Iversen, Ed

PURPOSE The emergence of large real-world clinical databases and tools to mine electronic medical records has allowed for an unprecedented look at large data sets with clinical and epidemiologic correlates. In clinical cancer genetics, real-world databases allow for the investigation of prevalence and effectiveness of prevention strategies and targeted treatments and for the identification of barriers to better outcomes. However, real-world data sets have inherent biases and problems (eg, selection bias, incomplete data, measurement error) that may hamper adequate analysis and affect statistical power. METHODS Here, we leverage a real-world clinical data set from a large health network for patients with breast cancer tested for variants in BRCA1 and BRCA2 (N = 12,423). We conducted data cleaning and harmonization, cross-referenced with publicly available databases, performed variant reassessment and functional assays, and used functional data to inform a variant's clinical significance applying American College of Medical Geneticists and the Association of Molecular Pathology guidelines. RESULTS In the cohort, White and Black patients were over-represented, whereas non-White Hispanic and Asian patients were under-represented. Incorrect or missing variant designations were the most significant contributor to data loss. While manual curation corrected many incorrect designations, a sizable fraction of patient carriers remained with incorrect or missing variant designations. Despite the large number of patients with clinical significance not reported, original reported clinical significance assessments were accurate. Reassessment of variants in which clinical significance was not reported led to a marked improvement in data quality. CONCLUSION We identify the most common issues with BRCA1 and BRCA2 testing data entry and suggest approaches to minimize data loss and keep interpretation of clinical significance of variants up to date.

Abstract As pathways of the immune system play an important role in multiple malignancies, we assessed the association between single-nucleotide polymorphisms (SNPs) in inflammation-related genes and risk of ovarian cancer in a multi-site case-control study. Using a custom array, we genotyped 930 epithelial ovarian cancer cases and 1,037 controls for SNPs in ALOX12, ALOX15, ALOX5, CCL11, CCL2, CCL3, CCR3, CRP, CXCL16, IL10, IL15RA, IL18, IL1A, IL1B, IL1RN, IL4R, IL6, IL6R, IL7R, IL8RA, IL8RB, IL9, NOS3, PTGS1, PTGS2, TLR2, and TNF. After analysis by logistic regression, SNPs with p<0.10 were evaluated in an independent data set of 3,143 cases and 2,102 controls from the Follow-up of Ovarian Cancer Genetic Association and Interaction Studies (FOCI) collaboration. Combined analysis revealed association with interleukin 1, alpha (IL1A) SNPs (rs17561 and rs4848300, r2=0.990, p=0.005) which varied by histological subtype (heterogeneity p=0.03). For example, IL1A rs17561, which results in a missense change in amino acid at position 114 and correlates with multiple inflammatory phenotypes, was inversely associated with risk of clear cell, mucinous, and endometrioid subtypes, but not with the serous subtype (Table 1). Additional data on IL1A rs17561 in an even larger collection of ovarian cancer cases and controls are pending and will be analyzed soon. We also found that genotype at rs1864414 in ALOX5 (arachidonate 5-lipoxygenase) was associated with decreased risk combined across subtypes (OR 0.86, 95% CI 0.78-0.95; p=0.002). Thus, inherited variation in IL1A and ALOX5 appears to have a role in ovarian cancer risk, though it is limited to rarer subtypes for IL1A. These data add to existing literature on the importance of inflammation in tumorigenesis and growing evidence of subtype-specific traits in ovarian cancer and may provide important clues on prevention. 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 2603. doi:1538-7445.AM2012-2603

Abstract Background: Genome-wide association studies (GWAS) for epithelial ovarian cancer (EOC), the most lethal gynecologic malignancy, have identified novel susceptibility loci. GWAS for survival after EOC have had more limited success. The association of each single-nucleotide polymorphism (SNP) individually may not be well suited to detect small effects of multiple SNPs, such as those operating within the same biologic pathway. Gene set analysis (GSA) overcomes this limitation by assessing overall evidence for association of a phenotype with all measured variation in a set of genes. Methods: To determine gene sets associated with EOC overall survival, we conducted GSA using data from two large GWAS (N cases = 2,813, N deaths = 1,116), with a novel Principal Component-Gamma GSA method. Analysis was completed for all cases and then separately for high-grade serous histologic subtype. Results: Analysis of the high-grade serous subjects resulted in 43 gene sets with P < 0.005 (1.7%); of these, 21 gene sets had P < 0.10 in both GWAS, including intracellular signaling pathway (P = 7.3 × 10−5) and macrolide binding (P = 6.2 × 10−4) gene sets. The top gene sets in analysis of all cases were meiotic mismatch repair (P = 6.3 × 10−4) and macrolide binding (P = 1.0 × 10−3). Of 18 gene sets with P < 0.005 (0.7%), eight had P < 0.10 in both GWAS. Conclusion: This research detected novel gene sets associated with EOC survival. Impact: Novel gene sets associated with EOC survival might lead to new insights and avenues for development of novel therapies for EOC and pharmacogenomic studies. Cancer Epidemiol Biomarkers Prev; 21(3); 529–36. ©2012 AACR.