Analysis of rare disruptive germline mutations in 2,135 enriched BRCA-negative breast cancers excludes additional high-impact susceptibility genes

Acknowledgements We thank all the subjects and families that participated in the research. We thank those at the ICR, past and present, for their assistance in patient recruitment, sample management, and management of the sequencing facility. We are grateful to all the clinicians and counsellors in The Breast and Ovarian Cancer Susceptibility Collaboration who have contributed to the recruitment and collection of samples. The full list of contributors is provided in the Appendix. Funding This work was supported by Cancer Research UK [grants numbers C8620/A8372, C8620/A8857]; the Institute of Cancer Research (no grant number); NHS to the Institute of Cancer Research and Royal Marsden ras part of a joint entity referred to as the National Institute of Health Research Specialist Biomedical Research Centre for Cancer.Peer reviewedPublisher PD

Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Funding Information: ADF has received a research grant from AstraZeneca, not directly related to the content of this manuscript. MWB conducts research funded by Amgen, Novartis and Pfizer. PAF conducts research funded by Amgen, Novartis and Pfizer. He received Honoraria from Roche, Novartis and Pfizer. AWK reports research funding to her institution from Myriad Genetics for an unrelated project. UM owns stocks in Abcodia Ltd. Rachel A. Murphy is a consultant for Pharmavite. The other authors declare no conflicts of interest. Publisher Copyright: © 2021, The Author(s).Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, “select and shrink for summary statistics” (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28–1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08–1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21–1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29–1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35–1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.publishersversionPeer reviewe

Menopausal hormone use and ovarian cancer risk: individual participant meta-analysis of 52 epidemiological studies.

BACKGROUND: Half the epidemiological studies with information about menopausal hormone therapy and ovarian cancer risk remain unpublished, and some retrospective studies could have been biased by selective participation or recall. We aimed to assess with minimal bias the effects of hormone therapy on ovarian cancer risk. METHODS: Individual participant datasets from 52 epidemiological studies were analysed centrally. The principal analyses involved the prospective studies (with last hormone therapy use extrapolated forwards for up to 4 years). Sensitivity analyses included the retrospective studies. Adjusted Poisson regressions yielded relative risks (RRs) versus never-use. FINDINGS: During prospective follow-up, 12 110 postmenopausal women, 55% (6601) of whom had used hormone therapy, developed ovarian cancer. Among women last recorded as current users, risk was increased even with <5 years of use (RR 1·43, 95% CI 1·31-1·56; p<0·0001). Combining current-or-recent use (any duration, but stopped <5 years before diagnosis) resulted in an RR of 1·37 (95% CI 1·29-1·46; p<0·0001); this risk was similar in European and American prospective studies and for oestrogen-only and oestrogen-progestagen preparations, but differed across the four main tumour types (heterogeneity p<0·0001), being definitely increased only for the two most common types, serous (RR 1·53, 95% CI 1·40-1·66; p<0·0001) and endometrioid (1·42, 1·20-1·67; p<0·0001). Risk declined the longer ago use had ceased, although about 10 years after stopping long-duration hormone therapy use there was still an excess of serous or endometrioid tumours (RR 1·25, 95% CI 1·07-1·46, p=0·005). INTERPRETATION: The increased risk may well be largely or wholly causal; if it is, women who use hormone therapy for 5 years from around age 50 years have about one extra ovarian cancer per 1000 users and, if its prognosis is typical, about one extra ovarian cancer death per 1700 users. FUNDING: Medical Research Council, Cancer Research UK

Genetically predicted circulating protein biomarkers and ovarian cancer risk.

OBJECTIVE: Most women with epithelial ovarian cancer (EOC) are diagnosed after the disease has metastasized and survival in this group remains poor. Circulating proteins associated with the risk of developing EOC have the potential to serve as biomarkers for early detection and diagnosis. We integrated large-scale genomic and proteomic data to identify novel plasma proteins associated with EOC risk. METHODS: We used the germline genetic variants most strongly associated (P <1.5 × 10-11) with plasma levels of 1329 proteins in 3301 healthy individuals from the INTERVAL study to predict circulating levels of these proteins in 22,406 EOC cases and 40,941 controls from the Ovarian Cancer Association Consortium (OCAC). Association testing was performed by weighting the beta coefficients and standard errors for EOC risk from the OCAC study by the inverse of the beta coefficients from INTERVAL. RESULTS: We identified 26 proteins whose genetically predicted circulating levels were associated with EOC risk at false discovery rate < 0.05. The 26 proteins included MFAP2, SEMG2, DLK1, and NTNG1 and a group of 22 proteins whose plasma levels were predicted by variants at chromosome 9q34.2. All 26 protein association signals identified were driven by association with the high-grade serous histotype that comprised 58% of the EOC cases in OCAC. Regional genomic plots confirmed overlap of the genetic association signal underlying both plasma protein level and EOC risk for the 26 proteins. Pathway analysis identified enrichment of seven biological pathways among the 26 proteins (Padjusted <0.05), highlighting roles for Focal Adhesion-PI3K-Akt-mTOR and Notch signaling. CONCLUSION: The identified proteins further illuminate the etiology of EOC and represent promising new EOC biomarkers for targeted validation by studies involving direct measurement of plasma proteins in EOC patient cohorts

Male breast cancer in BRCA1 and BRCA2 mutation carriers: pathology data from the Consortium of Investigators of Modifiers of BRCA1/2

Background: BRCA1 and, more commonly, BRCA2 mutations are associated with increased risk of male breast cancer (MBC). However, only a paucity of data exists on the pathology of breast cancers (BCs) in men with BRCA1/2 mutations. Using the largest available dataset, we determined whether MBCs arising in BRCA1/2 mutation carriers display specific pathologic features and whether these features differ from those of BRCA1/2 female BCs (FBCs). Methods: We characterised the pathologic features of 419 BRCA1/2 MBCs and, using logistic regression analysis, contrasted those with data from 9675 BRCA1/2 FBCs and with population-based data from 6351 MBCs in the Surveillance, Epidemiology, and End Results (SEER) database. Results: Among BRCA2 MBCs, grade significantly decreased with increasing age at diagnosis (P = 0.005). Compared with BRCA2 FBCs, BRCA2 MBCs were of significantly higher stage (P for trend = 2 x 10(-5)) and higher grade (P for trend = 0.005) and were more likely to be oestrogen receptor-positive [odds ratio (OR) 10.59; 95 % confidence interval (CI) 5.15-21.80] and progesterone receptor-positive (OR 5.04; 95 % CI 3.17-8.04). With the exception of grade, similar patterns of associations emerged when we compared BRCA1 MBCs and FBCs. BRCA2 MBCs also presented with higher grade than MBCs from the SEER database (P for trend = 4 x 10(-12)). Conclusions: On the basis of the largest series analysed to date, our results show that BRCA1/2 MBCs display distinct pathologic characteristics compared with BRCA1/2 FBCs, and we identified a specific BRCA2-associated MBC phenotype characterised by a variable suggesting greater biological aggressiveness (i.e., high histologic grade). These findings could lead to the development of gender-specific risk prediction models and guide clinical strategies appropriate for MBC management

High resolution melting for mutation scanning of TP53 exons 5-8.

BACKGROUND: p53 is commonly inactivated by mutations in the DNA-binding domain in a wide range of cancers. As mutant p53 often influences response to therapy, effective and rapid methods to scan for mutations in TP53 are likely to be of clinical value. We therefore evaluated the use of high resolution melting (HRM) as a rapid mutation scanning tool for TP53 in tumour samples. METHODS: We designed PCR amplicons for HRM mutation scanning of TP53 exons 5 to 8 and tested them with DNA from cell lines hemizygous or homozygous for known mutations. We assessed the sensitivity of each PCR amplicon using dilutions of cell line DNA in normal wild-type DNA. We then performed a blinded assessment on ovarian tumour DNA samples that had been previously sequenced for mutations in TP53 to assess the sensitivity and positive predictive value of the HRM technique. We also performed HRM analysis on breast tumour DNA samples with unknown TP53 mutation status. RESULTS: One cell line mutation was not readily observed when exon 5 was amplified. As exon 5 contained multiple melting domains, we divided the exon into two amplicons for further screening. Sequence changes were also introduced into some of the primers to improve the melting characteristics of the amplicon. Aberrant HRM curves indicative of TP53 mutations were observed for each of the samples in the ovarian tumour DNA panel. Comparison of the HRM results with the sequencing results revealed that each mutation was detected by HRM in the correct exon. For the breast tumour panel, we detected seven aberrant melt profiles by HRM and subsequent sequencing confirmed the presence of these and no other mutations in the predicted exons. CONCLUSION: HRM is an effective technique for simple and rapid scanning of TP53 mutations that can markedly reduce the amount of sequencing required in mutational studies of TP53.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Women's beliefs about breast cancer causation in a breast cancer case-control study

Objective: Our study sought to ascertain women's beliefs about breast cancer risk factors and whether these beliefs differed by demographic factors and personal and family history of breast cancer. Methods: Participants in a case-control study of breast cancer rated the effect of 37 exposures on the risk of being diagnosed with breast cancer. Chi-square tests were undertaken to measure differences in responses between cases and controls for each exposure. Logistic regression was undertaken to ascertain whether demographic factors and personal and family history of breast cancer affected participants' ability to correctly identify known breast cancer risk factors. Results: A total of 2742 participants completed the questionnaire, comprising 1109 cases and 1633 controls. Significant differences (p&lt;0.05) between cases and controls were found for 16 of the 37 exposures. Younger women and university-educated women were more likely to correctly identify known breast cancer risk factors. Women's perceptions about the effect of alcohol consumption on breast cancer risk, particularly regarding red wine, differed from that reported in the literature. Conclusions: Beliefs about risk factors for breast cancer may differ between cases and controls. Public health initiatives aimed at increasing awareness of breast cancer risk factors should consider that women's beliefs may differ by demographic factors and family history of breast cancer

Associations of common breast cancer susceptibility alleles with risk of breast cancer subtypes in BRCA1 and BRCA2 mutation carriers

Introduction: More than 70 common alleles are known to be involved in breast cancer (BC) susceptibility, and several exhibit significant heterogeneity in their associations with different BC subtypes. Although there are differences in the association patterns between BRCA1 and BRCA2 mutation carriers and the general population for several loci, no study has comprehensively evaluated the associations of all known BC susceptibility alleles with risk of BC subtypes in BRCA1 and BRCA2 carriers. Methods: We used data from 15,252 BRCA1 and 8,211 BRCA2 carriers to analyze the associations between approximately 200,000 genetic variants on the iCOGS array and risk of BC subtypes defined by estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and triple-negative-(TN) status; morphologic subtypes; histological grade; and nodal involvement. Results: The estimated BC hazard ratios (HRs) for the 74 known BC alleles in BRCA1 carriers exhibited moderate correlations with the corresponding odds ratios from the general population. However, their associations with ER-positive BC in BRCA1 carriers were more consistent with the ER-positive associations in the general population (intraclass correlation (ICC) = 0.61, 95% confidence interval (CI): 0.45 to 0.74), and the same was true when considering ER-negative associations in both groups (ICC = 0.59, 95% CI: 0.42 to 0.72). Similarly, there was strong correlation between the ER-positive associations for BRCA1 and BRCA2 carriers (ICC = 0.67, 95% CI: 0.52 to 0.78), whereas ER-positive associations in any one of the groups were generally inconsistent with ER-negative associations in any of the others. After stratifying by ER status in mutation carriers, additional significant associations were observed. Several previously unreported variants exhibited associations at P < 10-6 in the analyses by PR status, HER2 status, TN phenotype, morphologic subtypes, histological grade and nodal involvement. Conclusions: Differences in associations of common BC susceptibility alleles between BRCA1 and BRCA2 carriers and the general population are explained to a large extent by differences in the prevalence of ER-positive and ER-negative tumors. Estimates of the risks associated with these variants based on population-based studies are likely to be applicable to mutation carriers after taking ER status into account, which has implications for risk prediction