Abstract LB-177: Novel approaches for improving interpretation and predictive models of comparative genomic hybridization data

Abstract

Abstract As costs of genome wide analyses decline and become more accessible, their use in both human and animal cancer studies are generating increasing information regarding underlying cancer etiology. Comparative genomic hybridization (CGH) is providing valuable information relating copy number aberrations (CNAs) to cancer mechanisms and clinical outcomes. However, challenges exist to interpreting and fully utilizing these data. First, without matched tumor and healthy tissue samples from individuals, distinguishing naturally occurring copy number variations (CNVs) from CNAs is difficult. Second, the large search space of genome wide analyses makes finding combinations of CNAs with improved predictive potential compared to single CNAs challenging. Here we provide novel methods to address these challenges associated with CGH data. Many new resources (e.g. The Cancer Genome Atlas (TCGA)), are making large volumes of genomic data publically accessible. However, most datasets do not have matched normal and tumor tissue samples between subjects. We tested matched normal and tissue samples from 30 patients with colorectal, lung, and pancreatic cancer and compared CNVs and CNAs to findings in larger, non-matched samples in TCGA. Even with limited matched samples, this approach allows for the differentiation of CNVs from CNAs discovered in analyses of non-matched samples. In some cases, combinations of CNAs can provide improved predictive capability compared to any single CNA. However, it is computationally intractable to exhaustively test combinations of CNAs in a genome-wide study. To address this limitation, we use a novel approach for CNA feature reduction that minimizes the variance within CNA segments across subjects, and Random Forest Ensemble Classification. This approach provides CNA combinations with balanced accuracies of 83.5% and 94.9% for distinguishing 52 cases of canine ALL/AML and 71 cases of B-CLL/T-CLL, respectively. These two approaches address frequent limitations in the interpretation CGH data. Better distinguishing CNVs and interrogating CNA combinations, can provide additional information about the role of CNAs in disease mechanisms and improve treatment decisions. Citation Format: Daniel Rotroff, Matthew Breen, Alison Motsinger-Reif. Novel approaches for improving interpretation and predictive models of comparative genomic hybridization data. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-177. doi:10.1158/1538-7445.AM2015-LB-177