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Cancer research DOI:10.1158/0008-5472.CAN-09-1089

Integrative transcriptome analysis reveals common molecular subclasses of human hepatocellular carcinoma.

Publication TypeJournal Article
Year of Publication2009
AuthorsHoshida, Y, Nijman, SM, Kobayashi, M, Chan, JA, Brunet, JP, Chiang, DY, Villanueva, A, Newell, P, Ikeda, K, Hashimoto, M, Watanabe, G, Gabriel, S, Friedman, SL, Kumada, H, Llovet, JM, Golub, TR
JournalCancer research
Date Published2009/09/15

Hepatocellular carcinoma (HCC) is a highly heterogeneous disease, and prior attempts to develop genomic-based classification for HCC have yielded highly divergent results, indicating difficulty in identifying unified molecular anatomy. We performed a meta-analysis of gene expression profiles in data sets from eight independent patient cohorts across the world. In addition, aiming to establish the real world applicability of a classification system, we profiled 118 formalin-fixed, paraffin-embedded tissues from an additional patient cohort. A total of 603 patients were analyzed, representing the major etiologies of HCC (hepatitis B and C) collected from Western and Eastern countries. We observed three robust HCC subclasses (termed S1, S2, and S3), each correlated with clinical parameters such as tumor size, extent of cellular differentiation, and serum alpha-fetoprotein levels. An analysis of the components of the signatures indicated that S1 reflected aberrant activation of the WNT signaling pathway, S2 was characterized by proliferation as well as MYC and AKT activation, and S3 was associated with hepatocyte differentiation. Functional studies indicated that the WNT pathway activation signature characteristic of S1 tumors was not simply the result of beta-catenin mutation but rather was the result of transforming growth factor-beta activation, thus representing a new mechanism of WNT pathway activation in HCC. These experiments establish the first consensus classification framework for HCC based on gene expression profiles and highlight the power of integrating multiple data sets to define a robust molecular taxonomy of the disease.