Abstract 1182: Exploring longitudinal intra-tumor heterogeneity in cancer using whole genome sequencing and RNA rescue

Abstract

Abstract We present a methodology for confidently characterizing subclonal exonic variants by pairing whole genome sequencing (WGS) with transcriptomics (RNAseq). We demonstrate the utility of this method by studying subclonal population changes in longitudinally collected samples. Detection of variants specific to minority subclones has ordinarily only been achievable with very high-depth sequencing obtained by whole exome (WES) or targeted sequencing, and most available tools for studying subclonality require WES data as input. Although WGS provides more coverage breadth than WES, identifying subclonal mutations by WGS alone is difficult due to reduced read support for each variant. Detecting minority variants in WGS data is made especially difficult when tumor purity is low and subclonality is high. RNAseq is typically performed at high-depth and may be useful for replacing WES in detecting subclonal variants. Our method has two steps: 1) Use WGS to establish a low-confidence preliminary set of variants present in the tumor, and 2) bolster the confidence in those variants by observing whether the variants are expressed in the RNAseq. We used this methodology to generate WES-like data from WGS+RNAseq in simulated data, and show that we can correctly identify subclonal populations using publicly available tools designed for WES data. Additionally, we demonstrate using this method to characterize the evolutionary lineage of the tumor population in a patient who was biopsied multiple times throughout several treatments, for whom only WGS and RNAseq data is available. Our proposed method resulted in 60% more confident variants than its WGS-only counterpart. As tumor tissue availability is low and cost of sequencing is substantial, the choice of which molecular characterizations to perform is difficult. Here we show that paired WGS and RNAseq can be leveraged to infer information typically only obtained from WES, in addition to providing intergenic and expression information. Citation Format: Rahul Parulkar, Steve Benz, Charlie Vaske, Amie Radenbaugh, Christopher Szeto. Exploring longitudinal intra-tumor heterogeneity in cancer using whole genome sequencing and RNA rescue [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1182.