Classifying cancer genome aberrations by their mutually exclusive effects on transcription

Abstract

BackgroundMalignant tumors are typically caused by a conglomeration of genomic aberrations--including point mutations, small insertions, small deletions, and large copy-number variations. In some cases, specific chemotherapies and targeted drug treatments are effective against tumors that harbor certain genomic aberrations. However, predictive aberrations (biomarkers) have not been identified for many tumors. One way to address this problem is to examine the downstream, transcriptional effects of genomic aberrations and to identify characteristic patterns. Even though two tumors harbor different genomic aberrations, the transcriptional effects of those aberrations may be similar. These patterns could be used to inform treatment choices.\n\nResultsWe hypothesized that by grouping genomic aberrations according to their downstream transcriptional effects, we could identify drug-repurposing candidates that coincide with prior knowledge. To evaluate this hypothesis, we developed a computational pipeline that uses supervised machine learning to identify similarities among mutually exclusive genomic aberrations based on their transcriptional effects. We used data from 9300 tumors across 25 cancer types from The Cancer Genome Atlas to evaluate our approach. Our pipeline recapitulates known relationships in cancer pathways, and it identifies gene pairs known to predict responses to the same treatments.\n\nConclusionWe show that transcriptional data offer promise as a way to group genomic aberrations according to their downstream effects, and these groupings recapitulate known relationships. Our approach shows potential to help pharmacologists and clinical trialists narrow the search space for candidate gene/drug associations, including for rare mutations, and for identifying potential drug-repurposing opportunities.