Abstract 245: A comprehensive characterization of hyper-morph, hypo-morph, and neo-morph mutations in cancer
Authors: Somnath Tagore, Andrea Califano
Journal: Cancer Research (2021)

Abstract Although annotation of genomic mutations is a highly relevant and complex segment of the analysis of sequence-based genomic analyses, currently more than ten million variants lack functional annotation. While computational predictions of variant function are usually integrated into gene-based analyses of rare-variants, there is limited information for assessing variant function in the context of a particular disease. The goal of this study is to assess the mechanistic characterization of context-dependent hyper-, hypo-, and neomorph mutations in cancers. We developed a computational approach to characterize these mutations and assess the functional effects of mutations occurring in the same or different protein domains, and differentiate mutations in terms of their hyper-morph (gain-of-function), hypo-morph (loss-of-function), neutral, or neo-morph (establishing a novel function) potential. To elucidate the functional consequence of such mutations, we based our computational pipeline on inferring the activity of regulatory proteins such as transcription factors (TFs) and co-factors (co-TFs) using the VIPER algorithm. Our analytical pipeline integrates structural and functional information encompassing six topics : 1) structural domains affected by the mutation, 2) the overlap between mutation-specific TF/co-TFs, 3) differential activity signatures and signatures induced by established hyper-morph, hypo-morph and neutral or neo-morph mutations, 4) in vitro data generated by reporter assays, 5) the VIPER-inferred activity of each protein relative to a validated control, and 6) the fraction of proteins in a sample that are not affected by established hyper-morphs and hypo-morphs (candidate neo-morphs).The current repertoire considers 25 TCGA cohorts for 3830 proteins (oncoproteins/tumor suppressors). As an example, we validated our method on the TCGA Breast Cancer dataset (TCGA-BRCA) by predicting with very high confidence several neo-morphic phenotypes, including the previously-described PIK3CAE545, PIK3CAE542, PIK3CAH1047, PIK3CAQ546K and PIK3CAG1049R. Interestingly, PIK3CAE545K, classified previously as a gain-of-function mutation (in one TCGA-BRCA sample) or a loss-of-function mutation (in two other TCGA-BRCA samples), is predicted to be a neo-morph based on our approach. Further validation of these mutations using PIK3CA reporter assays led to the identification of several significant hypo-morphic signals in TP53 mutant samples. We defined this phenomenon as mutational mimicry (i.e. mutations in proteins mimicking those in established oncogenes) and we propose it as a tool for predicting tumor sensitivity/resistance to drugs. Citation Format: Somnath Tagore, Andrea Califano. A comprehensive characterization of hyper-morph, hypo-morph, and neo-morph mutations in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 245.