Leveraging a Multi-Omics Strategy for Prioritizing Personalized Candidate Mutation-Driver Genes: A Proof-of-Concept Study.

The expression of mutant forms of proteins (e.g., oncogenes and tumor suppressors) has implications in cancer biology and clinical practice. Initial efforts have been made to characterize the transcription of tumor-mutated alleles; however, few studies have been reported to link tumor-mutated alleles to proteomics. We aimed to characterize the transcriptional and translational patterns of tumor-mutated alleles. We performed whole-exome sequencing, RNA-seq, and proteome profiling in a hyper-mutated patient of hepatocellular carcinoma. Using the patient as a model, we show that only a small proportion of tumor-mutated alleles were expressed. In this case, 42% and 3.5% of the tumor-mutated alleles were identified to be transcribed and translated, respectively. Compared with genes with germline variations or without mutations, somatic mutations significantly reduced protein expression abundance. Using the transcriptional and translational patterns of tumor-mutated alleles, we classified the mutations into four types, and only one type may be associated with the liver cancer and lead to hepatocarcinogenesis in the patient. Our results demonstrate how tumor-mutated alleles are transcribed and translated, and how the expression enables the classification of somatic mutations that cause cancer. Leveraging multiple 'omics' datasets provides a new avenue for understanding patient-specific mutations that underlie carcinogenesis.