Hepatocellular carcinoma is the third leading cause of deaths from cancer worldwide. Infection with the hepatitis B virus is one of the leading risk factors for developing hepatocellular carcinoma, particularly in East Asia1. Although surgical treatment may be effective in the early stages, the five-year overall rate of survival after developing this cancer is only 50–70%2. Here, using proteomic and phospho-proteomic profiling, we characterize 110 paired tumour and non-tumour tissues of clinical early-stage hepatocellular carcinoma related to hepatitis B virus infection. Our quantitative proteomic data highlight heterogeneity in early-stage hepatocellular carcinoma: we used this to stratify the cohort into the subtypes S-I, S-II and S-III, each of which has a different clinical outcome. S-III, which is characterized by disrupted cholesterol homeostasis, is associated with the lowest overall rate of survival and the greatest risk of a poor prognosis after first-line surgery. The knockdown of sterol O-acyltransferase 1 (SOAT1)—high expression of which is a signature specific to the S-III subtype—alters the distribution of cellular cholesterol, and effectively suppresses the proliferation and migration of hepatocellular carcinoma. Finally, on the basis of a patient-derived tumour xenograft mouse model of hepatocellular carcinoma, we found that treatment with avasimibe, an inhibitor of SOAT1, markedly reduced the size of tumours that had high levels of SOAT1 expression. The proteomic stratification of early-stage hepatocellular carcinoma presented in this study provides insight into the tumour biology of this cancer, and suggests opportunities for personalized therapies that target it.
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The data that support the findings of this study—including clinical information, and proteome, phospho-proteome and gene expression data—are available within the paper and its Supplementary Information, or from CNHPP liver data portal (http://liver.cnhpp.ncpsb.org/). The thermo.raw files of proteome and phospho-proteome datasets can be obtained from PRIDE database (www.ebi.ac.uk/pride/archive, accession numbers PXD006512 and PXD008373)63 or iProX database (www.iprox.org, accession number IPX0000937000)64. Gene expression profiles by RNA-seq can be obtained from Gene Expression Omnibus (accession number GSE124535).
The uploading and sharing of individual genetic data from this project is not permissible, according to a review by the Human Genetic Resources Administration of China on the basis of regulations documented in the Interim Measures for the Administration of Human Genetic Resources. We have summaries of the data that are as detailed as possible, and which are available to other researchers. This includes the exonic and splicing mutations, given in Supplementary Table 3. Researchers who wish to gain access to allele frequency information and other summary statistics data are required to fill in a simple application form (http://liver.cnhpp.ncpsb.org/) and send an email to F.H. for identity verification purposes, to adhere to the Chinese regulations.
The TCGA RNA-seq data are publicly available at the Genomic Data Commons Data Portal (CDC, http://www.ncbi.nlm.nih.gov/geo). Additional publically available microarray data for HCC (Fudan cohort) was analysed from NCBI GEO repository (accession number GSE14520).
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This work was supported by Chinese Human Proteome Project, and partially supported by Chinese State Key Projects for Basic Research (973 Program) (no. 2014CBA02001), National Key R&D Program of China (no. 2016YFC0902400, 2017YFC0906603, 2018YFA0507502, 2016YFF0101405, 2013ZX10002009 and 2009ZX09503-002), Program of International S&T Cooperation (no. 2014DFB30020,2014DFB30030, 2014DFB30010, 2009DFB33070 and 2010DFA31260), National Natural Science Foundation of China (no. 81770581, 81570526, 81772551, 81802364, 8153077, 81672839, 81572823, 81772578, 8153077 and 81123001), Innovation project (16CXZ027), Beijing Science and Technology Project (Z161100002616036) and Shanghai 111 Project (B14019). We acknowledge the assistance of Z. Jin and P. Zhang for the immune-related data analyses, and C. Chang for the method development and evaluation of the missing-value imputing analyses (National Center for Protein Sciences (Beijing)). We thank L. Shen, W. Xue and Z. Li (Cancer Hospital & Institute, Peking University) for their examination of tumour cellularity and immune-cell infiltration; and B. Zhen, L. Tang and Y. Wang (State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics) for scientific management.
Nature thanks Josep M. Llovet and the other anonymous reviewer(s) for their contribution to the peer review of this work.