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. 2013 Sep;23(9):1422-33.
doi: 10.1101/gr.154492.113. Epub 2013 Jun 20.

Whole-genome sequencing identifies recurrent mutations in hepatocellular carcinoma

Zhengyan Kan  1 , Hancheng Zheng, Xiao Liu, Shuyu Li, Thomas D Barber, Zhuolin Gong, Huan Gao, Ke Hao, Melinda D Willard, Jiangchun Xu, Robert Hauptschein, Paul A Rejto, Julio Fernandez, Guan Wang, Qinghui Zhang, Bo Wang, Ronghua Chen, Jian Wang, Nikki P Lee, Wei Zhou, Zhao Lin, Zhiyu Peng, Kang Yi, Shengpei Chen, Lin Li, Xiaomei Fan, Jie Yang, Rui Ye, Jia Ju, Kai Wang, Heather Estrella, Shibing Deng, Ping Wei, Ming Qiu, Isabella H Wulur, Jiangang Liu, Mariam E Ehsani, Chunsheng Zhang, Andrey Loboda, Wing Kin Sung, Amit Aggarwal, Ronnie T Poon, Sheung Tat Fan, Jun Wang, James Hardwick, Christoph Reinhard, Hongyue Dai, Yingrui Li, John M Luk, Mao Mao
Affiliations

Whole-genome sequencing identifies recurrent mutations in hepatocellular carcinoma

Zhengyan Kan et al. Genome Res. 2013 Sep.

Abstract

Hepatocellular carcinoma (HCC) is one of the most deadly cancers worldwide and has no effective treatment, yet the molecular basis of hepatocarcinogenesis remains largely unknown. Here we report findings from a whole-genome sequencing (WGS) study of 88 matched HCC tumor/normal pairs, 81 of which are Hepatitis B virus (HBV) positive, seeking to identify genetically altered genes and pathways implicated in HBV-associated HCC. We find beta-catenin to be the most frequently mutated oncogene (15.9%) and TP53 the most frequently mutated tumor suppressor (35.2%). The Wnt/beta-catenin and JAK/STAT pathways, altered in 62.5% and 45.5% of cases, respectively, are likely to act as two major oncogenic drivers in HCC. This study also identifies several prevalent and potentially actionable mutations, including activating mutations of Janus kinase 1 (JAK1), in 9.1% of patients and provides a path toward therapeutic intervention of the disease.

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Figures

Figure 1.
Figure 1.
Activating mutations in Janus kinase 1 (JAK1). (A) Seven distinct somatic mutations in JAK1 in the context of protein domains and active sites. Two mutations (S703I and S729C) are recurrent, found in two samples each. (B) HEK293FT and Hep3B cells were transiently transfected with either empty vector, Flag-tagged JAK1 WT, or Flag-tagged JAK1 variants for 48 h, serum-starved for 4 h, and in the case of Hep3B, treated with vehicle or 10 ng/mL IL6 for 15 min. Resultant JAK1 and STAT3 activation determined by immunoblotting lysates with anti-pJAK1 (Y1034/1035) and -pSTAT3 (Y705), respectively. Comparable expression of Flag-tagged JAK1 constructs and protein loading was confirmed using anti-M2 Flag and -beta actin. Representative result is shown in two to six independent experiments. (C) A model of the interaction between the JAK1 JH1 and JH2 domains was generated using the crystal structure of the JH1 domain (3EYG) and a homology model of the JH2 domain built using Discovery Studio (accelrys.com). The two residues reported to be in contact (Lys 924 and Glu 637) that were used to orient the domains are highlighted (gray CPK), as are the seven residues found to be mutated in this study (green CPK), and the small-molecule CP-690,550 bound in the JH1 ATP site (ball-and-stick). (D) Tumor samples are ranked by JAK1 activation scores based on independently derived JAK1 gene expression signature (Flex et al. 2008). Colors indicate JAK1 mutation statuses and whether the mutation is activating in vitro as experimentally determined. (E) Wild-type JAK1 or activating JAK1 mutants (S703I, S729C, and L910P) transduced Ba/F3 cells were cultured in the absence of IL3 in triplicate. Cell numbers were counted at indicated time points and data presented as mean ± SD. (F) JAK1 activating mutant (S703I and S729C)-transduced cells were treated with either ruxolitinib or BMS-911453 for 3 d. Cell viability was measured and data presented as mean ± SD.
Figure 2.
Figure 2.
Landscape of genomic copy number alteration in HCC. (A) Shown is a genome-scale overview of copy number segments across 88 HCC samples rendered by IGV (Robinson et al. 2011). Each track in the upper panel represents one tumor sample, and the lower panels show the amplification and deletion G-score profiles across the genome. (B) Shown is a chromosomal overview of amplification and deletion peaks with the locations of putative cancer driver genes indicated. Arrows indicate focal peaks (<100 kb). Asterisks indicate genes also harboring nonsense mutations. (C) A log-scale plot of gene-level expression fold-change in CNV relative to non-CNV samples versus amplitude of copy gains. Genes with strong increase in expression driven by copy gains are labeled. Colors and shadings were used to indicate the CNV and cis-correlation statuses of genes. The size of the marker is proportional to the amplification G-score.
Figure 3.
Figure 3.
Frequently altered cancer pathways in HCC. Core pathway analysis identified frequent genomic alterations in multiple cancer pathways, including (A) Wnt, (B) JAK/STAT, (C) G1/S cell cycle, and (D) apoptosis pathways. Alterations include somatic mutation, DNA copy number changes correlated with gene expression, and HBV integration. Alteration frequency was represented as a percentage of all cases harboring a genomic alteration in one of the pathway genes shown. Gene expression up- or down-regulation in tumor relative to normal samples is shown but not included in the calculation of alteration frequencies. Alteration types and frequencies were represented by different colors and color gradients, respectively.
Figure 4.
Figure 4.
Molecular subclassification of HCC. (A) Gene expression profile of previously defined signatures (Hoshida et al. 2009) in three HCC subclasses. High and low expressions are represented by red and blue color, respectively. Seventy-six of the 88 tumors (86%) are assigned to subclasses with high confidence (FDR < 0.05). (B) Genetic and clinical profiles of the three HCC subclasses. Tumors with somatic SNVs in CTNNB1, TP53, and JAK1, HBV integrations into TERT and MLL4 (also known as KMT2B), AFP level >400 ng/mL, or tumor recurrence are represented by dark blue color. For tumor grade, poor, moderate, and well differentiation are represented by dark blue, light blue, and gray color, respectively. Tumors with missing tumor grade information are shown in white. (C) Kaplan-Meier survival plot for the three HCC subclasses. (D) Schematic summary of gene expression, genetic, and clinical profiles for each HCC subclass.
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