Abstract
561 Background: Recent massive sequencing studies of HCC genomes revealed many new genetic alterations that might be accountable for HCC development and provided comprehensive view of malignant disease. However, genomic profiling of tumors is limited by a loose correlation between genetic alterations and their functional products such as proteins and metabolites. To overcome current limitation, we generated genomic and proteomic data together from HCC tumors and performed integrated analysis of both data sets. Methods: We analyzed proteomic data and genomic data such as somatic mutations, mRNA expression, miRNA expression, and copy number alterations from 300 HCC tumors to uncover most correlated genomic alterations with proteins. Clinical significance of identified key protein features were validated in multiple independent cohorts of HCC patients. Results: Analysis revealed three subtypes of HCC with substantial difference in proteomic patterns. Based on proteomic characteristics, three subtypes were named to mesenchymal (MES), metabolically active (MA), and kinase-active and genome stable (KAGS) subtypes. When assessed clinical relevance, the overall survival rate of patients in MES subtype was significantly worse than those in MA and KAGS subtype (P = 0.001). Poor clinical outcomes of mesenchymal subtype is validated in multiple independent cohorts (in total of >500 patients). Gene network analysis with integrated genomic and proteomic data further revealed association of subtypes with currently available treatments of HCC such as sorafenib and immunotherapy. In addition, multiple in-depth analysis of integrated data identified potential therapeutic target candidates for each subtype. Functional validation with cell lines demonstrated that some of candidates are essential for survival of HCC cells. Conclusions: HCC is classified into three subtypes by integrating genomic and proteomic data. These analyses has identified potential therapeutic targets as well as biomarkers associated with therapeutic targets. Our study demonstrated merit of integrated analysis of proteomic data with genomic data to uncover potential driver genes of HCC development.
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