Abstract 3298: High-content phenotyping of somatic cancer mutations by functional variomics

Cancer genomes are highly complex with numerous somatic mutations identified across patient populations. Previous studies on genomic mutations have shed light on new means for cancer therapeutic interventions. With rapid advances in next-generation sequencing, accumulating genotypic information in the absence of efficient and systematic functional analyses of genomic aberrations will create a bottleneck in understanding genotype-phenotype relationships in cancer. To address these challenges, here we report a systems-level functional variomics approach integrating high-throughput phenotyping with robust computational analyses to investigate mutation-specific effects. This systematic functional platform consists of massively parallel mutagenesis, sensitive survival assays using growth factor-dependent cell models, and functional network perturbation profiling of mutations on signaling effects. We profile several thousands of genomic aberrations, including point mutations, gene fusions and indels, and significantly expand the repertoire of characterized actionable mutations. This study represents a valuable resource and provides insights in prioritizing cancer-causing mutations, and uncovering patient-specific disease mechanisms at a high resolution, a critical step towards personalized precision medicine. Citation Format: Nidhi Sahni, Patrick Kwok-Shing Ng, Kang Jin Jeong, Gordon B. Mills. High-content phenotyping of somatic cancer mutations by functional variomics [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 3298.