Whole-Genome Analysis of De Novo Somatic Point Mutations Reveals Novel Mutational Biomarkers in Pancreatic Cancer

Simple Summary Many studies have identified cancer subtypes based on the cancer driver genes, or the proportion of mutational processes in cancer genomes, however, none of these cancer subtyping methods consider these features together to identify cancer subtypes. Accurate classification of cancer individuals with similar mutational profiles may help clinicians to identify individuals who could receive the same types of treatment. Here, we develop a new statistical pipeline and use a novel concept, "gene-motif", to identify five pancreatic cancer subtypes, in which for most of them, targeted treatment options are currently available. More importantly, for the first time we provide a system-wide analysis of the enrichment of de novo mutations in a specific motif context of the driver genes in pancreatic cancer. By knowing the genes and motif associated with the mutations, a personalized treatment can be developed that considers the specific nucleotide sequence context of mutations within responsible genes. It is now known that at least 10% of samples with pancreatic cancers (PC) contain a causative mutation in the known susceptibility genes, suggesting the importance of identifying cancer-associated genes that carry the causative mutations in high-risk individuals for early detection of PC. In this study, we develop a statistical pipeline using a new concept, called gene-motif, that utilizes both mutated genes and mutational processes to identify 4211 3-nucleotide PC-associated gene-motifs within 203 significantly mutated genes in PC. Using these gene-motifs as distinguishable features for pancreatic cancer subtyping results in identifying five PC subtypes with distinguishable phenotypes and genotypes. Our comprehensive biological characterization reveals that these PC subtypes are associated with different molecular mechanisms including unique cancer related signaling pathways, in which for most of the subtypes targeted treatment options are currently available. Some of the pathways we identified in all five PC subtypes, including cell cycle and the Axon guidance pathway are frequently seen and mutated in cancer. We also identified Protein kinase C, EGFR (epidermal growth factor receptor) signaling pathway and P53 signaling pathways as potential targets for treatment of the PC subtypes. Altogether, our results uncover the importance of considering both the mutation type and mutated genes in the identification of cancer subtypes and biomarkers.