Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 [95% confidence interval (CI) 4.84–5.29] for men of European ancestry to 3.74 [95% CI 3.36–4.17] for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher [95% CI 2.14–2.22], and men of East Asian ancestry 0.73-times lower [95% CI 0.71–0.76], than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction.

Although genome-wide association studies (GWAS) for prostate cancer (PrCa) have identified more than 100 risk regions, most of the risk genes at these regions remain largely unknown. Here we integrate the largest PrCa GWAS (N = 142,392) with gene expression measured in 45 tissues (N = 4458), including normal and tumor prostate, to perform a multi-tissue transcriptome-wide association study (TWAS) for PrCa. We identify 217 genes at 84 independent 1 Mb regions associated with PrCa risk, 9 of which are regions with no genome-wide significant SNP within 2 Mb. 23 genes are significant in TWAS only for alternative splicing models in prostate tumor thus supporting the hypothesis of splicing driving risk for continued oncogenesis. Finally, we use a Bayesian probabilistic approach to estimate credible sets of genes containing the causal gene at a pre-defined level; this reduced the list of 217 associations to 109 genes in the 90% credible set. Overall, our findings highlight the power of integrating expression with PrCa GWAS to identify novel risk loci and prioritize putative causal genes at known risk loci.

Genome-wide association studies (GWAS) have transformed our understanding of susceptibility to multiple myeloma (MM), but much of the heritability remains unexplained. We report a new GWAS, a meta-analysis with previous GWAS and a replication series, totalling 9974 MM cases and 247,556 controls of European ancestry. Collectively, these data provide evidence for six new MM risk loci, bringing the total number to 23. Integration of information from gene expression, epigenetic profiling and in situ Hi-C data for the 23 risk loci implicate disruption of developmental transcriptional regulators as a basis of MM susceptibility, compatible with altered B-cell differentiation as a key mechanism. Dysregulation of autophagy/apoptosis and cell cycle signalling feature as recurrently perturbed pathways. Our findings provide further insight into the biological basis of MM.

BackgroundSeveral obesity-related factors have been associated with renal cell carcinoma (RCC), but it is unclear which individual factors directly influence risk. We addressed this question using genetic markers as proxies for putative risk factors and evaluated their relation to RCC risk in a mendelian randomization (MR) framework. This methodology limits bias due to confounding and is not affected by reverse causation.Methods and findingsGenetic markers associated with obesity measures, blood pressure, lipids, type 2 diabetes, insulin, and glucose were initially identified as instrumental variables, and their association with RCC risk was subsequently evaluated in a genome-wide association study (GWAS) of 10,784 RCC patients and 20,406 control participants in a 2-sample MR framework. The effect on RCC risk was estimated by calculating odds ratios (ORSD) for a standard deviation (SD) increment in each risk factor. The MR analysis indicated that higher body mass index increases the risk of RCC (ORSD: 1.56, 95% confidence interval [CI] 1.44–1.70), with comparable results for waist-to-hip ratio (ORSD: 1.63, 95% CI 1.40–1.90) and body fat percentage (ORSD: 1.66, 95% CI 1.44–1.90). This analysis further indicated that higher fasting insulin (ORSD: 1.82, 95% CI 1.30–2.55) and diastolic blood pressure (DBP; ORSD: 1.28, 95% CI 1.11–1.47), but not systolic blood pressure (ORSD: 0.98, 95% CI 0.84–1.14), increase the risk for RCC. No association with RCC risk was seen for lipids, overall type 2 diabetes, or fasting glucose.ConclusionsThis study provides novel evidence for an etiological role of insulin in RCC, as well as confirmatory evidence that obesity and DBP influence RCC risk.

The homologous recombination deficiency (HRD) score integrates three DNA-based measures of genomic instability, and has been understudied in prostate cancer. Given the recent FDA-approval of two PARP inhibitors for prostate cancer, HRD score analysis could help to refine treatment selection. We assessed HRD score (defined as the sum of loss-of-heterozygosity, telomeric allelic imbalance, and large-scale state transitions) in three cohorts of primary prostate cancer, including a Johns Hopkins University (JHU) cohort with germline mutations in BRCA2 , ATM or CHEK2 (n=64), the TCGA cohort (n=391), and the PROGENE cohort (n=102). In the JHU cohort, tumors with germline BRCA2 mutations had higher HRD scores (median=27) than those with germline ATM or CHEK2 mutations (median=16.5 [p=0.029] and 9 [p<0.001], respectively). For TCGA tumors without underlying HR pathway mutations, the median HRD score was 11, significantly lower than ovarian carcinoma lacking BRCA1/2 mutations (median=28). In the absence of HR gene mutations, the median HRD score was unexpectedly higher among prostate cancers with TP53 mutations versus those without (17 vs 11; p=0.015); this finding was confirmed in the PROGENE cohort (24 vs 16; p=0.001). Finally, among eight BRCA2 -altered patients who received olaparib, progression-free survival trended longer in those with HRD scores above versus below the median (14.9 vs 9.9 months). We conclude that HRD scores are low in primary prostate cancer and higher in cases with germline BRCA2 or somatic TP53 mutations. Germline BRCA2 -altered cases have significantly higher HRD scores than germline ATM -altered or CHEK2 -altered cases, consistent with the lower efficacy of PARP inhibitors among the latter.

Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (rg=0.57, p=4.6×10−8), breast and ovarian cancer (rg=0.24, p=7×10−5), breast and lung cancer (rg=0.18, p=1.5×10−6) and breast and colorectal cancer (rg=0.15, p=1.1×10−4). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis.

Several susceptibility loci for classical Hodgkin lymphoma have been reported. However, much of the heritable risk is unknown. Here, we perform a meta-analysis of two existing genome-wide association studies, a new genome-wide association study, and replication totalling 5,314 cases and 16,749 controls. We identify risk loci for all classical Hodgkin lymphoma at 6q22.33 (rs9482849, P = 1.52 × 10−8) and for nodular sclerosis Hodgkin lymphoma at 3q28 (rs4459895, P = 9.43 × 10−17), 6q23.3 (rs6928977, P = 4.62 × 10−11), 10p14 (rs3781093, P = 9.49 × 10−13), 13q34 (rs112998813, P = 4.58 × 10−8) and 16p13.13 (rs34972832, P = 2.12 × 10−8). Additionally, independent loci within the HLA region are observed for nodular sclerosis Hodgkin lymphoma (rs9269081, HLA-DPB1*03:01, Val86 in HLA-DRB1) and mixed cellularity Hodgkin lymphoma (rs1633096, rs13196329, Val86 in HLA-DRB1). The new and established risk loci localise to areas of active chromatin and show an over-representation of transcription factor binding for determinants of B-cell development and immune response.

No abstract