Objective: Hepatocellular carcinoma (HCC) is a highly heterogeneous disease with a dismal prognosis. However, driver genes and prognostic markers in HCC remain to be identified. It is hoped that in-depth analysis of HCC genomes in relation to available clinicopathological information will give rise to novel molecular prognostic markers. Methods: We collected genomic data of 1,061 HCC patients from previous studies, and performed integrative analysis to identify significantly mutated genes and molecular prognosticators. We employed three MutSig algorithms (MutSigCV, MutSigCL and MutSigFN) to identify significantly mutated genes. The GISTIC2 algorithm was used to delineate focally amplified and deleted genomic regions. Nonnegative matrix factorization (NMF) was utilized to decipher mutational signatures. Kaplan-Meier survival and Cox regression analyses were used to associate gene mutation and copy number alteration with survival outcome. Logistic regression model was applied to test association between gene mutation and mutational signatures. Results: We discovered 11 novel driver genes, including RNF213, VAV3 and TNRC6B, with mutational prevalence ranging from 1% to 3%. Seven mutational signatures were also identified in HCC, some of which were associated with mutations of classical driver genes (e.g., TP53, TERT) as well as alcohol consumption. Focal amplifications of TERT and other druggable targets, including AURKA, were also revealed. Targeting AURKA by a small-molecule inhibitor potently induced apoptosis in HCC cells. We further demonstrated that HCC patients with TERT amplification displayed shortened overall survival independent of other clinicopathological parameters. In conclusion, our study identified novel cancer driver genes and prognostic markers in HCC, reiterating the translational importance of omics data in the precision medicine era.
Xiangchun Li Xu Weiqi Wei Kang Sunny Wong Wang Mengyao Zhou Yong Xiaodong Fang Xiuqing Zhang 杨焕明 Wong Chi Hang Kafai To Stephenlam Chan Matthew Chan 沈祖堯 William ka kei Wu Jun Yu