Kristin L. White

Inherited Determinants of Ovarian Cancer Survival

Purpose: Due to variation of outcome among cases, we sought to examine whether overall survival in ovarian cancer was associated with common inherited variants in 227 candidate genes from ovarian cancer–related pathways including angiogenesis, inflammation, detoxification, glycosylation, one-carbon transfer, apoptosis, cell cycle regulation, and cellular senescence. Experimental Design: Blood samples were obtained from 325 women with invasive epithelial ovarian cancer diagnosed at the Mayo Clinic from 1999 to 2006. During a median follow-up of 3.8 years (range, 0.1-8.6 years), 157 deaths were observed. Germline DNA was analyzed at 1,416 single nucleotide polymorphisms (SNP). For all patients, and for 203 with serous subtype, we assessed the overall significance of each gene and pathway, and estimated risk of death via hazard ratios (HR) and 95% confidence intervals (CI), adjusting for known prognostic factors. Results: Variation within angiogenesis was most strongly associated with survival time overall (P = 0.03) and among patients with serous cancer (P = 0.05), particularly for EIF2B5 rs4912474 (all patients HR, 0.69; 95% CI, 0.54-0.89; P = 0.004), VEGFC rs17697305 (serous subtype HR, 2.29; 95% CI, 1.34-3.92; P = 0.003), and four SNPs in VHL. Variation within the inflammation pathway was borderline significant (all patients, P = 0.09), and SNPs in CCR3, IL1B, IL18, CCL2, and ALOX5 which correlated with survival time are worthy of follow-up. Conclusion: An extensive multiple-pathway assessment found evidence that inherited differences may play a role in outcome of ovarian cancer patients, particularly in genes within the angiogenesis and inflammation pathways. Our work supports efforts to target such mediators for therapeutic gain. Clin Cancer Res; 16(3); 995–1007

Ovarian Cancer Risk Associated with Inherited Inflammation-Related Variants

The importance of inflammation pathways to the development of many human cancers prompted us to examine the associations between single-nucleotide polymorphisms (SNP) in inflammation-related genes and risk of ovarian cancer. In a multisite case-control study, we genotyped SNPs in a large panel of inflammatory genes in 930 epithelial ovarian cancer cases and 1,037 controls using a custom array and analyzed by logistic regression. SNPs with P < 0.10 were evaluated among 3,143 cases and 2,102 controls from the Follow-up of Ovarian Cancer Genetic Association and Interaction Studies (FOCI) post-GWAS collaboration. Combined analysis revealed association with SNPs rs17561 and rs4848300 in the interleukin gene IL1A which varied by histologic subtype (P(heterogeneity) = 0.03). For example, IL1A rs17561, which correlates with numerous inflammatory phenotypes, was associated with decreased risk of clear cell, mucinous, and endometrioid subtype, but not with the most common serous subtype. Genotype at rs1864414 in the arachidonate 5-lipoxygenase ALOX5 was also associated with decreased risk. Thus, inherited variation in IL1A and ALOX5 seems to affect ovarian cancer risk which, for IL1A, is limited to rarer subtypes. Given the importance of inflammation in tumorigenesis and growing evidence of subtype-specific features in ovarian cancer, functional investigations will be important to help clarify the importance of inherited variation related to inflammation in ovarian carcinogenesis.

Candidate Gene Analysis Using Imputed Genotypes: Cell Cycle Single-Nucleotide Polymorphisms and Ovarian Cancer Risk

Polymorphisms in genes critical to cell cycle control are outstanding candidates for association with ovarian cancer risk; numerous genes have been interrogated by multiple research groups using differing tagging single-nucleotide polymorphism (SNP) sets. To maximize information gleaned from existing genotype data, we conducted a combined analysis of five independent studies of invasive epithelial ovarian cancer. Up to 2,120 cases and 3,382 controls were genotyped in the course of two collaborations at a variety of SNPs in 11 cell cycle genes (CDKN2C, CDKN1A, CCND3, CCND1, CCND2, CDKN1B, CDK2, CDK4, RB1, CDKN2D, and CCNE1) and one gene region (CDKN2A-CDKN2B). Because of the semi-overlapping nature of the 123 assayed tagging SNPs, we performed multiple imputation based on fastPHASE using data from White non-Hispanic study participants and participants in the international HapMap Consortium and National Institute of Environmental Health Sciences SNPs Program. Logistic regression assuming a log-additive model was done on combined and imputed data. We observed strengthened signals in imputation-based analyses at several SNPs, particularly CDKN2A-CDKN2B rs3731239; CCND1 rs602652, rs3212879, rs649392, and rs3212891; CDK2 rs2069391, rs2069414, and rs17528736; and CCNE1 rs3218036. These results exemplify the utility of imputation in candidate gene studies and lend evidence to a role of cell cycle genes in ovarian cancer etiology, suggest a reduced set of SNPs to target in additional cases and controls. (Cancer Epidemiol Biomarkers Prev 2009;18(3):935–44)

Assessment of Hepatocyte Growth Factor in Ovarian Cancer Mortality

Background: Invasive ovarian cancer is a significant cause of gynecologic cancer mortality. Methods: We examined whether this mortality was associated with inherited variation in approximately 170 candidate genes/regions [993 single-nucleotide polymorphisms (SNPs)] in a multistage analysis based initially on 312 Mayo Clinic cases (172 deaths). Additional analyses used The Cancer Genome Atlas (TCGA; 127 cases, 62 deaths). For the most compelling gene, we immunostained Mayo Clinic tissue microarrays (TMA, 326 cases) and conducted consortium-based SNP replication analysis (2,560 cases, 1,046 deaths). Results: The strongest initial mortality association was in HGF (hepatocyte growth factor) at rs1800793 (HR = 1.7, 95% CI = 1.3–2.2, P = 2.0 × 10−5) and with overall variation in HGF (gene-level test, P = 3.7 × 10−4). Analysis of TCGA data revealed consistent associations [e.g., rs5745709 (r2 = 0.96 with rs1800793): TCGA HR = 2.4, CI = 1.4–4.1, P = 2.2 × 10−3; Mayo Clinic + TCGA HR = 1.6, CI = 1.3–1.9, P = 7.0 × 10−5] and suggested genotype correlation with reduced HGF mRNA levels (P = 0.01). In Mayo Clinic TMAs, protein levels of HGF, its receptor MET (C-MET), and phospho-MET were not associated with genotype and did not serve as an intermediate phenotype; however, phospho-MET was associated with reduced mortality (P = 0.01) likely due to higher expression in early-stage disease. In eight additional ovarian cancer case series, HGF rs5745709 was not associated with mortality (HR = 1.0, CI = 0.9–1.1, P = 0.87). Conclusions: We conclude that although HGF signaling is critical to migration, invasion, and apoptosis, it is unlikely that HGF genetic variation plays a major role in ovarian cancer mortality. Furthermore, any minor role is not related to genetically-determined expression. Impact: Our study shows the utility of multiple data types and multiple data sets in observational studies. Cancer Epidemiol Biomarkers Prev; 20(8); 1638–48. ©2011 AACR.

Xenobiotic-metabolizing gene polymorphisms and ovarian cancer risk

Because selected xenobiotic‐metabolizing enzymes process pro‐carcinogens that could initiate ovarian carcinogenesis, we hypothesized that single nucleotide polymorphisms (SNPs) in the genes encoding xenobiotic‐metabolizing enzymes are associated with risk of ovarian cancer. Cases with invasive epithelial ovarian cancer (N = 1,571 including 956 of serous sub‐type) and controls (N = 2,046) from three studies were genotyped at 11 SNPs in EPHX1, ADH4, ADH1A, NQO2, NAT2, GSTP1, CYP1A1, and NQO1, following an initial SNP screen in a subset of participants. Logistic regression analysis of genotypes obtained via Illumina GoldenGate and Sequenom iPlex technologies revealed the following age‐ and study‐adjusted associations: EPHX1 rs1051740 with increased serous ovarian cancer risk [per‐allele odds ratio (OR) 1.17, 95% confidence interval (95% CI) 1.04–1.32, P = 0.01), ADH4 r1042364 with decreased ovarian cancer risk (OR 0.90, 95% CI: 0.81–1.00, P = 0.05), and NQO1 rs291766 with increased ovarian cancer risk (OR 1.11, 95% CI: 1.00–1.23, P = 0.04). These findings are consistent with prior studies implicating these genes in carcinogenesis and suggest that this collection of variants is worthy of follow‐up in additional studies.

Polymorphisms in NF-κB Inhibitors and Risk of Epithelial Ovarian Cancer

BackgroundThe nuclear factor-κB (NF-κB) family is a set of transcription factors with key roles in the induction of the inflammatory response and may be the link between inflammation and cancer development. This pathway has been shown to influence ovarian epithelial tissue repair. Inhibitors of κB (IκB) prevent NF-κB activation by sequestering NF-κB proteins in the cytoplasm until IκB proteins are phosphorylated and degraded.MethodsWe used a case-control study to evaluate the association between single nucleotide polymorphisms (SNPs) in NFKBIA and NFKBIB (the genes encoding IκBα and IκBβ, respectively) and risk of epithelial ovarian cancer. We queried 19 tagSNPs and putative-functional SNPs among 930 epithelial ovarian cancer cases and 1,037 controls from two studies.ResultsThe minor allele for one synonymous SNP in NFKBIA, rs1957106, was associated with decreased risk (p = 0.03).ConclusionConsidering the number of single-SNP tests performed and null gene-level results, we conclude that NFKBIA and NFKBIB are not likely to harbor ovarian cancer risk alleles. Due to its biological significance in ovarian cancer, additional genes encoding NF-κB subunits, activating and inhibiting molecules, and signaling molecules warrant interrogation.

Analysis of Over 10,000 Cases Finds No Association between Previously Reported Candidate Polymorphisms and Ovarian Cancer Outcome

Background: Ovarian cancer is a leading cause of cancer-related death among women. In an effort to understand contributors to disease outcome, we evaluated single-nucleotide polymorphisms (SNP) previously associated with ovarian cancer recurrence or survival, specifically in angiogenesis, inflammation, mitosis, and drug disposition genes. Methods: Twenty-seven SNPs in VHL, HGF, IL18, PRKACB, ABCB1, CYP2C8, ERCC2, and ERCC1 previously associated with ovarian cancer outcome were genotyped in 10,084 invasive cases from 28 studies from the Ovarian Cancer Association Consortium with over 37,000-observed person-years and 4,478 deaths. Cox proportional hazards models were used to examine the association between candidate SNPs and ovarian cancer recurrence or survival with and without adjustment for key covariates. Results: We observed no association between genotype and ovarian cancer recurrence or survival for any of the SNPs examined. Conclusions: These results refute prior associations between these SNPs and ovarian cancer outcome and underscore the importance of maximally powered genetic association studies. Impact: These variants should not be used in prognostic models. Alternate approaches to uncovering inherited prognostic factors, if they exist, are needed. Cancer Epidemiol Biomarkers Prev; 22(5); 987–. ©2013 AACR.

Risk of ovarian cancer and inherited variants in relapse-associated genes.

Background We previously identified a panel of genes associated with outcome of ovarian cancer. The purpose of the current study was to assess whether variants in these genes correlated with ovarian cancer risk. Methods and Findings Women with and without invasive ovarian cancer (749 cases, 1,041 controls) were genotyped at 136 single nucleotide polymorphisms (SNPs) within 13 candidate genes. Risk was estimated for each SNP and for overall variation within each gene. At the gene-level, variation within MSL1 (male-specific lethal-1 homolog) was associated with risk of serous cancer (p = 0.03); haplotypes within PRPF31 (PRP31 pre-mRNA processing factor 31 homolog) were associated with risk of invasive disease (p = 0.03). MSL1 rs7211770 was associated with decreased risk of serous disease (OR 0.81, 95% CI 0.66–0.98; p = 0.03). SNPs in MFSD7, BTN3A3, ZNF200, PTPRS, and CCND1A were inversely associated with risk (p<0.05), and there was increased risk at HEXIM1 rs1053578 (p = 0.04, OR 1.40, 95% CI 1.02–1.91). Conclusions Tumor studies can reveal novel genes worthy of follow-up for cancer susceptibility. Here, we found that inherited markers in the gene encoding MSL1, part of a complex that modifies the histone H4, may decrease risk of invasive serous ovarian cancer.

Variation at 8q24 and 9p24 and risk of epithelial ovarian cancer.

The chromosome 8q24 region (specifically, 8q24.21.a) is known to harbor variants associated with risk of breast, colorectal, prostate, and bladder cancers. In 2008, variants rs10505477 and rs6983267 in this region were associated with increased risk of invasive ovarian cancer (p < 0.01); however, three subsequent ovarian cancer reports of 8q24 variants were null. Here, we used a multi-site case-control study of 940 ovarian cancer cases and 1,041 controls to evaluate associations between these and other single-nucleotide polymorphisms (SNPs) in this 8q24 region, as well as in the 9p24 colorectal cancer associated-region (specifically, 9p24.1.b). A total of 35 SNPs from previous reports and additional tagging SNPs were assessed using an Illumina GoldenGate array and analyzed using logistic regression models, adjusting for population structure and other potential confounders. We observed no association between genotypes and risk of ovarian cancer considering all cases, invasive cases, or invasive serous cases. For example, at 8q24 SNPs rs10505477 and rs6983267, analyses yielded per-allele invasive cancer odds ratios of 0.95 (95% confidence interval (CI) 0.82-1.09, p trend 0.46) and 0.97 (95% CI 0.84-1.12, p trend 0.69), respectively. Analyses using an approach identical to that of the first positive 8q24 report also yielded no association with risk of ovarian cancer. In the 9p24 region, no SNPs were associated with risk of ovarian cancer overall or with invasive or invasive serous disease (all p values > 0.10). These results indicate that the SNPs studied here are not related to risk of this gynecologic malignancy and that the site-specific nature of 8q24.21.a associations may not include ovarian cancer.

Bayesian mixture models for the incorporation of prior knowledge to inform genetic association studies

In the last decade, numerous genome‐wide linkage and association studies of complex diseases have been completed. The critical question remains of how to best use this potentially valuable information to improve study design and statistical analysis in current and future genetic association studies. With genetic effect size for complex diseases being relatively small, the use of all available information is essential to untangle the genetic architecture of complex diseases. One promising approach to incorporating prior knowledge from linkage scans, or other information, is to up‐ or down‐weight P‐values resulting from genetic association study in either a frequentist or Bayesian manner. As an alternative to these methods, we propose a fully Bayesian mixture model to incorporate previous knowledge into on‐going association analysis. In this approach, both the data and previous information collectively inform the association analysis, in contrast to modifying the association results (P‐values) to conform to the prior knowledge. By using a Bayesian framework, one has flexibility in modeling, and is able to comprehensively assess the impact of model specification on posterior inferences. We illustrate the use of this method through a genome‐wide linkage study of colorectal cancer, and a genome‐wide association study of colorectal polyps. Genet. Epidemiol. 34:418–426, 2010.