifts (Ptrend = 0.38) [p for trend = 0.63). While in the NHS2, 20 years or more of rotating night shift work was associated with a significantly higher breast cancer risk . Women with 20 years or more of cumulative rotating night shift work exposure had a marginally significant higher risk of breast cancer  [In our study, we found that every additional year of night shifts increased the risk by 3.1% with Ptrend = 0.008. A study by Jones et al. found no significantly increased risks in relation to hours worked per night (Ptrend = 0.62), nights per week on night shift (Ptrend = 0.066), cumulative years of employment as a night shift worker (Ptrend = 0.51), and cumulative hours of night shift work (Ptrend = 0.51). There was a significant positive trend with average hours per week, adjusted for age only (Ptrend = 0.038) or fully adjusted (Ptrend = 0.035). There were no significantly increased risks with being a night shift worker in the last 10 years by type of occupation. There were no significant associations with the age the participant started night shift work (Ptrend = 0.89), whether night shift work started before ( = 0.38) . In a st = 0.38) . In the  = 0.38) . In our  = 0.74) . In the  = 0.74) . Engel e = 0.74) . In a No = 0.74) . In a No = 0.74) . In a cr = 0.74) .Schernhammer et al. analyzed the relationship between breast cancer risk and working night shifts in a Nurses’ Health Study. The authors observed a higher breast cancer risk among women who worked night shifts for 1–14 years  or 15–29 years  . In a coAn important factor related to night work is the chronotype. We did not include it in our study, which can be considered a limitation of the study. In the pilot study, we observed that the answers to the chronotype questions were too distant in time and posed too much difficulty for the respondents; therefore, we found them to be unreliable. In a case–control study of night shift work and breast cancer risk among women in the Danish military, Hansen and Lassen found that women who had ≥3 consecutive night shifts for ≥6 years and women with a morning chronotype preference had a higher breast cancer risk  . In a crThe strengths of the study were an extensive questionnaire and a large study group. The limitations of the study were the self-assessment questionnaire, the lack of reliable data on the relationship between chronotype and shift work tolerance, and that some of the questions concerned distant events. Another limitation was that the anthropometric indicators were evaluated using a self-assessment method.The negative impact of shift work, especially night work, on women’s health is unquestionable. In this study, we focused on the impact of night work and its intensity on breast cancer risk. The breast cancer risk factor for night-shift workers can be almost 4 times higher than for women who do not work nights. Night work is the third most crucial breast cancer risk factor (after a high BMI and a short or no breastfeeding period). However, the harmful effects of shift work are influenced by its intensity, frequency, and rotation. The forward rotation had a little lower impact than working nights, whereas more than 10 years of night shift or working in the system for more than three consecutive nights increased the risk of breast cancer. Since night work and its intensity is a modifiable risk factor, if women’s night work cannot be ruled out, it should be planned to be as harmless as possible. This means that night shifts should not occur one after another for more than three nights, a more favorable type of shift rotation is the forward one (night shift occurs after the afternoon one), and the years of night work should be as short as possible.


The lifetime risk of breast and ovarian cancer is significantly higher among women with genetic susceptibility or a strong family history. However, current risk assessment tools and clinical practices may identify only 10% of asymptomatic carriers of susceptibility genes. Bright Pink developed the Assess Your Risk (AYR) tool to estimate breast and ovarian cancer risk through a user-friendly, informative web-based quiz for risk assessment at the population level.This study aims to present the AYR tool, describe AYR users, and present evidence that AYR works as expected by comparing classification using the AYR tool with gold standard genetic testing guidelines.The AYR is a recently developed population-level risk assessment tool that includes 26 questions based on the National Comprehensive Cancer Network (NCCN) guidelines and factors from other commonly used risk assessment tools. We included all women who completed the AYR between November 2018 and January 2019, with the exception of self-reported cancer or no knowledge of family history. We compared AYR classifications with those that were independently created using NCCN criteria using measures of validity and the McNemar test.increased and average risk as the gold standard, based on the NCCN guidelines, we estimated the sensitivity and specificity for the AYR algorithm–generated risk categories as 100% and 89.9%, respectively (P<.001). The specificity improved when we considered the additional questions asked by the AYR to define increased risk, which were not examined by the NCCN criteria. By race, ethnicity, and age group; we found that the lowest observed specificity was for the Asian race (85.9%) and the 30 to 39 years age group (87.6%) for the AYR-generated categories compared with the NCCN criteria.There were 143,657 AYR completions, and most participants were either at increased or average risk for breast cancer or ovarian cancer . Using our estimates of These results demonstrate that Bright Pink’s AYR is an accurate tool for use by the general population to identify women at increased risk of breast and ovarian cancer. We plan to validate the tool longitudinally in future studies, including the impact of race, ethnicity, and age on breast and ovarian cancer risk assessment. BRCA) mutations or a strong family history [BRCA1 carriers, specifically, estimates suggest a 40% to 87% cumulative breast cancer risk by age 70 years, whereas for ovarian cancer risk, estimates range from 16% to 68% [In the United States, there are nearly 250,000 cases of breast cancer and 25,000 cases of ovarian cancer diagnosed annually ,2. Breas history -10. Amon% to 68% . HoweverA central component of breast and ovarian cancer prevention programs is education on personal risk, as determined by factors such as family history, genetic susceptibility, hormonal risk factors, and modifiable health behaviors -14. HoweTo achieve these goals, Bright Pink developed the Assess Your Risk (AYR) tool that provides a user-friendly, web-based survey to determine lifetime breast and ovarian cancer risk at the population level. Bright Pink was developed in collaboration with genetic counselors, public health practitioners, and clinicians to ensure alignment with standards of breast and ovarian cancer risk assessment but with a modern, consumer-facing interface that would be accessible to the general population. Since 2014, the Bright Pink AYR has been used by more than 1.2 million women to assess breast and ovarian cancer risk. The AYR tool offers a streamlined, educational, and actionable user experience with the goal of substantially increasing the number of women nationwide, as well as internationally, learning about breast and ovarian cancer risk.However, the AYR tool has not yet been compared with the gold standard clinical guidelines for identifying women at increased risk for breast and ovarian cancer who are eligible for consultation with a genetic counselor. The aims of this study are to (1) describe the AYR tool and its content, 2) describe the AYR tool user population, and (3) compare the AYR tool classification against the National Comprehensive Cancer Network (NCCN) guidelines for genetic and family high-risk assessment in breast and ovarian cancer  describe. MoreoveBright Pink, a national nonprofit organization based in Chicago, Illinois, was started in 2007 with the mission of empowering women to know their breast and ovarian cancer risk to manage their health proactively. For this study, eligible participants included Bright Pink AYR 3.0 users from October 2018 to March 2019, who were women aged ≥18 years. We included women of White non-Hispanic or Latinx, African American, Asian, or Hispanic or Latina race and ethnicity and all age groups. We excluded participants with a personal history of breast or ovarian cancer (excluding nonmelanoma skin cancer) or no knowledge of family history from calculations of agreement between the NCCN and the AYR. The goal of the NCCN criteria is to identify individuals who may be eligible for genetic testing and counseling for breast or ovarian cancer; therefore, we included women at increased risk for either breast or ovarian cancer.This study was approved by the Northwestern University Human Subjects Protection Program (review number STU00207333).Bright Pink developed the original AYR tool in 2014 to support the goals of modernizing and increasing access to risk assessment for breast and ovarian cancer at the population level. The AYR tool estimates breast and ovarian cancer risk through a user-friendly, informative web-based quiz with superior-rated readability [Genetic/Familial High-Risk Assessment: Breast and Ovarian [BRCA1/2 or other gene variants, (2) a family history of high-risk cancers such as triple-negative or male breast cancer, or (3) a family history of more than 3 cases of cancers on either side.The AYR 3.0 tool includes 26 questions  based on Ovarian . Howeverhigh category when they report already having a mutation in a breast cancer susceptibility gene identified through genetic testing. Specifically, AYR high risk is triggered by a personal history of positive genetic testing for genes associated with hereditary breast and ovarian cancer syndrome (BRCA1 and BRCA2), Lynch syndrome , or Peutz-Jeghers Syndrome (STK11) [increased risk is triggered by a personal history of positive genetic testing for other moderate to lower penetrance mutations  or a relative with a positive gene mutation test combined with no personal history of genetic testing. Moreover, a strong family history of high-risk breast or ovarian cancer, as well as a history of three or more cancers within the family, will trigger an increased risk in the AYR tool. Finally, AYR also uses components from the Gail and Tyrer-Cusick models, including a personal history of childhood radiation to the chest, abnormal breast biopsy, or polycystic ovary syndrome (PCOS) [average risk and provided with educational messaging focused on appropriate clinical screening guidelines and current evidence related to the impact of health behaviors on cancer prevention.On the basis of these NCCN criteria, Bright Pink’s AYR tool categorizes women into the e (PCOS) -24. WomeThe primary goal of the AYR is to estimate the lifetime risk of breast and ovarian cancer among women with no history of breast or ovarian cancer. We thus excluded women reporting a previous history of breast or ovarian cancer from the analysis because the focus of AYR is on cancer prevention and early detection. In this subset, we calculated descriptive statistics for the AYR participants overall then by average, increased, and high AYR breast and ovarian categories. We then examined measures of agreement between the NCCN and AYR criteria.average and increased categories based on the NCCN criteria for breast and ovarian cancer genetic testing. We then compared these with the categories assigned by the AYR algorithm when the quiz was taken. We assessed measures including sensitivity and specificity to estimate the ability of AYR to distinguish between women who are eligible for genetic testing (AYR increased risk) versus those who are at average risk and thus not eligible for genetic testing, considering our NCCN criteria variables as the gold standard. We also examined the positive predictive value (PPV) and negative predictive value (NPV) to determine whether the AYR tool correctly identifies eligible or ineligible women based on NCCN criteria among those in the AYR increased or average categories, respectively. A true positive was when AYR risk category matched with the independently created NCCN categories. We calculated McNemar test of agreement, with an α of .05.We used the AYR quiz questions to create our own 2, and most women reported that they limit alcohol intake to less than 2 drinks per day and did not smoke. However, 60.54%  of women reported that they did not meet the physical activity guidelines for 150 minutes of moderate-intensity activity per week.There were 143,657 AYR completions between October 2018 and March 2019, with complete data on health behaviors and family history. We examined the results for both breast  and ovar2. High-risk women were also more likely to have a personal history of dense breast, PCOS, or abnormal breast biopsy as well as a family history of high-risk cancers. For ovarian cancer , the International Breast Cancer Intervention Study (IBIS), the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) model, and BCRAPRO -29. TherBright Pink built the AYR for the general population as a tool to help inform women of their risk and to provide education on cancer prevention activities appropriate to their risk level. We believe that the patient-oriented interface, developed with the help of marketing experts, makes AYR a particularly user-friendly risk assessment tool with the potential to reach populations that existing clinical tools may not reach because of issues such as health care access. The Bright Pink AYR tool represents an advance in the field of population-level cancer prevention as a screening tool with the potential to improve the reach and use of web-based breast and ovarian risk assessment among diverse populations.The Bright Pink AYR tool has also demonstrated a strong reach to potentially underserved populations. Epidemiological data from the Surveillance, Epidemiology, and End Results Program demonstrate disparities in breast and ovarian cancer incidence and mortality by race -33. HoweBRCA1/2. The study found that women and those with higher education were more likely to report a healthy diet. The results showed no change in diet, vitamin use, or physical activity over time. However, there was no education component for health behaviors incorporated into the genetic counseling sessions. Another study examined health behaviors among participants of self-reported genetic testing  using data from the Health Information National Trends Survey 4 [The AYR tool is also unique compared with other tools in that health behaviors are collected and used to tailor email messages to promote cancer prevention behaviors. Previous studies have demonstrated the impact of education on health behaviors among high-risk populations. For example, Quach et al  examinedSurvey 4 . The resBRCA1 genetic testing on preventative cancer screening behavior over 2 years (n=408). Among women aged ≥40 years, 82% of mutation carriers followed guidelines for screening mammography in the first year and 67% in the second year, which was significantly increased from baseline and greater than the levels observed among noncarriers [Studies have examined the impact of risk assessment programs for breast or ovarian cancer on adherence to cancer prevention guidelines or recommendations for lifestyle risk factors, and most have identified opportunities for improvement -43. For carriers . These sThere are strengths and limitations of the current analysis. The strengths include the very large sample size of the completions of the AYR tool in a short period and a diverse population. However, although the population was diverse, the frequency by race and ethnicity was lower than expected for some populations. This reflects the opportunity to improve access and use of the AYR tool in diverse populations. Moreover, the questions asked by the Bright Pink AYR tool are categorical and often binomial, such as health or family history and physical activity questions. This limits the level of detailed data collected on high-risk cancers and the ability to evaluate factors such as age at cancer diagnosis and pathology or subtype of breast cancer. Finally, these data were cross-sectional. Longitudinal studies will be necessary to validate the accuracy of AYR risk assessment in breast and ovarian cancer risk prevention. However, overall, the results demonstrate that AYR accurately classifies women according to breast and ovarian cancer risk using existing gold standard criteria.These results demonstrate that Bright Pink’s AYR accurately classifies women at an increased risk of breast or ovarian cancer. The variation observed by race, ethnicity, and age group demonstrates the need to improve access and use of risk assessment tools in diverse populations. Overall, Bright Pink AYR is a valuable tool for use by the general population as well as patients and clinical providers for early detection and education, which will improve the prevention of breast and ovarian cancer.


Population-based genetic risk stratification and detection of early-stage breast cancers will improve approaches to prevent and reduce disease-associated mortalities. In this review, we summarize the latest discoveries in breast cancer susceptibility genetics and propose how these findings can be applied in the clinical arena to improve risk prediction and prevention of breast cancer. We also review the latest approaches and progress aimed at elucidating the functional consequences of both high and moderate penetrance genetic variation, which tend to lie in the protein coding regions of breast cancer susceptibility genes, and common low penetrance breast cancer risk alleles which tend to lie in non-protein coding DNA regions and affect gene regulation. For non-coding risk variation, there is no genetic code to interpret the function of common risk allele; and so, we provide the reader with an illustration of the step-by-step methods to understand their functional impact on breast cancer disease biology.BRCA1, BRCA2, PALB2, ATM, and TP53 are responsible for a proportion of breast cancer cases. However, breast cancer is a heterogeneous disease, and there is now strong evidence that different risk alleles can predispose to different subtypes of breast cancer. Here, we review the associations between the different genes and subtype-specificity of breast cancer based on the most comprehensive genetic studies published. Genome-wide association studies (GWAS) have also been used to identify an additional hereditary component of breast cancer, and have identified hundreds of common, low-penetrance susceptibility alleles. The combination of these low penetrance risk variants, summed as a polygenic risk score (PRS), can identify individuals across the spectrum of disease risk. However, there remains a substantial bottleneck between the discovery of GWAS-risk variants and their contribution to tumorigenesis mainly because the majority of these variants map to the non-protein coding genome. A range of functional genomic approaches are needed to identify the causal risk variants and target susceptibility genes and establish their underlying role in disease biology. We discuss how the application of these multidisciplinary approaches to understand genetic risk for breast cancer can be used to identify individuals in the population that may benefit from clinical interventions including screening for early detection and prevention, and treatment strategies to reduce breast cancer-related mortalities.Family history remains one of the strongest risk factors for breast cancer. It is well established that women with a first-degree relative affected by breast cancer are twice as likely to develop the disease themselves. Twins studies indicate that this is most likely due to shared genetics rather than shared epidemiological/lifestyle risk factors. Linkage and targeted sequencing studies have shown that rare high- and moderate-penetrance germline variants in genes involved in the DNA damage response (DDR) including  Breast cancer is the most common cancer among women worldwide accounting for around 12% of all female cancers . ApproxiBRCA1 and BRCA2 genes (BReast-CAncer susceptibility gene 1 and 2) carrying likely pathogenic variants that co-segregate with breast cancer in families [BRCA1 or BRCA2 pathogenic variants have an ~70% lifetime risk of being diagnosed with breast cancer by age 80 [BRCA1 and BRCA2 carriers are also at an increased risk of breast cancer with risk estimates of ~1–5% and 5–10% respectively, compared with the general male population where the life-time risks are ~0.1% [BRCA1 or BRCA2 is predicted to lead to loss of function (LOF) of these genes and increases genomic instability during breast cancer development [BRCA1 and BRCA2 gene, respectively [BRCA1 and 1731 unique BRCA2 deleterious variants [BRCA1 and BRCA2 account for only ~20% of familial breast cancer risk [The identification of the first breast cancer susceptibility genes dates back to the nineties when linkage studies led to the identification of the families ,7. Femaly age 80 . Male BRre ~0.1%  and havere ~0.1% ,13,14. Lelopment . A revieectively ; and as cer risk   pathway. These findings are summarized in PALB2 : Risks conferred by germline variants in the PALB2 gene are comparable to the risk conferred by BRCA2 in some familial cases. PALB2 plays a central role in DSB repair; BRCA1 recruits PALB2 in response to DSBs, followed by recruitment of BRCA2 forming a complex on DNA. Formation of this complex is necessary for recruitment of RAD51 and consequently RAD51-mediated homologous recombination [bination ,20.ATM (Ataxia telangiectasia mutated): Homozygous germline pathogenic variants in ATM are associated with ataxia-telangiectasia; but heterozygous variants also confer risk of breast cancer. The ATM gene encodes a protein kinase with a central role in DDR. Upon ATM activation by DSBs, it phosphorylates other genes involved in different stages of the DDR including BRCA1, TP53, and CHEK2 [nd CHEK2 ,22.CHEK2: After BRCA1 and BRCA2, this was the first breast cancer moderate risk gene to be identified after observing a common deletion (1100delC) in non-BRCA breast cancer families [families ,24. Funcfamilies .CDH1 (E-Cadherin 1): Association of lobular breast cancer (LBC) is associated with germline pathogenic variants in CDH1, normally associated with hereditary diffuse gastric cancer (HDGC) and non-HDGC families. The product of this gene is a calcium-dependent cell adhesion protein and a key player in epithelial cell–cell interactions, affecting cell mobility and proliferation [feration ,26,27.TP53: Germline pathogenic variants in this gene lead to the Li-Fraumeni syndrome, which is associated with the risk of developing a myriad of cancers including breast cancer. The p53 protein, commonly referred to as the guardian of the genome, is involved in a variety of DDR mechanisms via regulating cell-cycle arrest and apoptosis [poptosis ,29.PTEN (phosphatase and tensin homolog): Germline risk variants in PTEN are observed in Cowden disease, which is characterized by a high risk of breast cancer. It is a multifactorial tumor suppressor gene with protein phosphatase activity that is involved in the PI3K/AKT-mTOR signaling pathway that controls cell cycle [ll cycle ,31.BRIP1 [RAD51C, RAD50 [NBN [STK11 [RECQL [Other genes: Pathogenic variants in BRIP1 , RAD51C,C, RAD50 , NBN [34N [STK11 , and REC1 [RECQL  may also® (NCCN®) is an authority in publishing guidelines regarding the clinical management of breast cancer [The clinical importance of identifying additional breast cancer susceptibility genes is reflected in their use in multi-gene panel testing for the identification of individuals at risk of developing breast cancer ,38. The t cancer , followit cancer , and vart cancer ,41,42,43ATM, BRCA1, BRCA2, CHEK2, and PALB2 as the most prominent genes for breast cancer, harboring protein-truncating variants that confer significant disease risks (odds ratio of 2.10 to 10.57) to overall breast cancer risk. BARD1, RAD51C, RAD51D, PTEN, NF1, TP53, and MSH6 also confer elevated but more moderate risks (odds ratio of 1.76 to 3.06). Missense variants, although more functionally unclear, in CHEK2, ATM, TP53, BRCA1, and BRCA2 also showed evidence of association with overall breast cancer risk (odds ratio of 1.06 to 1.42) [BRCA1 and BRCA2, which were highly associated with breast cancer risk , pathogenic variants in PALB2 and CHEK2 were associated with a moderate risks of breast cancer  [NBN, BRIP1, RECQL, FANCC, FANCM, MRE11, MSH2, RAD50, RINT1, STK11, and XRCC2.Another confounding factor in accurate risk estimation may be due to the overall design of genetic studies that typically choose their patient population based on selective criteria, e.g., age at onset or limited sample size ,45,46. Tto 1.42) . Similarctively) . SeveralCombined, pathogenic variants in the known high- and moderate-penetrance genes account for ~25% of the heritability of breast cancers . The misCASP8 gene on chromosome 10p14 risk locus [Candidate gene-association studies: This approach focuses on the pathways or genes with a potential known biology in disease development. However, they are not agnostic and for breast cancer have largely been unsuccessful, perhaps with the exception of identifying a nonsynonymous risk associated variant in the coding region of the sk locus .FGFR2), 16q12 (TNRC9), 5q11 (MAP3K1), 11p15 (LSP1), as well as an intergenic risk locus (8q24) [Multistage Genome Wide Association Studies (GWAS): This methodology uses a subset of samples to identify the most associated SNVs (referred to as the tag SNVs), followed by analyzing the tag SNVs in a larger set of samples . The firs (8q24) . Furthers (8q24) , 6q25 [5s (8q24) , 5p12 [5s (8q24) , and 2q3s (8q24)  risk loc2 > 0.3). The imputation analysis increased the number of SNVs under investigation to approximately 11 million. Using this large-scale study, Michailidou et al. were able to identify 65 novel breast cancer risk loci, with a significant number mapping to the non-coding DNA regions, which overlapped regulatory elements and often enriched for transcription factor binding sites (TFBSs) [Large-scale GWAS studies and meta-analyses of multiple GWAS: In 2013, the Collaborative Oncological Gene–environment Study (COGS) used a custom array (iCOGS) and tested around 200,000 SNVs in each individual from the BCAC study. This included 45,290 and 41,880 cases and controls of European ancestry, respectively. This study not only confirmed the association between 23/27 previously identified risk loci, but also identified 41 additional novel loci associated with breast cancer risk . A furth (TFBSs) . As a re (TFBSs) , Figure  (TFBSs) .The identification of hundreds of common risk alleles for breast cancer can be used to improve the accuracy of the genetic risk prediction and population-based risk stratification. One main challenge is that the vast majority of individual common risk alleles for breast cancer are associated with very small disease risk contribution (OR < 1.5) . HoweverAnother utility of GWAS risk variants is the clinical opportunities that can arise from understanding of the novel biological mechanisms of breast cancer initiation and development, driven by germline GWAS risk variants. Innovative therapies such as poly (ADP-ribose) polymerase (PARP) inhibitor , or careBreast cancer is a heterogeneous disease which can be stratified into different clinical subtypes based on their biomarker expression . The different subtypes are associated with various therapeutic regimens and prognoses, which likely reflects differences in their underlying biology. Briefly, breast cancers are mainly divided into: (1) luminal A ; (2) luminal B ; (3) (HER2)-enriched; and (4) basal-like triple negative breast cancer (TNBC) , with the latter being the most aggressive form . ExpressBRCA1 variants and TNBC had been shown previously [TP53 germline variants may be more frequently associated with HER2-positive breast cancers than other subtypes [ATM and CHEK2 showed a stronger association with ER-positive breast cancer (odds ratio of 2.33 and 2.67). In the case of ER-negative breast cancer, stronger associations were observed for BARD1, BRCA1, BRCA2, PALB2, RAD51C, and RAD51D. BARD1, BRCA1, and BRCA2 also showed a stronger association with TNBC compared with other ER-negative breast cancers [CHEK2 and CDH1 are associated with ER-positive breast cancer, whereas BRCA1 is associated with ER-negative breast cancer [BARD1, RAD51C, and RAD51D with increased risks of ER-negative breast cancer and TNBC, whereas pathogenic variants in ATM, CDH1, and CHEK2 were associated with an increased risk of ER-positive breast cancer [Targeted sequencing analyses to identify pathogenic variants in both high- and moderate-penetrance breast cancer susceptibility genes have recently provided insights into the different genes and their breast cancer subtype associations. A correlation between eviously ,75, whersubtypes ,77. The  cancers . Notablyt cancer . On the t cancer . The subPEX14, 2q24.1, 2q31.1, ADAM29, EBF1, TCF7L2, 11q13.1, 11q24.3, 12p13.1, PTHLH, NTN4, 12q24, BRCA2, RAD51L1-rs2588809, and MKL1. Of these, PTHLH reached genome-wide significance [Common risk alleles identified by breast cancer GWAS also appear to confer subtype-specific risks, mainly by ER-status, suggesting the involvement of differential biological mechanisms in disease etiology. Purrington et al. performed a GWAS study on stage I TNBC cases, and identified breast cancer risk loci associated with ER-negative breast cancer . An addiificance . The difificance .While the majority of the GWAS-identified risk variants are associated with ER-positive breast cancer, a significant proportion of them are also associated with ER-negative disease. However, a subset of these risk variants are exclusively associated with ER-negative breast cancer. In this regard, Milne et al. recently investigated ER-negative breast cancer and identified 10 novel risk variants associated specifically with the ER-negative subtype. An ER-negative association was also detected for the previously identified GWAS risk variants, bringing the total number to 125 .CCND1, CHEK2, FGFR2), 29 (15%) with ER-negative disease , while the remainder (51%) were associated with similar risks for both ER-positive and ER-negative breast cancer development [More specifically, fine-mapping of all these breast cancer risk loci identified by GWAS found that of 196 strong risk signals, 66 (34%) had a higher risk for ER-positive breast cancer /Association for Molecular Pathology (AMP) has developed practice guidelines . ACMG/AMTP53 , PTEN [853 [PTEN , and CDHEN [CDH1 . This cuBRCA1 [Frameshift indels and nonsense variants are categorized as deleterious, because they introduce premature termination codons—for example the p.Tyr1853* (* represents a stop codon) variant in BRCA1 . While tBRCA1 . They deBRCA1 . In a laBRCA1 ,93.BRCA1 and BRCA2, respectively, reported in the ClinVar database [BRCA2, which leads to skipping of exons 5 and 6, due to the generation of a splicing site. Based on the functional results of this study, this variant was included in genetic testing [There still remain challenges in establishing the function of VUSs, which may be of clinical significance given they occur in highly penetrant susceptibility genes, and if established as truly pathogenic, would provide opportunities for clinical interventions such as risk-reducing surgery. In this regard, as of June 2021 there were 3083 and 5483 VUSs for database . An exam testing . HoweverBRCA1 known as the RING (exon2–5) and BRCT (exon15–23). This study used a functional scoring system to measure the deleterious effect of SNVs over time in a haploid cell line (HAP1), representative of the functional effect of the SNV on BRCA1 function. In this system, synonymous SNVs generate a score of 0, and variants with LOF effects produce a negative score . They were able to categorize SNVs into three main functional classes. Their results indicated that 72.5% of the variants were functional, 21.1% were non-functional, while 6.4% had intermediate function [In an effort to circumvent this issue, Findlay et al. used high-throughput saturation genome editing (SGE), a CRISPR/Cas9-based approach, to study the functional effect of potential SNVs (~4000) in the genomic regions encoding two functional domains of function . This obThe challenges are different for common low-risk alleles identified by GWAS. Although a large number of risk variants have been identified for breast cancer ,80, moreGWAS studies are usually based on genotype data from SNP-arrays and generally only identify risk variants that tag multiple other risk-associated variants in the region due to linkage disequilibrium. While a small number of variants at a locus may represent the most statistically significant risk allele(s) in reality, other statistically weaker variants in LD may be the causal disease drivers . Thus, tZNF404 and ANKLE1 genes in breast tissue [Non-coding risk variants may reside in the REs in a risk region including gene promoters, enhancers, silencers, and insulators, where they perturb TFBSs or local chromatin structure, ultimately resulting in changes of transcriptional output of one or more target gene(s) ,81,105. t tissue .ABHD8 at 19p13 [AKAP12/ESR1 at 6q25.1 [MYC at 8q24 [Relationships between inherited variation, expression, and trait can be investigated through approaches such as local expression quantitative trait locus (eQTL) analysis , PrediXcat 19p13 , AKAP12/t 6q25.1 , and MYC at 8q24 . Fachel  at 8q24 . More sp at 8q24 .cis or trans [Common variants that influence the activity of specific REs such as enhancers may affect target gene expression through a direct, physical interaction, acting in or trans ,119. Theor trans . Where eor trans . In the or trans . At a geor trans  and HiChor trans , can be or trans , ER [126or trans  on chromCREBBP, EP300, ESR1, GATA3, and MYC. Pathway analysis in ER-positive GWAS identified potential target genes with biological roles in mammary development (23%), immune system pathways (18%), and nuclear receptor pathways (17%). Interestingly, 19% of the ER-negative target genes belonged to the DNA integrity checkpoint processes and the immune system, and 16% were involved in apoptotic processes. These data also suggest underlying mechanistic differences or differential pathways specific to each breast cancer subtype [Fachel et al. used a prioritization approach for potential target gene identification, using multiple lines of evidence as discussed above including eQTL, functional annotation, and interaction-based data. In total, the authors were able to prioritize 191 potential target genes for 150 breast cancer risk regions, mostly breast cancer associated transcription factors and somatic driver genes including  subtype .Although using annotation and predictive tools will help to prioritize the list of the risk variants and target genes, the most likely causal risk variants and target genes need to be validated experimentally. Genome editing (GE) technologies can be used to assess the consequence of risk SNVs that lie in the non-coding genome, and to introduce protein coding changes that abrogate the function of potential target genes followed by testing the effect of gene editing on neoplastic development . VariousBRCA1 and BRCA2 pathogenic variant carriers where knowledge of the function of these genes led to the development of poly (ADP-ribose) polymerase (PARP) inhibition as a novel therapy for the treatment of breast cancers [A timely and refined breast cancer risk assessment on a population scale is challenging and relies on accurate risk estimates based on the individualized genetic profiles. An important step to achieve this seems to be subtype-specific risk stratification of individuals undergoing molecular genetic testing. Risk stratification will not only improve the accuracy of early detection and screening, but also allow targeted risk management and clinical care programs. Adding subtype-specificity in risk discrimination could benefit those people in need of screening and/or surveillance,  cancers . We envi