Modugno, Francesmary

Background The rarity of mutations in PALB2, CHEK2 and ATM make it difficult to estimate precisely associated cancer risks. Population-based family studies have provided evidence that at least some of these mutations are associated with breast cancer risk as high as those associated with rare BRCA2 mutations. We aimed to estimate the relative risks associated with specific rare variants in PALB2, CHEK2 and ATM via a multicentre case-control study. Methods We genotyped 10 rare mutations using the custom iCOGS array: PALB2 c.1592delT, c.2816T>G and c.3113G>A, CHEK2 c.349A>G, c.538C>T, c.715G>A, c.1036C>T, c.1312G>T, and c.1343T>G and ATM c.7271T>G. We assessed associations with breast cancer risk (42 671 cases and 42 164 controls), as well as prostate (22 301 cases and 22 320 controls) and ovarian (14 542 cases and 23 491 controls) cancer risk, for each variant. Results For European women, strong evidence of association with breast cancer risk was observed for PALB2 c.1592delT OR 3.44 (95% CI 1.39 to 8.52, p=7.1×10−5), PALB2 c.3113G>A OR 4.21 (95% CI 1.84 to 9.60, p=6.9×10−8) and ATM c.7271T>G OR 11.0 (95% CI 1.42 to 85.7, p=0.0012). We also found evidence of association with breast cancer risk for three variants in CHEK2, c.349A>G OR 2.26 (95% CI 1.29 to 3.95), c.1036C>T OR 5.06 (95% CI 1.09 to 23.5) and c.538C>T OR 1.33 (95% CI 1.05 to 1.67) (p≤0.017). Evidence for prostate cancer risk was observed for CHEK2 c.1343T>G OR 3.03 (95% CI 1.53 to 6.03, p=0.0006) for African men and CHEK2 c.1312G>T OR 2.21 (95% CI 1.06 to 4.63, p=0.030) for European men. No evidence of association with ovarian cancer was found for any of these variants. Conclusions This report adds to accumulating evidence that at least some variants in these genes are associated with an increased risk of breast cancer that is clinically important.

Abstract Breast, ovarian, and prostate cancers are hormone-related and may have a shared genetic basis, but this has not been investigated systematically by genome-wide association (GWA) studies. Meta-analyses combining the largest GWA meta-analysis data sets for these cancers totaling 112,349 cases and 116,421 controls of European ancestry, all together and in pairs, identified at P < 10−8 seven new cross-cancer loci: three associated with susceptibility to all three cancers (rs17041869/2q13/BCL2L11; rs7937840/11q12/INCENP; rs1469713/19p13/GATAD2A), two breast and ovarian cancer risk loci (rs200182588/9q31/SMC2; rs8037137/15q26/RCCD1), and two breast and prostate cancer risk loci (rs5013329/1p34/NSUN4; rs9375701/6q23/L3MBTL3). Index variants in five additional regions previously associated with only one cancer also showed clear association with a second cancer type. Cell-type–specific expression quantitative trait locus and enhancer–gene interaction annotations suggested target genes with potential cross-cancer roles at the new loci. Pathway analysis revealed significant enrichment of death receptor signaling genes near loci with P < 10−5 in the three-cancer meta-analysis. Significance: We demonstrate that combining large-scale GWA meta-analysis findings across cancer types can identify completely new risk loci common to breast, ovarian, and prostate cancers. We show that the identification of such cross-cancer risk loci has the potential to shed new light on the shared biology underlying these hormone-related cancers. Cancer Discov; 6(9); 1052–67. ©2016 AACR. This article is highlighted in the In This Issue feature, p. 932

Abstract Background: Despite a large body of literature evaluating the association between recreational physical activity and epithelial ovarian cancer (EOC) risk, the extant evidence is inconclusive, and little is known about the independent association between recreational physical inactivity and EOC risk. We conducted a pooled analysis of nine studies from the Ovarian Cancer Association Consortium to investigate the association between chronic recreational physical inactivity and EOC risk. Methods: In accordance with the 2008 Physical Activity Guidelines for Americans, women reporting no regular, weekly recreational physical activity were classified as inactive. Multivariable logistic regression was utilized to estimate the ORs and 95% confidence intervals (CI) for the association between inactivity and EOC risk overall and by subgroups based upon histotype, menopausal status, race, and body mass index. Results: The current analysis included data from 8,309 EOC patients and 12,612 controls. We observed a significant positive association between inactivity and EOC risk (OR = 1.34; 95% CI, 1.14–1.57), and similar associations were observed for each histotype. Conclusions: In this large pooled analysis examining the association between recreational physical inactivity and EOC risk, we observed consistent evidence of an association between chronic inactivity and all EOC histotypes. Impact: These data add to the growing body of evidence suggesting that inactivity is an independent risk factor for cancer. If the apparent association between inactivity and EOC risk is substantiated, additional work via targeted interventions should be pursued to characterize the dose of activity required to mitigate the risk of this highly fatal disease. Cancer Epidemiol Biomarkers Prev; 25(7); 1114–24. ©2016 AACR.

Abstract Background: Many epithelial ovarian cancer (EOC) risk factors relate to hormone exposure and elevated estrogen levels are associated with obesity in postmenopausal women. Therefore, we hypothesized that gene–environment interactions related to hormone-related risk factors could differ between obese and non-obese women. Methods: We considered interactions between 11,441 SNPs within 80 candidate genes related to hormone biosynthesis and metabolism and insulin-like growth factors with six hormone-related factors (oral contraceptive use, parity, endometriosis, tubal ligation, hormone replacement therapy, and estrogen use) and assessed whether these interactions differed between obese and non-obese women. Interactions were assessed using logistic regression models and data from 14 case–control studies (6,247 cases; 10,379 controls). Histotype-specific analyses were also completed. Results: SNPs in the following candidate genes showed notable interaction: IGF1R (rs41497346, estrogen plus progesterone hormone therapy, histology = all, P = 4.9 × 10−6) and ESR1 (rs12661437, endometriosis, histology = all, P = 1.5 × 10−5). The most notable obesity–gene–hormone risk factor interaction was within INSR (rs113759408, parity, histology = endometrioid, P = 8.8 × 10−6). Conclusions: We have demonstrated the feasibility of assessing multifactor interactions in large genetic epidemiology studies. Follow-up studies are necessary to assess the robustness of our findings for ESR1, CYP11A1, IGF1R, CYP11B1, INSR, and IGFBP2. Future work is needed to develop powerful statistical methods able to detect these complex interactions. Impact: Assessment of multifactor interaction is feasible, and, here, suggests that the relationship between genetic variants within candidate genes and hormone-related risk factors may vary EOC susceptibility. Cancer Epidemiol Biomarkers Prev; 25(5); 780–90. ©2016 AACR.

Abstract Background: While numerous susceptibility loci for epithelial ovarian cancer (EOC) have been identified, few associations have been reported with overall survival. In the absence of common prognostic genetic markers, we hypothesize that rare coding variants may be associated with overall EOC survival and assessed their contribution in two exome-based genotyping projects of the Ovarian Cancer Association Consortium (OCAC). Methods: The primary patient set (Set 1) included 14 independent EOC studies (4,293 patients) and 227,892 variants, and a secondary patient set (Set 2) included six additional EOC studies (1,744 patients) and 114,620 variants. Because power to detect rare variants individually is reduced, gene-level tests were conducted. Sets were analyzed separately at individual variants and by gene, and then combined with meta-analyses (73,203 variants and 13,163 genes overlapped). Results: No individual variant reached genome-wide statistical significance. A SNP previously implicated to be associated with EOC risk and, to a lesser extent, survival, rs8170, showed the strongest evidence of association with survival and similar effect size estimates across sets (Pmeta = 1.1E−6, HRSet1 = 1.17, HRSet2 = 1.14). Rare variants in ATG2B, an autophagy gene important for apoptosis, were significantly associated with survival after multiple testing correction (Pmeta = 1.1E−6; Pcorrected = 0.01). Conclusions: Common variant rs8170 and rare variants in ATG2B may be associated with EOC overall survival, although further study is needed. Impact: This study represents the first exome-wide association study of EOC survival to include rare variant analyses, and suggests that complementary single variant and gene-level analyses in large studies are needed to identify rare variants that warrant follow-up study. Cancer Epidemiol Biomarkers Prev; 25(3); 446–54. ©2016 AACR.

ABSTRACTEpithelial‐mesenchymal transition (EMT) is a process whereby epithelial cells assume mesenchymal characteristics to facilitate cancer metastasis. However, EMT also contributes to the initiation and development of primary tumors. Prior studies that explored the hypothesis that EMT gene variants contribute to epithelial ovarian carcinoma (EOC) risk have been based on small sample sizes and none have sought replication in an independent population. We screened 15,816 single‐nucleotide polymorphisms (SNPs) in 296 genes in a discovery phase using data from a genome‐wide association study of EOC among women of European ancestry (1,947 cases and 2,009 controls) and identified 793 variants in 278 EMT‐related genes that were nominally (P < 0.05) associated with invasive EOC. These SNPs were then genotyped in a larger study of 14,525 invasive‐cancer patients and 23,447 controls. A P‐value <0.05 and a false discovery rate (FDR) <0.2 were considered statistically significant. In the larger dataset, GPC6/GPC5 rs17702471 was associated with the endometrioid subtype among Caucasians (odds ratio (OR) = 1.16, 95% CI = 1.07–1.25, P = 0.0003, FDR = 0.19), whereas F8 rs7053448 (OR = 1.69, 95% CI = 1.27–2.24, P = 0.0003, FDR = 0.12), F8 rs7058826 (OR = 1.69, 95% CI = 1.27–2.24, P = 0.0003, FDR = 0.12), and CAPN13 rs1983383 (OR = 0.79, 95% CI = 0.69–0.90, P = 0.0005, FDR = 0.12) were associated with combined invasive EOC among Asians. In silico functional analyses revealed that GPC6/GPC5 rs17702471 coincided with DNA regulatory elements. These results suggest that EMT gene variants do not appear to play a significant role in the susceptibility to EOC.

Abstract Background: Genome-wide association studies (GWAS) have so far reported 12 loci associated with serous epithelial ovarian cancer (EOC) risk. We hypothesized that some of these loci function through nearby transcription factor (TF) genes and that putative target genes of these TFs as identified by coexpression may also be enriched for additional EOC risk associations. Methods: We selected TF genes within 1 Mb of the top signal at the 12 genome-wide significant risk loci. Mutual information, a form of correlation, was used to build networks of genes strongly coexpressed with each selected TF gene in the unified microarray dataset of 489 serous EOC tumors from The Cancer Genome Atlas. Genes represented in this dataset were subsequently ranked using a gene-level test based on results for germline SNPs from a serous EOC GWAS meta-analysis (2,196 cases/4,396 controls). Results: Gene set enrichment analysis identified six networks centered on TF genes (HOXB2, HOXB5, HOXB6, HOXB7 at 17q21.32 and HOXD1, HOXD3 at 2q31) that were significantly enriched for genes from the risk-associated end of the ranked list (P &lt; 0.05 and FDR &lt; 0.05). These results were replicated (P &lt; 0.05) using an independent association study (7,035 cases/21,693 controls). Genes underlying enrichment in the six networks were pooled into a combined network. Conclusion: We identified a HOX-centric network associated with serous EOC risk containing several genes with known or emerging roles in serous EOC development. Impact: Network analysis integrating large, context-specific datasets has the potential to offer mechanistic insights into cancer susceptibility and prioritize genes for experimental characterization. Cancer Epidemiol Biomarkers Prev; 24(10); 1574–84. ©2015 AACR.