Mutation timestamp and genome-scale metabolic model elucidate a novel anti-cancer drug target, mitochondrial aminoacyl-tRNA synthetase

Abstract

Cancer carcinogenesis is an evolutionary process in which somatic mutations and clonal selection play important roles. Understanding cancer evolution is commonly expected to accelerate anti-cancer drug target discovery. However, the cancer evolution model has not been investigated owing to their biological complexity. Here, I propose a new mathematical method to infer the cancer mutational sequences and its application for drug target discovery in colon cancer. At first, a new method called mutation timestamp illustrated the cancer evolution procedure across cancer types. Mutation timestamp was calculated with TCGA mutation profiles based on population genetics. Subsequently, to apply the knowledge of mutation timestamp to drug discovery, the cancer dependency map (DepMap), the RNA-seq data, and the ATAC-seq data were used to construct the colon cancer-specific DepMap correlation network. A network diffusion algorithm found the aminoacyl-tRNA biosynthesis subsystem network. Finally, genome-scale metabolic model simulation with the aminoacyl-tRNA biosynthesis subsystem network described the cancer metabolism reprogramming. In summary, this thesis suggests a novel systematical method to connect cancer genome to its phenotype.