Are reproductive factors and endometrial cancer risk associated among individuals in East Asia?In this pooled cohort study of 332 625 women including 1005 endometrial cancer cases from 13 Asian cohort studies, late menarche, early menopause, and a greater number of deliveries were associated with a lower risk of endometrial cancer.The findings from this large pooled analysis in Asia, which are consistent with previous evidence, may have an impact on the understanding of risk factors for endometrial cancer. This cohort study assesses the association of reproductive factors, such as number of deliveries, age at menarche, and age at menopause, with endometrial cancer risk among East Asian women. Despite evidence of an association between reproductive factors and endometrial cancer risk, prospective studies have been conducted mainly in non-Asian countries.To assess the association between reproductive factors, such as number of deliveries, age at menarche, or menopause, and endometrial cancer risk.This cohort study used pooled individual data from 13 prospective cohort studies conducted between 1963 and 2014 in the Asia Cohort Consortium. Participants were Asian women. Data analysis was conducted from September 2019 to April 2023.Reproductive factors were assessed using a questionnaire in each cohort.The main outcome was time to incidence of endometrial cancer. A Cox proportional hazards model was used to calculate hazard ratios (HRs) and 95% CIs.P for trend < .001). Compared with menarche at younger than 13 years, menarche at 17 years or older had an HR of 0.64 . Late menopause (age ≥55 years) showed an HR of 2.84  compared with the youngest age category for menopause (<45 years). Age at first delivery, hormone therapy, and breastfeeding were not associated with endometrial cancer risk.A total of 1005 endometrial cancer cases were detected among 332 625 women  during a mean (SD) of 16.5 (6.4) years of follow-up. Increasing number of deliveries was associated with a decreased endometrial cancer risk in a dose-response manner (≥5 deliveries vs nulliparous [reference]: HR, 0.37; 95% CI, 0.26-0.53; This large pooled study of individual participant data found that late menarche, early menopause, and a higher number of deliveries were significantly associated with a lower risk of endometrial cancer. These convincing results from Asian prospective studies add to the growing body of evidence for the association between reproductive factors and endometrial cancer. According to the Surveillance, Epidemiology, and End Results Program in the United States, the incidence rate was 28.0 among non-Hispanic White people and 22.6 among Asian and Pacific Islander people.4 Although previous studies have prospectively investigated reproductive factors and the risk of endometrial cancer in the United States and Europe,7 limited studies in this regard have emanated from Asian countries.8Endometrial cancer is the sixth most common cancer among women worldwide and accounted for 2% of new cancer cases and 1% of deaths in 2020.10 Reproductive factors, such as parity, age at menarche, and menopause, are potentially associated with differences in unopposed estrogen exposure among individuals and might lead to different endometrial cancer risks. Prospective studies have examined these associations,7 and meta-analyses including case-control and prospective studies have been published.15 According to an umbrella review, which included 171 meta-analyses of 1354 individual studies of 53 risk factors, parity, higher age at last birth, and higher age at menarche were associated with a decreased risk of endometrial cancer.16 Moreover, many previous studies have shown that multiparity, higher parity, and early menopause are associated with a decreased risk of endometrial cancer.15 However, the association of some factors, such as age at the first pregnancy or breastfeeding, with endometrial cancer risk has been controversial in prospective studies.14The endometrium modifies its structure in response to the menstrual cycle, with fluctuations in estrogen and progesterone levels. It has been hypothesized that unopposed estrogen exposure leads to endometrial hyperplasia, which increases the risk of endometrial cancer.3 Therefore, in this study, we pooled 13 Asian prospective cohort studies in the Asia Cohort Consortium (ACC), aiming to investigate the association between reproductive factors and endometrial cancer with a large sample size.In Asian countries, investigating factors associated with endometrial cancer in a single prospective cohort is likely to be difficult because the incidence rate of endometrial cancer is relatively low.19 Thirteen cohort studies were included in the endometrial cancer project, among which 8 were conducted in Japan,27 3 were conducted in Korea,30 1 in China,31 and 1 in Singapore.32 These cohort studies provided information on reproductive factors, the incidence of endometrial cancer, follow-up duration, and potential confounders, such as age, current smoking status, and alcohol drinking status at the baseline. We excluded participants with the following characteristics: male sex (n = 241 676); missing data regarding sex and age (n = 2422); history of endometrial cancer at the baseline survey (n = 1039); follow-up duration of 0 or less or missing (n = 1858); history of hysterectomy at baseline (n = 10 004); body mass index  of less than 14 or greater than 45 (n = 7571); and missing data on parity (n = 15 884). A total of 332 625 participants were included in this study.ACC is a consortium of prospective studies conducted in Asia. The details of the ACC have been described elsewhere.STROBE) reporting guideline.The institutional review board of the National Cancer Center Japan approved the protocol for the analysis in the ACC. Each cohort study protocol, including that pertaining to informed consent, was approved by the respective institutional ethics committees. Reporting of this study followed the Strengthening the Reporting of Observational Studies in Epidemiology , the number of deliveries , age at first delivery , age at menarche , menopausal status , age at menopause , breastfeeding status (never and ever), and hormone therapy (HT) status (never and ever) were asked in the baseline questionnaire of each prospective study. A limited number of cohorts included questions pertaining to breastfeeding and HT status. Since oral contraceptive (OC) use (never or ever) was asked in 5 cohorts, we did not assess the association of OC use with the risk of endometrial cancer. Also, since the number of deliveries was not assessed in the Life Span Study (LSS), results regarding the number of deliveries were not included. In addition, the Japan Collaborative Cohort Study (JACC) and LSS included questions about age at first pregnancy (not delivery).International Classification of Diseases for Oncology, Third Edition, codes for endometrial cancer are C54 and C55.9. First primary cancer was included in this study. Information on whether the endometrial cancer was primary, secondary, or more was collected in each cohort and submitted to the study coordinating center. Because information regarding morphological and histological codes was submitted for a limited number of cohorts, topological codes were used for the definition. Age (continuous), BMI (continuous), current smoking status (yes and no), and current alcohol consumption status at the baseline (yes and no) were considered as possible confounding variables in the model.The incidence of endometrial cancer was determined in each prospective study via linkage to local cancer registries. The 33 We created 2 statistical models: age-adjusted and multivariable-adjusted. The age-adjusted model incorporated adjustment for age at the baseline, while the multivariable-adjusted model incorporated adjustment for age at the baseline, current smoking status, current drinking status, and other reproductive factors . Regarding missing values for menopausal status, we defined postmenopausal when participants’ menopausal status was missing and their age was 54 years or older and those aged 44 years or younger were defined as premenopausal because the median age at menopause is approximately 50 years among Japanese women, and these statuses were plausible.34 On the other hand, the menopausal status of participants with age 45 to 53 years was left as a missing at the time of this variable cleaning. Next, 5 rounds of multiple imputation were performed on the pooled individual data for missing adjusted variables. Reproductive factors, age at the baseline, BMI, follow-up duration, and diagnosis of endometrial cancer were included in the multiple imputation procedure in SAS, and the Markov chain Monte Carlo method was used. The 5 calculated estimates were combined into the final estimates according to Rubin rule using the SAS MIANALYZE procedure. To calculate the P for trend, we used the mean value of each category when there was a range of values in that category. For example, for age 45 to 49 years in the second category of age at menopause, we applied 47 to calculate P for trend. We performed stratified analyses classified by BMI (greater or less than the median), menopausal status, and parity. In addition, because nulliparous women could not be distinguished from women who did not report age at first pregnancy in LSS, we further examined associations excluding LSS to confirm the consistency of the results. All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc), and P < .05 indicated statistical significance.This analysis was conducted from September 2019 to April 2023. In the present analysis, we examined the association between reproductive factors and endometrial cancer incidence using hazard ratios (HRs) and 95% CIs in the Cox proportional hazards model. The shared frailty model was applied to pooled individual data, and we used the variable of a cohort in the RANDOM statement of the PHREG procedure in SAS software.Among 332 625 women  from 13 cohort studies, a total of 1005 endometrial cancer cases were observed. The baseline characteristics of the cohort studies are presented in P for trend < .001). Among parous women, when the lowest category of number of deliveries (1-2) was set as a reference, the HRs for the highest category (≥5 deliveries) was 0.57  (P for trend = .003). The results adjusted for age as a categorical variable were similar and are shown in eTable 1 in The results pertaining to associations between reproductive factors and the risk of endometrial cancer are shown in P for trend = .004) and that of menopause (≥55 years) was 2.84 . Age at first delivery, HT use, and breastfeeding were not associated with endometrial cancer risk, although point estimates of the HR for HT use and ever breastfeeding were less than 1.0. We examined the influence of one study by excluding each study, but the results did not change largely  and menopause (<45 years) were defined as references, the HR of the highest age at menarche (≥17 years) was 0.64  of endometrial cancer, particularly in North America  but not in Asia .35 Consistent with these results, we did not find a significant association between breastfeeding and endometrial cancer. Breastfeeding, which suppresses ovulation, may reduce the risk of endometrial cancer. Although our pooled results can be regarded as including the maximum number of cases possible from Asian prospective studies, further studies including information on the duration of breastfeeding and a larger number of cancer cases might be required to establish evidence for the association between breastfeeding and endometrial cancer.The results regarding multiparity, age at menarche, and age at menopause in this study were consistent with previously published evidence. In an umbrella review,36 a different category of menarche age was used in Asian studies. We included cohorts in which data pertaining to age were acquired categorically, with the lowest and highest categories being the ages of 13 years or younger and 17 years or older, respectively, whereas studies conducted in Europe and the United States applied the lowest and highest age categories of 11 years and 14 or 15 years, respectively.11 On the other hand, the category of age at menopause was similar, although natural menopause has been shown to occur at a later age among Japanese Americans than in other ethnic groups.37 Some Western studies applied the lowest age category of 50 years38; however, an age category of 45 years or younger was used overall, regardless of the country.37 Although the categories varied slightly among studies and countries, the associations between age at menarche and menopause and endometrial cancer were consistent.Given that the age at menarche is likely to be higher in Asian populations than in European and US populations,39 The consistent results from Asian countries with lower BMI  are worth reporting to broaden the evidence. In a dose-response meta-analysis of BMI,40 the risk of endometrial cancer increased more obviously when BMI was 25 or greater. In our analysis, the P value for the interaction between the higher and lower BMI groups was not significant, and the trend of age at menopause in the low BMI group was significant . While some reproductive factors, such as the number of deliveries, did not show significant associations in the low-BMI group, it is possible that protective effects of these factors may occur in higher-BMI groups. However, the limited number of cases in the low-BMI group indicates the need for further studies with larger numbers of participants from lower-BMI populations. The presence of unopposed estrogen underlies the association between selected reproductive factors and the risk of endometrial cancer.41 Excess estrogen causes endometrial cell proliferation and is a risk factor for endometrial cancer.42 Parity, menarche, and menopause cause changes in estrogen and progesterone levels. For example, parous women have lower estrogen levels than nulliparous women.43 In line with this, higher age at last birth has been proposed to be associated with a lower risk of endometrial cancer because decreased estrogen levels accompanying pregnancy among women approaching menopause may have a protective effect against cancer. Moreover, OC use might reduce the cancer risk, and HT  is a potential risk factor for endometrial cancer.45 However, in the present study, age at last birth, OC use, and type of HT could not be investigated as exposure or confounding factors because the age at last birth and type of HT were not included in the questionnaires, and OC and HT use were uncommon in Asian countries, particularly in the 1990s. According to data from the United Nations in 2019, the estimated prevalence rates of OC use were 2.9% in Japan and 3.3% in Korea, compared with 15.1% in North America and 19.1% in European countries.46 Regarding HT use, the total prevalence reported in surveys of nurses in Japan was 13.8%, which was lower than that in non-Asian countries.47 Although most of the participating cohorts did not include information on the type of HT, a Korean study48 found that the proportion of HT use among women older than 40 years was 7.8%, and the rate of estrogen therapy was 3 times higher than that of estrogen-plus-progesterone therapy in 2002.48 In the future, such a study including HT type may be required because the lifestyle in Asian countries may be westernized.Moreover, age at menarche and menopause are influenced by BMI.This study has strengths. To our knowledge, it is the largest pooled analysis of Asian prospective studies with a large number of cases and long follow-up durations. Although generalizability of results was limited to Asians, individual data from multiple sites in Asia were collected and analyzed.49 further studies including endometrial cancer types are essential. Moreover, we did not collect information on the history of cancer other than endometrial cancer in this study, and participants with a history of cancer other than endometrial cancer were not excluded from analyses. Third, confounding factors other than age, country, smoking status, and drinking status were not included in the model. Physical activity, family history of endometrial cancer, and education level might be important confounding factors but could not be adjusted for in this study due to lack of data. Although further studies with full adjustment are ideal, we observed an HR trend that is consistent with the existing evidence, and these confounding factors may not have changed the results. Fourth, because we collected the data on reproductive factors at baseline, misclassification may have occurred, especially for factors such as menopausal status that change with time. Harmonization of time-varying data should be considered in future studies.However, our study also has several limitations. First, each cohort used a different questionnaire, and questions regarding reproductive factors differed slightly among the participating cohorts. Although we checked each questionnaire, and data cleaning was performed by a working group, JACC and LSS included questions about the age at first pregnancy, and categorical variables were applied to include all possible cohorts. Second, not all cohorts were asked about all reproductive factors investigated in this study, such as a history of hysterectomy, duration of breastfeeding, type of HT, and number of deliveries in LSS. Histological cancer types or types I and II classifications were not considered in this study because morphological information was obtained in 5 cohorts. As obesity and reproductive factors might be influenced more by type II tumors,In conclusion, among 1005 endometrial cancer cases recorded in 13 Asian cohort studies comprising 332 625 women, late menarche, early menopause, and a higher number of deliveries were significantly associated with a lower risk of endometrial cancer. These results are consistent with previous evidence emanating mainly from non-Asian countries. Breastfeeding was not significantly associated with endometrial cancer in this study. Since Asian countries are characterized by a low incidence of endometrial cancer, this study contributes to expanding the generalizability of evidence on the risk and protective factors for endometrial cancer.


We sought to investigate how penetrance of familial cancer syndromes varies with family history using a population-based cohort.We analysed 454,712 UK Biobank participants with exome sequence and clinical data (data collected between March 2006 and June 2021). We identified participants with a self-reported family history of breast or colorectal cancer and a pathogenic/likely pathogenic variant in the major genes responsible for hereditary breast cancer or Lynch syndrome. We calculated survival to cancer diagnosis .BRCA1 or BRCA2 variant had an increased risk of breast cancer that was higher in those with a first-degree family history  than those without (7.2 and 4.7). Penetrance to age 60 was also higher in those with a family history  versus those without . A similar pattern was seen in Lynch syndrome: individuals with a pathogenic MLH1, MSH2 or MSH6 variant had an increased risk of colorectal cancer that was significantly higher in those with a family history  than those without . Penetrance to age 60 was also higher for carriers of a pathogenic MLH1 or MSH2 variant in those with a family history  versus those without (20.5% CI 9.6–40.5 and 8.3% CI 2.1–30.4), but not for MSH6 . Relative risk increases were also observed both within and across conditions.Women with a pathogenic Individuals with pathogenic cancer syndrome variants may be at a less elevated risk of cancer in the absence of a first-degree family history, so in the context of results return, family history should be considered when counselling patients on the risks and benefits of potential follow-up care.10.13039/501100000265MRC (grant no MR/T00200X/1). The MRC had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.The current work is supported by the  At the initiation of the study (1st June 2021), we searched the PubMed database using the search terms [(BRCA1 OR BRCA2 OR MLH1 OR MSH2 OR MSH6) AND ] and identified a number of relevant studies. The literature concerning pathogenic variants for cancer predisposition in these studies is currently focussed around penetrance estimates in clinically ascertained cohorts and the effects of family history on this. Where population cohort data does exist, family history information is often not available or not interrogated to inform penetrance estimates.This study shows for the first time, using data from a large population cohort, that much of the risk conferred by a rare pathogenic variant associated with hereditary breast and ovarian cancer or Lynch syndrome is conferred by a first-degree family history of disease. This difference in penetrance in carrier individuals could be sufficient to impact on whether an individual might be eligible for specialist clinical care  guidance on familial breast cancer requires ≥30% lifetime risk to be eligible for referral to a specialist genetic clinic in the UK).It is imperative that individuals who receive genotype information indicating a predisposition to cancer are appropriately counselled as to their individual risk profile in the context of their family history of disease. For those ascertained outside of the standard clinical pathway, this will help to provide patients with more accurate information to allow them to make informed decisions about prophylactic options.,BRCA1 and BRCA2 testing is offered to individuals with breast or ovarian cancer and/or a known history of hereditary breast and ovarian cancer (HBOC),,Genetic testing for inherited cancer syndromes is offered to affected individuals based on various qualifying criteria.Traditionally, clinical genetics has used a phenotype-first approach to identify the most likely cases with an underlying genetic pathology.Increasingly, a genotype-first approach is being used to identify individuals with pathogenic variants.Here we use exome sequencing data and clinical records from 454,712 individuals from UK Biobank to estimate the population penetrance of pathogenic genetic variants for breast and colorectal cancer from two cancer-predisposition syndromes  and investigate the effect of first-degree relative (FDR) history of these cancers on these estimates.We used data from UK Biobank.The UK Biobank resource was approved by the UK Biobank Research Ethics Committee and all participants provided written informed consent to participate.−15, minimum read depth of 7 for SNVs and 10 for indels, and at least one sample per site passed the allele balance threshold >15% for SNVs and 20% for indels.Detailed sequencing methodology for UK Biobank samples is provided by Szustakowski et al.,BRCA1 (ENST00000357654) and BRCA2 (ENST00000380152) genes (hereafter collectively referred to as BRCA variants) and for Lynch syndrome in the MLH1 (ENST00000231790), MSH2 (ENST00000233146) and MSH6 (ENST00000234420) mismatch repair genes. We excluded PMS2 from our analysis due to the difficulty in variant calling caused by highly homologous sequences and pseudogene sequence exchange. We also did not include large deletions in EPCAM, a non-mismatch repair gene which is known to cause hypermethylation of MSH2. As described previously,Variants were considered in clinically relevant MANE select transcripts for HBOC in the Cancer registry data for breast and colorectal cancer was collected for all UK Biobank participants with exome sequencing data. Although both BRCA and Lynch syndrome variants are linked to multiple other cancers, family history information was only available for breast and bowel cancer and so the analysis was limited to these cancer types. ICD-9 and ICD-10 codes were used to identify individuals with breast cancer ; ICD-10 codes C50 ) and colorectal cancer , 1540 and 1541; ICD-10 codes C18, C19 and C20 ). The age at diagnosis was also extracted from the registry for these individuals.UK Biobank participants were asked about 12 specific illnesses within their family as part of the enrolment process. We used the fields 20,107 (illnesses of father), 20,110 (illnesses of mother) and 20,111 (illnesses of siblings) to create a new variable of positive first-degree family history for breast and bowel cancer. Bowel cancer was specifically the term used to ask participants about their family history so has been used here but it should be noted this may have yielded different results if the question had asked about CRC. There was no information recorded on illnesses of children or second-degree family history of these conditions, nor the age at which family members were diagnosed. We did not differentiate between whether an individual had one or multiple affected family members, we simply considered a binary FH yes/no. Genetic risk scores were also generated for breast and colorectal cancer to examine their influence on family history effects .All data analysis and statistical testing was performed in Stata (Version 16.1). Kaplan–Meier survival analysis was carried out to assess the relationship between individuals with a pathogenic BRCA or Lynch syndrome variant and first-degree family history. BRCA analysis was restricted to women  in field n3100 (sex) and whose genetic sex did not conflict with their reported answer to this question). A Cox linear regression model was built using mastectomy in the absence of cancer (to control for prophylactic treatment biasing outcome in BRCA carriers), breast or bowel screening, death, recruitment centre and sex  with time set as the age of participants. We did not remove individuals who had mastectomy from our analysis but addressed this by including it in the model as described above. Removing individuals with mastectomy could introduce a bias and risks losing individuals who had a cancer-related mastectomy. Sub-group analyses were also performed on those groups stratified by positive or negative first-degree family history. The resultant model was used to predict survival functions.https://www.stata.com/manuals13/stststest.pdf). Incidence-rate ratios were also calculated using person-time derived from the survival model, starting at birth and ending at age of participant at the last cancer registry update.Kaplan–Meier curves were generated using failure as the age when individuals were diagnosed with breast or colorectal cancer and time as the age of the participants. As we have cancer registry data going back prior to enrolment in the study, age at risk started from birth and ended at the age of participants at the final cancer registry data point we used for analysis. There was no additional censoring. Cox proportional hazard tests for equivalence of survival functions were used to interrogate inter-group differences. This test is a variation on the log-rank test and uses a COX proportional hazards model on indicator variables for each group and reports relative hazards (relative to a reference group that excludes each defining characteristic of that group) which are the exponentiated coefficients from the COX model, renormalised . The MRC had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.BRCA1 (BRCA1+) and 611 in BRCA2 (BRCA2+). Carriers were further categorised into those who had a FDR with breast cancer  and those who did not .We identified 230 women with a pathogenic variant in BRCA1  and BRCA2  (2 = 11.7 p < 0.001) to age 60 in BRCA1+/FH + women (44.7% 95% CI 32.2–59.3) compared to BRCA1+/FH- women (22.8% 95% CI 15.9–32.0). The predicted penetrance to age 60 in BRCA2+/FH + women was 24.1% (95% CI 17.5–32.6) versus 17.9% (95% CI 13.8–23.0) in BRCA2+/FH- women, though this difference was not statistically significant  and the Cox regression-based test for equality of survival curves demonstrated a significant difference between variant carriers with and without family history for both without) . The surnificant . Incidennificant .Fig. 1SuMLH1 (MLH1+), 71 in MSH2 (MSH2+) and 421 in MSH6 (MSH6+). Carriers were further categorised into those who had a FDR with bowel cancer  and those who did not .We identified 89 individuals with a pathogenic variant in MLH1 , MSH2  and MSH6   compared to MLH1+/FH- individuals (20.5% 95% CI 9.6–40.5). For MSH2+ individuals, the penetrance to age 60 was 38.3% (95% CI 21.5–61.8) for FH+ and 8.3% (95% CI 2.1–30.4) for FH-; for MSH6+ individuals, the penetrance to age 60 was 6.5% (95% CI 2.7–15.1) for FH+ and 8.3% (95% CI 5.1–13.2) for FH-  but not MSH2+/FH+  or MSH6+/FH + individuals  compared to FH-.Kaplan–Meier curves were generated for the different groups and the Cox regression-based test for equality of survival curves demonstrated a significant difference between variant carriers with and without family history for without) . The sur for FH- , trendinBRCA1/BRCA2) and colorectal cancer (MLH1/MSH2/MSH6) were also significant, with relative risks of family history of 1.72 (95% CI 1.34–2.20) and 1.93 (95% CI 1.13–3.27) respectively.Relative risk values were generated for all five genes and combined analysis performed across both syndromes for all pathogenic variant carriers, giving an overall increased risk of cancer in those with a family history of 1.76 (95% CI 1.40–2.20) versus those without . SubgrouBRCA1/2 variant are 1.5/1.9-times more likely to get breast cancer if they also have a first-degree family history of breast cancer, whilst individuals with a MLH1/MSH2/MSH6 variant are 2.3/2.5/1.4-times more likely to get colorectal cancer if they also have a first-degree family history of bowel cancer. Carriers with a family history are also more likely to develop cancer earlier versus those without. These risk increases are consistent with those previously observed in clinically ascertained cohortsMLH1/MSH2 carriers and age 35 for MSH6 carriers).Using data from a large population cohort, we have shown that much of the risk conferred by a rare pathogenic variant associated with HBOC or Lynch syndrome is conferred by a FDR family history of disease. In UK Biobank, women with a BRCA1/2 mutations was previously estimated in 49,960 individuals in UK Biobank, but the analysis did not evaluate family history.BRCA1/2 variant carriers was previously shown to be significantly different between those with and without a family history , but the study was limited to just three variants that are relatively common in the Ashkenazi Jewish population of Israel.Our results are consistent with comparable population studies of cancer susceptibility, but to the best of our knowledge, our study is the first to investigate the effect of family history alone in a clinically unselected population across multiple syndromes. The penetrance of Our findings are particularly important when considering reporting of secondary findings in individuals who are undergoing genomic sequencing for another indication. All five genes investigated here are included on both the ACMG Secondary Findings listWhilst existing family-based cohorts suffer from an ascertainment bias that is likely to overinflate penetrance estimates, the older cohort in the UK Biobank is likely to be confounded by survival bias,The true penetrance for an unselected population is likely to be somewhere between the figures generated from each context. Limiting our analyses to just breast and colorectal cancer (to match the available family history information) will also act to underestimate the true penetrance of these variants in causing any cancer. The UK Biobank is also not a representative population cohort, due to recognised recruitment biases,Additionally, a full family history was not recorded for participants in UK Biobank, so we relied upon self-reported illness in FDR as a proxy for family history and were only able to include breast and bowel cancers. Given the age of the UK Biobank cohort (recruited from 40 to 69 years old), these recollections could be biased towards more aggressive or early-onset disease, particularly in parents. The lack of second-degree family history information or that concerning children will also mean our cohort differences will be underestimated due to the presence of individuals with a family history in the FH negative group. We were unable to consider age of diagnosis of family members as this data was not present, nor were we powered to consider number of affected family members due to the small numbers in some sub-groups. Larger studies will be able to assess the impact of this on refining the penetrance estimates.Survival analysis, whilst a common and appropriate method for analysing this type of data is restricted by a number of assumptions. The Cox model assumes proportional hazards over time and linearity, which may not hold true for inherited cancer cohorts and may impact the estimates derived. The varying age of participants in UKBB also means that censoring on the right hand side of the survival curves results in incomplete data. This is partly why we decided to use age 60 as a penetrance cut-off due to more complete data at this timepoint.Finally, due to the rarity of individual pathogenic variants, we were limited by the size of the existing cohort. The small numbers of MLH1 and MSH2 variant carriers in particular may limit the actionabaility of our findings when considering guideline revisions. We have attempted to mitigate these issues slightly by including data from all eligible participants in UK Biobank , regardless of ethnicity or consanguinity. We are underpowered to detect differences in penetrance between individual variants, which will also be important for risk discussions with patients. Future research with larger cohorts is needed further improve risk prediction and investigate modifiers.,The findings of this study suggest that any universal policy of returning pathogenic cancer predisposing genetic variants found incidentally or through direct-to-consumer genetic testing of asymptomatic individuals should consider family history and other factors when counselling patients on the risks and benefits of follow-up care. It will be very difficult to counsel individuals as to their particular risk profile without further pedigree construction or investigations. If penetrance estimates from affected families are used, there is a danger of over-management of asymptomatic individuals with no family history of disease. These “patients-in-waiting” may be exposed to unnecessary surveillance or more invasive prophylactic procedures.,It has long been known, though is not widely appreciated, that penetrance estimates for pathogenic variants causing hereditary subtypes of common diseases are likely to be significantly inflated due to ascertainment bias.ARW, JT & KSR–data curation, methodology, software, writing–review & editing.JWH–data curation, formal analysis, investigation, validation, writing–review & editing.CFW & MNW–Conceptualization, Data curation, funding acquisition, investigation, methodology, resources, software, validation, writing–original draft & writing–review & editing.LJ–Conceptualization, Data curation, formal analysis, investigation, methodology, project administration, resources, validation, visualisation, writing–original draft & writing–review & editing.LJ, CFW & MNW accessed and verified the data.https://github.com/hdg204/Rdna-nexus. Variants classified as pathogenic and used in this study are defined in the methods and can be replicated, however a list is available on request.All data used in this study can be accessed via application to UK Biobank and approval via the data access committee. The authors are not permitted to directly share this data. The code used to perform GRS is available at The authors declare no competing interests.


The aim of this meta-analysis is to evaluate the impact of light at night (LAN) exposure on the risk of breast cancer across varying factors.We conducted a systematic search of literature up to July 15, 2023, including PubMed, Cochrane Library, and Embase databases, using keywords related to breast cancer and LAN exposure. Cohort study and case–control study literature on night light exposure and breast cancer risk were included. Statistical analyses were performed using Stata software version 17.0. To address heterogeneity among different studies, we employed a random-effects model for analysis and assessed publication bias using funnel plots and Egger’s test.2 = 31.3%, p < 0.001). In the subgroup analysis, the results of the analysis for study types  and the results for light exposure types  are presented. In the analysis conducted for continents, the highest breast cancer risk was observed in the Asian population  and in the analysis of estrogen receptor status . We also conducted an analysis on menopausal status and various lifestyles but did not find any statistically significant findings.We included 13 case–control and 8 cohort studies with 734,372 participants worldwide. In the Newcastle-Ottawa Scale (NOS) assessments, the average score was 7.43 (ranging from 5 to 9). The overall meta-analysis demonstrated a significant association between exposure to LAN and risk of breast cancer (RR = 1.12; 95% CI: 1.06–1.17; IOur study demonstrates that LAN exposure is associated with an increased risk of breast cancer, particularly in the Asian population. Among the existing hypotheses, the idea that LAN exposure leads to a decrease in melatonin is widely accepted. However, until the mechanism of this effect is clearly elucidated, it is not recommended to take melatonin supplements for breast cancer prevention without medical advice. We hope to conduct more high-quality research, especially concerning the investigation of other environmental confounding factors, to further advance this field. The incidence and mortality of breast cancer have increased among women worldwide , 2. In 2Breast cancer, as a highly heterogeneous and multifactorial disease , has beeLight pollution is an emerging environmental issue that has intensified with urbanization and industrialization. Over the past few decades, light pollution has posed an increasing threat to human health . BetweenExisting hypotheses suggest that exposure to LAN may inhibit the natural surge of melatonin during the night , 54, theA series of epidemiological studies have examined the impact of LAN exposure on breast cancer risk, considering factors such as classification of this exposure, hormone receptor status, race, menopausal status, and others. In various studies examining different types of LAN exposure, previous research has reported a positive association between outdoor LAN exposure and breast cancer risk , 47. HowIn the past 2 years, a recent meta-analysis included an analysis of BMI and reanalyzed the impact of breast cancer risk in different estrogen receptor status populations . Additio22.1The present meta-analysis was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses PRISMA;  guidelin2.2We searched the PubMed, Cochrane Library, and Embase databases up to July 15, 2023. We employed Medical Subject Headings (MeSH) and relevant keywords. We drew upon the previous methodologies of meta-analysis. , 59–64. 2.3The study selection process was carried out by two reviewers (LZN and LZL) in accordance with the predetermined inclusion and exclusion criteria. Both reviewers independently screened the literature and removed duplicate and irrelevant articles based on titles and abstracts. Subsequently, the full texts of potentially eligible articles were obtained and thoroughly reviewed to ensure the inclusion of all relevant studies. Any disagreements between the two reviewers were resolved through discussion until a consensus was reached.2.4The inclusion criteria were as follows: (a) inclusion of an independent exposure variable pertaining to LAN exposure; (b) consideration of an outcome variable related to breast cancer; (c) incorporation of case–control or cohort study designs. The exclusion criteria were as follows: (a) reviews, conference proceedings, or commentaries; (b) duplicate literature (retaining the variant with the most comprehensive information).2.5The data extraction process was conducted independently by the first reviewer (LZN), and upon its completion, cross-verification and consultation was performed with the second reviewer (LZL). A predesigned data extraction form was utilized, and the following data were extracted: study type (case–control or cohort), first author, publication year, source of cases, source of controls, number of cases, number of controls, exposure definition, breast cancer definition, classification of exposure, information collection period and scope, baseline age, and values of outcome variables (odds ratio or hazard ratio) determined based on various confounding factors. Any potential discrepancies were addressed through discussions with LZL to achieve a consensus.2.6The quality of our studies was evaluated using the Newcastle-Ottawa Scale (NOS) , which e2.72 values (The adjusted relative ratios (RRs) and 95% confidence intervals (CIs) for each observational study were used to assess the association between breast cancer incidence and LAN exposure. Heterogeneity was evaluated using I2 values  and Coch2 values . Further2 values . Based o2 values , we used2 values . The pri33.1A total of 2,964 observational studies were initially identified after excluding duplicates. After conducting title and abstract screening, 2,871 articles were subsequently excluded. An in-depth review was conducted on 93 articles, and after the further exclusion of 72 articles, 21 studies were eligible for the meta-analysis . The pri3.2The 21 studies included in this study were published between 2001 and 2023 and involved a total of 734,372 participants. Of these studies, 11 originated from North America , 52, 3 fn = 9) (n = 10) (n = 14) (n = 14)  , 50, 52,(n = 10) , 51. Add(n = 14) , 50, 51,(n = 14) , 50, 52.n = 4) of studies examined the habit of watching TV while sleeping (n = 5) looked at the use of bedroom shutters (n = 3) explored the habit of waking up and turning on lights  assessments conducted on the various studies, the average score for the included research was 7.43 (ranging from 5 to 9). A score of 7 is considered as the cut-off point for high-quality research. Studies with a score of 7 or above are regarded as of higher quality. The scores of the included studies are illustrated in 3.42 = 31.3%, p < 0.001; A comprehensive analysis of 21 studies  revealed3.52 = 0%) was observed for continents, white race, and postmenopausal individuals, which was considered the main factor contributing to the slight heterogeneity observed in the overall analysis.We conducted subgroup analyses on the 21 included studies to explore the impact of various factors on the incidence of breast cancer . Based o2 = 0.0%, p < 0.001) was significantly higher than that in North America . However, the breast cancer incidence rate in Europe , despite being analyzed, did not exhibit any statistically significant difference. When considering racial differences, the incidence of breast cancer was higher in the white population , with no statistically significant difference observed in the Black population . Additionally, subgroup analysis for menopausal status did not yield statistically significant differences between the premenopausal  and postmenopausal groups .Regarding confounding factors, in our subgroup analyses that focused on different continents, we observed a clear disparity in breast cancer incidence rates associated with exposure to the LAN. Specifically, the breast cancer incidence rate in Asia  had a slightly higher promoting effect on breast cancer incidence than indoor LAN exposure . In the analysis of estrogen receptor status, LAN exposure exhibited a slightly stronger impact on breast cancer risk for cases with estrogen receptor positivity  compared to cases with estrogen receptor negativity . Subgroup analyses for different study types revealed that LAN exposure had a higher promoting effect on individual breast cancer incidence in case–control studies  compared to cohort studies .We also conducted analyses on various lifestyle factors summarized in the literature. When considering TV on while sleeping as an effect-modifying factor, the impact of LAN exposure on breast cancer did not show statistical significance . This observation holds true for “Turn on the light when you wake up”  and “Bedroom shutters (open)” . The complete dataset can be found in In terms of effect-modifying factors, similar to the meta-analysis conducted in 2021 , we cond3.6p = 0.746). Similar findings of bias were observed across various subgroups . Notably, our findings demonstrated a more substantial risk association among the case–control study population, contradicting the results reported by Urbano et al.  (n = 2)  , and sub (n = 2) , 47. Whi (n = 2) , we coul (n = 2)  was used (n = 2) , 50, 51, (n = 2)  categori (n = 2)  used onl (n = 2) , we have5Our meta-analysis provides evidence to support the hypothesis that exposure to LAN is associated with an elevated risk of breast cancer incidence, thereby adding valuable insights to the existing body of research. Although we did not observe a statistically significant correlation between lifestyle factors and the association between LAN and breast cancer incidence, our findings indicate a stronger relationship in certain subgroups, such as study populations located in Asia or investigations focusing on outdoor LAN exposure. These results contribute to the identification of important avenues for future research and offer meaningful recommendations.ZIL: Conceptualization, Data curation, Formal analysis, Software, Supervision, Validation, Writing – original draft, Writing – review & editing. ZHL: Conceptualization, Funding–acquisition, Writing – review & editing. HC: Data curation, Investigation, Supervision, Writing – review & editing. YL: Writing – review & editing-Methodology/Investigation. NT: Investigation, Supervision, Writing – review & editing. HL: Investigation, Supervision, Writing – review & editing.


Obesity is associated with an increased breast cancer risk in postmenopausal women and may contribute to worse outcomes. Black women experience higher obesity and breast cancer mortality rates than non-Black women. We examined associations between race, obesity, and clinical tumor stage with breast cancer prognosis.We conducted a prospective cohort study in 1,110 breast cancer patients, using univariable and multivariable Cox regression analyses to evaluate the effects of obesity, race/ethnicity, and clinical tumor stage on progression-free and overall survival (PFS and OS).2). In univariable analyses, tumor stage III-IV was associated with worse PFS and OS compared to tumor stage 0-II . Multivariable analysis revealed an association between Black race and worse PFS in obese  and non-obese  women with tumors staged 0-II. Obesity alone was not associated with worse PFS or OS.22% of participants were Black, 64% were Hispanic White, and 14% were non-Hispanic White or another race. 39% of participants were obese (body mass index [BMI] ≥ 30 kg/mResults suggest a complex interrelationship between obesity and race in breast cancer prognosis. The association between Black race and worse PFS in tumor stages 0-II underscores the importance of early intervention in this group. Future studies are warranted to evaluate whether alternative measures of body composition and biomarkers are better prognostic indicators than BMI among Black breast cancer survivors. The increasing prevalence of obesity in the United States presents a public health challenge. About 42.4% of American adults are obese and non-Hispanic Black adults are disproportionately affected. Obesity Women who are obese are more likely to present at a later stage of the disease, with larger tumors and more positive lymph nodes at the time of diagnosis., 7, 8 AdMultiple mechanisms underly the association between obesity and breast cancer outcomes. Increased aromatase activity in adipose tissue raises circulating estrogen levels. Estrogen has a proliferative effect on breast tissue, contributing to incidence and recurrence. Second, The prevalence of obesity varies by race and breast cancer prognosis. AlthoughMultiple studies have identified differences in the expression of cancer-associated genes in Black women compared to White women.-19 BecauThere is accumulating evidence suggesting that the relationship between obesity, race, and clinical tumor stage in breast cancer prognosis is complex., 20-26 TWe evaluated 1,115 post-surgical breast cancer patients scheduled to receive adjuvant radiation therapy (RT) at the Sylvester Comprehensive Cancer Center (SCCC) and Jackson Memorial Hospital (JMH) in Miami, Florida, between 2008 and 2014. Participants were recruited for a case-control study and/or a study assessing RT-induced skin toxicity to the intact breast. Each patient completed a self-administered questionnaire with (1) demographic information, (2) self-reported race and ethnicity, (3) self-reported height and weight, and (4) assessment of breast cancer risk factors . Clinical and pathological tumor characteristics were obtained from pathology reports and medical records. Informed consent was obtained from all participants at the time of enrollment. This study was approved by the Institutional Review Board at the University of Miami and Jackson Memorial Hospital.Inclusion criteria included female patients aged 18 or older who were diagnosed with breast carcinoma stages 0-IV (American Joint Committee on Cancer), were scheduled to receive treatments at SCCC or JMH between 2008 and 2014 and were able and willing to provide informed consent. Exclusion criteria included patients who were aged less than 18, received prior radiation to the currently treated breast or chest wall, were undergoing concurrent chemotherapy, or were unable to provide written consent. We had a final sample size of 1,110 following the exclusion of participants who were lost to follow-up or had missing information.We evaluated race (non-Black or Black), obesity status (obese or non-obese), age at diagnosis (< 60 years or ≥ 60 years), and smoking status  as patient covariates. Former smoking was defined as having smoked 100 or more lifetime cigarettes and current smoking was defined as active smoking. Clinical variables, including estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and triple-negative (TN) status, as well as clinical tumor stage (0-II or III-IV), were determined using medical records. The clinical tumor stage was based on the American Joint Committee on Cancer staging scheme. Body masParticipants were followed for up to 13 years through a review of the medical records, with the evaluation completed as of July 31, 2021. Progression-free survival (PFS) was defined as the time elapsed from diagnosis to the earliest date of disease progression . Overall survival (OS) was defined as the time elapsed from diagnosis to death. Event-free patients were censored at the date of the last follow-up.P < 0.05. The heterogeneity of obesity effect by race and by tumor stage was assessed in multivariable analyses stratified by clinical tumor stage (0-II and III-IV). Data analysis was conducted using SAS .Descriptive statistics  are presented for patient and clinical characteristics stratified by obesity status. The bivariate association of obesity status and patient and clinical characteristics was assessed by a Chi-square test for categorical variables. PFS was defined as the time from diagnosis to recurrence, metastasis, secondary breast cancer, death, or last follow-up, whichever occurred first. OS was defined as the time from diagnosis to death or last follow-up. Event-free patients were censored at the date of the last follow-up. Selected covariates associated with obesity or with PFS or OS were included in the multivariable Cox regression model based on univariable analysis and literature review. PFS and OS were estimated by the Kaplan-Meier method and the associations with race and obesity were assessed by a log-rank test. Univariable Cox proportional hazard analysis was used for potential covariables on time-to-event outcomes of PFS and OS. Multivariable Cox proportional hazard analysis was used to assess the association between pretreatment obesity and race category with PFS and OS adjusted for selected covariables. Results were reported as hazard ratios (HR) with 95% confidence intervals (95% CI). Statistical significance was set at a threshold of The study population included 1,110 breast cancer patients, 918 (82.7%) of whom were progression-free by the last follow-up and 192 (17.3%) of whom experienced disease progression . 75 women (6.8%) experienced two or more outcomes. The mean age at diagnosis was 54.7 years (range: 24.5–85.0). 30.5% of participants were ≥ 60 years old at diagnosis and 69.5% were < 60 years old. Most of our sample self-identified as Hispanic White (64.1%), 21.7% of participants identified as Black, and 14.1% identified as Non-Hispanic White or another race/ethnicity. Non-Hispanic White and Other categories were combined due to their similar progression-free and overall survival. 76.4% of participants had stage 0-II disease upon enrollment and 23.6% of participants had stage III-IV disease.2. A significantly higher proportion of obese participants were ≥ 60 years old at diagnosis , Black , never smokers (40.4% vs. 39.6% for former smokers vs. 23.7% for current smokers) and had ≥ 2 comorbid conditions . Additional patient and tumor characteristics are summarized in 38.7% of participants were obese, defined as BMI ≥ 30 kg/mUnivariable analysis in Univariable analysis in For combined race and obesity at clinical tumor stages 0-II A, being This study uses a large racially and ethnically diverse population to evaluate patient and clinical characteristics associated with worse PFS and OS in early  and advanced  breast cancer. Race and obesity status were combined in multivariable models to evaluate their joint effects. In the early breast cancer group, obese and non-obese Black women had significantly higher hazards of progression compared to non-Black, obese women. Our results emphasize the importance of race as a prognostic indicator in breast cancer that, when combined with obesity status, may contribute to worse outcomes. We know from prior studies that Black women are more likely to have worse breast cancer prognosis despite a similar risk of developing breast cancer compared to their White counterparts. Reasons SOS1, a gene that is activated by a compound secreted from adipocytes, implicated in anti-apoptotic pathways, and has been linked to breast cancer progression and metastasis, in Black women compared to White women, as well as altered expression of its epigenetic regulatory elements.[SOS1 is activated by a compound secreted from adipocytes. Finally, resistin is another gene that may mediate this link, as it is associated with obesity, insulin resistance, and breast cancer risk, and is expressed higher in the tumors of Black women than in White women.[We found a significant association between combined race and obesity with worse PFS in early, but not advanced breast cancer A and 4B.elements. SOS1 is te women., 24 Likete women. They didte women. ImportanAlthough previous studies suggest that obesity is independently associated with breast cancer incidence, recurrence, and worse clinical outcomes,-6 we didThe lack of independent association of obesity with PFS or OS may also be attributed to the limitations of BMI as a measure of obesity. Emerging evidence suggests that BMI is an oversimplified metric, as it does not distinguish between muscle and adipose, nor does it describe patterns of adipose distribution.-31 AdipoKaplan Meier survival analyses revealed that non-Black obese women had the lowest hazard of progression among all participants in the tumor stage 0-II group . Non-BlaThe stratified multivariable analyses demonstrate an association of TNBC with worse PFS and OS in the advanced breast cancer group. The association with worse outcomes in this cohort is best explained by the aggressiveness of TNBC. TNBC lacks hormone receptor expression and is thus not susceptible to hormonal therapies, leading to worse outcomes., 34 AddiMultivariable Cox models revealed a 2.82-fold increased hazard of death in former smokers compared to never smokers in the early-stage breast cancer group  A. These This study has several strengths. First, a prospective study design is appropriate to assess the patient and clinical characteristics associated with PFS and OS. In addition, we utilize a large racially and ethnically diverse cohort that we followed for up to 13 years, enabling us to evaluate inter-group differences in outcomes. Moreover, although many previous studies characterize the patient and clinical characteristics associated with breast cancer risk, few studies focus specifically on outcomes. This work builds upon our previous study in which we used a PRS to evaluate genetic predisposition to obesity in this same racially and ethnically diverse cohort of breast cancer patients using GWAS data. We foundThis study has several limitations. First, the lack of association between race, obesity, and worse PFS or OS in advanced breast cancer could be attributed to the small sample size in this group. Future studies with larger sample sizes are necessary to elucidate potential differences. Second, our use of BMI as a primary outcome may not accurately reflect differences in adiposity. Our decision to use BMI was based on its extensive use in previous studies and the patient data that was available from the study enrollment survey. Third, our patient population was highly enriched for Hispanic White women, ; results may therefore not be generalizable to all populations. Finally, our study did not evaluate variables such as socioeconomic status or access to healthcare, which may also contribute to breast cancer outcomes.Our findings suggest a complex relationship between obesity and race in breast cancer prognosis. The significant association of combined Black race and obesity status with worse PFS in early-stage breast cancer highlights the importance of targeted early intervention strategies among Black women with breast cancer due to the higher hazard of progression of early-stage disease. An interaction of race and obesity at the molecular level may contribute to the observed differences in PFS; future studies are needed to characterize how environmental and lifestyle factors may alter the expression of cancer-associated genes. Finally, the lack of independent association between BMI alone and PFS or OS may suggest that alternate measures of body composition better illustrate the role of obesity in breast cancer outcomes. Future studies should evaluate whether central obesity and adiposity are more sensitive prognostic indicators than BMI among Black breast cancer survivors.


Investigation of the excess cancer risk among healthcare workers in hospitals is crucial. We constructed a large, nationwide retrospective cohort including 107,646 healthcare workers in hospitals. We found significantly increased standardized incidence ratio (SIR) of all-cancer  and breast cancer  among female healthcare workers. These results imply that potential carcinogens of hospital workers should be assessed, especially female workers in the hospital, including nurses.Objectives: Healthcare workers in hospitals (HHCWs), a notably increasing workforce, face various occupational hazards. A high incidence of cancer among HHCWs has been observed; however, the cancer incidence status among HHCWs in South Korea is yet to be studied. This study aimed to assess cancer incidence among HHCWs in South Korea. Methods: We constructed a retrospective cohort of HHCWs using National Health Insurance claims data, including cancer incidence status and vital status, from 2007 to 2015. Those who had worked in hospitals for at least three years were defined as HHCWs. Standardized incidence ratios (SIRs) for all cancer types and standardized mortality ratios were calculated. Results: A total of 107,646 HHCWs were followed up, and the total follow-up duration was 905,503 person-years. Compared to the total workers, female HHCWs showed significantly higher SIR for all cancers . The incidence of breast cancer among female HHCWs was significantly higher compared to that among total workers . Conclusions: Our findings indicate that female HHCWs have an elevated probability of developing cancer, which suggests that occupational risk factors such as night-shift work, anti-neoplastic medications, stressful jobs, and ionizing radiation should be assessed. Further investigation and occupational environment improvement activities are required. Healthcare workers (HCWs) constitute a fast-growing sector of the workforce in most countries, and this trend has accelerated since the coronavirus disease 2019 pandemic. Health professionals, health associate professionals, personal care workers in health services, health management and support personnel, and other health service providers can be classified as HCWs who provide care and services to patients  malignant neoplasm; [C00–C14] lip, oral cavity, and pharynx; [C15–C26] digestive organs; (C16) stomach; (C18) colon; (C22) liver and intrahepatic bile ducts; (C25) pancreas; [C30–C39] respiratory and intrathoracic organs; (C33–C34) trachea and malignant neoplasm of bronchus and lung; [C40–C41] bone and articular cartilage; [C43–C44] skin; [C45–C49] mesothelial and soft tissue; [C50–C50] breast; [C51–C58] female genital organs; [C60–C63] male genital organs; [C64–C68] urinary tract; [C70–C72] brain and other parts of central nervous system; [C81–C96] lymphoid; hematopoietic and related tissue (C82–C85); non-Hodgkin’s lymphoma; and (C91–C95) leukemia. Mortality cases were defined as participants who died before the end of follow-up (31 December 2015), identified using the mortality registration data of Statistics Korea.Demographic variables of participants were collected from the NHIS database, including age (years), sex , and household income. To describe the distribution of the ages, we grouped participants as 25–34, 35–44, 45–54, and 55–64 years. The NHIS database provides household income in a range from 1 (lowest) to 20 (highest). Household income levels were divided into four groups .p < 0.002 was considered significant and was approximately calculated as 0.05 divided by 21. For the sensitivity analysis, we defined HHCWs differently from the main analysis; participants whose workplaces were registered as a “hospital” for at least one year were designated as HHCWs (“Dynamic”). All analyses were performed using SAS version 9.4 . This study was approved by the Institutional Review Board at the Yonsei University (Y-2017-0100).An indirect standardization method was used to calculate the standardized incidence ratios (SIR) using age-specific expected cases of the control group. We calculated the SIRs and standardized mortality ratios (SMRs) of HHCWs compared to all workers and public officials in South Korea. The observed cases of death were divided by the expected cases of death, with the standardization of sex and five-year age groups from 25 to 64 years. The age- and sex-specific mortality rates of all workers and public officials during the study period were used as a reference. The SIRs and SMRs were calculated in both cohorts for 21 specific outcomes. The Bonferroni method was used to control the overall testing error rate for multiple comparisons. A value of p = 0.001) was significantly higher than in the total workers. Female HHCWs showed higher SIRs of breast cancer (C50). The expected and observed cases of breast cancer among female HHCWs were 310 and 376, respectively, which resulted in an SIR of 1.21 . The all-cause SMR was significantly higher among female HHCWs than that of all the workers .p = 0.001) and 1.15  among men and women, respectively. Among men, significantly higher SIRs than public officials were observed for respiratory and intrathoracic organs , and non-Hodgkin’s lymphoma . Among women, the highest cancer incidence was observed for breast cancer ; followed by female genital organs ; mesothelial and soft tissue ; and digestive organs .In p = 0.003) and mesothelial and soft tissue cancer  in female HHCWs were marginally significantly higher than in the total workers. Cancers in male genital organs were significantly lower in male HHCWs than in public officials . All-cause mortality was significantly higher in male and female HCWs than in public officials, at SIR of 1.33  and 1.32 , respectively.We constructed a retrospective cohort of 107,646 HHCWs, with a total follow-up duration of 905,503 person-years. Compared to the total workers, female HHCWs showed a significantly higher SIR for all cancers. Breast cancer was the most common cancer and was more frequently observed among female HHCWs than among all workers and public officials. Compared to public officials, male and female HHCWs showed significantly higher SIRs for all cancers. The digestive organ was the most common cancer site among male HHCWs, whereas the breast and female genital organs were the most common cancer sites among female HHCWs. These cancers showed significantly higher SIRs in HHCWs than in public officials.Epidemiological studies have reported a high cancer risk among all HCWs compared to other medical staff in hospitals. The results of our study showed an increased SIR for all cancers among female HHCWs; however, this has not been consistently reported in previous studies. Rix et al. investigated cancer incidence in Danish HCWs using census data and cancer register records. They studied a total of 13,955 men and 75,052 women during a 17-year period (1970–1987) and found the highest incidence of brain cancer among male doctors  and of breast cancer among female dentists , female doctors , and female registered nurses  .In our study, a higher incidence of breast cancer was observed among female HHCWs compared to the general population. Considering that the majority of HHCWs were nurses, these results are consistent with previous reports on breast cancer among nurses. Kjaer et al. investigated cancer incidence in a cohort of 92,140 female Danish registered nurses from 1980 to 2003 and reported a significantly increased SIR of breast cancer  compared with the general population, although the SIR of all cancers was not increased  . AlthougOccupational risk factors for breast cancer may include night-shift work and exposure to anti-neoplastic drugs . The IntOccupational stress can be a carcinogenic risk factor for HCWs. A meta-analysis performed in 2003 to investigate the association between stressful life events and breast cancer risk reviewed 27 relevant studies and concluded that stressful life events were significantly associated with breast cancer risk; however, publication bias was observed . AdditioIonizing radiation is also a potential carcinogen in HCWs. A recent review found 16 epidemiological studies on ionizing radiation exposure among HCWs and concluded that there could be an elevated risk of cancer owing to high and prior exposure to radiation . Lee et In a Korean study, Shin et al. analyzed the differences in mortality between Korean medical doctors and the general population. The SMR for all causes of death among doctors was low  . This paBecause the fixed-job classification of the industry was more conservative, HHCWs could be identified more reliably. There was a significant difference in follow-up duration (person-years) between the fixed and dynamic cohorts. The majority of HHCWs are female; the World Health Organization (WHO) reported that women account for 67% of the global healthcare workforce . HoweverOur study is a novel and large-scale study that investigated increased cancer risk among HHCWs in South Korea using representative data for HHCWs in South Korea from the NHIS. However, our study has some limitations. First, there could be a misclassification of cancer cases. A study comparing the Korean Central Cancer Registry and the NHIS showed that the primary diagnosis has a sensitivity of 91.5–97.9% and a positive prediction of 81.8–94.1% . HoweverWe constructed a cohort of 107,646 HHCWs with a follow-up duration of 905,503 person-years and found an increased incidence of all cancers, including breast cancer, and increased mortality. Increased SIRs for all cancers and breast cancer were noted among female HHCWs. Our data showed that workers in hospitals face a high risk of cancer, which implies that occupational risk factors, such as night-shift work, exposure to antineoplastic drugs and ionizing radiation, and job stress, should be investigated among HHCWs in South Korea. The findings of this study suggest that more detailed research and intervention programs for occupational environments are required.


Objective Epidemiological studies have shown evidence of the effect of sex hormones in the pathogenesis of breast cancer, and have suggested a relationship of the disease with variations in genes involved in estrogen synthesis and/or metabolism. The aim of the present study was to evaluate the association between the CYP3A4*1B gene polymorphism (rs2740574) and the risk of developing breast cancer.Methods In the present case-control study, the frequency of the CYP3A4*1B gene polymorphism was determined in 148 women with breast cancer and in 245 women without the disease. The DNA of the participants was extracted from plasma samples, and the gene was amplified by polymerase chain reaction. The presence of the polymorphism was determined using restriction enzymes.Results After adjusting for confounding variables, we have found that the polymorphism was not associated with the occurrence of breast cancer . We have also found no association with the presence of hormone receptors, with human epidermal growth factor receptor 2 (HER2) overexpression, or with the rate of tumor cell proliferation.Conclusion We have not observed a relationship between the CYP3A4*1B gene polymorphism and the occurrence of breast cancer. BRCA1 and BRCA2.Breast cancer is the most common type of cancer in the female population, second only to cases of non-melanoma skin cancer. The mortality rate due to the disease presents an upward curve,CYP3A4 gene, which plays a key role in the metabolism of estrogens, catalyzing its hydroxylation in the liver; it contributes with other enzymes that also participate in this process, both intrahepatically and extrahepatically. In the hydroxylation process catalyzed by these enzymes, estradiol is converted to 2-hydroxyoestradiol, a hormone metabolite that has a low carcinogenic potential.CYP3A4 is an enzyme of the cytochrome P450 family, encoded by the Several studies have shown that exposure to estrogen plays an important role in the etiology of breast cancer.Recently, numerous researchers have focused their studies on some gene polymorphisms of estrogen metabolism and, apparently, the influence of these changes on the risk of developing breast cancer is low. However, as these are common changes, it is plausible that they may be responsible for a large number of cases of the disease.CYP3A4 gene, the CYP3A4*1B variant is one of the most common polymorphisms, and has been associated with specific types of cancer, including breast cancer.Of the many single nucleotide polymorphisms (SNPs) that have been identified in the CYP3A4 gene polymorphism with breast cancer.In the present clinical, cross-sectional case-control study, we have evaluated the potential relationship of the We studied 393 women recruited between 2013 and 2015, who were followed-up in the Mastology Sector of the Division of Gynecology of Faculdade de Medicina do ABC . The project was approved by the Ethics in Research Committee of the institution under the number 169/2010. The participants were divided into 2 groups: 148 women with a histologically confirmed diagnosis of breast cancer (case group), and 245 women without the disease, with normal clinical and mammographic examinations (control group). For the patients with breast cancer, an immunohistochemical analysis of the tumor was performed to determine the presence of estrogen receptors, detected using the EP1 clone. Clinical data were collected with the use of a questionnaire. The following data were recorded: age, age at menarche and last menstruation, number of pregnancies, previous use of hormonal medications, breastfeeding, history of smoking, alcohol consumption, and endocrine diseases. The patients included were informed about the study and signed a consent form.CYP3A4 gene polymorphism was determined following the polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) procedure described by Voso et al.MspI restriction enzyme, and the restriction fragments were separated by electrophoresis in 3.0% agarose stained with ethidium bromide. At the end of the analysis, A/A homozygotes should present a single 165-bp band, G/G homozygotes should present 2 bands of 142 and 23 bp, and A/G heterozygotes should present 3 bands of 165, 142 and 23 bp ), following the manufacturer's instructions. The presence of the nd 23 bp .t-test. The Hardy-Weinberg equilibrium was also tested using the chi-squared test. After the stratification of the groups, the effect of the CYP3A4 gene polymorphism on breast cancer development was estimated by the odds ratio (OR), obtained by the binary logistic regression model, using IBM SPSS Statistics for Windows, version 23.0 . The confidence interval (CI) adopted was 95%, and the value for rejection of the null hypothesis was set at 0.05 or 5% (α ≤ 0.05).To assess the association between the study groups and the categorical variables, we have used the frequency chi-squared test, whereas the continuous variables were analyzed using the unpaired p < 0.0001). The family history of breast cancer (p = 0.04) was more frequent in women who presented with the disease, with a difference of almost 10% between the groups.The clinical and epidemiological characteristics of the case and control groups are described in The genotyping and the frequency of the alleles are described in CYP3A4*1B polymorphism was not directly associated with tumor occurrence . In addition, no statistically significant difference was found between the polymorphisms when they were analyzed according to the estrogen receptor status, to human epidermal growth factor receptor 2 (HER2) overexpression or non-overexpression, or to cell proliferation rate represented by Ki67, as shown in Due to the low incidence of the GG genotype in the studied population, we have chosen to analyze the results comparing the wild homozygous group (AA) with the polymorphic group (AG + GG). After adjusting for oral contraceptive use and family history of breast cancer, the presence of the G allele and the GG (AG + GG) genotype of the CYP3A4*1B gene homozygous variant or by the high frequency of the wild variant when compared with that of the polymorphism, although this hypothesis has not been clearly discussed in the literature.The distribution of the genotypes is not in genetic equilibrium according to the Hardy-Weinberg principle, which has also been observed in some of the previous studies that evaluated the same polymorphism. This fact can be explained by the excess of the CYP3A4 gene, located in the chromosome 7q21.3-q22.1, may influence the level or function of the CYP3A4 protein.CYP3A4 gene, and the most common variant is the CYP3A4*1B gene, an A290G substitution in the 5′ flanking region.CYP3A4*1B gene polymorphism was hypothesized to cause reduced CYP3A4 gene expression.CYP3A4*1B gene polymorphism were not directly associated with the occurrence of breast cancer, as shown in table 2.Genetic factors have been described as modifiers of estrogen levels and good candidates for breast cancer predisposition alleles.p = 0.212).CYP3A4*1B gene, and found no association.CYP3A4*1B gene, even when the outcome was adjusted for age and menopausal status .CYP3A4*1B gene is related to the risk of cancer.The association between this polymorphism and the disease has already been studied by groups from several countries, without a direct relationship being established. A Chilean study found a higher frequency of the polymorphism in patients with breast cancer when compared with healthy women, although the difference was not statistically significant .Genetic variations in enzymes involved in steroidogenesis have been suggested to play a role not only in the risk of breast cancer, but also in the age at menarche.p = 0.059).The possible relationship between the polymorphism in question and breast cancer was suggested by Kadlubar et alp < 0.0001).A factor with strong involvement that has not yet been established as a risk factor is the use of oral contraceptives, which, in our study, was related to a higher incidence of the disease (p = 0.004).Approximately 5 to 10% of breast cancer cases are familial and occur earlier than those in the general population. The BRCA1 and BRCA2 mutations are primarily responsible for hereditary breast cancer.A stratified analysis according to HER2 or to estrogen receptor expression in neoplastic cells showed no relationship with the occurrence of the polymorphism studied. Similarly, Ki67–a tumor cell proliferation index –was not a factor associated with the greater presence of polymorphic alleles. We believe, however, that more studies are needed to confirm any of the proposed hypotheses due to the lack of evidence in the literature on the subject.CYP3A4*1B gene on the genesis of breast cancer. More studies and a larger case sample are necessary to confirm the effects on the risk of breast cancer to assist in the screening and follow-up of patients at increased risk of the disease.We note that the controversy remains over the influence of the CYP3A4*1B gene do not play a key role in breast cancer development.The main results of the present study suggest that the G allele and the GG genotype of the The small sample size and the breast cancer risk factors were among the limitations of the present study that might have affected the detection of differences between the groups.CYP3A4*1B gene polymorphism and the occurrence of breast cancer.We did not observe a relationship between the


The ability to accurately predict whether a woman will develop breast cancer later in her life, should reduce the number of breast cancer deaths. Different predictive models exist for breast cancer based on family history, BRCA status, and SNP analysis. The best of these models has an accuracy  of about 0.65. We have developed computational methods to characterize a genome by a small set of numbers that represent the length of segments of the chromosomes, called chromosomal-scale length variation (CSLV).We built machine learning models to differentiate between women who had breast cancer and women who did not based on their CSLV characterization. We applied this procedure to two different datasets: the UK Biobank (1534 women with breast cancer and 4391 women who did not) and the Cancer Genome Atlas (TCGA) 874 with breast cancer and 3381 without.We found a machine learning model that could predict breast cancer with an AUC of 0.836 95% CI (0.830.0.843) in the UK Biobank data. Using a similar approach with the TCGA data, we obtained a model with an AUC of 0.704 95% CI . Variable importance analysis indicated that no single chromosomal region was responsible for significant fraction of the model results.In this retrospective study, chromosomal-scale length variation could effectively predict whether or not a woman enrolled in the UK Biobank study developed breast cancer.The online version contains supplementary material available at 10.1186/s40246-023-00482-8. Over 600,000 women die annually from breast cancer around the world , but breBreast cancer predictive models based on genetics already exist. The effectiveness of these predictive models can be characterized by the area under the receiver operating characteristic curve, known as the AUC. One commonly used predictive model, the Gail model , has an Family history is encoded in the germline genetics. In fact, models based on detailed germline genetics should perform better than models based on family history alone, since family history is often incomplete; limited to just a generation or two, and genetic factors present in relatives might not be inherited. The more recent approach to predicting breast cancers is to incorporate polygenic risk scores.Polygenic risk scores, computed from linear combinations of SNPs, should provide superior predictions compared to models that rely on family history questionnaires, but they do not show a substantial improvement. The most complete study to date used a group of 313 SNPs to predict breast cancer with an AUC of 0.630 (95% CI 0.628–0.651) [One plausible reason that polygenic risk scores do not substantially increase the AUC for breast cancer prediction models is that these polygenic risk scores only consider linear combinations of SNPs . DetaileWe have introduced a new method of computing genetic risk scores based on chromosomal scale length variation –13. ChroAfter characterizing each person with a series of number derived from their chromosomal scale length variation, we can use the power of modern machine learning algorithms to identify patterns in the germline genetics. The purpose of this paper is to evaluate how well, measured by the AUC, that chromosomal scale length variation can predict breast cancer in patients.To test how well chromosomal length variation can predict breast cancer we acquired germline genetic data on breast cancer patients and non-breast cancer patients (for a control group) from two different data sources, the Cancer Genome Atlas (TCGA) , 14, 15 The Cancer Genome Atlas (TCGA) characterized molecular differences in 33 different human cancers , 15. TheEach patient’s germline DNA was extracted from the normal blood samples. A single laboratory processed all germline DNA samples. Each patient’s germline DNA was genotyped by single nucleotide polymorphisms (SNPs) using an Affymetrix SNP 6.0 array. This SNP data were then processed (by the TCGA project) through a bioinformatics pipeline, which included the packages Birdsuite  and DNAcThe Genomic Data Commons, an NCI sponsored data repository, contains most of the TCGA data . In the We refer to the final TCGA dataset we used as the masked copy number variation dataset. This dataset originates from normal blood samples extracted from 8826 different patients: 4692 females and 4134 males. The patients’ ages ranged from 10 to 90 years old.This dataset contains about 695,000 different copy number variations that appear in at least one patient. Copy number variations are genomic regions characterized by the chromosome number, a start position, an end position, and a copy number value. The copy number value is represented as the log base 2 of the ratio of copies present to the expected number of copies, two. A “0” would represent the expected number of copies log2(1), a negative number indicates deletions, and a positive number indicates multiple copies. In the TCGA dataset, normal regions, those with a log_2 CNV equal to 0 are not recorded.While most copy number variations output by this TCGA pipeline are relatively short, less than the size of a gene, we noted that a few are relatively long, consisting of most of the chromosome’s entire length. For instance, the copy number variation output by the TCGA pipeline that we use to characterize chromosome 1 is 244 megabases long, while the full length of chromosome 1 is 249 megabases. This process produced a dataset with 8,826 rows (each representing a different patient) and 23 columns (each representing one of the chromosomes 1–22 and the X chromosome).We created a case/control study to differentiate between people with breast cancer and those without breast cancer. For the case/control study, we included all cases in the TCGA dataset that included “normal blood” samples from women patients with a breast cancer diagnosis. No cases were excluded. Patients included also have measurements of copy number variation from DNA derived from normal blood in the database.Controls for the TCGA dataset include all women in the dataset who had “normal blood” samples without a breast cancer diagnosis. We included only women (no men) in the control sample. Due to the nature of TCGA, each woman in the “control” sample had another type of cancer diagnosis (not breast cancer). The final dataset included 874 women with breast cancer and 3381 women as controls.To ensure that the results from the TCGA dataset were not due to systematic effects in producing the data, we tested the same methods in a second independent dataset, the UK Biobank. Because of differences in the way in which the data was made available, we could not directly test the predictive power of a model developed on TCGA data with UK Biobank data and vice versa.We obtained data from the UK Biobank under Application Number 47850. The UK Biobank project collected genetic data and medical records from about 500,000 people who were between the ages of 40 and 69 during the 2006–2010 recruitment years. Most have supplied biological samples and filled out questionnaires about their health. Most of the participants’ medical records are linked, through the National Health Service, to the UK Biobank records. This linkage provides for ongoing follow-up of health conditions .As previously described , we firsWe next computed the mean l2r value for different portions of each chromosome for each patient in the dataset, which we refer to as the “length”. We compute the length for each person’s chromosome using the l2r files by taking the average of all l2r values measured across that chromosome. A value of 0 represents the nominal average length of that portion of the chromosome. We call this dataset the chromosome-scale length variation (CSLV) dataset.This CSLV dataset was joined with the UK Biobank Health records dataset. UK Biobank matched the person in the Public Health England data with UK Biobanks internal records to produce the person’s encoded participant ID. The dataset we have, provided by UK Biobank, contains the participant ID and whether the participant had been diagnosed with breast cancer.The UK Biobank dataset that we used consisted of measurements at 820,967 genetic markers across 23 chromosomes for each of 488,377 different patients. From the UK Biobank population, we constructed a dataset of positive cases that included all women who both self-reported having been diagnosed with breast cancer and were identified by cancer registries as having been diagnosed with breast cancer, a total of 1534 women. We then constructed a control dataset from a pool of 10,000 UK Biobank participants. From this pool of 10,000 we excluded all men and any women that had any type of cancer diagnosis, either self-reported or from a cancer registry. This gave a control group of 4391 cancer-free.We quantified the germ line DNA of each of these women with 88 numbers, each representing the length variation of one quarter of each of 22 chromosomes. We did not use the X chromosome.For both the TCGA data and the UK Biobank data, we set up as a binary classification supervised learning task that was trying to distinguish between patients diagnosed with breast cancer from those not diagnosed with breast cancer.We performed the analysis using the statistical language R. The data were reformatted for analysis, and then was fed into the machine-learning algorithm. The data included whether the subject had breast cancer, and measurements of copy number variation at distinct locations across the genome derived from the patient’s peripheral blood sample. The data fed into the machine-learning algorithm did not include age since germline DNA should not depend upon age. The results are independent of the patient’s age.We used the H2O package in R for machine learning. This package implements several common machine learning algorithms, including gradient boosting machines, deep learning neural networks, distributed random forests, and generalized linear models. The best performing algorithms for our datasets are invariably stacked ensembles, which are combinations of other machine learning algorithms. This combination often provides superior results to any particular algorithm , 23. TheMachine learning We used the H2O Automatic Machine Learning (automl) function to identify a good machine learning model. The automl function is given a specific amount of time and then proceeds to train and tune a series of models, searching for the best model. To obtain confidence intervals, we repeated the training multiple times (at least 10) with different initial randomization. This process provides independent test/train splits to the data.The TCGA dataset we used included 874 women with breast cancer and 3381 women who did not have breast cancer as controls. We used 10 × cross validation, so the test set had 87 women with breast cancer and 338 women as controls.Statistical tests were performed in R. We computed the 95% confidence intervals using the R command t.test. Normality was first confirmed with the Shapiro test. More information is available in Additional file Using the TCGA dataset, consisting of 4,255 women (874 with breast cancer and 3381 controls), we found a classifier with an AUC of 0.704 CI (0.702–0.706) for identifying breast cancer, see Fig. Using our subset of the UK Biobank dataset, consisting of 5925 women  with 88 measurements for each, we found a classifier with an AUC of 0.836 with a 95% CI  for identifying breast cancer, see Fig. To simulate a real-world application, we then split the UK Biobank dataset into a training set and a test set. The test set consisted of 889 women (233 with breast cancer and 656 without). After obtaining a model from the training set, we applied the model to a test set. The model returns a score for each woman in the test set. The higher the score, the more likely the woman is to have breast cancer. We ranked each woman by the assigned score and then evaluated how accurate the model was for each decile. For instance, about 25% of the women in the test set had breast cancer, but 85% of women who scored in the highest decile had breast cancer. See Table We quantified the importance of the different variables (regions of chromosomes) using the summary plot of different SHAP (Shapley Additive exPlanations) contributions. This summary plot assigns each variable an importance for different predictions . The SHAGenetic risk scores have been developed for many different ailments –30, inclBreast cancer was the first target of genetic risk scores with the discovery of BRCA1 . A 2015 Machine learning techniques have been used to build polygenic risk scores to predict other complex traits , 38. ForWe evaluated different machine learning algorithms. We used the h2o platform for machine learning and selected the best algorithm by weighing computation time and AUC. The h2o platform evaluated four different algorithms . For the TCGA data, the gradient boosting machine algorithm and the deep learning algorithm provided comparable AUCs, but the gradient boosting algorithm was faster. For the UKBiobank data, the deep learning algorithm provided larger AUCs than all other algorithms evaluated.Several factors could contribute to the difference between the results we see in TCGA compared to the UK Biobank. The TCGA dataset includes only cancer patients, so the normal people in our TCGA analysis of breast cancer are women who do not have breast cancer but do have some other type of cancer. The TCGA dataset is also more controlled. Each breast tumor in the TCGA study was confirmed by a pathologist to be invasive and either ductal or lobular. The UK Biobank relies more on medical records indicating a diagnosis of “breast cancer.” This diagnosis may include a number of different conditions. Second, the genetic characterization data we have from the UK Biobank is much more extensive than the data from TCGA. The TCGA dataset we constructed relied on the TCGA bioinformatics pipeline, which did not always report a number for factors we were interested in. Many patients had no values reported for a specific chromosome “length,” presumably because it was close normal length and it was not considered a copy number variation. Since we had access to the UK Biobank SP array measurements, we could compute a value for every patient. Third, the UK Biobank population is much more uniform than the TCGA population. The TCGA patients were selected to represent a diverse population. About 76% of the TCGA breast cancer patients were identified as “white”, while about 95% of the UK Biobank participants are categorized as “white.”The tests have not been optimized. The UKBiobank test could be further improved in two ways. First, the results might improve with further training. The training was done with a desktop computer . We believe additional training time could slightly improve the AUC. Second, we used 88 numbers to characterize each genome, splitting the 22 chromosomes into four equal parts. Some of these regions were highly correlated to others. We might be able to further improve the AUC by splitting the chromosomes into finer parts and ignoring those parts that are highly correlated to existing parts of the dataset.Understanding the predictions made here is difficult. Although risk prediction and association studies share common methods, the end goals differ. Association studies often try to identify alterations in specific genes that can be mechanistically tied to specific diseases. Risk prediction, however, is only concerned with maximizing the predictive power (18). One method of understanding machine learning models is through examination of variable importance, identifying which regions contribute the most to the model’s predictions. Figure We considered whether the results were due to two common problems faced by GWAS studies: batch effects  or populPopulation stratification occurs in case/control studies when the cases and controls contain different proportions of genetically discernable subclasses. Most TCGA samples were collected in the United States from a racially diverse group. The typical process to correct for population stratification in GWAS is to use principal component analysis, but that process in inherently linear and cannot be used with non-linear machine learning techniques.Cancer is the result of a complex interaction between genetics and the environment. In some cases, for instance, lung cancer and smoking or mesothelioma and asbestos exposure, the required environmental exposure is significant and well known. In other cases, the required environmental exposure is minor and not well known. The genetic signature identified here is a necessary, but not sufficient factor in developing the cancer. Since this is a retrospective study of people who already developed cancer, sufficient environmental exposure has already occurred. A prospective study would need to be performed to determine the effect of environmental exposure on how effective these predictions are.In this retrospective study, chromosomal-scale length variation could effectively predict whether or not a woman enrolled in the UK Biobank study developed breast cancer.Additional file 1. Supplemental Methods and Results.