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Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma

Ally, A; Balasundaram, M; Carlsen, R; Chuah, E; Clarke, A; Dhalla, N; Holt, RA; ... Laird, PW; + view all (2017) Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma. Cell , 169 (7) 1327-1341.e23. 10.1016/j.cell.2017.05.046. Green open access

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Abstract

Liver cancer has the second highest worldwide cancer mortality rate and has limited therapeutic options. We analyzed 363 hepatocellular carcinoma (HCC) cases by whole-exome sequencing and DNA copy number analyses, and we analyzed 196 HCC cases by DNA methylation, RNA, miRNA, and proteomic expression also. DNA sequencing and mutation analysis identified significantly mutated genes, including LZTR1, EEF1A1, SF3B1, and SMARCA4. Significant alterations by mutation or downregulation by hypermethylation in genes likely to result in HCC metabolic reprogramming (ALB, APOB, and CPS1) were observed. Integrative molecular HCC subtyping incorporating unsupervised clustering of five data platforms identified three subtypes, one of which was associated with poorer prognosis in three HCC cohorts. Integrated analyses enabled development of a p53 target gene expression signature correlating with poor survival. Potential therapeutic targets for which inhibitors exist include WNT signaling, MDM4, MET, VEGFA, MCL1, IDH1, TERT, and immune checkpoint proteins CTLA-4, PD-1, and PD-L1.

Type: Article
Title: Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.cell.2017.05.046
Publisher version: http://dx.doi.org/10.1016/j.cell.2017.05.046
Language: English
Additional information: © 2017 The Author. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Cell Biology, RNA-SEQ DATA, LIVER-CANCER, MUTATIONAL LANDSCAPE, THERAPEUTIC TARGETS, EXPRESSION PROFILES, SEQUENCING DATA, GENE, DNA, PREDICTION, DISCOVERY
URI: https://discovery.ucl.ac.uk/id/eprint/10038903
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