Jonathan Tyrer

Evaluating the performance of the Breast and Ovarian Analysis of Disease Incidence Algorithm model in predicting 10-year breast cancer risks in UK Biobank

Abstract Background The Breast and Ovarian Analysis of Disease Incidence Algorithm (BOADICEA) model predicts breast cancer risk using cancer family history, epidemiological, and genetic data. We evaluated its validity in a large prospective cohort. Methods We assessed model calibration, discrimination and risk classification ability in 217 885 women (6838 incident breast cancers) aged 40-70 years of self-reported White ethnicity with no previous cancer from the UK Biobank. Age-specific risk classification was assessed using relative risk thresholds equivalent to the absolute lifetime risk categories of less than 17%, 17%-30%, and 30% or more, recommended by the National Institute for Health and Care Excellence guidelines. We predicted 10-year risks using BOADICEA v.6 considering cancer family history, questionnaire-based risk factors, a 313–single nucleotide polymorphisms polygenic score, and pathogenic variants. Mammographic density data were not available. Results The polygenic risk score was the most discriminative risk factor (area under the curve [AUC] = 0.65). Discrimination was highest when considering all risk factors (AUC = 0.66). The model was well calibrated overall (expected-to-observed ratio = 0.99, 95% confidence interval [CI] = 0.97 to 1.02; calibration slope = 0.99, 95% CI = 0.99 to 1.00), and in deciles of predicted risks. Discrimination was similar in women aged younger and older than 50 years. There was some underprediction in women aged younger than 50 years (expected-to-observed ratio = 0.89, 95% CI = 0.84 to 0.94; calibration slope = 0.96, 95% CI = 0.94 to 0.97), which was explained by the higher breast cancer incidence in UK Biobank than the UK population incidence in this age group. The model classified 87.2%, 11.4%, and 1.4% of women in relative risk categories less than 1.6, 1.6-3.1, and at least 3.1, identifying 25.6% of incident breast cancer patients in category relative risk of at least 1.6. Conclusion BOADICEA, implemented in CanRisk (www.canrisk.org), provides valid 10-year breast cancer risk, which can facilitate risk-stratified screening and personalized breast cancer risk management.

Validation of the BOADICEA model for epithelial tubo-ovarian cancer risk prediction in UK Biobank

Abstract Background The clinical validity of the multifactorial BOADICEA model for epithelial tubo-ovarian cancer (EOC) risk prediction has not been assessed in a large sample size or over a longer term. Methods We evaluated the model discrimination and calibration in the UK Biobank cohort comprising 199,429 women (733 incident EOCs) of European ancestry without previous cancer history. We predicted 10-year EOC risk incorporating data on questionnaire-based risk factors (QRFs), family history, a 36-SNP polygenic risk score and pathogenic variants (PV) in six EOC susceptibility genes (BRCA1, BRCA2, RAD51C, RAD51D, BRIP1 and PALB2). Results Discriminative ability was maximised under the multifactorial model that included all risk factors (AUC = 0.68, 95% CI: 0.66–0.70). This model was well calibrated in deciles of predicted risk with calibration slope=0.99 (95% CI: 0.98–1.01). Discriminative ability was similar in women younger or older than 60 years. The AUC was higher when analyses were restricted to PV carriers (0.76, 95% CI: 0.69–0.82). Using relative risk (RR) thresholds, the full model classified 97.7%, 1.7%, 0.4% and 0.2% women in the RR < 2.0, 2.0 ≤ RR < 2.9, 2.9 ≤ RR < 6.0 and RR ≥ 6.0 categories, respectively, identifying 9.1 of incident EOC among those with RR ≥ 2.0. Discussion BOADICEA, implemented in CanRisk (www.canrisk.org), provides valid 10-year EOC risks and can facilitate clinical decision-making in EOC risk management.

Rare germline genetic variation in PAX8 transcription factor binding sites and susceptibility to epithelial ovarian cancer

Abstract Common genetic variation throughout the genome and rare coding variants identified to date explain about half of the inherited genetic component of epithelial ovarian cancer risk. It is likely that rare variation in the noncoding genome will explain some of the unexplained heritability, but identifying such variants is challenging. The primary problem is a lack of statistical power to identify individual risk variants by association, as power is a function of sample size, effect size, and allele frequency. Power can be increased by using burden tests, which test for the association of carriers of any variant in a specified genomic region. This has the effect of increasing the putative effect allele frequency. PAX8 is a transcription factor that plays a critical role in tumor progression, migration, and invasion. Furthermore, regulatory elements proximal to target genes of PAX8 are enriched for common ovarian cancer risk variants. We hypothesized that rare variation in PAX8 binding sites is also associated with ovarian cancer risk but unlikely to be associated with risk of breast, colorectal, or endometrial cancer. We have used publicly available, whole-genome sequencing data from the UK 100,000 Genomes Project to evaluate the burden of rare variation in PAX8 binding sites across the genome. Data were available for 522 ovarian cancers, 2984 breast cancers, 2696 colorectal cancers, 836 endometrial cancers, and 2253 noncancer controls. Active binding sites were defined using data from multiple PAX8 and H3K27 chromatin immunoprecipitation sequencing experiments. We found no association between the burden of rare variation in PAX8 binding sites (defined in several ways) and risk of ovarian, breast, or endometrial cancer. An apparent association with colorectal cancer was likely to be a technical artifact as a similar association was also detected for rare variation in random regions of the genome. Despite the null result, this study provides a proof-of-principle for using burden testing to identify rare, noncoding germline genetic variation associated with disease. Larger sample sizes available from large-scale sequencing projects, together with improved understanding of the function of the noncoding genome, will increase the potential of similar studies in the future.

Characterizing somatic mutations in ovarian cancer germline risk regions

Abstract Epithelial ovarian cancer (EOC) genetics research has been focused on germline or somatic mutations independently. Emerging evidence suggests that the somatic mutational landscape can be shaped by the germline genetic background. In this study, we aim to unravel the role of somatic alterations within EOC germline susceptibility regions by incorporating functional annotations. We investigate somatic events, including mutational signatures, point mutations, copy number alterations, and transcription factor binding disruptions, within 33 EOC germline susceptibility regions. Our analysis identifies significant associations between candidate germline susceptibility genes and somatic mutational signatures known to be key risk factors for EOC, such as mismatch repair deficiency, age-related mutagenesis, and homologous recombination deficiency. In addition, we find somatic point mutations and copy number alterations are significantly enriched in histotype-specific active enhancers and promoters within EOC risk loci. Furthermore, we examine the impact of germline variants and somatic mutations on transcription factor binding sites, identifying cancer developmental transcription factor motifs frequently affected by both types of mutations. Overall, our study highlights the importance of integrating germline and somatic mutations with regulatory and epigenomic data to gain insights into the genetic basis of EOC.

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

Abstract Rare, germline loss-of-function variants in a handful of DNA repair genes are associated with epithelial ovarian cancer. The aim of this study was to evaluate the role of rare, coding, loss-of-function variants across the genome in epithelial ovarian cancer. We carried out a gene-by-gene burden test with various histotypes using data from 2573 non-mucinous cases and 13,923 controls. Twelve genes were associated at a False Discovery Rate of less than 0.1 of which seven were the known ovarian cancer susceptibility genes BRCA1, BRCA2, BRIP1, RAD51C, RAD51D, MSH6 and PALB2. The other five genes were OR2T35, HELB, MYO1A and GABRP which were associated with non-high-grade serous ovarian cancer and MIGA1 which was associated with high-grade serous ovarian cancer. Further support for the association of HELB association comes from the observation that loss-of-function variants in HELB are 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.

Folate Intake and Ovarian Cancer Risk among Women with Endometriosis: A Case–Control Study from the Ovarian Cancer Association Consortium

Abstract Background: Although folate intake has not been associated with an increased risk of ovarian cancer overall, studies of other cancer types have suggested that high folate intake may promote carcinogenesis in precancerous lesions. Women with endometriosis (a potential precancerous lesion) have an increased risk of developing ovarian cancer; however, whether high folate intake increases risk in this group is unknown. Methods: We conducted a pooled analysis of six case–control studies from the Ovarian Cancer Association Consortium to investigate the association between folate intake and risk of ovarian cancer among women with and without self-reported endometriosis. We included 570 cases/558 controls with and 5,171/7,559 without endometriosis. We used logistic regression to estimate odds ratios (OR) and 95% confidence intervals for the association between folate intake (dietary, supplemental, and total) and ovarian cancer risk. Finally, we used Mendelian randomization (MR) to evaluate our results using genetic markers as a proxy for folate status. Results: Higher dietary folate intake was associated with an increased risk of ovarian cancer for women with endometriosis [OR, 1.37 (1.01–1.86)] but not for women without endometriosis. There was no association between supplemental folate intake and ovarian cancer risk for women with or without endometriosis. A similar pattern was seen using MR. Conclusions: High dietary folate intake may be associated with an increased risk of ovarian cancer among women with endometriosis. Impact: Women with endometriosis with high folate diets may be at increased risk of ovarian cancer. Further research is needed on the potential cancer-promoting effects of folate in this group.

Adapting the BOADICEA breast and ovarian cancer risk models for the ethnically diverse UK population

Abstract Background BOADICEA is a widely used algorithm for predicting breast and ovarian cancer risks, using a combination of genetic and lifestyle, hormonal and reproductive risk factors. However, it has largely been developed using data from White/European individuals, limiting its applicability to other ethnicities. Here, we updated BOADICEA to provide ethnicity-specific risk estimates. Methods We utilised data from multiple sources to derive estimates for the distributions and effect sizes of risk factors in major UK ethnic groups (White, Black, South Asian, East Asian, and Mixed), along with ethnicity-specific population cancer incidences. We also developed a method for deriving adjusted polygenic scores for individuals of mixed genetic ancestry. Results The predicted average absolute risks were smaller in all non-White ethnic groups than in Whites, and the risk distributions were narrower. The proportion of women classified as at moderate or high risk of breast or ovarian cancer, according to national guidelines, was considerably smaller in non-Whites. Discussion The updated BOADICEA, available in the CanRisk tool ( www.canrisk.org ), is based on more appropriate estimates for non-White women in the UK. Further validation of the model in prospective studies is required. Considering these findings, risk classification guidelines for non-White women may need to be revised.