Koestler, Devin C.

Abstract Background Advances in next-generation DNA sequencing technologies are now enabling detailed characterization of sequence variations in cancer genomes. With whole-genome sequencing, variations in coding and non-coding sequences can be discovered. But the cost associated with it is currently limiting its general use in research. Whole-exome sequencing is used to characterize sequence variations in coding regions, but the cost associated with capture reagents and biases in capture rate limit its full use in research. Additional limitations include uncertainty in assigning the functional significance of the mutations when these mutations are observed in the non-coding region or in genes that are not expressed in cancer tissue. Results We investigated the feasibility of uncovering mutations from expressed genes using RNA sequencing datasets with a method called Variant Detection in RNA(VaDiR) that integrates 3 variant callers, namely: SNPiR, RVBoost, and MuTect2. The combination of all 3 methods, which we called Tier 1 variants, produced the highest precision with true positive mutations from RNA-seq that could be validated at the DNA level. We also found that the integration of Tier 1 variants with those called by MuTect2 and SNPiR produced the highest recall with acceptable precision. Finally, we observed a higher rate of mutation discovery in genes that are expressed at higher levels. Conclusions Our method, VaDiR, provides a possibility of uncovering mutations from RNA sequencing datasets that could be useful in further functional analysis. In addition, our approach allows orthogonal validation of DNA-based mutation discovery by providing complementary sequence variation analysis from paired RNA/DNA sequencing datasets.

Abstract Both genetic and epigenetic factors influence the development and progression of epithelial ovarian cancer (EOC). However, there is an incomplete understanding of the interrelationship between these factors and whether they interact to impact disease risk. Given that the development of ovarian cancer is complex, we aimed to identify DNA methylation marks that are candidates for mediating ovarian cancer genetic risk. We used 214 cases and 214 age-matched controls from the Mayo Clinic Ovarian Cancer Study. Pretreatment, blood-derived DNA was profiled for genome-wide methylation (Illumina Infinium HumanMethylation27 BeadArray) and single nucleotide polymorphisms (SNPs, Illumina Infinium HD Human610-Quad BeadArray). We employed a three-step filtering procedure, followed by the Causal Inference Test (CIT), to distinguish CpG sites that mediate genetic risk, from those that are consequential or independently acted on by genotype. Controlling for the estimated distribution of immune cells and other key covariates, we identified 1,993 out of 25,926 (7.7%) CpGs that were significantly differentially methylated between cases and controls (FDR, q < 0.05). The relationship between methylation and case-control status among these 1,993 CpGs was found to be highly consistent with the results of an independent study. Implementation of the CIT test revealed 13 CpG/SNP pairs, comprising 13 unique CpGs and 17 unique SNPs, which represent potential methylation-mediated relationships between genotype and EOC risk. These findings provide additional insight into EOC etiology and may serve as novel biomarkers for EOC susceptibility. Citation Format: Devin C. Koestler, Prabhakar Chalise, Mine S. Cicek, Julie M. Cunningham, Sebastian Armasu, Melissa C. Larson, Jeremy Chien, Matthew Block, Kimberly R. Kalli, Thomas A. Sellers, Ellen L. Goode, Brooke L. Fridley. Integrative genomic analysis identifies epigenetic marks that mediate genetic risk for epithelial ovarian cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 260. doi:10.1158/1538-7445.AM2014-260