CRY2, rs2253820 in PER1, rs2289591 in PER1 and rs3027188 in PER1 were positively associated with the risk of breast cancer.Rare studies had been conducted in the Chinese population regarding the impact of circadian disruptors on breast cancer risk. This study found that a short duration of sleep was significantly associated with breast cancer. Besides, rs2292912 in CRY2, rs2253820 in PER1, rs2289591 in PER1 and rs3027188 in PER1 were positively associated with the risk of breast cancer. This study supported that the short duration of sleep and four SNPs in crucial circadian genes played a role in the development of breast cancer.Studies had suggested an association between circadian disruptors  and breast cancer, while rare studies had been conducted in the Chinese population. This study was a case-control study conducted to explore the impact of circadian disruptors on the risk of breast cancer in China. Four hundred and sixty-four cases and 464 controls, admitted from the Department of Breast Surgery, Cancer Hospital, Chinese Academy of Medical Sciences, were included in this study. Adjusting age, BMI group, smoking, alcohol consumption, menopausal status, family history of breast cancer, duration of breastfeeding, age at menarche, number of pregnancies, age at first full-term pregnancy, use of estrogen and use of oral contraceptive, multivariate logistic regression analysis showed that the risk of breast cancer was higher in short sleep duration group . Meanwhile, rs2292912 in  GLOBOCAN 2020  estimateMore and more evidence showed that there was an association between circadian rhythm disruption and cancer risk ,7,8. CirBesides, only light exposure at night, caused by night shift work, domestic light at night , and so on, can affect circadian rhythm ,15. SincCircadian rhythms are the result of complex physiological processes in which the external and internal environments interact, and these physiological processes are regulated by a set of circadian genes . In partSeveral studies had suggested an association between circadian disruptors  and breast cancer risk ,25,30,31Using an unmatched case-control study design, participants were all obtained from the Department of Breast Surgery, Cancer Hospital, Chinese Academy of Medical Sciences between July 2011 and December 2012. Participants were eligible if: (I) female; (II) consented to participate in this study and completed the questionnaire survey. Patients of previously diagnosed malignant tumor, non-primary breast cancer, and recurrent breast cancer were excluded from this study. In the end, 464 newly diagnosed breast cancer patients were collected as the case group. The control group was 464 histologically-confirmed benign breast disease patients collected from the same department during the same period.Before the survey, we got informed consent from each participant. All participants were then interviewed face-to-face with well-trained investigators using a structured questionnaire. This questionnaire covered demographic characteristics , personal behavior , history of disease, family history of breast cancer, use of estrogenic drugs, night work, shift work, domestic light exposure at night  and sleep duration. 2), overweight (24.0 to 27.9 kg/m2) and obesity (≥28.0 kg/m2). Cigarette smoking was defined as cumulative smoking greater than 100 cigarettes. Alcohol consumption refers to drinking alcohol at least once a week for more than six months. Menopause refers to the absence of menstruation for six consecutive months or more. Family history of breast cancer refers to having more than one first-degree relative  who was diagnosed with breast cancer. Night work was defined as ever having worked between midnight and 5 a.m. for more than one year, and shift work was defined as ever having worked at night, or on night shifts, or on call at night for more than one year.Based on the guidelines for the prevention and control of overweight and obesity in adults in China , body mahttps://www.ncbi.nlm.nih.gov/, (accessed on 1 January 2016)) and published articles  . A case-We found that domestic light exposure at night may be a potential risk factor for breast cancer, while none of the results were statistically significant. In this study, women not pulling curtains  and light on  during sleep may have a higher risk of breast cancer. A case-control study in Connecticut conducted by Li et al.  found thWe observed that short duration of sleep  increased the risk of breast cancer, and long sleep duration  may be a risk factor for breast cancer. The most recent meta-analyses published in 2021, including 15 prospective studies and 65,410 breast cancer cases, found that there was no association between short sleep duration , long sleep duration  and breast cancer . A prospCRY2, rs2253820 in PER1, rs2289591 in PER1 and rs3027188 in PER1, were significantly associated with breast cancer risk. A case-control study from Korea, with 941 cases and 959 controls, found that night-shift work could increase breast cancer risk in women carrying the CG genotype of rs2292912 in CRY2  [PER1  was significantly associated with Parkinson’s disease risk in Han-nationality Chinese [PER1  was associated with more aggressive prostate cancer risk under a dominant genetic model [PER1 and breast cancer risk [Our analysis showed that four SNPs, including rs2292912 in  Chinese . In a caic model . A case-cer risk .The current study had several advantages. First of all, our study collected detailed information about domestic light exposure at night, such as bedroom brightness and outdoor light while sleeping. Second, we explored the association between light exposure at night in occupational settings and domestic settings, which was comprehensive. Meanwhile, several limitations of this study need to be acknowledged. First, the sample size of this study was limited. Furthermore, it was difficult for people to accurately report their light exposure at night because of recall bias, resulting in unavoidable misclassification of circadian disruption. At last, due to the lack of relevant data, we were unable to perform a prognostic analysis on the overall survival of patients.In summary, the present study conducted on Chinese women suggested that a short duration of sleep and four SNPs in crucial circadian genes played a role in the development of breast cancer. More epidemiological studies need to be implemented in a larger population to further explore the relationship between circadian disruption and breast cancer.


Identifying women with high risk of breast cancer is necessary to study high‐risk experiences and deliver risk‐management care. Risk prediction models estimate individuals' lifetime risk but have rarely been applied in community‐based settings among women not yet receiving specialized care. Therefore, we aimed: (1) to apply three breast cancer risk prediction models  to a racially diverse, community‐based sample of women, and (2) to assess risk prediction estimates using survey data.high‐risk subsample required ≥20% lifetime risk per ≥1 model. Descriptive statistics were used to compare the proportions of women identified as high risk by each model.An online survey was administered to women who were determined by a screening instrument to have potentially high risk for breast cancer. Risk prediction models were applied using their self‐reported family and medical history information. Inclusion in the N = 1053 women were initially eligible and completed the survey. All women, except one, self‐reported the information necessary to run at least one model; 90% had sufficient information for >1 model. The high‐risk subsample included 717 women, of which 75% were identified by one model only; 96% were identified by IBIS, 3% by Claus, <1% by Gail. In the high‐risk subsample, 20% were identified by two models and 3% by all three models.Assessing breast cancer risk using self‐reported data in a community‐based sample was feasible. Different models identify substantially different groups of women who may be at high risk for breast cancer; use of multiple models may be beneficial for research and clinical care. Risk prediction models have rarely been applied to community‐based settings to identify women with high risk for breast cancer. This study applied three breast cancer risk prediction models to a racially diverse, community‐based sample. The results highlight differences between models in the identification of high‐risk women. Lay SummaryVarious risk prediction models incorporate different information to estimate an individual's lifetime risk of breast cancer. These models have mostly been used in clinical settings, omitting women who are not receiving healthcare. This study compared three different risk prediction models  among a community‐based sample to identify which women have high risk of getting breast cancer, using survey data collected directly from women. We found large differences in the number of women identified as high risk from each of the three models. We conclude that multiple models should be used to identify high‐risk women from the community.1In the United States, women with ≥20% lifetime risk of developing breast cancer are considered to be at high risk, compared to the 12% general population risk, and are recommended to receive risk‐management care.Numerous breast cancer risk prediction models have been developed.Validation studies have observed substantial differences between the Gail, Claus, and IBIS models. The IBIS model may often provide the best predictive accuracy for breast cancer, while the Gail and Claus models more frequently underestimate breast cancer risk, particularly for women with strong family history of breast/ovarian cancer.The lack of systematic cancer risk assessment screening is a missed opportunity for both clinical care and research.Therefore, the goal of this study was to apply three breast cancer risk prediction models in a purposive racially diverse, community‐based sample of women using survey data. Our aims were (1) to investigate the proportion of women eligible to receive risk estimation by the Gail, Claus, and IBIS models and (2) to assess the resulting risk estimates.22.1Daughter Sister Mother Project.This study is part of the amazon.com gift card after completing the survey . Given our online data collection format, we then reviewed each completed survey to identify and remove fraudulent surveys. Fraudulent surveys, resulting from individuals completing the survey repeatedly or deploying programmed bots to harvest incentive payments, are a common problem in online studies.Recruitment materials advertised the study for white and African American women 18–75 years old, with a family history of breast cancer or a BRCA mutation, who had never had cancer themselves. Interested individuals completed an online screening instrument designed to distinguish women with potentially high risk for breast cancer. The screener asked up to 12 questions about race, ethnicity, age, family breast cancer history, and personal medical and genetic testing history. Individuals who passed through the screener were deemed eligible to continue and were invited to provide electronic informed consent and then participate in the online study survey. Surveys were administered October 2018–August 2019 using the REDCap data collection system, available on any Internet‐enabled device. Participants took an average of 31 min to complete the survey and received a $10 2.22.2.1BRCA1, BRCA2, or other pathogenic variants).Participants were asked about history of genetic testing and the result, if applicable  and ovarian cancers and ages of diagnoses for their mother, sisters, daughters, aunts, grandmothers, half‐sisters, female first cousins, nieces, father, and brothers.2.2.3Sociodemographic information was collected using standardized questions about age, race, education, and annual household income.2.2.4A dichotomous variable was created to indicate whether participants had ever seen a breast, cancer, or genetics specialist.Participants answered the following question: “What do you think your chance is of developing breast cancer in your lifetime?”. Responses were categorical ranging from 0% to 100% in 20% increments.2.32.3.1We applied the Gail , Claus , and IBIS (version 7) risk prediction models to estimate each participant's lifetime risk of breast cancer.2.3.2Participants were categorized into one of two subsamples based on self‐reported BRCA status and risk prediction estimates.Includes participants with ≥20% lifetime breast cancer risk according to ≥1 risk prediction model or self‐report of a pathogenic BRCA1/2 variant.Includes participants that the screener determined as potentially having high risk of breast cancer, who completed the survey, but lifetime risk estimates were <20% according to all three risk estimation models.2.3.3n = 717) and average‐risk (n = 336) subsamples. We also calculated the number of models applied each participant and cataloged reasons that models could not be run.Descriptive statistics were conducted to compare demographic characteristics of our high‐risk (2.3.4n = 36) of the high‐risk subsample. We report median and ranges of risk scores calculated by each model, stratified by BRCA status. Discrepancies in risk scores were calculated as follows: (1) if all three models could be run, discrepancy = absolute difference between largest and smallest risk scores, (2) if only two models could be run, discrepancy = absolute difference between the two risk scores, (3) if only one model could be run, discrepancy was not calculated.Within the high‐risk subsample, we calculated percentages of women identified as high risk by each model. We conducted a quality control check by re‐estimating all models for a random 5% sample  were more likely to be ≥55 years old, Black or African American (even after adjustment for income and education), had lower household income, education, perceived breast cancer risk, and were less likely to have seen a cancer, breast, or genetics specialist (p < 0.05).Of the 1053 women who completed the survey, 717 (68%) were included in the high‐risk subsample. Compared to the high‐risk subsample, members of the average‐risk subsample (The high‐risk subsample was comprised of 65% White women and 35% Black or African American women. Most (95%) were recruited from nonclinical sources and 54% had never seen a specialist. The majority (65%) were 25–50 years old, had at least a college degree (74%), and reported a family history of at least one first‐degree relative with breast cancer (59%). Twenty percent reported having BRCA1/2 pathogenic variants , 47% had two models run , and 43% had all three models run  having seen a specialist, and (2) having three models run compared to 1–2 models run. Participants recruited from Facebook had 2.4 times higher odds of having three models run compared to other recruitment sources. No differences were observed in the number of models run across race, income, education, or perceived risk of breast cancer .3.33.3.1The high‐risk subsample included 709 women who had ≥20% lifetime risk from ≥1 model and eight women whose risk scores were <20% but self‐reported pathogenic BRCA variants. Of the 709 women, 76% had ≥20% risk according to only one model , 20% had ≥20% risk according to two models, and 3% had ≥20% risk according to all three models . Table Estimated lifetime risks were 32.5% higher, on average, among women with reported pathogenic BRCA variants compared to women without  for those with pathogenic BRCA variants and 15.5% (range: 0.1%–74.7%) for those without pathogenic BRCA variants. For women who had all three models run (none of whom reported BRCA mutations), the median discrepancy was 14.3% (range: 1.4%–32.7%) Table .4In our racially diverse, community‐based sample of 1053 women, the Gail, Claus, and IBIS models identified substantially different proportions of women as having ≥20% lifetime risk for breast cancer. The IBIS model identified the highest proportion of women as high risk, followed by Claus, then Gail. Only 3.2% of women were identified as high risk by all three models. Our community‐based research sample provides useful insights for identifying high‐risk women outside of clinical care settings that can inform both research and healthcare delivery.Prior studies have most often applied risk prediction models to identify high‐risk women using electronic medical record (EMR) data and have reported similar differences in risk estimate patterns between models. Several large studies among mammogram patients have shown that the Gail model under‐identified potentially high‐risk women compared to IBIS, Claus, and other criteria such as the U.S. Preventive Service Task Force (USPSTF) family history criteria.Recently, alternative screening methods have been tested to identify potentially high‐risk women who should be referred to genetic counseling or testing within primary care. These methods  do not provide lifetime risk estimates but are more time‐efficient and provide a threshold to indicate the need for a clinical referral.The advantages of our community‐based approach to identifying high‐risk women compliment those of other approaches that also attempt to overcome the limitations of clinic‐based identification strategies. Contacting at‐risk family members through state cancer registries is one step in the right direction toward community‐based identification of high‐risk women, but relying on registry patients to respond to surveys and offer contact information for willing family members limits the potential to identify all women who may be at high risk. For example, in one study, 33% of cancer survivors responded to the initial survey request, and 52% of their at‐risk relatives subsequently responded.Among the high‐risk subsample in this study, discrepancies between risk models were larger among women with pathogenic BRCA variants compared to women without pathogenic BRCA variants. This pattern could be explained by several potential mechanisms. Pathogenic BRCA variant carriers can have lifetime risks from 41% to 90%, and this may be a stronger driver of risk discrepancies than other risk factors..Several limitations are relevant to this study. Our data were entirely self‐reported, therefore pathogenic BRCA mutations and medical history were not objectively verified. Furthermore, family history data collected using surveys may be more susceptible to errors than family history information collected via clinician–patient discussion.Several opportunities exist to improve identification of high‐risk women to facilitate better linkage to clinical care and increase capacity for research.5Our study demonstrated success in applying risk prediction models using survey data to identify high‐risk women through community‐based recruitment. The risk prediction results also demonstrated that the selection of risk prediction models can make a substantial difference in the identification of high‐risk women. Relying on any single model as a basis for screening or identification of high‐risk women is likely to miss a high proportion of potentially eligible women. Concurrent use of multiple models is likely most advisable.All authors have no conflict of interest to disclose.Rachel J. Meadows: Conceptualization, data analyses, writing, and editing. Wilson Figueroa: Conceptualization, writing, and reviewing. Kate P. Shane‐Carson: Conceptualization, data analyses, and reviewing. Tasleem J. Padamsee: Funding acquisition, conceptualization, writing, reviewing, and editing.This research was approved by the Ohio State University Institutional Review Board (Protocol #2017C0212) prior to study commencement. Informed consent was obtained electronically from all research participants.Risk prediction models have rarely been applied to community‐based settings to identify women with high risk for breast cancer. This study applied three breast cancer risk prediction models to a racially diverse, community‐based sample and the results highlight differences between models in the identification of high‐risk women.Appendix S1Click here for additional data file.


Our results indicate that adherence to healthy lifestyles is associated with lower overall cancer incidence and all-cause mortality risk among MetS individuals. However, causality cannot be made due to the nature of observational studies.This study investigated the association between a healthy lifestyle with all-cause, cause-specific mortality, and cancer incidence among individuals with metabolic syndrome (MetS). Healthy lifestyle scores were created based on MetS management guidelines, including never/quitting smoking, moderate drinking, good sleep, healthy diet, sufficient exercise, social support, and less sedentary behaviour. Weighted healthy lifestyle scores were further constructed and classified into three groups: unfavourable (lowest quintile), intermediate (quintiles 2–4), and favourable (highest quintile) lifestyles. We included 87,342 MetS participants from the UK Biobank. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using multivariate-adjusted Cox proportional hazards regression. During a median follow-up of 12.54 years, 6739 deaths were reported; during a median follow-up of 10.69 years, 10,802 new cancer cases were documented. We found a favourable lifestyle was inversely associated with all-cause mortality , cause-specific mortality from respiratory disease, cancer, digestive disease, cardiovascular disease (HR < 1;  Metabolic syndrome (MetS) is a public health problem as well as a clinical problem, with nearly a quarter of the world’s population suffering from it ,2. It isThe international panel’s therapeutic guidelines for MetS advocates for lifestyle management ,9, whichIn this study, we used data from the UK Biobank to investigate associations between a healthy lifestyle  and all-cause mortality, mortality from cancer, CVD, respiratory and digestive disease, as well as incident cancer among individuals with MetS.n = 12,317), lost to follow-up (n = 296), and those aged <40 years (n = 2). In addition, a total of 30,041 participants with missing information and 7593 participants who died or had cancer within the first three years after recruitment were also excluded. Overall, 87,342 participants with MetS at baseline were included in this study . All the study participants signed an informed consent form. This study was performed under the approval of the UK Biobank (Application number: 78563).MetS was defined as the occurrence of three or more of the following risk factors according to the most recent joint interim statement of the International Diabetes Federation (IDF) and National Heart, Lung and Blood Institute (NHLBI)/American Heart Association (AHA): central obesity, elevated BP, increased fasting blood glucose (FBG), reduced high-density lipoprotein cholesterol (HDL-C), and increased triglycerides (TG) . Waist cHealthy lifestyle scores were created based on seven recommended lifestyle behaviours  ,20, obta−1 × factor−1 + β−2 × factor−2 + …+ β−7 × factor−7) ÷ (β−1 + β−2 +…+ β−7) × 7 . St. St36]. 2 as the recommended level. Fifth, never smoking was redefined as recommended behaviour. All statistical analyses were performed using Stata , and graphs were plotted using R . R Foundation for Statistical Computing, Vienna, Austria). All p-values were two-sided, and p-values < 0.05 were considered statistically significant.Sensitivity analyses were performed to examine the robustness of the estimates. First, given the large sample size, we excluded participants with missing values of subjective data to avoid imputations affecting the authenticity of the subjective data. Second, we excluded participants with baseline cancer, those who died, or those who were diagnosed with cancer in the first three years of follow-up to rule out potential reverse causation. Third, the unweighted healthy lifestyle scores were classified into three groups: scoring 0–2, 3–5, and 6–7, to investigate all-cause mortality, cause-specific mortality, and incident cancer risk. Fourth, we reconstructed lifestyle scores by defining 18.5 ≤ BMI < 25 kg/mp < 0.05). The characteristics of participants with MetS excluded from the analysis are shown in The baseline characteristics of participants, according to all-cause mortality and overall cancer incidence, are presented in p-value < 0.05). Never/quit smoking  was associated with a lower risk of overall cancer incidence (As shown in ncidence .p-trend < 0.05)  had HRs of 0.57 (95% CI: 0.53–0.62) for all-cause mortality and 0.84 (95% CI: 0.79–0.90) for overall cancer incidence compared to those with an unfavourable lifestyle (lowest quintile). Notably, a favourable lifestyle was associated with lower site-specific cancer risk in the digestive, respiratory, and intrathoracic organs ( < 0.05) .p-value for trend < 0.001)  were from cancer, 1731 (25.7%) from CVD, 535 (7.9%) from respiratory disease, 321 (4.8%) from digestive disease, and 1440 (21.4%) from all other causes. Based on the Cox proportional hazards regression curves of cumulative risk, all-cause mortality risk decreased monotonically across healthy lifestyle scores, and a higher healthy lifestyle score was associated with a lower risk of cause-specific mortality from respiratory disease, cancer, digestive disease, CVD, and other causes (< 0.001) . The sam< 0.001) . The mul< 0.001) . Similar< 0.001) .p for interaction < 0.001) (p-interaction < 0.001).Further analyses stratified by age at baseline, sex, employment status, education level, socioeconomic status, BMI categories, cholesterol medication, hypertension medication, insulin medication, family history of hypertension, DM, and cancer, with unfavourable lifestyle as the reference group, yielded a similar pattern of associations; favourable and intermediate lifestyles were inversely associated with all-cause mortality risk. Notably, crossover analysis also indicated that associations of favourable and intermediate lifestyles with all-cause mortality incidence were consistently stronger in participants with MetS who were women, employed, and had lower BMI (< 0.001) . Stronge2 or redefining never smoking as a recommended behaviour  was associated with a lower risk of all-cause and cause-specific mortality from cancer, respiratory diseases, digestive diseases, and CVD. Nearly one-fifth of deaths among populations with MetS could be attributed to nonadherence to six to seven recommended lifestyle behaviours. Notably, the contributing beneficial effect of adherence to six to seven health lifestyle behaviours to respiratory and digestive diseases is better than cancer and CVD diseases, possibly due to the other diseases, such as cancer, are more susceptible to genetic or other lifestyle factors . MeanwhiLifestyle factors affecting the risk of incident cancer and mortality have received widespread interest from researchers. A large cohort study with 444,399 members of the general population from the United States and the UK found that a healthy lifestyle  was associated with a reduced risk of all-cause mortality regardless of socioeconomic status . Additiop < 0.05) [Additionally, a previous study among Swedish women found that quitting smoking and weight reduction were inversely associated with the risk of cancer, including breast cancer  . Moreove < 0.05) . These fTo our knowledge, no previous study has examined the associations between lifestyle factors, cancer incidence, and mortality risk among adults with MetS. Two recent systematic reviews and meta-analyses found that lifestyle interventions improve the risk factors and prognosis of MetS ,41. HoweMultiple mechanisms were proposed to explain the associations between lifestyle factors, all-cause, cause-specific mortality, and cancer incidence. The release of carcinogens from tobacco products, including tobacco-specific nitrosamines and volatile organic compounds, causes complex mutations in key cancer genes , and proThis study has several limitations. First, lifestyle information was collected through self-reports and only investigated once, which may have caused measurement errors. Lifestyles are not static, and repeated measures should be considered in future studies. Second, although a weighted lifestyle score was created, with the assumption that each factor has a different effect on mortality and cancer, to examine the effect of healthy lifestyle factors on the outcomes, it was still difficult to fully explain the complex interactions between lifestyle factors, and the combined lifestyle factors did not answer the question about the different factors with diverting effects to complex diseases. Third, a limited number of lifestyle factors were included in this study, and other environmental and lifestyle factors may also have an effect on cancer or death risk. Fourth, although the study had adjusted for potential confounders and participants without cancer at baseline were followed up for a median of approximately 12 years, there may still be unmeasured confounders and reverse causality. Fifth, the participants in this study were primarily Caucasians, with a baseline age of 40–70 years. Further research on other ethnic groups is required. Finally, due to the limitations of observational studies, it is difficult to derive causality; well-designed interventional studies are warranted in the near future.This study, which is based on a large, nationwide UK Biobank cohort, suggests that adherence to a favourable lifestyle is significantly associated with a lower risk of mortality and incident cancer among populations with MetS. These findings highlight the importance of lifestyle modifications in reducing the disease burden for individuals with MetS. However, whether the observed associations are causal remains to be further determined.


Acrylamide is classified as a probable human carcinogen by the IARC but epidemiological evidence on the carcinogenicity of acrylamide from dietary sources is limited. This study aimed to investigate the associations between dietary acrylamide and breast cancer risk in the NutriNet-Santé cohort.This prospective cohort study included 80,597 French women (mean [SD] age at baseline: 40.8 [14] years) during a mean (SD) follow-up of 8.8 (2.3) years. Acrylamide intake was evaluated using repeated 24h dietary records (n = 5.5 [SD 3.0]), linked to a comprehensive food composition database. Associations between acrylamide intake and breast cancer risk  were assessed by Cox hazard models adjusted for known risk factors.The mean (SD) dietary acrylamide intake was 30.1 (21.9) µg/d . During follow-up, 1016 first incident breast cancer cases were diagnosed . A borderline significant positive association was observed between acrylamide intake and breast cancer risk overall (HRQ4 vs Q1= 1.21 [95% CI: 1.00-1.47]) and a positive association was observed with premenopausal cancer (HRQ4 vs Q1= 1.40 [95% CI: 1.04-1.88]). Restricted cubic spline analyses suggested evidence for non-linearity of these associations, with higher HR for intermediate (Q2) and high (Q4) exposures. Receptor-specific analyses revealed a positive association with estrogen receptor-positive breast cancer, which represented 86% of total cancer cases. Acrylamide intake was not associated with post-menopausal breast cancer.Results from this large prospective cohort study suggest the potential deleterious role of dietary acrylamide in breast cancer etiology, especially in premenopausal women, and provide new insights that should encourage further mitigation strategies to reduce the content of acrylamide in food.Results from this large prospective cohort study suggest that dietary acrylamide exposure is associated with an increased risk of breast cancer, especially in premenopausal women.These epidemiological findings are in line with experimental data that conducted IARC, EFSA and other public health institutions worldwide to classify acrylamide as a probable carcinogen for humans.


Breast cancer diagnosed between a negative screening mammogram and the next regularly scheduled mammographic exam is called interval breast cancer. It is often diagnosed at more advanced stages than screening-detected cancers. While body mass index (BMI) is a risk factor for postmenopausal breast cancer overall and can influence the accuracy of mammography, the association of BMI with postmenopausal interval breast cancer is unclear. Using data from the Women’s Health Initiative, a national study among postmenopausal women, we found that lower BMI was significantly associated with a higher risk of interval breast cancers diagnosed within 1 year of a negative mammogram after adjustment for multiple risk factors. These findings suggest that obesity is associated with a lower risk of postmenopausal interval breast cancer. Future research using body composition measures is warranted to confirm our findings.2) was measured at baseline. Associations between obesity and incidence of interval cancer were determined by sequential logistic regression analyses. In multivariable-adjusted models, obesity was inversely associated with interval breast cancer risk [OR (95% CI) = 0.65 ]. The inverse association persisted after excluding women diagnosed within 2 years [OR (95% CI) = 0.60 ] or 4 years [OR (95% CI) = 0.56 ] of enrollment, suggesting consistency of the association regardless of screening practices prior to trial entry. These findings warrant confirmation in studies with body composition measures.Interval breast cancer refers to cancer diagnosed after a negative screening mammogram and before the next scheduled screening mammogram. Interval breast cancer has worse prognosis than screening-detected cancer. Body mass index (BMI) influences the accuracy of mammography and overall postmenopausal breast cancer risk, yet how is obesity associated with postmenopausal interval breast cancer incidence is unclear. The current study included cancer-free postmenopausal women aged 50–79 years at enrollment in the Women’s Health Initiative who were diagnosed with breast cancer during follow-up. Analyses include 324 interval breast cancer cases diagnosed within one year after the participant’s last negative screening mammogram and 1969 screening-detected breast cancer patients. Obesity (BMI ≥ 30 kg/m Interval breast cancer refers to cancer emerging after a non-suspicious mammography screen, and prior to the next scheduled screen. Previous studies have found that interval breast cancer has greater clinical severity at diagnosis, including higher average histological grade, larger tumor size, more metastatic local lymph nodes, and a worse prognosis compared to screening-detected cancers ,3,4,5,6.The current study objective was to investigate the role of BMI at enrollment with subsequent interval breast cancer risk using data from the Women’s Health Initiative (WHI) Clinical Trials . A previ2 are 30% more likely to receive false-negative results compared to those with BMI > 25 kg/m2 among postmenopausal women 50–59 years old /height squared [meters squared]) and categorized based on National Heart Lung and Blood categories : underweAll covariates were self-reported by participants in the WHI enrollment questionnaires completed between 1993–1998. Covariates considered in this analysis included: age at study entry, highest education, parity, family history of breast cancer, waist-to-hip ratio (WHR), comorbidity, energy expended from recreational physical activity (MET-hr/wk) and current smoking and alcohol use status . WHR wast-tests for continuous variables and Chi-square tests for categorical variables. Continuous BMI analysis was represented as per one-unit increases; categorical BMI was calculated with normal weight (BMI: 18.5–24.6) as reference.We first compared anthropometric, reproductive, lifestyle and health behavior characteristics between women diagnosed with interval breast cancer against those with screening-detected breast cancer. Bivariate associations between BMI and each of the studied covariates were analyzed using Covariates were selected for inclusion in logistic regression models based on unadjusted analyses including analyses of BMI with multiple variables and IBC risk with multiple variables, as well as risk factors for breast cancer previously identified in the literature . Covarian = 151) and their controls (n = 212), and those receiving both estrogen and progestin therapy (n = 398) and their controls (n = 297). Since the original WHI study found women receiving estrogen and progestin had significantly increased risk of breast cancer [We conducted sensitivity analyses to tease out the screening practice impact prior to trial entry by excluding (1) all breast cancer cases diagnosed within 2 years after enrolling into the WHI study and (2) all breast cancer cases diagnosed within 4 years after enrolling into the WHI study. We also conducted stratified analyses by early-stage vs. late-stage breast cancer. The current analytic study participants included those receiving estrogen therapy alone  (p < 0.0001), age at menarche (p < 0.0001) and age at first live birth (p < 0.0001), instance of previous breast biopsy (p = 0.0004), number of comorbidities (p < 0.0001), education level (p < 0.0001), alcohol use (p < 0.0001) and membership in WHI Hormone Therapy study arms (p < 0.0001). Those with a higher BMI were also more likely to be randomly assigned to the hormone intervention group at baseline. We did not find associations between BMI and age at enrollment or diagnosis, family history of breast cancer, parity, or Dietary Modification trial arm membership. -hrs/wk) . Chi-squOur study shows increasing BMI was inversely associated with risk of postmenopausal interval breast cancer, which differs from previously reported positive associations between obesity and overall postmenopausal invasive breast cancer risk . In unadPrevious studies of premenopausal women with early-onset breast cancer have shown obesity is associated with a reduced risk of breast cancer; that is, the higher the BMI, the lower the risk . A changOur data are also consistent with interval breast cancer being masked by denser breast tissue found in lean women, a potential interaction that requires further study. Dense breast tissue has a higher proportion of false-negative mammographic results . Premeno2, those with a relatively high trunk fat  are at significantly elevated risk of invasive breast cancer and show altered concentrations of circulating inflammatory and metabolic factors [A limitation of our study is that our sample lacked objective measures of body composition to appropriately differentiate between fat mass and fat-free mass and their distribution around the body. We used BMI and waist -hip -ratio (WHR) measurements in the analyses. BMI represents a person’s general (subcutaneous) adiposity ; however factors ,42. WHR  factors ; in thisOur study has several strengths, including a large population-based cohort with a long -follow -up period of 19 years (median), extensive case ascertainment and mammography history, and collection of detailed information on covariates. We were able to exclude women who were non-compliant with screening recommendations in order to focus only on screen-compliant women. Our study is limited by the fact that we were unable to consider the independence or interaction between breast density and BMI, given that density data were not collected. The results from this study may not be generalizable to non-US population.We found that lower BMI was associated with higher risk of postmenopausal interval breast cancer diagnosed <1 year from a normal screening mammogram, when compared with screening-detected breast cancer. Our findings need confirmation in future studies with objective measures of body composition and fat distribution. In addition, there is need for additional research on screening techniques and prediction models beyond mammography.


To examine the association between consumption of ultra-processed foods and risk of colorectal cancer among men and women from three large prospective cohorts.Prospective cohort study with dietary intake assessed every four years using food frequency questionnaires.Three large US cohorts.Men (n= 46 341) from the Health Professionals Follow-up Study (1986-2014) and women (n=159 907) from the Nurses’ Health Study  and the Nurses’ Health Study II  with valid dietary intake measurement and no cancer diagnosis at baseline.Association between ultra-processed food consumption and risk of colorectal cancer, estimated using time varying Cox proportional hazards regression models adjusted for potential confounding factors.3216 cases of colorectal cancer  were documented during the 24-28 years of follow-up. Compared with those in the lowest fifth of ultra-processed food consumption, men in the highest fifth of consumption had a 29% higher risk of developing colorectal cancer , and the positive association was limited to distal colon cancer . These associations remained significant after further adjustment for body mass index or indicators of nutritional quality of the diet . No association was observed between overall ultra-processed food consumption and risk of colorectal cancer among women. Among subgroups of ultra-processed foods, higher consumption of meat/poultry/seafood based ready-to-eat products  and sugar sweetened beverages  among men and ready-to-eat/heat mixed dishes among women  was associated with increased risk of colorectal cancer; yogurt and dairy based desserts were negatively associated with the risk of colorectal cancer among women .In the three large prospective cohorts, high consumption of total ultra-processed foods in men and certain subgroups of ultra-processed foods in men and women was associated with an increased risk of colorectal cancer. Further studies are needed to better understand the potential attributes of ultra-processed foods that contribute to colorectal carcinogenesis. Colorectal cancer is the third most commonly diagnosed malignancy among both men and women in the United States and the second leading cause of death from cancer worldwide.In a multicentric, population based case-control study in Spain, Romaguera and colleagues reported a positive association between ultra-processed food consumption and the risk of colorectal cancer.We used data from three large prospective cohorts in the US. The Nurses’ Health Study (NHS) included 121 700 registered female nurses aged 30 to 55 years at baseline in 1976,In this analysis, we used 1986 for the NHS (n=102 267), 1991 for the NHS II (n=108 453), and 1986 for the HPFS as the baseline, since when the Food Frequency Questionnaires (FFQs) were the most complete and comparable across studies.Dietary intake was assessed by validated semiquantitative FFQs with about 130 food items, administered every four years since 1986 in NHS and HPFS and 1991 in NHS II.We further estimated the percentage of total energy from ultra-processed foods for each participant as the exposure variable in a sensitivity analysis. To further investigate whether the association between overall ultra-processed food consumption and colorectal cancer was driven by specific food groups, we further categorized ultra-processed foods into mutually exclusive subgroups (supplementary table A).On each biennial follow-up questionnaire, participants were asked to report any cancer diagnosis in the previous two years. After receiving permission from the study participants, study physicians blinded to exposure data reviewed the medical records and pathological reports to confirm the diagnosis and extract information on anatomic location. We used information from various sources, including next of kin, the National Death Index, death certificates, and medical records, to confirm the diagnosis in participants who died from colorectal cancer but had not reported a diagnosis on a questionnaire.Self-administered questionnaires were sent to participants biennially to assess medical and lifestyle factors, including smoking, physical activity, alcohol intake, endoscopy status, regular use of aspirin and other non-steroidal anti-inflammatory drugs, family history of colorectal cancer, weight, and height, as well as menopausal status and postmenopausal hormone use for women, in all three cohorts as previously described.We calculated person years of follow-up from the return date of the first FFQ until the date of death, diagnosis of colorectal cancer, or end of follow-up , whichever came first. To better represent long term habitual intake and minimize random measurement errors, we calculated the cumulative average of energy adjusted servings of ultra-processed food intake per day and then categorized them into fifths. For example, in the NHS, we used the cumulative average of ultra-processed food intake from 1986 and 1990 as the exposure for the follow-up period from 1990 to 1994 and the cumulative average from 1986, 1990, and 1994 for the follow-up period between 1994 and 1996. Owing to the high within individual correlations in dietary intake between adjacent data cycles, we carried forward non-missing dietary intake data from the previous data cycle to replace missing data in the next cycle. We treated covariates similarly.We used Cox proportional hazards regression models to estimate the hazard ratios and 95% confidence intervals for risk of colorectal cancer in association with ultra-processed food intake. To account for repeated measures of food intake and covariates over time, we constructed the model on the basis of a counting process data structure for handling time varying covariates.In the multivariable model, we adjusted for potential confounding factors, including race (white/non-white), family history of cancer (yes or no), history of endoscopy (yes or no), physical activity , smoking status and pack years of smoking , total alcohol intake , total caloric intake (in fifths), and regular aspirin use (yes or no) and additionally for menopausal status (yes or no) and post-menopausal hormone use (yes or no) in women.On the basis of previous findings on the different pattern of the association of dietary factors with colorectal cancer risk between the sexes, we did the analyses among men (in HPFS) and women (in pooled data from NHS and NHS II) separately. We used the likelihood ratio test to examine the heterogeneity of risk estimates by sex in pooled data of the three cohorts. We examined the potential non-linear relation between ultra-processed food consumption and risk of colorectal cancer by using the restricted cubic spline analysis.As body mass index is a possible intermediate step linking ultra-processed food consumption with risk of colorectal cancer, we additionally adjusted for body mass index (model S1) in the multivariable model as a sensitivity analysis. Similarly, we adjusted for variables that could serve as both confounders and mediators, including western dietary pattern score (model S2),To further overcome the potential collider stratification bias due to the adjustment of time varying covariates in the Cox models, we emulated a target trial and applied the parametric g-formula to estimate the effect of a hypothetical intervention of limiting ultra-processed food consumption on risk of colorectal cancer.The association between ultra-processed food consumption and risk of colorectal cancer may be modified by genetic predisposition and other lifestyle factors such as physical activity, smoking, and body weight.We used SAS software, version 9.4 for UNIX, for all analyses. We set statistical significance at a two sided P value <0.05.This study used data from three large prospective cohort studies that were not specifically designed for the current aim. Therefore, we were not able to involve patients in setting the research question or the outcome measures, or in the design and implementation of the study. We plan to disseminate these findings to participants in our annual newsletter and the general public in a press release.Over the 24 to 28 years of follow-up, we documented 1294 cases of colorectal cancer among men in HPFS and 1922 cases among women in NHS and NHS II. Among both men and women, compared with those in the lowest fifth of ultra-processed food intake, participants in the highest fifth were more likely to be current smokers or smoke more pack years; to have a higher body mass index, lower consumption of alcohol, and a lower level of physical activity; and to consume a lower amount of dietary fiber, folate, calcium, vitamin D, and whole grains and a higher intake of fat, added sugars, and processed meats . They weAfter multivariable adjustments, men who consumed ultra-processed foods in the highest fifth had a 29% higher risk of developing colorectal cancer than did those in the lowest fifth  . HoweverSensitivity analyses showed similar results after further adjustment for body mass index (model S1) (supplementary table D), western dietary pattern score (model S2), or the AHEI-2010 score (model S3). Adjustment for intake of foods and nutrients including processed meats, fruits, vegetables, whole grains, calcium, vitamin D, and folate (model S4) attenuated the association of ultra-processed food consumption with overall colorectal cancer risk among men to non-significant , whereas the association with distal colon cancer remained significant . In addition, the results were not materially altered after recategorization of the undetermined food items as ultra-processed foods (supplementary table E), use of percentage of total energy of ultra-processed foods as the exposure variable (supplementary table F), or exclusion of participants with more than 10 missing items in the FFQs at baseline (supplementary table G) in sensitivity analyses.In the emulated targeted trial analysis, compared with no intervention (on average 6.6 servings per day of consumption), the dietary intervention scenario of restricting ultra-processed food consumption to four servings per day was associated with a risk ratio of 0.94  for developing colorectal cancer and 0.82 (0.72 to 0.94) for developing distal colon cancer among men (supplementary table H). We found no association among women. We did not observe any significant interactions between potential effect modifiers and ultra-processed food consumption for colorectal cancer risk  (supplementary table I).Among subgroups of ultra-processed foods , we obseIn this analysis of three large prospective cohorts with nearly three decades of follow-up, we found a positive association between consumption of ultra-processed food and risk of colorectal cancer among men, and the association was limited to distal colon cancer. Among subgroups of ultra-processed foods, we observed a positive association with colorectal cancer for higher consumption of meat/poultry/seafood based ready-to-eat products and sugar sweetened beverages among men and higher consumption of ready-to-eat/heat mixed dishes among women; we observed a negative association with colorectal cancer for yogurt and dairy based desserts among women.This study is among the first that has detected a positive association between ultra-processed food consumption and risk of colorectal cancer among men. Consumption of ultra-processed foods may contribute to poorer overall dietary quality,Why the association was seen in men but not in women is unclear. A previous review study found that the associations of dietary patterns with colorectal cancer were more consistently significant and stronger in men than women.The differing role of diet on colorectal cancer risks at specific anatomic subsites remains unclear. Stronger associations for overall dietary patterns,Strengths of this study include the prospective cohort design and high follow-up rate, which minimized recall and selection bias. Secondly, the detailed and repeated measurement of diet and other covariates enabled us to use cumulative averages of dietary intake and all the other quantitative factors to decrease measurement errors further and reduce residual confounding. The large number of cases of colorectal cancer for each subsite enabled us to examine the association of ultra-processed food with risk of colorectal cancer by anatomic subsites with sufficient statistical power.The study also has several limitations. Firstly, owing to the study’s observational nature, residual confounding due to unmeasured confounders and measurement error of covariates cannot be ruled out. Secondly, as FFQs collect intake from only a limited number of pre-defined items representing the primary source of energy and nutrients in the study population, they cannot cover the full spectrum of ultra-processed foods consumed. Additionally, FFQs used in the three cohorts were not designed to classify food intake by levels of processing, which may lead to non-differential misclassification of the exposure. For example, nine food items lacked sufficient details in the resource documents to support their classification.In conclusion, this study found that high consumption of total ultra-processed foods in men and certain subgroups of ultra-processed foods in men and women was associated with an increased risk of colorectal cancer. The findings support the public health importance of limiting certain types of ultra-processed foods for better health outcomes in the population. Further studies are needed to better understand the potential attributes of ultra-processed foods that contribute to colorectal carcinogenesis.Accumulating evidence suggests that high consumption of ultra-processed foods is associated with a higher risk of several chronic diseasesFew studies have assessed the association between ultra-processed food intake and colorectal cancer risk, and the findings are mixed owing to limitations in study design and sample sizesHigh consumption of total ultra-processed foods in men and certain subgroups of ultra-processed foods in men and women was associated with an increased risk of colorectal cancerThe findings support the public health importance of limiting certain types of ultra-processed foods for better health outcomes in the population


BRCA1/2. Women with pathogenic variants in these genes have a 40-80% lifetime risk of breast cancer. Past studies have shown that genetic counseling can help women and their families make informed decisions about genetic testing and early cancer detection or risk-reduction strategies. However, H/L are 3.9-4.8 times less likely to undergo genetic testing than NHW women. We developed a program to outreach and educate the H/L community about hereditary breast cancer, targeting monolingual Spanish-speaking individuals in California. Through this program, we have assessed cancer screening behavior and identified women who might benefit from genetic counseling in a population that is usually excluded from cancer research and care.Breast cancer is the most common cancer among women in the U.S. and the leading cause of cancer death among Hispanics/Latinas (H/L). H/L are less likely than Non-H/L White (NHW) women to be diagnosed in the early stages of this disease. Approximately 5-10% of breast cancer can be attributed to inherited genetic mutations in high penetrance genes such as The “Tu Historia Cuenta” program is a promotores-based virtual outreach and education program including the cities of San Francisco, Sacramento, and Los Angeles. Participants responded to three surveys: a demographic survey, a breast cancer family history survey, and a feedback survey. Survey responses were described for participants and compared by area where the program took place using chi-square, Fisher exact tests, and t tests. Multinomial logistic regression models were used for multivariate analyses.We enrolled 1042 women, 892 completed the cancer family history survey and 62 (7%) provided responses compatible with referral to genetic counseling. We identified 272 women (42.8% ages 40 to 74 years) who were due for mammograms, 250 women (24.7% ages 25 to 65 years) due for Papanicolaou test, and 189 women (71.6% ages 50+) due for colorectal cancer screening. These results highlight the need of additional support for programs that spread awareness about cancer risk and facilitate access to resources, specifically within the H/L community. Breast cancer is the most common cancer among women in the United States , 2 and tBRCA1 and BRCA2 have a 40-80% lifetime risk of breast cancer compared to 12% risk in the general population  are uniquely positioned to bridge the gap between the H/L community and the health care system  women 21-75 years of age, 2) Spanish-speaking or bilingual, and 3) self-identifying as H/L. Participants provided verbal informed consent. Data from all surveys were de-identified. The study was approved by the University of California, San Francisco Institutional Review Board.THC is a promotores-led outreach and education program with materials developed using a continuous stakeholder engagement approach as previously described . The oneWomen identified as having strong family history based on their score in the breast cancer-family history survey received a recommendation to discuss their family history with a doctor and potentially a genetic counselor. For those without a usual source of care, we provided resources and support to facilitate access.The demographic survey contained questions including city of residence, zip code, age, number of years residing in the U.S., number of people in the household, and employment status. Information regarding English-language proficiency , medical insurance , and educational attainment  was obtained. In addition, the demographic survey contained questions regarding genetic testing such as previous knowledge and exposure to genetic testing, and interest in genetic testing. A subset of questions targeted cancer screening behavior .The family history of breast cancer survey was adapted from a previously validated survey  and collThe feedback survey was given to participants at the end of the education session. This survey was anonymous and had nine questions to help understand how useful participants found the information provided and whether they felt motivated to share the information learned with family and friends and to seek additional information regarding breast cancer.Average, dispersion (standard deviation-SD) and proportion measures were used to describe the characteristics of the participants and their survey responses. We used chi-square, Fisher’s exact test, and two-sided t-tests to compare characteristics and responses between participants in the three areas of outreach: San Francisco, Sacramento, and Los Angeles County, as well as by breast cancer family history score  and screening status.We used multivariate multinomial logistic regression analyses to assess the association between different demographic factors and screening behavior among THC participants. The ‘never’ screened category group was defined as reference in all regression models. All analyses were conducted in RStudio version 4.1.2 .A total of 1042 Spanish-speaking H/L women residing in San Francisco County, Sacramento and Los Angeles provided demographic information after registering for the THC education session. The average age of participants was 43 years and ranged between 21 and 73 years  (Most participants expressed interest in learning about genetics (98%), and only 1.3% of the individuals stated that they were not interested in learning about genetics or how genetics could be used to prevent or detect cancer early. More than half of the participants reported that they had not heard about genetic tests before (52.2%) (Among women within the age range of mammography screening guidelines (40-74 years), 56.1% were current with their mammogram , and 42.8% of the participants were due for mammograms . Of the 163 women who had never had a mammogram, 14% were navigated into the Every Women Counts program (EWC) , and of Cervical cancer screening for women between the ages of 21 to 65 years was observed for 82.1%, with 73.3% of the participants having obtained a Papanicolaou test within the last 3 years. Among participants 50 years of age and older, 23.5% reported ever having colorectal cancer screening (Most participants in the program expressed interest in learning about genetics and breast cancer (~98%), however, a larger proportion of participants who resided in the San Francisco area were aware of genetic testing (62.9%) compared to participants in Los Angeles County (42.3%) and Sacramento (41.3%) , while a lower proportion was observed among participants in Los Angeles County (52.5%); this difference was not statistically significant    (N=62), 7 (11.3%) had received genetic counseling before participating in THC, 8 (12.9%) reported having been diagnosed with breast cancer before the age of 50 years, and one woman (1.6%) after the age of 50 years. Among the 43 participants originally identified as 6+ that moved to the ‘limited family history’ category, 3 (7%) reported a breast cancer diagnosis after age 50 years  (15%) compared to foreign-born individuals (6%). Furthermore, the proportion of 6+ score individuals was higher among those with a university degree (16%) compared to women with lower level of educational attainment (4-9%)  responded to the anonymous feedback survey  were still unclear by the end of the session for 4-5.5% of participants who responded , who due to their limited English proficiency, socioeconomic and health insurance status, and cultural barriers, might not have access to adequate information and resources for breast cancer prevention, particularly, for prevention of hereditary breast cancer. The demographic characteristics of the program participants were consistent with the target population and supports the crucial role of promotores in connecting with underserved communities , 55, 56.We limited the program to women older than 21 years of age, and the average age for all participants was 43 years, with some variation by geographic area, with San Francisco participants being younger than those in Los Angeles and Sacramento. The difference in the average age of participants at the different locations might be a reflection of the age of promotores in the different groups, since the average age of an individual’s networks is likely to be concordant with their own age. For programs working with promotores, this may be important as it helps demonstrate that promotores may recruit individuals within their social circles that resemble some of their own characteristics. Having promotores of similar age of the target population of a specific program may be important.California’s Medicaid-managed care legislation established a two-plan model in 14 counties with the largest Medicaid population . MedicaiA study of breast cancer screening among H/L age 40 years and older in San Diego County found that 76.2% of women had received a mammogram in the past 2 years , which iThe THC participants who had never obtained a mammogram reported a higher average number of household members, which is a measure that correlates with socioeconomic status, thereby suggesting that participants who never had a mammogram within the THC study may also be those in the lowest income bracket.Participants expressed interest in learning about hereditary breast cancer and genetics despite limited knowledge at the time of registration. The proportion of participants identified as having strong family history of breast cancer (~7%), is concordant with other estimates in studies assessing breast cancer family history in unaffected women –68. The There were 116 individuals whose breast cancer family history survey scores changed after a second conversation with promotores. Over-reporting of cancer family history has been noted in previous studies , 70. TheA strength of this study was that we were able to connect to a population that is often excluded from health studies . Another strength was that researchers worked closely with promotores to ensure the relevance and accessibility of the study materials and process, while engaging community members to obtain their perspective and perceptions of the program . Due to The study has some limitations. Participants were enrolled through the work of two organizations and individuals were recruited from promotores’ social circles and networks. Consequently, results from this study may not be generalizable to the overall population of Spanish-speaking H/L in San Francisco, Sacramento, and Los Angeles County. Additionally, the education program advertised learning about hereditary breast cancer, which could have influenced people to participate if they had a personal interest based on their family history of cancer. However, the percentage of individuals in the THC study identified as candidates for genetic counseling (7%) was slightly less than what has been reported for the general population of unaffected women in the U.S. (8% to 12%) –68, suggOverall, participants found the THC education session to be useful, and most of the participants reported willingness to share the information they acquired in the session with their friends and family. We hope the sharing of information will lead to greater awareness about hereditary breast cancer in California Spanish-speaking H/L communities.The THC promotores-led outreach, education and breast cancer family history assessment program implemented in San Francisco, Sacramento, and Los Angeles in June 2020 has reached more than 1000 Spanish-speaking H/L. Since then, we have identified 62 women (7%) which based on survey responses could benefit from genetic counseling, 272 (42.8%) women due for mammograms (64 of whom we have navigated to the EWC program), 250 (24.7%) due for Papanicolaou test, and 189 (71.6%) due for colorectal cancer screening. Follow-up of the THC participants who were referred to and/or navigated to genetic counseling and testing will be important to assess the long-term impact of the program on the prevention of advanced breast cancer diagnosis among Spanish-speaking H/L with strong family history of the disease.The results from the THC study highlight the need for additional programs targeted to this underserved population in order to spread awareness about cancer risk and facilitate access to resources for prevention.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by University of California, Davis IRB. The patients/participants provided their verbal informed consent to participate in this study. The current study is not subject to HIPAA rules and therefore waived from written informed consent requirements.LT performed the statistical analysis and wrote the manuscript. LF conceived and designed the study and provided overall supervision of data analyses and manuscript writing. YD contributed to the conception and design of the study and to manuscript revision. FP controlled the quality of the data, organized the database, and contributed to manuscript revision. MH and AM supervised data collection. XH and VZ contributed to data curation and manuscript revision. EZ, SN, and LC-C contributed to manuscript revisions. All authors contributed to the article and approved the submitted version.EZ, SN, LGC-C and LF received support from the California Initiative to Advance Precision Medicine (contract OPR18111). LF receives support from the National Cancer Institute (R01CA204797). LGC-C cancer health disparities and community engagement efforts receive support from the NCI , National Center for Advancing Translational Science (UL1TR001860), the Heart, BrEast and BrAin HeaLth Equity Research (HEAL-HER) Program and the Auburn Community Cancer Endowed Chair in Basic Science. The opinions expressed in this article are the author’s own and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government.We want to thank the ‘Tu Historia Cuenta’ participants for taking the time to attend educational sessions and respond to study surveys. In addition, this study would not have been possible without the passion, resilience and energy of the promotores who faced and overcame the difficulties of implementing a virtual outreach and education hereditary breast cancer program in the middle of the Covid19 pandemic.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Breast cancer is the most common disease among women, with over  Cancer mortality rates are falling due to recent advancements around earlier diagnosis and improved therapeutic options. However, further work is still needed considering that breast cancer is still one of the most frequent cancers in Europe, and it is the second leading cause of cancer mortality ,2. A breWomen who have had early stage breast cancer are at risk of recurrence, either locally, regionally or at distant sites. Approximately iagnosis . At the Artificial intelligence techniques are emerging to resolve medical issues such as diagnosis, prognosis, drug design, and testing ,9,10,11 Researchers would like to find papers of interest, contributions, and evidence in order to prevent repetition and to enhance their results given the variety of approaches, investigations, and published papers on predicting the risk of breast cancer recurrence using artificial intelligence. There are some reviews on the risk of recurrence in breast cancer based on identifying machine learning techniques and comparing results . HoweverThe study of the application of artificial intelligence techniques to predict the risk of recurrence of breast cancer was conducted according to the methodology of Kitchenham and Charter . KitchenThis review aims to explore the literature surrounding artificial intelligence techniques, features, training and testing methodologies, model evaluation metrics, and use of artificial intelligence to predict the risk of recurrence of breast cancer. Considering there are different strategies, studies, and a significant amount of published papers on predicting the risk of recurrence of breast cancer using artificial intelligence, researchers need to identify publications of interest, contributions, and evidence in order to avoid repetition and to improve their results. In recognition of the gap within the existing literature, we conducted a systematic literature review using electronic bibliographic databases from January 2011 to November 2021.RQ1: What artificial intelligence techniques have been used to predict the risk of recurrence of breast cancer and what is their performance?RQ2: What type of features have been used?RQ3: What were the common training and testing methodologies used?RQ4: What model evaluation metrics have been used, and what are the advantages and disadvantages of these metrics?RQ5: What systems have been implemented in clinical practice, or validated in a real-world context?The research questions that we aimed to address were:We conducted a systematic search of the literature in the following scientific and academic databases and search engines: Springer, Google Scholar, PubMed, and IEEE. The searches were conducted in English. Only studies using artificial intelligence techniques to predict the risk of recurrence of breast cancer were selected.Selecting appropriate search terms was a key step; keywords that were too broad yielded an unwieldy number of irrelevant publications, but terms that were too particular seemed to overlook significant research. This required some experimentation with a range of terms to select the key words for a broad and inclusive review of the application of artificial intelligence techniques to predict the risk of recurrence of breast cancer. We performed a search using the following query:("Predicting Breast Cancer Recurrence" OR "Risk of Recurrence Breast Cancer Prediction" OR "Recurrence Prediction Breast Cancer") AND We searched for studies reported between 1st January 2011 and 1st November 2021. A total of 492 papers were found at this stage before excluding irrelevant papers. In order to verify the quality of the selected studies, each study that met the inclusion criteria was abstracted by a reviewer and a questionnaire was completed for each paper. Each question was designed to elicit information about potential limitations in the quality of the study. The evaluation questions were: (i) Was the artificial intelligence solution well described ?; (ii) Was the study population  well described?; (iii) Was the data type  well described?; (iv) Were the evaluation metrics well described? Answers that showed quality problems were assessed to see whether they were significant enough to diminish confidence in the results.Artificial intelligence has made a substantial contribution to cancer research. Despite the fact that deep learning classifiers have dominated many research areas, traditional machine learning models are more widely used  have been used most commonly for breast cancer recurrence risk prediction—used in 17 studies. Naïve Bayes and Decision Trees have also been used extensively in this research, with 16 and 14 studies respectively. Bayesian Neural Networks and Multivariate Logistic Regression were the least used with only two studies for each. The distribution of literature based on artificial intelligence prediction methods is shown in The type of data used to train a prediction model can significantly affect the performance of the model, and impact on the model’s reliability and prediction outcomes . Most ofRegarding patient characteristics, the majority of studies  [With the introduction of computer systems, the digitisation of clinical examination and medical records in healthcare systems has become a standard and widely accepted practice. However, there are challenges with breast cancer recurrence dataset size and class balance, given that around  51.61%) ,47,49,52 51.61%) ,27,32,36Data selection is an important phase in artificial intelligence training and testing procedures. Selecting data has a direct impact on the performance of the resulting model . Samplinenerated . HoweverHealth data often contain a lot of missing values. Missing values can be caused by failure to record data due to a lack of standards or by data corruption. Handling missing data is crucial during data preprocessing since many artificial intelligence algorithms do not handle missing values, thereby affecting their performance. In our analysis, excluding cases with incomplete data was the most commonly used strategy  specificity  Dataset size and class balance. Most of these studies had a limited number of patients, <1000, especially for a common disease such as breast cancer. However, number of patients developing recurrence, follow-up recurrence window, and difficulty in keeping patients records for a long time present challenges to collecting data on the risk of recurrence. None of the studies had balanced data, recurrence cases are less than datasets . Neverthdatasets  and syntdatasets . (ii) Sadatasets . (iii) Ddatasets . (iv) Vadatasets ,61. All datasets .Our fourth research question is to identify and critically appraise what model evaluation metrics are being used, and what are the advantages and disadvantages. Given the class imbalance present in the associated datasets, it is encouraging to find that most of the studies used specificity, sensitivity, accuracy, and AUC—20, 19, 18, and 16 out of the 31, respectively. Some studies have discussed precision and accuracy metrics in term of trustworthiness for imbalanced classes [Finally, none of the 31 studies shows evidence of being used in clinical practice or validated in a real-world context. Translating artificial intelligence proposals into medical practice or increasing the likelihood of them being validated in a real-world setting is still an open problem. However, artificial intelligence implementation needs to be rigorously assessed to be embraced responsibly ,79, consPredicting the risk of recurrence in breast cancer is crucial for choosing proper treatment methods, as well as reducing morbidity and mortality . Our litOn the one hand, according to this review, artificial intelligence techniques have performed well on independent and ensemble approaches, which is consistent with previous literature reviews . Large dIn summary, translation of artificial intelligence approaches into medical practice remains a challenge. However, in order to increase changes of acceptance within clinical context, artificial intelligence implementations must be thoroughly evaluated ,79.


BRCA1, BRCA2, PALB2, CHEK2, ATM, and TP53 via gene-panel sequencing. We conducted complex segregation analyses and fitted genetic models in which breast cancer incidence depended on the effects of known susceptibility genes and other unidentified major genes and a normally distributed polygenic component. The proportion of familial variance explained by the six genes was 46% at age 20–29 years and decreased steadily with age thereafter. After allowing for these genes, the best fitting model for the residual familial variance included a recessive risk component with a combined genotype frequency of 1.7% (95% CI: 0.3%–5.4%) and a penetrance to age 80 years of 69% (95% CI: 38%–95%) for homozygotes, which may reflect the combined effects of multiple variants acting in a recessive manner, and a polygenic variance of 1.27 (95% CI: 0.94%–1.65), which did not vary with age. The proportion of the residual familial variance explained by the recessive risk component was 40% at age 20–29 years and decreased with age thereafter. The model predicted age-specific familial relative risks consistent with those observed by large epidemiological studies. The findings have implications for strategies to identify new breast cancer-susceptibility genes and improve disease-risk prediction, especially at a young age.Rare pathogenic variants in known breast cancer-susceptibility genes and known common susceptibility variants do not fully explain the familial aggregation of breast cancer. To investigate plausible genetic models for the residual familial aggregation, we studied 17,425 families ascertained through population-based probands, 86% of whom were screened for pathogenic variants in  A large population-based family study, with gene-panel sequencing data, investigated the genetic models that explain the residual breast cancer familial aggregation after considering known susceptibility genes. The results may have implications for strategies to identify new breast cancer-susceptibility genes and improve disease-risk prediction, especially at a young age. The familial relative risk (FRR) of breast cancer for having an affected first-degree relative is on average about 1.8 but is greater the younger the age at cancer diagnosis of the relative(s), the greater the number of affected relatives, and the younger the age of the consultand.There is a substantial familial aggregation of breast cancer (MIM: BRCA1 (MIM: 113705), BRCA2 (MIM: 600185), and PALB2 (MIM: 610355) and intermediate-risk PVs in genes such as CHEK2 (MIM: 604373) and ATM (MIM: 607585)BARD1 (MIM: 601593), RAD51C (MIM: 602774), RAD51D (MIM: 602954), TP53 (MIM: 191170), PTEN (MIM: 601728), and NF1 (MIM: 613113), are also associated with breast cancer risk, but to a lesser extent, and account for a small proportion of the familial aggregation.High-risk pathogenic variants (PVs) in the currently known breast cancer-susceptibility genes 1 MIM: 11705, BRCA2 MIM: 60373 and AMIM: 60015, and PAB2 MIM: 6355 and iBRCA1 and BRCA2 PVs and a polygenic component representing the combined multiplicative effects of a large number of unknown genetic variants each making a small contribution to the variation in risk.,,PALB2, CHEK2, and ATM, polygenic risk scores, lifestyle- and hormone-related risk factors, and mammographic densityBARD1, RAD51C, and RAD51D.Segregation analyses have been used to develop pedigree-based statistical models of breast cancer susceptibility and predict breast cancer risk on the basis of family history, genotype, and other factors.BRCA1, BRCA2, PALB2, CHEK2, ATM, and TP53 were available. Our objectives were (1) to estimate key genetic model parameters simultaneously to further improve the accuracy of existing genetic models and (2) to investigate the genetic models of inheritance that best explain the familial aggregation of breast cancer not accounted for by the known breast cancer-susceptibility genes and polygenic factors.It was not previously possible to fit segregation analysis models that included the joint effects of all known breast cancer-susceptibility genes simultaneously because of the lack of data on PVs in some genes. Another challenge has been small sample sizes that limit the statistical power to distinguish different inheritance models. In this study, we conducted the analysis of this kind by using data from families ascertained through large population-based series of affected and unaffected probands for which data on PVs in The sample included 17,425 three-generation families ascertained via population-based sampling of breast cancer probands from two studies: 2,712 families from the population-based case-control family study within the Australian Breast Cancer Family Registry (ABCFR) and 14,713 families from the prospective Studies of Epidemiology and Risk Factors in Cancer Heredity (SEARCH) study in the UK.167000) diagnoses of the probands and all their adult female first- and second-degree relatives identified at baseline. ABCFR was designed to be enriched for breast cancer diagnoses at younger ages: 55% of case probands and 52% of control probands included in this analysis were younger than 40 years at diagnosis and recruitment, respectively. Of the probands, 92% reported having White ethnicity.ABCFR,SEARCHABCFR was approved by the Human Research Ethics Committee of the University of Melbourne. SEARCH was approved by the National Research Ethics Service Committee East of England—Cambridge South. All participants provided written consent.BRCA1, BRCA2, PALB2, CHEK2, ATM, and TP53. PVs included predicted protein-truncating variants (PTVs) and an additional subset of rare missense variants (population frequency < 0.001) for BRCA1, BRCA2, and TP53. The risks associated with this subset of missense variants have been shown to be similar to those associated with PTVs,We studied six major cancer-susceptibility genes: In ABCFR, PVs were identified by gene-panel testing for 2,305 probands  and 770 relatives and by other tests conducted before gene-panel testing was widely available  for 2,31i in birth cohort k, from country c and at age t, the incidence of cancer s, λi, was assumed to depend on the genetic factors according to the following model:λ0 is the baseline incidence, n is the number of major genotypes, and Gμi is an indicator variable that takes the value of 1 if the woman has major genotype μ and 0 otherwise. Pi(t) is the breast cancer age-specific polygenic component that was assumed to be normally distributed with zero mean and variance σP2(t), the same across countries and birth cohorts, representing the multiplicative effects of a large number of variants each associated with a small increment in breast cancer risk. No polygenic component was included for ovarian cancer. The polygenic component was approximated by the hypergeometric polygenic model (HPM),R has a binomial distribution  and N, the number of loci used in the HPM, was 3.We conducted complex segregation analysis by using the pedigree analysis software MENDEL following a similar approach to that used in previous studies.P2(t) from this model reflects the total breast cancer familial variance under the polygenic susceptibility model.P2(t) is termed the polygenic variance, it also captures the effects of non-genetic factors contributing to the risks for relatives being correlated and whose existence is suggested by analysis of twin pairs in the Nordic Twin Study.P2(t) to decrease linearly with age.We firstly fitted a model with a polygenic component only, without any major genes. The polygenic variance σBRCA1, BRCA2, PALB2, CHEK2, and ATM, as well as the polygenic component. We sequentially extended this model to include the effects of PVs in TP53, and a hypothetical major gene for which we investigated different models of inheritance . We used the decrease in σP2(t) as major genes were included to express the proportions of breast cancer familial variance explained by PVs in those genes.We then fitted a model equivalent to the BOADICEA (versions 4 and 5) in terms of the number of major genes, which included the effects of PVs in m + 1 alleles, where m is the number of major genes considered in the model. That is, we assumed separate risk alleles representing the presence of a PV in BRCA1, BRCA2, PALB2, CHEK2, ATM, TP53, and the hypothetical gene and a normal allele. We assumed a dominant inheritance for the risk alleles in the order of the seven major genes above; therefore, there was a total of nine possible major genotypes μ: women with BRCA1 PVs, women with BRCA2 PVs, women with PALB2 PVs, women with CHEK2 PVs, women with ATM PVs, women with TP53 PVs, the hypothetical gene risk allele homozygotes, the hypothetical gene risk allele heterozygotes, and women without PVs. This simplification is unlikely to affect results because women with PVs in more than one major gene are very rare and would not contribute materially to the analysis; from our data, 0.1% of the women were such , we used the method previously describedTo reduce computational time, we fitted the major gene component by using a single locus comprising ere such . To compBRCA1 and BRCA2 PVs and breast cancer RRs for PALB2, CHEK2, and ATM PVs were fixed at estimates from previous studies,,,BRCA1, BRCA2, PALB2, and CHEK2 PVs  were estimated. The variances of the parameter estimates were obtained by inverting the observed information matrix. To allow for the restricted ranges of the parameter values and provide estimates likely to be more nearly normally distributed, we used transformed values for the parameters in the model: allele frequencies were logit transformed, RRs were log transformed, and σP2(t) was square-root transformed. We compared nested models by using the likelihood ratio test and non-nested models by using the Akaike information criterion. All statistical tests were two sided, and results with a p < 0.05 were treated as statistically significant.The age-specific breast and ovarian cancer RRs  to age t, F(t), asλ(t) is the estimated incidence at age t averaged over the polygenic effects, based on the UK population incidence for women born in 1940–1949 (the median birth year of the probands was 1948). The 95% confidence interval (CI) of F(t) was calculated via a parametric bootstrap: a sample of 10,000 draws was taken from the multivariate normal distribution that the maximum likelihood estimates would be expected to follow under asymptotic likelihood theory; for each age, a corresponding sample of 10,000 cumulative risks was calculated with the formula above, and the 2.5th and 97.5th percentiles of this distribution were taken to be the 95% CI limits.For BRCA1, BRCA2, PALB2, CHEK2, ATM, or TP53  probands were screened, and 892 (5.9%) were found to carry a PV in  or TP53 . Of the  or TP53 , and 164 or TP53 .P2(t) was 3.86  at age 20–29 years , decreasing to 0.72  at age 70–79 years  was 2.25  at age 20–29 years, decreasing to 1.11  at age 70–79 years. The proportion of the total breast cancer familial variance attributed to these five major genes was therefore 42% at 20–29 years and decreased steadily with age thereafter  was 1.53 , and there was no evidence that σP2(t) depended on age (p = 0.2).We then additionally fitted a sixth hypothetical major gene . All thrTP53. No PVs in the other genes were identified for the probands of these families from the gene-panel testing data. We therefore hypothesized that the hypothetical gene might reflect the effects of TP53 PVs and further extended the models to include TP53.Most of the difference in log-likelihoods between the dominant inheritance model for the hypothetical gene and its equivalent nested model without the gene was attributed to a small number of families . For allTP53 in addition to the five major genes while fitting an age-decreasing σP2(t) improved the model fit  and the estimated cumulative risk of breast cancer to age 80 years for TP53 PVs was 45.0%   that σP2(t) decreased with age. On the basis of the model with an age-decreasing σP2(t), the proportion of the total breast cancer familial variance attributed to TP53 was 3.5% at age 20–29 years and decreased with age thereafter; in terms of the residual familial variance after adjusting for the effects of BRCA1, BRCA2, PALB2, CHEK2, and ATM, the proportion decreased from 6.1% at age 20–29 years  . After f29 years .Figure 2TP53 and an age-constant σP2(t), we incorporated an additional hypothetical gene (P2(t) was estimated to be 1.27  and there was no evidence that it depended on age (p = 0.81). The proportion of the total breast cancer familial variance explained by the hypothetical gene was 21.6% at age 20–29 years and decreased steadily with age thereafter; in terms of the residual familial variance after adjusting for the effects of BRCA1, BRCA2, PALB2, CHEK2, ATM, and TP53, the proportion was 39.7% at age 20–29 years and decreased thereafter  was added . For allBRCA1, BRCA2, PALB2, CHEK2, ATM, and TP53 genes were 0.089%, 0.157%, 0.067%, 0.432%, 0.187%, and 0.020%, respectively, corresponding to frequencies of individuals with BRCA1, BRCA2, PALB2, CHEK2, ATM, and TP53 PVs of 0.178%, 0.313%, 0.133%, 0.862%, 0.374%, and 0.039%, respectively.Similar results were found from the sensitivity analyses in which the PV test sensitivity was reduced to 80% . With thThe age-specific FRRs associated with a family history in a first-degree relative predicted by the best fitting recessive inheritance model were consistent with those observed by the largest combined analysis of epidemiological studiesBRCA1, BRCA2, PALB2, CHEK2, ATM, and TP53 play more important roles at younger ages; the proportion of the total breast cancer familial variance explained by these genes was 46% at age 20–29 years and decreased steadily with age thereafter. GWASs have identified several hundred common genetic variants associated with breast cancer risk;,,BRCA1, BRCA2, PALB2, CHEK2, ATM, TP53, and the PRS, with the proportion greater at younger ages (44% at ages 20–29 years) and decreased steadily with age thereafter.This study provides insights into the genetic susceptibility of breast cancer. In terms of explaining why women of the same age differ in risk, the known susceptibility genes, including BRCA1, BRCA2, PALB2, CHEK2, ATM, and TP53, is consistent with the results from previous analyses.,,,,Although our study sample overlapped with the data used in three previous analyses,We also cannot rule out the role of non-genetic familial factors in explaining familial aggregation of breast cancer, as we only considered genetic models. The Nordic Twin Study of breast cancer found evidence for non-genetic effects shared by twins, especially at younger ages, which would also be consistent with that the FRR is greater for sister pairs than for mother-daughter pairsOur results also do not necessarily imply there is a single additional susceptibility gene (indeed this is highly unlikely), nor imply one or two genes contribute most of the effect, because such gene(s) would probably have been identified through linkage or association studies. It is more likely that the recessive component reflects the combined effects of multiple variants (and in more than one gene or non-coding region), each potentially being associated with different effects, although the actual number of variants and genes cannot be inferred from segregation analysis alone. Nevertheless, our results could inform the design of sequencing studies to try to identify such variants by focusing on analyzing the following types of samples that do not have PVs in the known genes: (1) families with multiple affected siblings; (2) families of young women  with breast cancer; and (3) a combination of (1) and (2).,,Breast cancer FRR depends on pathological subtype, and the risks associated with both known breast cancer-susceptibility genes and PRS differ across subtypes.We calculated the age-specific proportion of breast cancer familial variance explained by PVs in major genes as the difference in the polygenic variance between nested models. The results in Although our study—by design—did not include probands diagnosed after age 70 years, the analyses are still informative for familial aggregation after age 70 years because it was the phenotypes  of relatives, not of probands, that contributed to the analyses, as the likelihood of observing the family phenotypes was conditioned on the proband’s phenotype. Our study included 22,253 relatives from 12,393 families  who were censored after age 70 years, and 470 of them were diagnosed with breast cancer.BARD1, RAD51C, and RAD51D, which are incorporated in the most recent version of BOADICEA (version 6).Our study did not consider the PVs in other recently established breast cancer-susceptibility genes such as TP53 PVs occur among women diagnosed at a very young age.TP53 PVs are lacking. Two studies of sarcoma families reported a breast cancer cumulative risk for TP53 PVs of approximately 37% to age 80 years and 54% to age 70 years, respectively.,TP53 PVs with cancer family history. On the other hand, the estimates in our study are based on families from population-based studies of breast cancer, and our analysis has adjusted for the ascertainment process and allowed for the modification of cancer risks by the residual familial component. As a result, our estimate represents the average risk for women with TP53 PVs unselected for family history and would be expected to be lower than the estimates for those with family history of breast cancer—similar to the patterns observed previously for the risks for women with BRCA1/2 PVs.,TP53 PV risks are assumed to be modified by the residual familial component, the breast cancer risk predicted by the model will vary by the exact family history, and the risks for those with one or two affected relatives will be higher than the average risk.We previously conducted a population-based study of women with breast cancer diagnosed at a young age in ABCFR and confirmed that germline TP53 PVs. However, the extent to which including the additional genes and using the updated polygenic variance improves breast cancer-risk prediction overall needs to be investigated. Moreover, such a revised model would need to be validated with independent studies.The findings of the current study can be used to update breast cancer-risk models, in particular BOADICEA, which currently considers the effects of PVs in known major genes and a polygenic component.BRCA1, BRCA2, PALB2, CHEK2, and ATM, respectively, on the basis of typical clinically testing approaches,,BRCA1 and BRCA2 were slightly greater than, and the estimates for PALB2, CHEK2, and ATM were similar to, those assumed in the BOADICEA, which had been derived from large population-based targeted-sequencing data and adjusted for the test sensitivity of targeted-sequencing.To allow for the possibility that not all PVs can be detected by the test methods used, our analysis included a test sensitivity parameter, which was assumed to be ∼90%. The actual sensitivity is difficult to estimate and will depend on the methods used. The current implementation of BOADICEA assumes sensitivities of 89%, 96%, 92%, 98%, and 94% for PALB2, CHEK2, ATM, and TP53 in addition to BRCA1 and BRCA2 while modeling the residual familial variance of breast cancer as a function of age with a polygenic component.Our study has several strengths. First, it included data from more than 17,000 three-generational families ascertained via probands from population-based studies and screened for high- and moderate-risk PVs in the major known susceptibility genes. Second, our study incorporated the explicit age-specific effects of PVs in The study has also some limitations. First, family history in some families was self-reported and therefore subject to reporting errors, though reporting of breast cancer in first-degree relatives is generally considered to be accurate.In conclusion, by considering the explicit effects of established major breast cancer-susceptibility genes and polygenes and using the largest sample size of its kind, our analysis estimates the proportion of breast cancer familial aggregation that is explained by established susceptibility genes and variants and provides evidence for an additional recessive risk component, which could explain a substantial proportion of the residual familial aggregation, especially at a younger age. Our findings are informative for the design of sequencing studies to identify novel breast cancer-susceptibility genes and modeling breast cancer genetic susceptibility for disease risk prediction.