Citations

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.

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 < 0.05 and FDR < 0.05). These results were replicated (P < 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.

Abstract Background: Treatment options for women with ovarian cancer remain very limited and acquired resistance to current therapies is very common. Altered DNA repair capability in epithelial ovarian cancer (EOC) may underlie response to both standard therapy and novel treatments, such as PARP inhibitors. Molecular sub-classification of high-grade serous ovarian cancer (HG-SOC) may uncover potential drug targets and possible mechanisms of drug resistance. Understanding the contribution of DNA repair and other driver mutations to drug response and resistance requires the development of molecularly annotated preclinical models reflective of the clinic. Methods: A patient derived xenograft (PDX) cohort has been generated from consecutive, chemotherapy-naïve human HG-SOC and stratified according to in vivo response to standard chemotherapy, DNA repair capability and molecular characteristics, including next generation sequencing by Foundation Medicine. Resistance to therapy is driven by re-treating relapsed PDX in vivo providing invaluable “paired samples” (pre and post drug treatment), which are difficult to obtain from patients, to allow clonal evolution analysis of mechanisms of drug response and resistance. Results: The xenograft success rate was 83%. Of ten HG-SOC PDX, all exhibited mutations in TP53, five in BRCA1/2 (two of which were germline) and two were methylated for BRCA1. In vivo cisplatin response, determined as platinum sensitive (progression-free interval (PFI) ≥100 d, n=4), platinum resistant (PFI <100 d, n=3) or platinum refractory (n=3), was largely consistent with patient outcome. Three of four platinum sensitive HG-SOC PDX contained DNA repair gene mutations, and the fourth was methylated for BRCA1. In contrast, all three platinum refractory PDX overexpressed dominant oncogenes (CCNE1, LIN28B and/or BCL2). Molecular analysis of this cohort has revealed actionable targets for novel therapeutic strategies. In vivo studies, including with PARP inhibitors, are underway. Conclusion: PDX with histologic, molecular and therapeutic annotation, as well as clinical outcome data allow interrogation of molecular aberrations and drug resistance in vivo. This will inform targeting of novel therapies and the design of clinical trials for women. Citation Format: Monique D. Topp, Lynne Hartley, Michele Cook, Valerie Heong, Emma Boehm, Lauren McShane, Jan Pyman, Orla McNally, Sumi Ananda, Maria I. Harell, Dariush Etemadmoghadam, Laura Galletta, Kathryn Alsop, Gillian Mitchell, Stephen B. Fox, Jeff B. Kerr, Karla J. Hutt, Scott H. Kaufmann, Australian Ovarian Cancer Study (AOCS), Elizabeth M. Swisher, David D. Bowtell, Matthew M. Wakefield, Clare L. Scott. Using molecularly characterized patient-derived models to delineate underlying drivers and vulnerabilities of epithelial ovarian cancer. [abstract]. In: Proceedings of the AACR Special Conference: Cancer Susceptibility and Cancer Susceptibility Syndromes; Jan 29-Feb 1, 2014; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(23 Suppl):Abstract nr 38. doi:10.1158/1538-7445.CANSUSC14-38

Abstract Part of the substantial unexplained familial aggregation of breast cancer may be due to interactions between common variants, but few studies have had adequate statistical power to detect interactions of realistic magnitude. We aimed to assess all two-way interactions in breast cancer susceptibility between 70 917 single nucleotide polymorphisms (SNPs) selected primarily based on prior evidence of a marginal effect. Thirty-eight international studies contributed data for 46 450 breast cancer cases and 42 461 controls of European origin as part of a multi-consortium project (COGS). First, SNPs were preselected based on evidence (P < 0.01) of a per-allele main effect, and all two-way combinations of those were evaluated by a per-allele (1 d.f.) test for interaction using logistic regression. Second, all 2.5 billion possible two-SNP combinations were evaluated using Boolean operation-based screening and testing, and SNP pairs with the strongest evidence of interaction (P < 10−4) were selected for more careful assessment by logistic regression. Under the first approach, 3277 SNPs were preselected, but an evaluation of all possible two-SNP combinations (1 d.f.) identified no interactions at P < 10−8. Results from the second analytic approach were consistent with those from the first (P > 10−10). In summary, we observed little evidence of two-way SNP interactions in breast cancer susceptibility, despite the large number of SNPs with potential marginal effects considered and the very large sample size. This finding may have important implications for risk prediction, simplifying the modelling required. Further comprehensive, large-scale genome-wide interaction studies may identify novel interacting loci if the inherent logistic and computational challenges can be overcome.

Abstract Background: There are several well-established environmental risk factors for ovarian cancer, and recent genome-wide association studies have also identified six variants that influence disease risk. However, the interplay between such risk factors and susceptibility loci has not been studied. Methods: Data from 14 ovarian cancer case–control studies were pooled, and stratified analyses by each environmental risk factor with tests for heterogeneity were conducted to determine the presence of interactions for all histologic subtypes. A genetic “risk score” was created to consider the effects of all six variants simultaneously. A multivariate model was fit to examine the association between all environmental risk factors and genetic risk score on ovarian cancer risk. Results: Among 7,374 controls and 5,566 cases, there was no statistical evidence of interaction between the six SNPs or genetic risk score and the environmental risk factors on ovarian cancer risk. In a main effects model, women in the highest genetic risk score quartile had a 65% increased risk of ovarian cancer compared with women in the lowest [95% confidence interval (CI), 1.48–1.84]. Analyses by histologic subtype yielded risk differences across subtype for endometriosis (Phet < 0.001), parity (Phet < 0.01), and tubal ligation (Phet = 0.041). Conclusions: The lack of interactions suggests that a multiplicative model is the best fit for these data. Under such a model, we provide a robust estimate of the effect of each risk factor that sets the stage for absolute risk prediction modeling that considers both environmental and genetic risk factors. Further research into the observed differences in risk across histologic subtype is warranted. Cancer Epidemiol Biomarkers Prev; 22(5); 880–90. ©2013 AACR.

Abstract Background: The five year survival rate for women with ovarian cancer (OC) is less than 40%. Crucial to improving the outcomes for ovarian cancer patients is the development of accurate pre-clinical models of human epithelial OC, which can be used to unravel the mechanisms underlying its evolution and behaviour. Methods: We have generated a novel xenograft model of human high-grade serous OC (HG-SOC) in order to enable preclinical molecular and drug response assessment of individual HG-SOC: this includes a comparison of standard OC xeno-transplantion approaches (intra-peritoneal, subcutaneous and sub renal capsular), with the novel use of the rodent ovarian bursa, the orthotopic site. Utilising the rodent bursa may better replicate the relevant microenvironment and increase the success of EOC xeno-transplantion. Other aspects designed to optimize the model include the use of NOD-SCID-IL-2rg recipient mice, systemic estrogen supplementation and transplantation of fresh human HG-SOC fragments which have had no prior in vitro culture. Histological, functional and molecular analysis of the novel xenograft cohort (at baseline and following xenotransplantation) includes histological review; documentation of in vitro Homologous Recombination (HR) DNA repair and drug response capabilities (using novel α-irradiation and explant drug assays); classification according to molecular subtype (Tothill classfier); documentation of NHEJ pathway status, BRCA1/2 status and other DNA repair gene status. In vivo drug treatment studies are being performed, with the choice of treatment targeted to the specific molecular characteristics of the HG-SOC in question. Results: Fourteen consecutive chemotherapy naive potentially HG-SOC samples have been collected. Corresponding clinical data has been collected. Data concerning DNA repair capability and response to DNA damaging agents will be presented, including IHC for markers of DNA damage (γH2AX), DNA repair (RAD51) and apoptosis (capsase 3 cleavage). HG-SOC in this cohort have been classified according to Tothilll (Tothill et al 2008). Eight appropriate HG-SOC have been transplanted and 5 of the first 6 have successfully xenografted, with phenotyping of xenografts underway. In vivo analysis of response to cisplatin treatment and other relevant therapeutics will be presented. Conclusions: A novel xenograft model has been developed of human HG-SOC, which includes comparative characterization of important prognostic features both in the baseline panel of fresh human HG-SOC and in subsequent xenografts. This clinically, functionally and molecularly annotated consecutive xenograft cohort of HG-SOC will provide outstanding utility for the development of improved therapeutic approaches. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3276. doi:1538-7445.AM2012-3276

Abstract There is increasing evidence that neighboring stromal cells are not uninvolved bystanders in the pathway to cancer. We hypothesized that SNPs in genes expressed as a consequence of interactions between ovarian cancer cells and the host micro-environment could contribute to ovarian cancer susceptibility. We therefore used a two-stage approach to comprehensively evaluate common single nucleotide polymorphisms (SNPs) in the Ovarian Cancer Association Consortium (OCAC) for 173 genes involved in stromal-epithelial interactions. Cases with epithelial ovarian cancer (n=675) and controls (n=1,162) were genotyped at 1536 SNPs using an Illumina GoldenGate™ BeadArray array. Logistic regression was used to estimate odds ratios (ORs) and adjust for potential confounders. Based on Positive Predictive Value estimates, three SNPs - PODXL rs1013368, ITGA6 rs13027811 and MMP3 rs522616 - were selected for replication using TaqMan genotyping in 3,734 serous invasive cases and 10,067 controls from 16 OCAC case-control studies. An additional 18 SNPs with Pper-allele <0.05 in the test set, and which fitted into an iPLEX design, were selected for replication by a subset of five OCAC studies (n=1771 serous invasive cases; n=4022 controls). The associations observed in the test set for the PODXL, ITGA6 and MMP3 SNPs were completely attenuated in the replication set (adj. Pper-allele ≥0.5). However, TERT rs7726159 showed evidence of a log additive association with ovarian cancer risk in the replication study (Pper-allele =0.03). Combined analysis of the test and replication sets for this SNP showed a significant association with risk of serous ovarian cancer among non-Hispanic whites [adj. ORper-allele 1.14 (1.04 - 1.24) p = 0.003]. A stronger association was observed for serous invasive cases when data for all ethnicities were included [adj. ORper-allele 1.18 (1.09 - 1.28) p = 2.46 × 10−5]. Stratification by histological subtype and malignant potential showed that TERT rs7726159 was also associated with risk of serous borderline tumors, but not with any other subtypes. Although TERT variants have not been previously reported to be associated with ovarian cancer, multiple genome-wide association studies have reported associations with this locus and risk of glioma, basal cell carcinoma, and several cancer types that are known to originate in the epithelium, including lung, bladder, prostate and cervical cancer. Further analysis of this locus is necessary to definitively identify the causal SNP(s). Our study adds to the growing evidence that, as well as 8q24, the TERT locus at 5p15.33, is a general cancer susceptibility locus. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4727.