People with active cancer are recognised as at risk of COVID-19 complications, but it is unclear whether the much larger population of cancer survivors is at elevated risk. We aimed to address this by comparing cancer survivors and cancer-free controls for (i) prevalence of comorbidities considered risk factors for COVID-19; and (ii) risk of severe influenza, as a marker of susceptibility to severe outcomes from epidemic respiratory viruses.We included survivors (≥1 year) of the 20 most common cancers, and age, sex and general practice-matched cancer-free controls, derived from English primary care data linked to cancer registrations, hospital admissions and death registrations. Comorbidity prevalences were calculated 1 and 5 years from cancer diagnosis. Risk of hospitalisation or death due to influenza was compared using Cox models adjusted for baseline demographics and comorbidities.108,215 cancer survivors and 523,541 cancer-free controls were included. Cancer survivors had more diabetes, asthma, other respiratory, cardiac, neurological, renal, and liver diseases, and less obesity, compared with controls, but there was variation by cancer site. There were 205 influenza hospitalisations/deaths, with cancer survivors at higher risk than controls . Haematological cancer survivors had large elevated risks persisting for >10 years . Survivors of other cancers had evidence of raised risk up to 5 years from cancer diagnosis only .Risks of severe COVID-19 outcomes are likely to be elevated in cancer survivors. This should be taken into account in policies targeted at clinical risk groups, and vaccination for both influenza, and, when available, COVID-19, should be encouraged in cancer survivors. Few data are available to date on how COVID-19 affects cancer survivors. We searched PubMed with the keywords “influenza cancer survivors” to identify studies that compared severe influenza outcomes in cancer survivors and in a control group. No study was identified.In this matched cohort study of routinely collected electronic health records, we demonstrated raised risks of influenza hospitalisation or mortality in survivors from haematological malignancies for >10 years after diagnosis, and in survivors from solid cancers up to 5 years after diagnosis.Cancer survivorship appears to be an important risk factor for severe influenza outcomes, suggesting that cancer survivors may also be at raised risk of poor COVID-19 outcomes. This should be taken into account in public health policies targeted at protecting clinical risk groups. Influenza vaccination should be encouraged in this group, and may need to be extended to a wider population of medium- to long-term cancer survivors than currently recommended.Alt-text: Unlabelled box1As of 7 July 2020, the novel Coronavirus disease 2019 (COVID-19) has been diagnosed in over 11.6 million individuals with more than 539,000 deaths reported worldwide Current guidance on who should be considered vulnerable has been largely based on policies developed for previous epidemic respiratory viruses, notably influenza. For example, vaccination against influenza is only recommend for individuals under active treatment for cancer and for up two years following some treatments and haematological cancersWe therefore aimed to investigate whether cancer survivors are likely to be a high-risk group for severe outcomes during the current COVID-19 pandemic in two ways: first, by comparing the prevalence of risk factors currently used to guide COVID-19 policy between site-specific cancers survivors and cancer free controls; second, by comparing the risk of influenza hospitalisation or death between cancer survivors and cancer free controls, as a way of exploring susceptibility to severe outcomes from epidemic respiratory viruses.2This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (Supplementary Checklist S1).2.1We carried out a population-based cohort study amongst 1-year survivors of the 20 most common site-specific cancers matched to cancer-free controls. We used primary care data from Clinical Practice Research Datalink (CPRD GOLD)2.2Cohorts of adult cancer survivors (aged ≥18 years) were identified for each of the 20 most common cancer sites . For the primary analysis, we counted hospitalisations with a primary diagnosis of influenza, and deaths with an underlying cause of influenza. In a sensitivity analysis, we broadened the definition to include hospitalisations/deaths with any code for influenza present.a priori as of potential importance in determining risk of severe COVID-19 outcomes, namely asthma, chronic respiratory disease (other than asthma), chronic heart disease, chronic liver disease, chronic neurological disease, chronic kidney disease, diabetes, obesity, sickle cell disease and splenectomy. Other causes of immunosuppression were not included due to overlap with cancer and its treatment. In a secondary analysis we also described the total number of comorbidities (0 vs 1 vs ≥2 comorbidities from the aforementioned list). Full variable definitions and code lists are provided in Supplementary Table S1.Age and sex were matching factors. Other covariates were index of multiple deprivation quintile, smoking status , and common comorbidities identified 2.4Prevalence of COVID-19 related risk factors in cancer survivors and controls: amongst cancer survivors and controls alive and under follow-up in CPRD GOLD at the index date (i.e. 1 year after cancer diagnosis for cancer survivors) and 4 years later (5 years after diagnosis), we calculated the proportion with each morbidity of interest for all cancers combined and individual cancer sites. The numerator included those with any history of the relevant comorbidity at the given time point, except for obesity, which was classified based on the most recent body mass index (BMI) measure available at that time point.Risk of influenza hospitalisation and mortality in cancer survivors and controls: Individuals were followed up from the index date until the earliest occurrence of the outcome, death without the outcome, or end of study period. Follow-up was not censored at the end of data collection in CPRD GOLD because the main analysis did not require post-baseline primary care data. We then fitted Cox proportional hazards models with time since index date as the timescale, initially accounting only for matching factors  through stratification by matched set and then additionally adjusting for the presence of risk factors at the index date . We examined the role of time since cancer diagnosis, by fitting a time-updated variable indicating time of cancer survivorship .Since haematological malignancies directly affect the immune system and treatments may have long-term immune consequences, we stratified results by haematological versus other cancers by fitting a three-level cancer survivorship variable. Due to limited power, we did not break cancer sites down further. In a post hoc analysis, the exposure variable included each haematological malignancy separately  and four groups of solid cancers ; Wald tests were used after model estimation to test the null hypothesis of heterogeneity of effect amongst subgroups.As a secondary analysis, we explored mediation of any raised risk of the primary outcome by development of recognised risk factors during follow-up, by adjusting for time-updated risk factor variables . This analysis was additionally censored at the end of follow-up in CPRD GOLD, since it relies on post-baseline primary care data.Patients with missing data on BMI, smoking or deprivation were excluded from the cohorts (see above), therefore all models were based on complete case analyses. Multiple imputation was not used, as the missingness was considered likely to be not at random in the primary care setting ,16, and Sensitivity analyses: We conducted two main sensitivity analyses. First, we broadened our definition of the outcome to include influenza recorded anywhere in the hospitalisation or death record, to account for the possibility of differential prioritisation of influenza codes between cancer survivors and controls. Second, we adjusted for time-updated influenza vaccination status and ever receipt of a pneumococcal vaccine, as cancer survivors may be more likely to receive influenza and pneumococcal vaccinations than general population controls due to higher engagement with healthcare, or vaccination indicated by immunosuppression following cancer and its treatment, which may protect against influenza and influenza-related death from secondary bacterial pneumonia. Influenza vaccinations were considered current from the date of vaccination until the start of the following influenza season in September. As vaccination records were ascertained from primary care data, these analyses were additionally censored at the end of CPRD follow up; we also re-ran the primary model with this additional censoring in order to provide a similarly censored comparator for the sensitivity and mediation analysis models.Ethics: This study was approved by the London School of Hygiene & Tropical Medicine Ethics Committee  and the Independent Scientific Advisory Committee for the Medicines and Healthcare products Regulatory Agency database research (20_082). Individual consent was not required for this study. CPRD supplies anonymised data for public health research; individuals are free to opt-out from having their data included in the database.Role of funding source: The study funders had no role in study design; in the collection, analysis, and interpretation of data; and in the writing of the article.3This study included 108,215 cancer survivors, of which 9685 had prior haematological malignancies, and 523,541 individuals with no history of cancer . Median 3.1For all cancers combined, we observed higher absolute prevalence of all risk factors for severe COVID-19 except for obesity and sickle cell disease/splenectomy in 1-year cancer survivors, compared to the cancer-free comparison group  during a median follow-up time from the index date of 4.7 years in cancer survivors (IQR 1.9–8.4 years) and 6.2 years in controls (IQR 3.3–9.9 years); follow-up exceeded 10 years for 19,273 (18%) cancer survivors and 128,132 (25%) controls. The risk of influenza hospitalization or death was 2.7 times higher (95%CI 2.12–3.44) in cancer survivors compared to people with no history of cancer after accounting for matching factors only . ControlStratification by cancer group  showed substantial differences. Haematological cancer survivors had 15 times higher risk of a severe influenza outcome compared to people without cancer , and further stratifying by time since cancer diagnosis, the hazard ratio was 29.56 (95%CI 10.20–85.66) for those 1 to <5 years from diagnosis, falling to 9.56 (95%CI 4.39–20.84) and 10.06 (95%CI 2.47–40.93) for those 5 to <10, and 10+ years from diagnosis respectively. Associations were smaller for non-haematological cancer survivors. The overall adjusted HR was 1.38 but compatible with chance variation (95%CI 0.92–2.07). However, stratification by time since diagnosis suggested a doubling of risk in those 1 to <5 years from diagnosis  with no raised risk in longer-term survivors.3.3n = 320) but a very similar pattern of results to the primary analysis (Supplementary Table S3). In analyses that censored at end of CPRD follow-up, fewer events were included (n = 167) but hazard ratios were generally larger than in the primary analysis . Additional control for time-updated exposure to influenza and pneumococcal vaccination led to similar but slightly stronger associations , while adjusting for mediators led to slightly weaker associations , but in both cases patterns of results were similar. There was no strong statistical evidence of a variation in the HRs amongst survivors of leukaemia, non-Hodgkin lymphoma and multiple myeloma (p = 0.08), or amongst survivors from the different solid cancers (p = 0.42).Using hospitalisations and deaths with any mention of influenza in the outcome definition led to more events being included . Multiple validation studies have demonstrated the validity of CPRD primary care data for measuring disease phenotypes including cancer, especially when combined with additional linked data sources There are some important limitations. We analysed severe influenza in an attempt to inform COVID-19 policy but despite both being infectious respiratory illnesses, it is not certain that risk factors for severe influenza will have the same associations with COVID-19. Our approach follows that of policy makers who have assumed parallels with influenza in the absence of mature COVID-19 data The high prevalence of several established COVID-19 risk factors in cancer survivors, and the increased risk of influenza hospitalisation and death in survivors of haematological cancers even many years from diagnosis, and in survivors from other cancers in the first five years of survivorship, indicate a likely increased risk of severe COVID-19 outcomes in these patient groups. Early direct evidence from the COVID-19 pandemic appears to be consistent with this. These findings suggest that cancer survivorship should be considered a potentially important risk factor for severe COVID-19 outcomes in public health policy. At present, while UK policy defines those with active cancers and/or receiving treatments as high-risk for COVID-19 complications, the much larger overall population of cancer survivors does not appear in either moderate or high-risk groupingsstreptococcus pneumoniae may also be considered Our results also have implications for preventive medicine in the coming autumn and winter, when influenza and SARS-CoV-2 are expected to coexist in the population. Improving influenza vaccination coverage amongst cancer survivors should be a priority, as the vaccine is both effective and safe,30 but cFuture studies should focus on the risk of severe COVID-19 in cancer survivors, explore the role of comorbidities and prior exposure to specific anti-cancer therapies, disaggregating data by cancer site when possible.In conclusion, survivors of haematological malignancies had substantially elevated risks of influenza hospitalisation or death persisting for at least 10 years after cancer diagnosis, while risk was doubled for survivors of other cancers for up to 5 years from diagnosis. In addition, cancer survivors had higher prevalence of several chronic conditions associated with severe COVID-19, compared to people with no history of cancer. This should be taken into account in public health policies targeted at protecting clinical risk groups. Influenza vaccination should be encouraged in this group, and may need to be extended to a wider population of medium- to long-term cancer survivors than currently recommended.Dr. McDonald reports grants from NIHR Health Protection Research Unit in Immunisation, during the conduct of the study; Dr. Stanway reports personal fees from Roche, personal fees from Eli Lilly, personal fees from Novartis, personal fees from Clinigen, outside submitted work; Dr. Bhaskaran reports grants from Wellcome Trust, grants from Royal Society, during the conduct of the study; all other authors have no conflicts of interest to disclose.


Previous studies have investigated the association between the use of bisphosphonates and the development of breast cancer, which presented controversial results. Thus, this meta-analysis was conducted to summarize the current evidence of the association of bisphosphonate use with breast cancer risk.  A comprehensive search was conducted in PubMed, ISI Web of Knowledge, the Cochrane Library, and Embase from inception to March 2019 by two researches, who independently selected trials, retrieved relevant data, and assessed study quality. The summary relative risk (RR) for the use of bisphosphonates on the risks of developing breast cancer was calculated using a random-effect model.  The present meta-analysis, which included four case-control studies, involving 55052 breast cancer cases, and seven retrospective cohort studies, involving 14641 breast cancer cases, assessed the effect of bisphosphonates on breast cancer risk. The random-effect model meta-analysis found a reduced risk of breast cancer with exposure to bisphosphonates with pooled RR of 0.87 . The short-term use of bisphosphonates (<1 year) did not render significant alteration , while a significant 26% risk reduction of breast cancer was noted with long-term use (>1 year) . A protective effect of bisphosphonates was shown in contralateral breast cancer . In terms of the type of bisphosphonates, a significant inverse relationship was noted for etidronate, with pooled RR of 0.87 (95% CI: 0.80 to 0.96). CRD42018105024) (registered on 29 August 2018). This meta-analysis suggested that the use of bisphosphonates was associated with reduced risk of breast cancer, including contralateral breast cancer. Compared to other types of bisphosphonates, only etidronate showed a significant inverse relationship. Additionally, the long-term use (>1 year) of bisphosphonates was more significant in lowering breast cancer risk. Further randomized controlled trials are needed to verify this association. This trial is registered with PROSPERO (registration number:  Bisphosphonates are extensively used to treat osteoporosis, as well as prevent and treat skeletal destructive lesions caused by malignancy, particularly among postmenopausal women , 2. SeveNumerous epidemiologic studies have investigated a relationship between bisphosphonate use and breast cancer risk –16. For However, subsequent publications of two large studies have showed opposite results , 10, andCRD42018105024).This meta-analysis followed the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) . The revWe systematically searched from inception to March 2019, using the PubMed, ISI Web of Knowledge, Cochrane Library, and Embase databases. All relevant published articles about human subjects were identified using the following terms: (breast or mammary gland), (neoplasm∗or tumor∗or cancer∗or carcinoma∗), and . No publication date or language restrictions were adopted. In addition, a manual search of references cited in the selected articles and published reviews on the same topic was conducted to obtain any additional relevant trials.Using standard forms, two authors independently assessed the trials for candidates based on the inclusion and exclusion criteria in the meta-analysis. In cases of divergent views, a consensus was reached through discussion or in consultation with the third author. Studies were considered eligible if they (1) focused on the association between bisphosphonate use and breast cancer risk; (2) reported relative risk (RR), odds ratio (OR), or hazard ratio (HR) with 95% CI (or sufficient data to compute these); and (3) included a trial with the most relevant information with respect to repetitive trials. The exclusion criteria were review articles, case reports, letters to the editor, editorials, and if the outcome of interest was not reported, even after contacting the corresponding authors.Data was abstracted independently by two reviewers, with inconformity addressed by consensus and discussion with a third reviewer. The following information was collected from each included trial, such as the first author, year of publication, nation, study design, period, study database, age of participants, menopausal status, number of breast cancer cases, number of participants, prevalence of breast cancer, type of breast cancer, type of bisphosphonates, definition of exposure, measure of bisphosphonates use, average exposure period, adjustment factors in multivariate analysis, and risk estimates with 95% CI. We collected the OR (or HR) in the main analysis that adjusted the greatest degree of potential covariates. If possible, outcome data were separately divided into each subgroup. According to the number of breast cancer cases and the number of subjects being lower or higher than the median of the entire studies, they were classified as the “low” or “high” subgroup, respectively. The primary outcome was the relative risk for usage of bisphosphonates and the incidence of breast cancer.The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of the cohort studies , in whicThe primary summary estimate of interest was the adjusted RR for the incidence of breast cancer with bisphosphonate use. Based on adjusted RR estimates and 95% CI, we generate overall estimates for the effect of bisphosphonates on breast cancer by using a DerSimonian-Laird random-effect model. Compared to the fixed-effect model, the random-effect model is more robust due to incorporating into the weighing scheme both within-study and between-study variations . Given tQ statistic and quantified by I2 statistics (P > 0.10 or I2 > 50% considered significant heterogeneity). Sensitivity analysis was conducted by eliminating each study at a time to estimate its influence on incorporative RR in the meta-analysis. Additionally, sensitivity analysis was also investigated by removing studies with characteristics different from the others.We assessed statistical heterogeneity among studies by using the Cochran P value or a large chi-square statistic relative to its degree of freedom constructs evidence of heterogeneity beyond chance.To assess whether the association between incidence of breast cancer and bisphosphonate use was modified by clinical parameters, we also did prior-specified subgroups based on study location (Western country vs. Eastern country), study design (cohort vs. case-control), menopausal status , measurement of bisphosphonate use , study quality , number of breast cancer cases (>1000 vs. ≤1000), and number of participants (>20000 vs. ≤20000). Analysis was conducted to evaluate whether the difference was statistically significant between the subgroups. Additionally, subgroup differences were tested by chi-square test—that is, whether the observed differences are aligned with chance alone within the subgroups. A low Metaregression analysis (MRA) was conducted to investigate the potential impacts of heterogeneity and confounders on outcomes. Publication year, average age, number of breast cancer cases, number of participants, prevalence of breast cancer, and average exposure period were considered as variables. We assessed publication bias for primary outcomes by funnel plot inspection, in which 10 or more studies provided data.P values are double-tailed, and the statistical significance was set at 0.05.All meta-analyses were conducted with RevMan version 5.3  and Stata version 14 . All The flow chart of the literature selection procedure is shown in A total of eleven studies, including 69693 women with breast cancer, were published between 2010 and 2017, focusing on bisphosphonates and the risk of breast cancer. P = 0.02 and I2 = 52%, heterogeneity existed between the studies. In a sensitivity analysis, after removing one study by Monsees et al. [P = 0.06 and I2 = 45%) showed a relative decrease from high to moderate heterogeneity  could decrease the risk of breast cancer. Nevertheless, alendronate , risedronate , clodronate , and zoledronic acid  did not render any significant risk reduction in the incidence of breast cancer .  FigureP > 0.05).P = 0.025) for the publication year in individual studies. While the average age (P = 0.433), number of breast cancer cases (P = 0.597), number of participants (P = 0.687), prevalence of breast cancer (P = 0.888), and average exposure period (P = 0.865) did not explain the demonstrated heterogeneity , including contralateral breast cancer . This protective efficacy of bisphosphonates was noted in women who taken bisphosphonates with long-term use (>1 year). The benefit varied between different types of bisphosphonates, specifically etidronate, which significantly lowered breast cancer risk by 13%.Our results generally conformed with two previous meta-analyses , 18 thatd rate, 13% and 9%, respectively) and in conformity with the Early Breast Cancer Trialists' Collaborative Group meta-analysis [Structurally, bisphosphonates are categorized into older first-generation non-nitrogen-containing moieties and newer second-generation nitrogen-containing moieties, including etidronate, clodronate, and tiludronate as well as pamidronate, alendronate, ibandronate, risedronate, and zoledronate. Our meta-analysis found that only etidronate, not alendronate, showed a protective efficacy on the risk of breast cancer, in contrast with a published meta-analysis  in whichanalysis  in whichanalysis . It is nIn our meta-analysis, the antitumor effectiveness of the usage of bisphosphonates on breast cancer risk was observed only after more than 1 year of receiving bisphosphonates, which was also seen in two previous meta-analyses focusing on breast cancer , 18. IntIn terms of the efficacy of bisphosphonates for different types of breast cancer, our risk-stratification analysis suggested that bisphosphonates have anti-breast cancer effects only in contralateral breast cancer , rather than in invasive breast cancer and DICS, inconsistent with a previous meta-analysis  in that Meanwhile, this meta-analysis was also inevitably associated with several limitations, as shown below. First, marked heterogeneity existed across the included studies in terms of nonuniform study design, population characteristics, and definitions of exposure bisphosphonates. However, using sensitivity and metaregression analyses, we have found the sources of potential heterogeneity between studies. Second, though it was attempted to adjust for potential confounding factors in each eligible trial, a meta-analysis is not able to resolve residual or unknown confounders that could be inherent in the included studies. Third, due to lack of sufficient data, we cannot perform further subgroup analysis in terms of intake form (daily use vs. once a week use). Final, after cessation of treatment, we were not able to evaluate the period of time on whether the protective efficacy of bisphosphonate is lost or not. Further well-designed researches are warranted to resolve these issues.Taken together, the results from this meta-analysis found that exposure to bisphosphonates was associated with decreased breast cancer incidence, including contralateral breast cancer. Compared with other types of bisphosphonates, only etidronate showed a significant inverse relationship. In additional, the long-term use (>1 year) of bisphosphonates was more significant in reducing the risk of breast cancer. Further in-depth longitudinal researches are urgently desirable to generate more precise estimates considering all potential confounders, different types of bisphosphonates, cumulative dose, duration of bisphosphonates use, and effects of bisphosphonates on different types of breast cancer.


To investigate the association between sleep duration and breast cancer incidence, we examined the association in a large UK prospective study and conducted a meta-analysis of prospective studies.In the Million Women Study, usual sleep duration over a 24-h period was collected in 2001 for 713,150 participants without prior cancer, heart problems, stroke, or diabetes (mean age = 60 years). Follow-up for breast cancer was by record linkage to national cancer registry data for 14.3 years on average from the 3-year resurvey. Cox regression models yielded multivariable-adjusted breast cancer relative risks (RR) and 95% confidence intervals (CIs) for sleep duration categories. Published prospective studies of sleep duration and breast cancer risk were included in a meta-analysis, which estimated the inverse-variance weighted average of study-specific log RRs for short and for long versus average duration sleep.After excluding the first 5 years to minimize reverse causation bias in the Million Women Study, 24,476 women developed breast cancer. Compared with 7–8 h of sleep, the RRs for <6, 6, 9, and >9 h of sleep were 1.01 , 0.99 (0.96–1.03), 1.01 (0.96–1.06), and 1.03 (0.95–1.12), respectively. In a meta-analysis of 14 prospective studies plus the Million Women Study, including 65,410 breast cancer cases, neither short (RR < 7 h = 0.99 [0.98–1.01]) nor long (RR > 8 h = 1.01 [0.98–1.04]) versus average duration sleep was associated with breast cancer risk.The totality of the prospective evidence does not support an association between sleep duration and breast cancer risk. We examined sleep duration and breast cancer risk in a large UK cohort with a mean follow-up of 14.3 years and 24,476 incident cases. To our knowledge, this is the largest individual prospective analysis published to date. We excluded the first 5 years of follow-up in our analyses to minimize potential reverse causation bias, whereby pre-clinical disease might cause an increase or decrease in sleep duration. The findings suggested no association between sleep duration and breast cancer risk. The findings were robust across sensitivity analyses. We performed a meta-analysis of 15 prospective studies including our study, with a total of 65,410 breast cancer cases. The totality of the prospective evidence does not show that breast cancer risk varies by sleep duration.Short sleep duration has been hypothesized to increase breast cancer risk . The ligResults from meta-analyses on the association of sleep duration with breast cancer risk are inconsistent , 17. WhiWe aimed to examine the association between usual sleep duration in a 24-h period and breast cancer risk using data from the large-scale prospective UK Million Women Study. We excluded the first 5 years of follow-up to minimize possible reverse causation bias, whereby preclinical cancer might cause changes in sleeping patterns. To put published results into context, we performed a systematic review and meta-analysis of prospective data on the association of 24-h or overnight sleep duration with breast cancer risk.http://www.millionwomenstudy.org/).The details of the Million Women Study have been previously described . BrieflyThe current analyses were based on the 3-year resurvey questionnaire completed in 1999–2005, which included questions on sleep patterns for the first time . As simiNearly all women were postmenopausal at baseline. Other personal characteristics used in these analyses were collected from the 3-year resurvey questionnaires, except for region of residence, educational attainment, Townsend deprivation index, strenuous exercise, age at menarche, height, age at first birth, and parity, which were recorded at the time of recruitment. Responses of “not known,” “not sure,” and “not answered” were treated as missing values. Information was missing for less than 10% for each covariate, apart from alcohol intake (12%).Information on cancer registrations and deaths, including cause-specific details, coded to the 10th revision of the International Classification of Diseases (ICD-10) , were obAmong participants who returned the 3-year questionnaire and did not have a prior cancer or breast carcinoma in situ registration (except non-melanoma skin cancer), we further excluded women: (1) with missing data on sleep patterns or an invalid sleep duration; (2) reporting use of sleeping pills or with a prior hospital diagnosis of insomnia, hypersomnia, or sleep apnea ; or (3) with a previous history of stroke, diabetes, or heart problem. This left the final sample of 713,150 women for the analysis .Cox regression models using time in study as the underlying time variable estimated hazard ratios, henceforth called relative risks (RRs) and 95% confidence intervals (CIs) of sleep duration categories. Person-years were calculated from the date the 3-year baseline questionnaire was completed to the date of first incident invasive cancer (except non-melanoma skin cancer [ICD-10 C44]) date of death, date of loss to follow-up, or the end of follow-up , whichever was earliest. If women had a diagnosis of any other cancer type or breast carcinoma in situ (ICD-10 D05), they were censored at the date of that cancer registration. To minimize reverse causation bias, we excluded the first 5 years of follow-up.2), alcohol intake , ever use of hormone replacement therapy (HRT) , strenuous exercise per week , smoking status and number of cigarettes per day , age at menarche , parity and age at first birth , family history of breast cancer , and height . As nighttime sleep duration  may be more relevant to the risk of breast cancer in terms of the biological hypothesis, and daytime napping was found to be associated with breast cancer in a previous analysis in the Million Women Study  years) included in these analyses, 23% reported ≤6 h of sleep, 68% reported 7–8 h of sleep, and 10% reported ≥9 h of sleep. A total of 36,173 breast cancer cases were diagnosed over a mean of 14.3  years of follow-up, of which 24,476 cases were diagnosed after the first 5 years of follow-up.Of the 713,150 women  years after baseline, sleep duration  showed moderate agreement . The Spearman non-parametric correlation was 0.66. When assessed as a continuous variable, the Bland–Altman plot showed good agreement of sleep duration in the two reports (mean difference: 0.01 h [95% limits of agreement: −2 to +2 h]).In a subset of 13,109 women who returned a repeat questionnaire 1.7 . The prevalence of daytime napping, current smoking, and not being in paid work were the highest among women sleeping >9 h. Most reproductive risk factors were similar across sleep duration categories , 0.99 (0.96–1.03), 1.01 (0.96–1.06), and 1.03 (0.95–1.12), respectively . Breast The search strategy identified a total of 743 records . After eOur systematic review identified 14 previously published prospective studies  1–14]. . 1–14]. When relevant results from the 15 studies were combined , the overall RRs for short  or long  sleep durations, respectively, compared to average sleep duration  were 0.99 (0.98–1.01) and 1.01 (0.98–1.04) . Althougp for heterogeneity for short sleep vs average sleep = 0.6; p for heterogeneity for long sleep vs average sleep = 0.1) or type of sleep duration . The RR for long sleep versus average sleep duration among postmenopausal women in six studies was 1.02 (0.98–1.05), whereas that of short sleep duration was 0.99 (0.97–1.01). The RRs for shortest average sleep duration were 0.99 (0.96–1.02) for 15 studies and 1.00 (0.97–1.03) for 10 studies with analyses excluding the first few years of follow-up or cases diagnosed soon after baseline.Our subgroup analyses did not show statistically significant heterogeneity by geographical region  may affect the likelihood of short sleep being associated with greater exposure to light at night. Overall though, evidence from meta-analyses of the associations of night shift work  and insoIn summary, despite the different populations studied and the inclusion and exclusion criteria for each prospective study in the meta-analyses, there is no heterogeneity in the findings across studies. The totality of the evidence thus suggests little or no effect of sleep duration on breast cancer risk.zsaa166_suppl_Supplemenarty_MaterialsClick here for additional data file.


Family history of breast cancer is a key risk factor, accounting for up to 10% of cancers. We evaluated the proactive assessment of familial breast cancer (FBC) risk in primary care. Eligible women (30 to 60 years) were recruited from eight English general practices. Practices were trained on FBC risk assessment. In four randomly-assigned practices, women were invited to complete a validated, postal family history questionnaire, which practice staff inputted into decision support software to determine cancer risk. Those with increased risk were offered specialist referral. Usual care was observed in the other four practices. In intervention practices, 1127/7012 women (16.1%) returned family history questionnaires, comprising 1105 (98%) self-reported white ethnicity and 446 (39.6%) educated to University undergraduate or equivalent qualification, with 119 (10.6%) identified at increased breast cancer risk and offered referral. Sixty-seven (56%) women recommended referral were less than 50 years old. From 66 women attending specialists, 26 (39.4%) were confirmed to have high risk and recommended annual surveillance (40–60 years) and surgical prevention; while 30 (45.5%) were confirmed at moderate risk, with 19 offered annual surveillance (40–50 years). The remaining 10 (15.2%) managed in primary care. None were recommended chemoprevention. In usual care practices, only ten women consulted with concerns about breast cancer family history. This study demonstrated proactive risk assessment in primary care enables accurate identification of women, including many younger women, at increased risk of breast cancer. To improve generalisability across the population, more active methods of engagement need to be explored.Trial registration: CRUK Clinical Trials Database 11779.The online version of this article (10.1007/s10689-020-00188-z) contains supplementary material, which is available to authorized users. Breast cancer is the commonest cancer in women in most countries , accountFamily history of breast cancer is a key risk factor, with the risk increasing according to the number of relatives  and firsTaking a three-generation family history can identify women at increased familial risk for breast cancer –15. EnglIdentification of familial risk of breast cancer can lead to successful prevention strategies. Prophylactic bilateral mastectomy reduces breast cancer mortality and morbidity in higher familial risk women, particularly those with BRCA1/2 genetic mutations , 20. FurIn UK primary care, standard usual (reactive) practice is to assess women opportunistically when they present with concerns about their family history . There aThis intervention study was performed between May 2014 and September 2015. The study was approved by East Midlands National Research Ethics Service (NRES) Committee (Reference 14/EM/0009). This involved recruiting family practices in central England. Initially, sixteen family practices expressed an interest in the study. After four practices were deemed outside of the catchment areas and another four declined after further information provided, eight practices were recruited to the study. In all eight practices, primary care teams  received a one-hour education session on the identification of familial breast cancer, based on current national guidelines for England (NICE), including referral pathway for familial cancer risk assessment in hospital specialist care. In the recruitment area, the pathway involved general practitioners referring to the breast cancer service. Prior to the clinic appointment, the patient is sent a family history questionnaire by the service. This questionnaire is reviewed with the familial cancer nurse specialist, who will perform a breast cancer risk assessment. If women in moderate or high-risk categories are interested in chemoprevention, they are offered a further appointment with breast consultant to discuss pros and cons of treatment. Those women at very high risk are referred to regional clinical genetics service to consider genetic testing.We randomly selected four practices who expressed an interest to adopt this proactive approach, using decision-support tool, and matched them by deprivation   for compBased on current English (NICE) guidelines, there were three possible outcomes following assessment of women’s family history information by the decision support tool; average risk, increased risk or uncertain risk. Table The following measures were assessed: return of completed family history questionnaires (FHQ); accuracy of data entry from questionnaires to primary care FaHRAS tool achieved by primary care administrative staff ; numbers and proportions of women identified at “increased” risk and at “average” risk, before and after taking account of the “uncertain risk” category; and numbers and proportions of women confirmed to be classified at increased risk following specialist assessment and their subsequent management. Finally, at the end of the intervention period, recruited patients’ hospital and primary care records were reviewed to identify if recommended preventive surveillance or treatment was offered.In the four other “usual care” practices, women consulting their practice with concerns about breast cancer in their family history, over the same period were provided with care as usual.Analysis was descriptive, with all outcome measures reported as numbers and percentages. Characteristics of the study population were presented as means and standard deviations for normally distributed continuous variables, medians and interquartile range for non-normally distributed continuous variables, and numbers and percentages for categorical variables. Descriptive analyses were conducted in STATA 15.In the four practices using a proactive approach and decision support, 1127 (16.1%) of 7012 eligible women returned completed family history questionnaires for assessment. A further 10 women who consulted in these four proactive practices, with family history concerns, also completed this questionnaire opportunistically. The sociodemographic profile of recruited women is shown in Table Administrative staff at the practices correctly entered over 99% (1134 of 1137) of patients’ family history data from these questionnaires. For three women, data were incorrectly inputted into software or information on the questionnaire was misinterpreted. In the four usual care practices, 10 women consulted in the same period with concerns about family history of breast cancer.Risk assessment outcomes for the 1127 women proactively invited and assessed with decision support are shown in Fig. Among the 999 women at average risk, 734 (65.1%) had no family history of breast, ovarian or prostate cancer, and 265 (23.5%) had a family history of such cancers but were assessed as near population risk. 163 of the 265 women were identified by the decision software alone and the remaining 102 women confirmed as near population risk after decision support software recommended discussion with a familial cancer specialist (“uncertain risk” category).Among the 128 women at increased risk with specialist referral recommended, 64 (50%) women were confirmed by decision software alone, and the remaining 64 (50%) women initially assessed as ‘uncertain’ by decision software were then confirmed to be at increased risk after discussion with a specialist. Of these 128 women at increased risk, 71 women were under 50, and 68 had a family history of breast cancer only, 18 women had a family history of ovarian cancer only, 32 women had a family history of both breast and ovarian cancer, and 1 woman had a family history of prostate cancer. In the remaining 9 women, there were 5 women who had familial colorectal cancer histories, one woman with a cancer syndrome family history, one woman with a sarcoma family history and two women were referred prior to receiving their risk assessment (see Table S2 for full details).Outcomes of specialist referral are provided in Table At the time of data collection in hospital care, 53 of 119 (43.7%) women identified in primary care at increased risk did not have further specialist assessment. This included 16 women referred by their GP who did not attend specialist review (13) or who were referred to another secondary specialist (3); 11 women were not referred because they declined referral (6) or had previously been assessed by familial specialist or genetic services (5); and 21 women who did not respond to their GPs’ further invitations to discuss their results and referral. Finally, 5 women were lost to follow up and no information was available.Of the further 10 women assessed for familial breast cancer risk in the four “proactive” practices with breast cancer concerns, 3  were identified at increased familial breast cancer risk by decision support with referral recommended. In the other four “usual care” practices, none of the 10 women consulting had a documented family history risk assessment conducted as recommended by national (NICE) guidelines.This study has found proactive invitation and decision support in primary care enabled the accurate identification of women, including many younger women, at significantly increased risk of breast cancer. Following a single invitation, 16% of eligible women provided their family history information and over 10% of them were newly identified, by decision support in primary care, at increased familial breast cancer risk with specialist referral recommended. The study also demonstrates primary care administrative staff can be trained to enter family history questionnaire, with over 99% of family history data correctly entered into the decision support software. The majority of women referred were confirmed at high or at moderate familial cancer risk by a familial cancer specialist, enabling the initiation of regular mammographic surveillance. One younger woman, aged 46, was thus diagnosed with early breast cancer within the study period itself.Previous application of decision support in primary and secondary care have improved family history recording and reduced unnecessary referrals , 34, 38.Women’s engagement following a single postal invitation was relatively modest but the intervention itself reached a large number of women in a short period of time using a primary care unsolicited postal survey with no reminders. This led to over 1100 women from four family practices returning family history information, with over 10% identified at increased familial risk. Nevertheless, 84% of invited women did not return a questionnaire. Patients who responded and returned a questionnaire had a similar age distribution compared to non-responders. If a similar 10% of non-responders also have an increased familial risk this would represent a large group of women and a significant unmet clinical need. While some selected response might be expected from those with a relevant family history, on-screen reminders and invitations might increase uptake and identification of risk in practice . This stTo our knowledge, this is the first study of proactive familial breast cancer risk assessment in women with unrecognised risk in UK primary care practice. The US Family Healthware Impact Trial , similarFollowing specialist assessment, recommendations for surveillance, genetic testing and chemoprevention were aligned with English national (NICE) guidelines, recent studies and best practice in clinical genetics , 24, 25.A significant proportion of women who responded and were confirmed to be at increased risk were from younger age groups. As the UK National Breast Screening programme only starts at 50, these women would not have been detected by this screening programme. However, as mentioned earlier, there is strong evidence that surveillance of these younger women with increased familial risk improves disease outcomes , 23. We This intervention study, with robust experimental design, was completed over a relatively short follow-up period, hence not all the referral outcomes could be verified or followed-up. Related to this, longer term outcomes related to genetic testing and cancer diagnoses could not be ascertained. Due to a small number of participants (n = 10) recruited through the four practices adopting usual opportunistic approach, comparison and statistically analysis between the participants in the proactive practices compared to usual care practices was not feasible. The opportunistic recruitment may be improved by patient-specific reminders in electronic health records . Also, cThis study suggests the considerable promise of proactively engaging women to have familial cancer risk assessment initiated by postal invitation. This is a relatively low cost strategy and can identify women at increased risk of breast cancer, who would otherwise not access the service, such as, younger women. Moreover, familial risk identification is particularly relevant in women under 50 who are not routinely offered mammographic screening, and who currently present at a later stage of breast cancer (with more years of life to lose).The study demonstrates that in women aged 30 to 60 years, who respond and complete a validated family history questionnaire, up to 10% could be identified at increased breast cancer risk who may require specialist referral. This would require clear management pathways to manage finite specialist resources. Using current national guidelines, as many as 11% of women’s family histories were considered “uncertain risk” requiring primary care physician to discuss these with a specialist. This could be avoided if more refined primary care decision support were introduced. The use of such decision support software should be actively considered in order to support early detection with the role of better identifying those in need of referral and facilitating pathway improvements. Management pathways between primary care and specialists also needs to reinforce recommendation on chemoprevention for women at moderate and high familial risk of breast cancer.Parallel qualitative study of women’s and primary care providers’ experiences of this approach is needed and will be reported separately. Further research might explore the use of decision support (compared to none) in primary care in a definitive clinical trial evaluating identification of women at increased familial breast cancer risk through systematic recruitment in both arms. Preceding the trial, there needs to be further intervention development to facilitate recruitment of women from underserved populations . EconomiSupplementary file1 (DOCX 23 kb)Below is the link to the electronic supplementary material.


To evaluate the robustness of multiple machine learning classifiers for breast cancer risk estimation in the presence of incomplete or inaccurate information. m in one experiment, and randomly corrupted the existing information with a probability of pc in another experiment. We considered three prominent machine-learning classifiers such as Logistic regression (LR), Random Forests (RF) and a custom Neural Network (NN) architecture and compared their degradation of discrimination performance as a function of increasing probability of missing or inaccurate data. Open data for this study was obtained from the BCSC Data Resource (http://breastscreening.cancer.gov/). We conducted two ablation-type experiments to compare the robustness of different classifiers where we randomly switched known information to missing with a missing probability of pm and pc from 0 to 1, NN resulted in better performance in terms of AUC compared to RF and LR as long as less than half the data was missing/inaccurate . However, for missing (pm) or corruption (pc) probabilities above 0.5, LR gave similar performance as the custom NN. RF resulted in overall poorer performance when the data had additional missing or incorrect entries. LR, RF and custom NN resulted in an Area Under Curve (AUC) of 0.645, 0.643 and 0.649, respectively, on a test set with 500,000 total observations. When we manipulated the data by varying probabilities pIn cases where the input information is missing or inaccurate, our experiments show that the proposed custom NN provides reliable risk estimates in medical datasets like BCSC. These results are particularly important in health care applications where not every attribute of the individual participant might be available. The World Health Organization estimates that 1 in 12 women are diagnosed with some breast abnormality in their lifetime . In developing countries like India, mortality rates approach 50% . The national incidence and mortality rates of breast cancer in India are approximately 25.8 and 12.7 per 100,000 women, respectively . This incidence rises to 35-40 per 100,000 women in large metropolitan cities  . There is a critical imbalance of 1 radiologist per 100,000 persons across India, suggesting that access to appropriate expertise and screening facilities are major factors impacting detrimentally on breast cancer mortality . There is low public awareness and limited acceptability of breast cancer screening among Indian women. Significant efforts are needed to improve early breast cancer detection facilities for improving access . It has been found that risk-based population screening is more effective than unselective population screening. The use of multifactorial mathematical models to predict individual risk may help to promote self-awareness and equip individuals with the necessary information to address their situation. Previous studies show that an individual’s absolute risk is modulated by known health and environmental factors , presence of any BRCA genetic mutation  and having a family history of breast cancer . Among the prominent risk models, the Gail model  assesses risk based on a participant’s current age, age at menarche, age at first childbirth, race, ethnicity, number of first-degree relatives with a history of breast cancer and the number of breast biopsy examinations. This risk model was based on the data collected from the Breast Cancer Detection Demonstration Project (BCDDP) in 1980. The Claus model  derives the risk score from age and detailed family cancer history involving the number of first- and second-degree relatives and the age of onset of breast cancer in a relative. Amir et al.  used age, body mass index, age at first childbirth, age at menarche, menopausal status, the number of first- and second-degree relatives with cancer, age of onset of breast cancer in a relative, bilateral cancer in a relative, ovarian cancer in a relative, hormonal exposure and benign breast disease history to predict risk. Recently, Barlow et al.  used data collected through the Breast Cancer Surveillance Consortium (BCSC)  to assess risk using a comprehensive list of risk factors, as shown in However, the above widely used risk prediction models in literature are no longer usable if any of the required parameters are missing or incorrect. Complete medical histories may not always be available in the real world; especially in developing countries, individuals often choose not to disclose details due to fear of social stigmatization, or they may report incorrect information due to a flawed recollection. A predictive classifier algorithm intended to be used for risk estimation in this setting should, therefore, be tested for robustness with respect to either missing and/or incorrect data. Our focus in this investigation was to compare the robustness of risk prediction models under the constraint of limited or incorrect information.In a recent paper, Deist et al., (2018) compared various machine learning (ML) classifiers for medical situations such as lung cancer, head and neck cancer, laryngeal cancer and meningioma detection. They observed that relatively simple approaches such as logistic regression and random forests resulted in broadly better discriminatory performance if complete and accurate information was available. However, this analysis was only applied to complete cases with no missing values and assumed no corrupted values.In this paper, we evaluated the robustness of multiple machine learning-based classifier algorithms for risk prediction in the presence of incomplete and inaccurate information. For this, we added simulated incomplete and inaccurate information into a large medical dataset and measured the degradation of discrimination performance. Section 2 of this paper discusses the dataset and our proposed methodology, followed by results in section 3. In section 4, we discussed the comparative performance of the machine learning classifiers. We also considered a visualization of results for better clinical interpretation of the predicted risk scores.DatasetThe data for this study was obtained from the BCSC Data Resource (more information at http://breastscreening.cancer.gov/). The BCSC  dataset aggregated risk factors of women who attended breast cancer screening from 1st January 1996 to 31st December 2002 from seven data registries covering most mammography clinic locations in the United States. The detailed information of 2.4 million observations, of women aged between 35 and 85 years, attending a mammography clinic was collected by a questionnaire for the BCSC study. Specific exclusions were: women with prior breast cancer or breast augmentation, or women who had already undergone breast cancer screening in the preceding 12 months. The primary endpoint was diagnostic confirmation of either ductal carcinoma in situ or invasive breast carcinoma after one year. Overall, the participation rate was highest in the 50-54 age group as shown in The BCSC dataset recorded: menopausal status, age, breast density, race, Hispanic ethnicity, Body Mass Index (BMI), age at first childbirth, number of first-degree relatives affected with breast cancer (NRBC), information about previous breast medical procedures, result from last mammogram, incidence of surgical menopause and treatment by Hormone Replacement Therapy (HRT). All data elements were coded as categorical variables.In total, 2 392 998 rows of observations were available of 1,007,660 unique subjects that met the following criteria: (1) at least one previous mammogram within the preceding 5 years, (2) had not undergone mammography in the last one year from the time of registration and (3) had no prior history of breast cancer. Each BCSC risk factor was categorized according to the coding schema shown in Selection of machine learning classifiers We selected three of the most commonly used types of ML classifiers for our analysis – namely logistic regression, random forest and deep neural networks. While this selection of ML-based classifiers is similar to a recent paper , our focus is on analyzing the performance of these models in the presence of additional missing elements and inaccurate data entry. Logistic regression  models aim to optimize the linear relationship between the log of odds (logit) of the dependent variable (outcome) and the independent variables such that the overall error between predicted and true outcomes for different observations is minimized. Random forests  create a large ensemble of individually distinct decision trees, such that the final classification is derived from the majority voting result taken over all trees. In our experiments, we have considered a random forest of 200 decision trees with a maximum depth of 10 for each decision tree.Neural Networks (NN) establish complex non-linear relationships between the inputs and the output using interconnected hidden layers. Given that we have a sufficiently large dataset, use of NN was feasible . We experimented with a variety of NN configurations and found that a simple 3-layer NN architecture generalized well for breast cancer risk prediction on the BCSC training dataset.The first stage of the NN architecture consisted of non-linear transformations to produce 64 different non-linear variations of each individual risk factor using (1x1) convolution filters and ReLU activations. The resulting 64 channels were then convolved with 32 (1x1) filters followed by ReLU activations to reduce the dimensionality. The resulting intra-input variations were mixed using a fully inter-connected dense layer consisting of 1,024 nodes to learn the non-linear inter-relations across different risk factors. A dropout layer  was applied immediately after the dense layer for regularization and to avoid over-fitting. The final output layer consisted of two output nodes with soft-max activation, representing the class probabilities of getting cancer in the next year and the probability of not getting cancer in next year, respectively.The starting weights of the NN were initialized randomly. We then used an Adam optimizer for updating NN parameters iteratively based on the training set because of its reported superior results. The Adam optimizer was set to learning rate 0.001 and a batch size of 15,000 and dropout probability of 0.5 was used for training. Categorical cross-entropy  was used as the loss function to measure the error between predicted and true probability distributions. The output corresponding to the probability of cancer obtained from the soft-max layer was used as the absolute final risk score in our analysis. The NN was implemented with the Keras  python library and a Tensorflow back-end . Performance evaluationAs mentioned in the Introduction, incomplete or inaccurate information may be encountered during real-world health data collection, which could result in inaccurate risk estimation. A good classifier needs to be robust to these errors and should result in a risk value that is close to the true risk, in spite of missing or inaccurate information. To quantify the robustness of different classifiers in this setting, we conducted the two experiments as described below.Experiment 1: Robustness to incomplete datam < 1’ during validation and testing. We varied the value of ‘pm’ in steps of 0.1. We first trained each of the classifiers by introducing additional 10% of missing values in the training set.In order to evaluate the robustness of the classifiers to missing or incomplete data, we randomly switched one of the existing known values to missing values with a probability of ‘0 < pExperiment 2: Robustness to inaccurate datac < 1’. As before, we varied the value of pc in steps of 0.1.In a similar manner to Experiment 1, we randomly modified the data by replacing the feature values with a different value, with probability of ‘0 < pTo assess the degradation of discrimination performance, we used an Area Under Receiver Operating Characteristic curve (AUC) metric . AUC measures the probability of correctly classifying the subjects according to a binary outcome. For perfect classification, we would obtain an AUC value of 1. For a model that had no discriminatory power beyond random guessing, we would expect an AUC value of 0.5. It is generally interpreted that a higher value of AUC suggests better performance in differentiating between the two available outcomes. Training and cross-validation settingsThe entire BCSC dataset was randomly divided into a model development set (75%) and a model testing set (25%). The training and validation sets had the same proportion of non-cancer and cancer findings. For cross-validation, we further split the training set in a ratio of 3:1 into training and cross-validation subsets.We first compared the performance of all three classifiers on the original BCSC dataset. Performance of classifiers with incomplete informationm for logistic regression, random forests and the NN on the cross-validation and model testing sets, respectively. As expected, the performance of all the classifiers were degraded with an increase in the percentage of missing information. Overall, the mean AUC was observed to be high for NN when compared to other classifiers up to pm < 0.5. But when the percentage of missingness exceeded 50%, both logistic regression and NN performed equally well. The values of mean AUCs at pm = 0 were slightly different from Performance of classifiers with inaccurate informationc for all the three classifiers, on the cross-validation and model testing sets, respectively. Similar to the above, the mean AUC reduced with the increased percentage of incorrect information for all the classifiers. NN resulted in superior performance in terms of AUC on the cross-validation sets, whereas on the independent test set, the performance was very close to logistic regression and random forests when more than 50% of the data was incorrect. It is important to note that the mean AUCs at pc = 0 were slightly different from the mean AUC’s at pm = 0 since these values were obtained by introducing the incorrect feature values with 0.1 corruption probability during training.c > 0.5) or missing ( pm > 0.5), NN and logistic regression resulted in similar performance. The performance of RF remained low compared to other classifiers with varying missing data. The primary aim of this work was to evaluate three commonly used classifiers in terms of their robustness towards missing and inaccurate data when predicting breast cancer incidence risk. The original BCSC dataset itself came with approximately 20% of missing data. NN showed superior results to logistic regression and random forests on this dataset as shown in m) and incorrect information (pc) during the training of NN instead of fixed ‘0.1’ as discussed in our experimentations. This may aid the NN to see the possible instances of missing or inaccurate information that might happen in the real setting.Overall, the NN was observed to be a marginally superior choice for risk estimation of breast cancer from structured data like BCSC dataset, even when there was a large amount of missing and incorrect information. We also believe that these superior results with NN could be replicated on other health care datasets where there are high chances of incomplete or inaccurate information. In addition, the use of NN reduces the need for explicit feature engineering steps such as feature elimination, dimensionality reduction and missing values imputation, due to the capability of NN to learn non-linear transformations from inputs to outputs. To effectively train an NN to deal with incomplete and inaccurate information, future work could attempt to increase the probabilities of missing  involving age alone as a risk criterion. We have currently hosted the risk estimation model proposed in this paper on our online website at https://www.niramai.org for public access. At this time, the wider clinical generalizability of this study is limited on a few fronts. First, we have not yet been able to independently validate the models using a more recent dataset or in the Indian population, due to a current lack of open access anonymized data. That is the focus of our future work and we further plan to validate our models in the Indian setting. Since the model is already trained to understand a diverse spread of risk factors from the BCSC data, convergence of a revised model in a new population setting is expected to be achieved much faster. Secondly, the combination of risk factors encompassing genetics, family history and individual phenotype is hypothesized to improve our ability to estimate breast cancer risk. Currently, there are no publicly available datasets with all the risk factors for the same population. A last observation would be that it is possible to either impute the missing values or detect potentially incorrect information by means of a conditional generative adversarial framework , but is outside the scope of the present study.In conclusion, we compared three commonly used classes of machine learning classifiers - logistic regression, random forests and neural networks - for their robustness towards missing values and inaccurately reported values that are inherently present in population survey datasets such as the BCSC. The NN yielded marginally higher AUCs even when the data is missing or incorrect especially when their individual incidences are less than 50% for a breast cancer dataset. However, the performance of NN and logistic regression was equivalent when missing/inaccurate data was above 50%. Additional work is needed to validate the models in the Indian population, if these models are to be used to aid in selecting women for breast cancer screening.


HOXB13 p.G84E is a prostate cancer risk allele, we evaluated the association between HOXB13 germline mutations and breast cancer risk in a previous study consisting of 3,270 familial non-BRCA1/2 breast cancer cases and 2,327 controls from the Netherlands. Although both recurrent HOXB13 mutations p.G84E and p.R217C were not associated with breast cancer risk, the risk estimation for p.R217C was not very precise. To provide more conclusive evidence regarding the role of HOXB13 in breast cancer susceptibility, we here evaluated the association between HOXB13 mutations and increased breast cancer risk within 81 studies of the international Breast Cancer Association Consortium containing 68,521 invasive breast cancer patients and 54,865 controls. Both HOXB13 p.G84E and p.R217C did not associate with the development of breast cancer in European women, neither in the overall analysis , nor in specific high-risk subgroups or breast cancer subtypes. Thus, although involved in breast cancer progression, HOXB13 is not a material breast cancer susceptibility gene.In breast cancer, high levels of homeobox protein Hox-B13 (HOXB13) have been associated with disease progression of ER-positive breast cancer patients and resistance to tamoxifen treatment. Since  BRCA1, BRCA2, CDH1, PTEN, STK11, TP53, PALB2, ATM, CHEK2 and NBN1. In addition, genome-wide association studies (GWASs) have identified over 170 common low penetrance alleles each conferring a small increased risk to develop breast cancer3. Importantly, the risks these low penetrance alleles confer combine multiplicatively and since these variants are so common in the population women in the top 1% of risk have a 4.4- and 2.8-fold increased risk to develop ER-positive and ER-negative breast cancer, respectively4. Still, to identify better those women at risk for developing breast cancer and establish more precise risk estimates, we need to explain the remainder of the genetic heritability of breast cancer.Breast cancer is a complex disease and several classes of germline variants have been identified that together explain about half of the total genetic heritability of breast cancer. These include rare germline mutations in high and moderate penetrance breast cancer susceptibility genes HOXB13 p.G84E germline mutation ; rs138213197:C > T) was found to be associated with an increased risk to develop prostate cancer by linkage analysis and candidate gene sequencing of 200 genes at the 17q21-22 linkage region5. Since then, several studies have validated this association and meta-analyses have shown the prostate cancer risk to be 3- to 4-fold increased for male carriers8. Moreover, the p.G84E mutation also associated with early-onset prostate cancer, multiple affected relatives and highly aggressive disease8. Considering the evidence, there is a strong consensus for including the HOXB13 gene in genetic testing for hereditary prostate cancer9.In this respect, the rare RFX6 gene which encodes a driver of prostate cancer progression. Interestingly, HOXB13 is preferentially recruited to the risk allele of a prostate cancer risk associated SNP, rs339331, located in an enhancer element upstream of RFX6, thereby enhancing RFX6 expression and promoting more aggressive disease10. Moreover, HOXB13 also pioneers binding of the constitutively active splice variant 7 of the androgen receptor (AR-V7) to open chromatin of castrate-resistant prostate cancer (CRPC) genomes to upregulate target oncogenes11. Importantly, AR-V7 plays an important role in the anti-AR therapy resistance12.In recent years, we have also begun to understand the role of HOXB13 in prostate cancer progression. HOXB13 acts as a transcription factor and, together with the androgen receptor (AR) and FOXA1, regulates expression of the HOXB13 to IL17BR expression ratio was associated with a high risk of recurrence and poor outcome for estrogen receptor (ER)-positive breast cancer patients15. Furthermore, high expression of HOXB13 predicted a poor response to tamoxifen therapy by suppressing ER and activating the mTOR pathway via IL617. Interestingly, a significant fraction of breast cancer risk SNPs have been found to alter the affinity of chromatin for pioneer factor FOXA1 with which HOXB13 interacts in prostate cancer cells18. To date, several studies have investigated the association between the germline HOXB13 p.G84E mutation and breast cancer risk, however, these have led to contradictory results21.In breast cancer, HOXB13 also plays an important role in disease progression. A high HOXB13 in 1,250 familial non-BRCA1/2 breast cancer cases and 800 controls. We identified two recurrent HOXB13 mutations in the female Dutch population, the known prostate cancer risk allele p.G84E, but also p.R217C ; rs139475791:G > A). We found that neither p.G84E nor p.R217C were associated with an increased breast cancer risk  in 3,270 familial non-BRCA1/2 breast cancer patients and 2,327 controls22. Considering the low carrier allele frequency  and the very wide confidence intervals for the association between p.R217C and breast cancer risk, larger studies are needed to provide more conclusive evidence. Furthermore, we wanted to replicate our findings for the p.G84E mutation. Therefore, we have genotyped 68,521 breast cancer cases and 54,865 controls from 81 studies in the Breast Cancer Association Consortium (BCAC) for the HOXB13 p.G84E and p.R217C mutations.In a previous study, we have sequenced the entire coding region of i.e. Sweden, Denmark and the Netherlands in controls)  breast cancer risk ) and p.R268Q ; rs748782183:C > T)22. These two mutations had not been investigated before due to their low frequency in the Dutch population. However, the present study enabled us to assess their frequency in a global context. The p.P190L mutation was most prevalent in the African population and absent in the Asian population 19. However, in a larger study conducted by Akbari et al., no such association between the p.G84E mutation and an increased breast cancer risk was observed among 4,037 cases, of which 1,082 were familial, and 2,762 controls 20. A study by Laitinen et al. consisting of 986 breast cancer patients  and 1,449 controls also did not reveal an association for overall breast cancer risk and p.G84E among Finnish women21. Results of these three studies have been pooled in a fixed-effects meta-analysis by Cai et al. and did not find a significant association between HOXB13 p.G84E and an increased breast cancer risk 7. We also did not observe an increased breast cancer risk associated with the p.G84E mutation in our previous study of 3,270 familial non-BRCA1/2 breast cancer cases and 2,327 controls 22. The results of the current study concur with these observations in that HOXB13 p.G84E does not appear to act as a breast cancer susceptibility allele, neither in overall analyses  nor in analyses enriching for particular (high-risk) subgroups.The current study is by far the largest study that has been performed evaluating the association with an increased breast cancer risk for germline 22. Since the estimation of the breast cancer risk for this mutation was not very precise in our previous study, we sought to re-evaluate the association between p.R217C and increased breast cancer risk in the current study. As for p.G84E, we did not find any association between p.R217C and an increased breast cancer risk, neither in overall analyses , nor in subgroup analyses. Interestingly, the p.R217C mutation had been described before among a few prostate cancer cases, but Xu et al. reported that p.R217C did not co-segregate with prostate cancer in the two families they identified24. In concordance with this, OncoArray summary association results from the PRACTICAL consortium show that, indeed, p.R217C is also not a material prostate cancer susceptibility allele , while p.G84E is associated with an increased prostate cancer risk in this data set 25.Besides p.G84E, we also identified p.R217C to be a recurrent mutation in the female Dutch population17, germline mutations in the HOXB13 gene seem to associate with the development of prostate cancer only8. This suggests distinct biological pathways associated with HOXB13 function in breast and prostate tissue. In prostate cancer, HOXB13 co-localizes with AR and acts as a repressor of AR target genes to modulate AR hormonal responses27. In breast cancer, ER and HOXB13 have been shown to regulate each other’s expression28. Thus, in both tissue types hormonal responses are closely interlinked with HOXB13 function. More research is needed, however, to understand better the differential roles of HOXB13 in disease initiation and progression.Although HOXB13 plays an important role in both breast and prostate cancer progressionHOXB13 germline mutations p.G84E and p.R217C are not associated with an increased risk to develop breast cancer. HOXB13 is therefore not a material breast cancer susceptibility gene.To conclude, in our large study consisting of 68,521 invasive breast cancer cases and 54,865 controls from 81 BCAC studies we provide strong evidence that the rare, but recurrent 3. All studies provided core data on disease status and age at diagnosis while only a subset of the studies provided data on menopausal status, ER, PR and ERBB2 status, family history and bilateral breast cancer. All 81 BCAC studies were approved by their relevant governing research ethics committee and all participants provided written informed consent. The experimental protocol was approved by the Medical Ethical Committee of the Erasmus Medical Center Rotterdam and the study was carried out in accordance with the Code of Conduct of the Federation of Medical Scientific Societies in the Netherlands (https://www.federa.org/gedragscodes).In this study, BCAC consists of 81 case-control studies of unrelated women with participants of European, Asian and African ancestry contributing 68,521 patients with invasive breast cancer and 54,865 controlse.g. breast, ovarian, prostate, lung, colorectal) for several distinct reasons as detailed in29. Approximately 72,000 SNPs were selected specifically for their relevance to breast cancer. Details of the genotype calling and quality control for OncoArray are described elsewhere29. In brief, samples were excluded when the call rate was below 95% or when these were probable duplicates, close relatives or samples with extreme heterozygosity. Ancestry was computed using a principal component analysis (PCA). Variants were excluded using the following criteria: an overall call rate <99% or <95% in any consortium, minor allele frequency (MAF) < 0.001, poor intensity and clustering metrics, deviation from the expected frequency as observed in the 1000 Genomes Project and deviation from the Hardy-Weinberg equilibrium . A total of 494,763 SNPs passed the quality control and included the following four HOXB13 missense variants: c.251 G > A , c.569 C > T (p.P190L), c.649 C > T  and c.803 G > A (p.R268Q).Genotyping of the 81 BCAC studies was performed previously using the OncoArray, a custom-designed Illumina Infinium BeadChip. About half of the approximately 533,000 OncoArray SNPs were selected as a ‘GWAS backbone’ (Illumina HumanCore) with the remainder of SNPs selected by the disease-based consortia representing the main cancer sites  and women diagnosed with a contralateral breast cancer) as well as stratification for hormone receptor status  to evaluate subtype-specific breast cancer risk. All P-values were two-sided and P < 0.05 was considered to be statistically significant after correction for multiple testing by the Bonferroni procedure. Logistic regression analyses were performed using R version 3.3.3.The association between Supplementary Information.