Abstract 2011: Separating the signal from the noise: DNA variation as a perturbagen distinguishes between causal and follower genes associated with tumorigenesis and progression in a heptacellular carcinoma (HCC) cohort

Abstract

Abstract Given genomic instability as a defining characteristic of cancer, one of the central problems in identifying those genes that drive cancer onset and progression has been distinguishing these causal genes from the massive number of other genes that change as a result of the instability, but that are not central to the biological processes associated with cancer onset and progression. The study presented here integrates DNA variation with gene expression and RNA sequencing data in normal and matched tumor tissues collected from hepatocellular carcinoma (HCC) patients to distinguish between genes that cause tumorigenesis and cancer progression and those that respond to these events or that tag along with the causal genes as a consequence of genomic instability. Tumor and adjacent normal tissues were collected from an HCC cohort, and both DNA and RNA isolated from these tissues were profiled using gene expression and SNP genotyping arrays. On a smaller subset of individuals, we characterized the HCC transcriptome in both tumor and adjacent normal tissues using long reads from Pacific Biosciences’ single-molecule real-time (SMRT) sequencing platform. Transcript sequencing has previously been limited to messages less than ∼1100 bases in length for sequencing in one contiguous read. However, the SMRT platform's ability to sequence >3,000-base transcripts in a single read provided the ability to characterize aberrant cancer transcripts of this length. An integrative genomics analysis of these data revealed that the process of tumorigenesis resulted in rearrangements of gene networks in which networks specific to normal tissue function were disrupted and networks critical to tumor formation/progression were created. These changes were predicted to be driven by somatic copy number variation (CNV) that also associated with disease free survival and other outcome parameters. Validation of the inferred causal relationships were carried out by genetically mapping the recurrence rate and magnitude of somatic CNV using genome-wide genetic association testing in the HCC cohort, where CNVs were considered as a quantitative trait and tested for association to germ line SNP genotypes. By associating SNP genotypes with somatic CNVs, statistical procedures to infer causal relationships between somatic CNVs and associated gene expression traits could be applied to validate that changes in copy number lead to changes in expression that in turn drive changes in network states associated with tumorigenesis. These causal relationships were further confirmed using a treatment that promotes HCC tumorigenesis. Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend. 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 2011.