Risks of second primary cancers among 584,965 female and male breast cancer survivors in England: a 25-year retrospective cohort study.

Background: Second primary cancers (SPCs) after breast cancer (BC) present an increasing public health burden, with little existing research on socio-demographic, tumour, and treatment effects. We addressed this in the largest BC survivor cohort to date, using a novel linkage of National Disease Registration Service datasets. Methods: The cohort included 581,403 female and 3562 male BC survivors diagnosed between 1995 and 2019. We estimated standardized incidence ratios (SIRs) for combined and site-specific SPCs using incidences for England, overall and by age at BC and socioeconomic status. We estimated incidences and Kaplan-Meier cumulative risks stratified by age at BC, and assessed risk variation by socio-demographic, tumour, and treatment characteristics using Cox regression. Findings: Both genders were at elevated contralateral breast (SIR: 2.02 (95% CI: 1.99-2.06) females; 55.4 (35.5-82.4) males) and non-breast (1.10 (1.09-1.11) females, 1.10 (1.00-1.20) males) SPC risks. Non-breast SPC risks were higher for females younger at BC diagnosis (SIR: 1.34 (1.31-1.38) <50 y, 1.07 (1.06-1.09) ≥50 y) and more socioeconomically deprived (SIR: 1.00 (0.98-1.02) least deprived quintile, 1.34 (1.30-1.37) most). Interpretation: Enhanced SPC surveillance may benefit BC survivors, although specific recommendations require more detailed multifactorial risk and cost-benefit analyses. The associations between deprivation and SPC risks could provide clinical management insights. Funding: CRUK Catalyst Award CanGene-CanVar (C61296/A27223). Cancer Research UK grant: PPRPGM-Nov 20∖100,002. This work was supported by core funding from the NIHR Cambridge Biomedical Research Centre (NIHR203312)]. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

Exome sequencing identifies HELB as a novel susceptibility gene for non-mucinous, non-high-grade-serous epithelial ovarian cancer.

Rare, germline loss-of-function variants in a handful of genes that encode DNA repair proteins have been shown to be associated with epithelial ovarian cancer with a stronger association for the high-grade serous hiostotype. The aim of this study was to collate exome sequencing data from multiple epithelial ovarian cancer case cohorts and controls in order to systematically evaluate the role of coding, loss-of-function variants across the genome in epithelial ovarian cancer risk. We assembled exome data for a total of 2,573 non-mucinous cases (1,876 high-grade serous and 697 non-high grade serous) and 13,925 controls. Harmonised variant calling and quality control filtering was applied across the different data sets. We carried out a gene-by-gene simple burden test for association of rare loss-of-function variants (minor allele frequency < 0.1%) with all non-mucinous ovarian cancer, high grade serous ovarian cancer and non-high grade serous ovarian cancer using logistic regression adjusted for the top four principal components to account for cryptic population structure and genetic ancestry. Seven of the top 10 associated genes were associations of the known ovarian cancer susceptibility genes BRCA1, BRCA2, BRIP1, RAD51C, RAD51D, MSH6 and PALB2 (false discovery probability < 0.1). A further four genes (HELB, OR2T35, NBN and MYO1A) had a false discovery rate of less than 0.1. Of these, HELB was most strongly associated with the non-high grade serous histotype (P = 1.3×10-6, FDR = 9.1×10-4). Further support for this association comes from the observation that loss of function variants in this gene are also associated with age at natural menopause and Mendelian randomisation analysis shows an association between genetically predicted age at natural menopause and endometrioid ovarian cancer, but not high-grade serous ovarian cancer.

Risk Factors for Ovarian Cancer by BRCA Status: A Collaborative Case-Only Analysis.

BACKGROUND: Women with an inherited pathogenic variant in BRCA1 or BRCA2 have a greatly increased risk of developing ovarian cancer, but the importance of behavioral factors is less clear. We used a case-only design to compare the magnitude of associations with established reproductive, hormonal, and lifestyle risk factors between BRCA mutation carriers and noncarriers. METHODS: We pooled data from five studies from the Ovarian Cancer Association Consortium including 637 BRCA carriers and 4,289 noncarriers. Covariate-adjusted generalized linear mixed models were used to estimate interaction risk ratios (IRR) and 95% confidence intervals (CI), with BRCA (carrier vs. noncarrier) as the response variable. RESULTS: IRRs were above 1.0 for known protective factors including ever being pregnant (IRR = 1.29, 95% CI; 1.00-1.67) and ever using the oral contraceptive pill (1.30, 95% CI; 1.07-1.60), suggesting the protective effects of these factors may be reduced in carriers compared with noncarriers. Conversely, the IRRs for risk factors including endometriosis and menopausal hormone therapy were below 1.0, suggesting weaker positive associations among BRCA carriers. In contrast, associations with lifestyle factors including smoking, physical inactivity, body mass index, and aspirin use did not appear to differ by BRCA status. CONCLUSIONS: Our results suggest that associations with hormonal and reproductive factors are generally weaker for those with a pathogenic BRCA variant than those without, while associations with modifiable lifestyle factors are similar for carriers and noncarriers. IMPACT: Advice to maintain a healthy weight, be physically active, and refrain from smoking will therefore benefit BRCA carriers as well as noncarriers.

Allometric versus traditional body-shape indices and risk of colorectal cancer: a Mendelian randomization analysis.

BACKGROUND: Traditional body-shape indices such as Waist Circumference (WC), Hip Circumference (HC), and Waist-to-Hip Ratio (WHR) are associated with colorectal cancer (CRC) risk, but are correlated with Body Mass Index (BMI), and adjustment for BMI introduces a strong correlation with height. Thus, new allometric indices have been developed, namely A Body Shape Index (ABSI), Hip Index (HI), and Waist-to-Hip Index (WHI), which are uncorrelated with weight and height; these have also been associated with CRC risk in observational studies, but information from Mendelian randomization (MR) studies is missing. METHODS: We used two-sample MR to examine potential causal cancer site- and sex-specific associations of the genetically-predicted allometric body-shape indices with CRC risk, and compared them with BMI-adjusted traditional body-shape indices, and BMI. Data were obtained from UK Biobank and the GIANT consortium, and from GECCO, CORECT and CCFR consortia. RESULTS: WHI was positively associated with CRC in men (OR per SD: 1.20, 95% CI: 1.03-1.39) and in women (1.15, 1.06-1.24), and similarly for colon and rectal cancer. ABSI was positively associated with colon and rectal cancer in men (1.27, 1.03-1.57; and 1.40, 1.10-1.77, respectively), and with colon cancer in women (1.20, 1.07-1.35). There was little evidence for association between HI and colon or rectal cancer. The BMI-adjusted WHR and HC showed similar associations to WHI and HI, whereas WC showed similar associations to ABSI only in women. CONCLUSIONS: This large MR study provides strong evidence for a potential causal positive association of the allometric indices ABSI and WHI with CRC in both sexes, thus establishing the association between abdominal fat and CRC without the limitations of the traditional waist size indices and independently of BMI. Among the BMI-adjusted traditional indices, WHR and HC provided equivalent associations with WHI and HI, while differences were observed between WC and ABSI.

Novel breast cancer susceptibility loci under linkage peaks identified in African ancestry consortia.

BACKGROUND: Expansion of genome-wide association studies across population groups is needed to improve our understanding of shared and unique genetic contributions to breast cancer. We performed association and replication studies guided by a priori linkage findings from African ancestry (AA) relative pairs. METHODS: We performed fixed-effect inverse-variance weighted meta-analysis under three significant AA breast cancer linkage peaks (3q26-27, 12q22-23, and 16q21-22) in 9241 AA cases and 10 193 AA controls. We examined associations with overall breast cancer as well as estrogen receptor (ER)-positive and negative subtypes (193,132 SNPs). We replicated associations in the African-ancestry Breast Cancer Genetic Consortium (AABCG). RESULTS: In AA women, we identified two associations on chr12q for overall breast cancer (rs1420647, OR = 1.15, p = 2.50×10-6; rs12322371, OR = 1.14, p = 3.15×10-6), and one for ER-negative breast cancer (rs77006600, OR = 1.67, p = 3.51×10-6). On chr3, we identified two associations with ER-negative disease (rs184090918, OR = 3.70, p = 1.23×10-5; rs76959804, OR = 3.57, p = 1.77×10-5) and on chr16q we identified an association with ER-negative disease (rs34147411, OR = 1.62, p = 8.82×10-6). In the replication study, the chr3 associations were significant and effect sizes were larger (rs184090918, OR: 6.66, 95% CI: 1.43, 31.01; rs76959804, OR: 5.24, 95% CI: 1.70, 16.16). CONCLUSION: The two chr3 SNPs are upstream to open chromatin ENSR00000710716, a regulatory feature that is actively regulated in mammary tissues, providing evidence that variants in this chr3 region may have a regulatory role in our target organ. Our study provides support for breast cancer variant discovery using prioritization based on linkage evidence.

Estimation of age of onset and progression of breast cancer by absolute risk dependent on polygenic risk score and other risk factors.

BACKGROUND: Genetic, lifestyle, reproductive, and anthropometric factors are associated with the risk of developing breast cancer. However, it is not yet known whether polygenic risk score (PRS) and absolute risk based on a combination of risk factors are associated with the risk of progression of breast cancer. This study aims to estimate the distribution of sojourn time (pre-clinical screen-detectable period) and mammographic sensitivity by absolute breast cancer risk derived from polygenic profile and the other risk factors. METHODS: The authors used data from a population-based case-control study. Six categories of 10-year absolute risk based on different combinations of risk factors were derived using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm. Women were classified into low, medium, and high-risk groups. The authors constructed a continuous-time multistate model. To calculate the sojourn time, they simulated the trajectories of subjects through the disease states. RESULTS: There was little difference in sojourn time with a large overlap in the 95% confidence interval (CI) between the risk groups across the six risk categories and PRS studied. However, the age of entry into the screen-detectable state varied by risk category, with the mean age of entry of 53.4 years (95% CI, 52.2-54.1) and 57.0 years (95% CI, 55.1-57.7) in the high-risk and low-risk women, respectively. CONCLUSION: In risk-stratified breast screening, the age at the start of screening, but not necessarily the frequency of screening, should be tailored to a woman's risk level. The optimal risk-stratified screening strategy that would improve the benefit-to-harm balance and the cost-effectiveness of the screening programs needs to be studied.

An updated PREDICT breast cancer prognostic model including the benefits and harms of radiotherapy.

PREDICT Breast ( www.breast .predict.nhs.uk ) is a prognostication tool for early invasive breast cancer. The current version was based on cases diagnosed in 1999-2003 and did not incorporate the benefits of radiotherapy or the harms associated with therapy. Since then, there has been a substantial improvement in the outcomes for breast cancer cases. The aim of this study was to update PREDICT Breast to ensure that the underlying model is appropriate for contemporary patients. Data from the England National Cancer Registration and Advisory Service for invasive breast cancer cases diagnosed 2000-17 were used for model development and validation. Model development was based on 35,474 cases diagnosed and registered by the Eastern Cancer Registry. A Cox model was used to estimate the prognostic effects of the year of diagnosis, age at diagnosis, tumour size, tumour grade and number of positive nodes. Separate models were developed for ER-positive and ER-negative disease. Data on 32,408 cases from the West Midlands Cancer Registry and 100,551 cases from other cancer registries were used for validation. The new model was well-calibrated; predicted breast cancer deaths at 5-, 10- and 15-year were within 10 per cent of the observed validation data. Discrimination was also good: The AUC for 15-year breast cancer survival was 0.809 in the West Midlands data set and 0.846 in the data set for the other registries. The new PREDICT Breast model outperformed the current model and will be implemented in the online tool which should lead to more accurate absolute treatment benefit predictions for individual patients.

Long-term outcomes of hysterectomy with bilateral salpingo-oophorectomy: a systematic review and meta-analysis.

OBJECTIVE: This study aimed to provide an up-to-date systematic review of "the long-term outcomes of bilateral salpingo-oophorectomy at the time of hysterectomy" and perform a meta-analysis for the reported associations. DATA SOURCES: Our study updated a previous systematic review by searching the literature using PubMed, Web of Science, and Embase for publications between January 2015 and August 2022. STUDY ELIGIBILITY CRITERIA: Our study included studies of women who had a hysterectomy with bilateral salpingo-oophorectomy vs women who had a hysterectomy with ovarian conservation or no surgery. METHODS: The quality of the evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluations. Adjusted hazard ratios were extracted and combined to obtain fixed effect estimates. RESULTS: Compared with hysterectomy or no surgery, hysterectomy with bilateral salpingo-oophorectomy in young women was associated with decreased risk of breast cancer (hazard ratio, 0.78; 95% confidence interval, 0.73-0.84) but with an increased risk of colorectal cancer (hazard ratio, 1.27; 95% confidence interval, 1.10-1.47). In addition, it was associated with an increased risk of total cardiovascular diseases, coronary heart disease, and stroke with hazard ratios of 1.18 (95% confidence interval, 1.11-1.25), 1.17 (95% confidence interval, 1.10-1.25), and 1.20 (95% confidence interval, 1.10-1.31), respectively. Compared with no surgery, hysterectomy with bilateral salpingo-oophorectomy before the age of 50 years was associated with an increased risk of hyperlipidemia (hazard ratio, 1.44; 95% confidence interval, 1.25-1.65), diabetes mellitus (hazard ratio, 1.16; 95% confidence interval, 1.09-1.24), hypertension (hazard ratio, 1.13; 95% confidence interval, 1.06-1.20), dementia (hazard ratio, 1.70; 95% confidence interval, 1.07-2.69), and depression (hazard ratio, 1.39; 95% confidence interval, 1.22-1.60). The evidence on the association with all-cause mortality in young women showed substantial heterogeneity between the studies (I2=85%; P<.01). CONCLUSION: Hysterectomy with bilateral salpingo-oophorectomy was associated with multiple long-term outcomes. The benefits of the addition of bilateral salpingo-oophorectomy to hysterectomy should be balanced against the risks.

Polymorphisms in genes of melatonin biosynthesis and signaling support the light-at-night hypothesis for breast cancer.

Light-at-night triggers the decline of pineal gland melatonin biosynthesis and secretion and is an IARC-classified probable breast-cancer risk factor. We applied a large-scale molecular epidemiology approach to shed light on the putative role of melatonin in breast cancer. We investigated associations between breast-cancer risk and polymorphisms at genes of melatonin biosynthesis/signaling using a study population of 44,405 women from the Breast Cancer Association Consortium (22,992 cases, 21,413 population-based controls). Genotype data of 97 candidate single nucleotide polymorphisms (SNPs) at 18 defined gene regions were investigated for breast-cancer risk effects. We calculated adjusted odds ratios (ORs) and 95% confidence intervals (CI) by logistic regression for the main-effect analysis as well as stratified analyses by estrogen- and progesterone-receptor (ER, PR) status. SNP-SNP interactions were analyzed via a two-step procedure based on logic regression. The Bayesian false-discovery probability (BFDP) was used for all analyses to account for multiple testing. Noteworthy associations (BFDP < 0.8) included 10 linked SNPs in tryptophan hydroxylase 2 (TPH2) (e.g. rs1386492: OR = 1.07, 95% CI 1.02-1.12), and a SNP in the mitogen-activated protein kinase 8 (MAPK8) (rs10857561: OR = 1.11, 95% CI 1.04-1.18). The SNP-SNP interaction analysis revealed noteworthy interaction terms with TPH2- and MAPK-related SNPs (e.g. rs1386483R ∧ rs1473473D ∧ rs3729931D: OR = 1.20, 95% CI 1.09-1.32). In line with the light-at-night hypothesis that links shift work with elevated breast-cancer risks our results point to SNPs in TPH2 and MAPK-genes that may impact the intricate network of circadian regulation.

Predicted Proteome Association Studies of Breast, Prostate, Ovarian, and Endometrial Cancers Implicate Plasma Protein Regulation in Cancer Susceptibility.

BACKGROUND: Predicting protein levels from genotypes for proteome-wide association studies (PWAS) may provide insight into the mechanisms underlying cancer susceptibility. METHODS: We performed PWAS of breast, endometrial, ovarian, and prostate cancers and their subtypes in several large European-ancestry discovery consortia (effective sample size: 237,483 cases/317,006 controls) and tested the results for replication in an independent European-ancestry GWAS (31,969 cases/410,350 controls). We performed PWAS using the cancer GWAS summary statistics and two sets of plasma protein prediction models, followed by colocalization analysis. RESULTS: Using Atherosclerosis Risk in Communities (ARIC) models, we identified 93 protein-cancer associations [false discovery rate (FDR) < 0.05]. We then performed a meta-analysis of the discovery and replication PWAS, resulting in 61 significant protein-cancer associations (FDR < 0.05). Ten of 15 protein-cancer pairs that could be tested using Trans-Omics for Precision Medicine (TOPMed) protein prediction models replicated with the same directions of effect in both cancer GWAS (P < 0.05). To further support our results, we applied Bayesian colocalization analysis and found colocalized SNPs for SERPINA3 protein levels and prostate cancer (posterior probability, PP = 0.65) and SNUPN protein levels and breast cancer (PP = 0.62). CONCLUSIONS: We used PWAS to identify potential biomarkers of hormone-related cancer risk. SNPs in SERPINA3 and SNUPN did not reach genome-wide significance for cancer in the original GWAS, highlighting the power of PWAS for novel locus discovery, with the added advantage of providing directions of protein effect. IMPACT: PWAS and colocalization are promising methods to identify potential molecular mechanisms underlying complex traits.

Polygenic scores in cancer.

Since the publication of the first genome-wide association study for cancer in 2007, thousands of common alleles that are associated with the risk of cancer have been identified. The relative risk associated with individual variants is small and of limited clinical significance. However, the combined effect of multiple risk variants as captured by polygenic scores (PGSs) may be much greater and therefore provide risk discrimination that is clinically useful. We review the considerable research efforts over the past 15 years for developing statistical methods for PGSs and their application in large-scale genome-wide association studies to develop PGSs for various cancers. We review the predictive performance of these PGSs and the multiple challenges currently limiting the clinical application of PGSs. Despite this, PGSs are beginning to be incorporated into clinical multifactorial risk prediction models to stratify risk in both clinical trials and clinical implementation studies.

A framework for assessing interactions for risk stratification models: the example of ovarian cancer.

Generally, risk stratification models for cancer use effect estimates from risk/protective factor analyses that have not assessed potential interactions between these exposures. We have developed a 4-criterion framework for assessing interactions that includes statistical, qualitative, biological, and practical approaches. We present the application of this framework in an ovarian cancer setting because this is an important step in developing more accurate risk stratification models. Using data from 9 case-control studies in the Ovarian Cancer Association Consortium, we conducted a comprehensive analysis of interactions among 15 unequivocal risk and protective factors for ovarian cancer (including 14 non-genetic factors and a 36-variant polygenic score) with age and menopausal status. Pairwise interactions between the risk/protective factors were also assessed. We found that menopausal status modifies the association among endometriosis, first-degree family history of ovarian cancer, breastfeeding, and depot-medroxyprogesterone acetate use and disease risk, highlighting the importance of understanding multiplicative interactions when developing risk prediction models.

Genetically proxied glucose-lowering drug target perturbation and risk of cancer: a Mendelian randomisation analysis.

AIMS/HYPOTHESIS: Epidemiological studies have generated conflicting findings on the relationship between glucose-lowering medication use and cancer risk. Naturally occurring variation in genes encoding glucose-lowering drug targets can be used to investigate the effect of their pharmacological perturbation on cancer risk. METHODS: We developed genetic instruments for three glucose-lowering drug targets (peroxisome proliferator activated receptor γ [PPARG]; sulfonylurea receptor 1 [ATP binding cassette subfamily C member 8 (ABCC8)]; glucagon-like peptide 1 receptor [GLP1R]) using summary genetic association data from a genome-wide association study of type 2 diabetes in 148,726 cases and 965,732 controls in the Million Veteran Program. Genetic instruments were constructed using cis-acting genome-wide significant (p<5×10-8) SNPs permitted to be in weak linkage disequilibrium (r2<0.20). Summary genetic association estimates for these SNPs were obtained from genome-wide association study (GWAS) consortia for the following cancers: breast (122,977 cases, 105,974 controls); colorectal (58,221 cases, 67,694 controls); prostate (79,148 cases, 61,106 controls); and overall (i.e. site-combined) cancer (27,483 cases, 372,016 controls). Inverse-variance weighted random-effects models adjusting for linkage disequilibrium were employed to estimate causal associations between genetically proxied drug target perturbation and cancer risk. Co-localisation analysis was employed to examine robustness of findings to violations of Mendelian randomisation (MR) assumptions. A Bonferroni correction was employed as a heuristic to define associations from MR analyses as 'strong' and 'weak' evidence. RESULTS: In MR analysis, genetically proxied PPARG perturbation was weakly associated with higher risk of prostate cancer (for PPARG perturbation equivalent to a 1 unit decrease in inverse rank normal transformed HbA1c: OR 1.75 [95% CI 1.07, 2.85], p=0.02). In histological subtype-stratified analyses, genetically proxied PPARG perturbation was weakly associated with lower risk of oestrogen receptor-positive breast cancer (OR 0.57 [95% CI 0.38, 0.85], p=6.45×10-3). In co-localisation analysis, however, there was little evidence of shared causal variants for type 2 diabetes liability and cancer endpoints in the PPARG locus, although these analyses were likely underpowered. There was little evidence to support associations between genetically proxied PPARG perturbation and colorectal or overall cancer risk or between genetically proxied ABCC8 or GLP1R perturbation with risk across cancer endpoints. CONCLUSIONS/INTERPRETATION: Our drug target MR analyses did not find consistent evidence to support an association of genetically proxied PPARG, ABCC8 or GLP1R perturbation with breast, colorectal, prostate or overall cancer risk. Further evaluation of these drug targets using alternative molecular epidemiological approaches may help to further corroborate the findings presented in this analysis. DATA AVAILABILITY: Summary genetic association data for select cancer endpoints were obtained from the public domain: breast cancer ( https://bcac.ccge.medschl.cam.ac.uk/bcacdata/ ); and overall prostate cancer ( http://practical.icr.ac.uk/blog/ ). Summary genetic association data for colorectal cancer can be accessed by contacting GECCO (kafdem at fredhutch.org). Summary genetic association data on advanced prostate cancer can be accessed by contacting PRACTICAL (practical at icr.ac.uk). Summary genetic association data on type 2 diabetes from Vujkovic et al (Nat Genet, 2020) can be accessed through dbGAP under accession number phs001672.v3.p1 (pha004945.1 refers to the European-specific summary statistics). UK Biobank data can be accessed by registering with UK Biobank and completing the registration form in the Access Management System (AMS) ( https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access ).

Risks of second non-breast primaries following breast cancer in women: a systematic review and meta-analysis.

BACKGROUND: Second primary cancer incidence is rising among breast cancer survivors. We examined the risks of non-breast second primaries, in combination and at specific cancer sites, through a systematic review and meta-analysis. METHODS: We conducted a systematic search of PubMed, Embase, and Web of Science, seeking studies published by March 2022. We included studies that reported standardized incidence ratios (SIRs), with associated standard errors, assessing the combined risk of second non-breast primaries following breast cancer. We performed meta-analyses of combined second primary risks, stratifying by age, follow-up duration, and geographic region. We also assessed second primary risks at several specific sites, stratifying by age. The inverse variance method with DerSimonian-Laird estimators was used in all meta-analyses, assuming a random-effects model. Associated biases and study quality were evaluated using the Newcastle-Ottawa scale. RESULTS: One prospective and twenty-seven retrospective cohort studies were identified. SIRs for second non-breast primaries combined ranged from 0.84 to 1.84. The summary SIR estimate was 1.24 (95% CI 1.14-1.36, I2: 99%). This varied by age: the estimate was 1.59 (95% CI 1.36-1.85) when breast cancer was diagnosed before age 50, which was significantly higher than in women first diagnosed at 50 or over (SIR: 1.13, 95% CI 1.01-1.36, p for difference: < 0.001). SPC risks were also significantly higher when based on Asian, rather than European, registries (Asia-SIR: 1.47, 95% CI 1.29-1.67. Europe-SIR: 1.16, 95% CI 1.04-1.28). There were significantly increased risks of second thyroid (SIR: 1.89, 95% CI 1.49-2.38), corpus uteri (SIR: 1.84, 95% CI 1.53-2.23), ovary (SIR: 1.53, 95% CI 1.35-1.73), kidney (SIR: 1.43, 95% CI 1.17-1.73), oesophagus (SIR: 1.39, 95% CI 1.26-1.55), skin (melanoma) (SIR: 1.34, 95% CI 1.18-1.52), blood (leukaemia) (SIR: 1.30, 95% CI 1.17-1.45), lung (SIR: 1.25, 95% CI 1.03-1.51), stomach (SIR: 1.23, 95% CI 1.12-1.36) and bladder (SIR: 1.15, 95% CI 1.05-1.26) primaries. CONCLUSIONS: Breast cancer survivors are at significantly increased risk of second primaries at many sites. Risks are higher for those diagnosed with breast cancer before age 50 and in Asian breast cancer survivors compared to European breast cancer survivors. This study is limited by a lack of data on potentially confounding variables. The conclusions may inform clinical management decisions following breast cancer, although specific clinical recommendations lie outside the scope of this review.

Evaluation and comparison of different breast cancer prognosis scores based on gene expression data.

BACKGROUND: Breast cancer is one of the three most common cancers worldwide and is the most common malignancy in women. Treatment approaches for breast cancer are diverse and varied. Clinicians must balance risks and benefits when deciding treatments, and models have been developed to support this decision-making. Genomic risk scores (GRSs) may offer greater clinical value than standard clinicopathological models, but there is limited evidence as to whether these models perform better than the current clinical standard of care. METHODS: PREDICT and GRSs were adapted using data from the original papers. Univariable Cox proportional hazards models were produced with breast cancer-specific survival (BCSS) as the outcome. Independent predictors of BCSS were used to build multivariable models with PREDICT. Signatures which provided independent prognostic information in multivariable models were incorporated into the PREDICT algorithm and assessed for calibration, discrimination and reclassification. RESULTS: EndoPredict, MammaPrint and Prosigna demonstrated prognostic power independent of PREDICT in multivariable models for ER-positive patients; no score predicted BCSS in ER-negative patients. Incorporating these models into PREDICT had only a modest impact upon calibration (with absolute improvements of 0.2-0.8%), discrimination (with no statistically significant c-index improvements) and reclassification (with 4-10% of patients being reclassified). CONCLUSION: Addition of GRSs to PREDICT had limited impact on model fit or treatment received. This analysis does not support widespread adoption of current GRSs based on our implementations of commercial products.

Association of Frequent Aspirin Use With Ovarian Cancer Risk According to Genetic Susceptibility.

Importance: Frequent aspirin use is associated with reduced ovarian cancer risk, but it is unknown whether genetic factors modify this association. Understanding effect modifiers is important given that any use of aspirin for ovarian cancer chemoprevention will likely need to focus on specific higher-risk subgroups. Objective: To evaluate whether the association between frequent aspirin use and ovarian cancer is modified by a polygenic score (PGS) for nonmucinous ovarian cancer. Design, Setting, and Participants: We pooled individual-level data from 8 population-based case-control studies from the Ovarian Cancer Association Consortium conducted in the US, UK, and Australia between 1995 and 2009. We included case patients and control participants with both genetic data and data on frequent aspirin use. Case patients with mucinous ovarian cancer were excluded. Data were analyzed between November 1, 2021, and July 31, 2022. Exposures: Frequent aspirin use, defined as daily or almost daily use for 6 months or longer. Main Outcomes and Measures: The main outcome was nonmucinous epithelial ovarian cancer. We used logistic regression to estimate odds ratios (ORs) and 95% CIs and likelihood ratio tests to investigate effect modification by the PGS. Results: There were 4476 case patients with nonmucinous ovarian cancer and 6659 control participants included in this analysis. At study enrollment, the median (IQR) age was 58 (50-66) years for case patients and 57 (49-65) years for control participants. Case patients and control participants self-reported that they were Black (122 [3%] vs 218 [3%]), White (3995 [89%] vs 5851 [88%]), or of other race and ethnicity (348 [8%] vs 580 [9%]; race and ethnicity were unknown for 11 [0%] vs 10 [0%]). There were 575 case patients (13%) and 1030 control participants (15%) who reported frequent aspirin use. The 13% reduction in ovarian cancer risk associated with frequent aspirin use (OR, 0.87 [95% CI, 0.76-0.99]) was not modified by the PGS. Consistent ORs were observed among individuals with a PGS less than (0.85 [0.70-1.02]) and greater than (0.86 [0.74-1.01]) the median. Results were similar by histotype. Conclusions and Relevance: The findings of this study suggest that genetic susceptibility to ovarian cancer based on currently identified common genetic variants does not appear to modify the protective association between frequent aspirin use and ovarian cancer risk. Future work should continue to explore the role of aspirin use for ovarian cancer prevention among individuals who are at higher risk for ovarian cancer.

Associations of a Breast Cancer Polygenic Risk Score With Tumor Characteristics and Survival.

PURPOSE: A polygenic risk score (PRS) consisting of 313 common genetic variants (PRS313) is associated with risk of breast cancer and contralateral breast cancer. This study aimed to evaluate the association of the PRS313 with clinicopathologic characteristics of, and survival following, breast cancer. METHODS: Women with invasive breast cancer were included, 98,397 of European ancestry and 12,920 of Asian ancestry, from the Breast Cancer Association Consortium (BCAC), and 683 women from the European MINDACT trial. Associations between PRS313 and clinicopathologic characteristics, including the 70-gene signature for MINDACT, were evaluated using logistic regression analyses. Associations of PRS313 (continuous, per standard deviation) with overall survival (OS) and breast cancer-specific survival (BCSS) were evaluated with Cox regression, adjusted for clinicopathologic characteristics and treatment. RESULTS: The PRS313 was associated with more favorable tumor characteristics. In BCAC, increasing PRS313 was associated with lower grade, hormone receptor-positive status, and smaller tumor size. In MINDACT, PRS313 was associated with a low risk 70-gene signature. In European women from BCAC, higher PRS313 was associated with better OS and BCSS: hazard ratio (HR) 0.96 (95% CI, 0.94 to 0.97) and 0.96 (95% CI, 0.94 to 0.98), but the association disappeared after adjustment for clinicopathologic characteristics (and treatment): OS HR, 1.01 (95% CI, 0.98 to 1.05) and BCSS HR, 1.02 (95% CI, 0.98 to 1.07). The results in MINDACT and Asian women from BCAC were consistent. CONCLUSION: An increased PRS313 is associated with favorable tumor characteristics, but is not independently associated with prognosis. Thus, PRS313 has no role in the clinical management of primary breast cancer at the time of diagnosis. Nevertheless, breast cancer mortality rates will be higher for women with higher PRS313 as increasing PRS313 is associated with an increased risk of disease. This information is crucial for modeling effective stratified screening programs.

Validation of a Genetic-Enhanced Risk Prediction Model for Colorectal Cancer in a Large Community-Based Cohort.

BACKGROUND: Polygenic risk scores (PRS) which summarize individuals' genetic risk profile may enhance targeted colorectal cancer screening. A critical step towards clinical implementation is rigorous external validations in large community-based cohorts. This study externally validated a PRS-enhanced colorectal cancer risk model comprising 140 known colorectal cancer loci to provide a comprehensive assessment on prediction performance. METHODS: The model was developed using 20,338 individuals and externally validated in a community-based cohort (n = 85,221). We validated predicted 5-year absolute colorectal cancer risk, including calibration using expected-to-observed case ratios (E/O) and calibration plots, and discriminatory accuracy using time-dependent AUC. The PRS-related improvement in AUC, sensitivity and specificity were assessed in individuals of age 45 to 74 years (screening-eligible age group) and 40 to 49 years with no endoscopy history (younger-age group). RESULTS: In European-ancestral individuals, the predicted 5-year risk calibrated well [E/O = 1.01; 95% confidence interval (CI), 0.91-1.13] and had high discriminatory accuracy (AUC = 0.73; 95% CI, 0.71-0.76). Adding the PRS to a model with age, sex, family and endoscopy history improved the 5-year AUC by 0.06 (P < 0.001) and 0.14 (P = 0.05) in the screening-eligible age and younger-age groups, respectively. Using a risk-threshold of 5-year SEER colorectal cancer incidence rate at age 50 years, adding the PRS had a similar sensitivity but improved the specificity by 11% (P < 0.001) in the screening-eligible age group. In the younger-age group it improved the sensitivity by 27% (P = 0.04) with similar specificity. CONCLUSIONS: The proposed PRS-enhanced model provides a well-calibrated 5-year colorectal cancer risk prediction and improves discriminatory accuracy in the external cohort. IMPACT: The proposed model has potential utility in risk-stratified colorectal cancer prevention.