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 tumor types and treatments. 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.MethodsWe used data from 9300 tumors across 25 cancer types from The Cancer Genome Atlas. We used supervised machine learning to evaluate our ability to distinguish between tumors that had mutually exclusive genomic aberrations in specific genes. An ability to accurately distinguish between tumors with aberrations in these genes suggested that the genes have a relatively different downstream effect on transcription, and vice versa. We compared these findings against prior knowledge about signaling networks and drug responses.ResultsOur analysis recapitulates known relationships in cancer pathways and identifies gene pairs known to predict responses to the same treatments. For example, in lung adenocarcinomas, gene-expression profiles from tumors with somatic aberrations in EGFR or MET were negatively correlated with each other, in line with prior knowledge that MET amplification causes resistance to EGFR inhibition. In breast carcinomas, we observed high similarity between PTEN and PIK3CA, which play complementary roles in regulating cellular proliferation. In a pan-cancer analysis, we found that genomic aberrations in BRAF and VHL exhibit downstream effects that are clearly distinct from other genes.ConclusionWe 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.
Highlights
Malignant tumors are typically caused by a conglomeration of genomic aberrations—including point mutations, small insertions, small deletions, and large copy-number variations
Machine-learning analysis Our analysis focused exclusively on The Cancer Genome Atlas (TCGA) samples for which data were available for all three molecular types
The lowest area under the receiver operating characteristic curve (AUROC) was 0.58 for KRAS. These findings suggest that EGFR mutations have a distinct effect upon transcription levels in lung adenocarcinomas
Summary
Malignant tumors are typically caused by a conglomeration of genomic aberrations—including point mutations, small insertions, small deletions, and large copy-number variations. 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. A single tumor contains anywhere from tens to millions of genomic aberrations—including point mutations, small insertions, small deletions, and large copy number variations—that differ from the patient’s normal cells [1,2,3,4]. Knowledge of these aberrations may be useful in guiding therapeutic decisions. It may be economically infeasible to develop targeted therapies for every rare mutation, we may be able to repurpose existing cancer treatments by identifying similarities in tumor biology between tumors that harbor rare and common aberrations
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