Cited by 2,490Cancer Genomics and DiagnosticsHepatocellular Carcinoma Treatment and PrognosisChromatin Remodeling and Cancer

Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma

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Ally, Adrian, Balasundaram, Miruna, Carlsen, Rebecca, Chuah, Eric, Clarke, Amanda, Dhalla, Noreen, Holt, Robert A., Jones, Steven J.M. +230 more·2017·Cell

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.

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Crossref · 10.1016/j.cell.2017.05.046

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  • Human HCCs with CTNNB1 and ARID2 mutations from two independent studies also showed up-regulation of many of the same WNT pathway genes (NOTUM, AQP9, TBX3, ABCB11, GLUL) as observed in mouse models.

    “Notably, human HCCs with CTNNB1 and ARID2 mutations from two independent studies (25, 35) also showed up-regulation of many of the same WNT pathway genes (NOTUM, AQP9, TBX3, ABCB11, GLUL) as observed in our mouse models (fig. S6B and table S4).”

    From The origin of hepatocellular carcinoma depends on metabolic zonation (2026) · cites this paper as [25]

    The Results sections on Somatic Mutations and Other Signaling Pathways (pages unavailable) report CTNNB1 and ARID2 mutations in human HCC and frequent alterations in WNT signaling. They do not report that HCCs with those mutations up-regulate NOTUM, AQP9, TBX3, ABCB11, or GLUL. Searches for the named genes and their expression did not establish that part of the claim. (LLM, conf=0.83)

    Experimental — accuracy not yet measured
  • CpGs in CG-poor regions are generally methylated, while CpGs within CGIs remain unmethylated in normal cells and aberrantly hypermethylated in cancer.

    “The CpGs in CG-poor regions are generally methylated, while CpGs within CGIs remain unmethylated in normal cells49 and aberrantly hypermethylated in cancer2,50.”

    From Alternative promoters in CpG depleted regions are prevalently associated with epigenetic misregulation of liver cancer transcriptomes (2023) · cites this paper as [2]

    The Results (Methylation Profiling; page unavailable) report cancer-specific CpG hypermethylation in hepatocellular carcinoma compared with normal tissue. They do not establish that the hypermethylated CpGs are within CpG islands or that this pattern applies to cancer generally. The earlier claims about CG-poor regions and normal cells are not attached to this citation. (LLM, conf=0.86)

    Experimental — accuracy not yet measured
  • RNA-seq analysis of the TCGA HCC dataset showed a strong negative correlation between BAAT, CYP7A1, CYP7B1, and CYP8B1 expression and CD8A, PDCD1, and IFNG expression.

    “Furthermore, RNA-seq analysis of the HCC dataset from The Cancer Genome Atlas (TCGA) Pan-Cancer Atlas (25) demonstrated a strong negative correlation between BAAT, CYP7A1, CYP7B1, and CYP8B1 expression and CD8A, PDCD1, and IFNG expression (Fig. 2M and fig. S6C),”
  • Among 14 frequently mutated or copy number-altered genes in HCC, CTNNB1 mutations were most likely to co-occur with ARID2 mutations.

    “Among 14 frequently mutated or copy number–altered genes in HCC (25), CTNNB1 mutations were most likely to co-occur with ARID2 mutations (Fig. 1A).”
  • CTNNB1 is among the most frequently mutated genes in HCC, with mutation frequencies TERT promoter 44%, TP53 31%, and CTNNB1 27%.

    “We focused on CTNNB1 because it is among the most frequently mutated genes in HCC [mutation frequency: TERT promoter (44%), TP53 (31%), CTNNB1 (27%)] (25).”

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Authors
Ally, A., Balasundaram, M., Carlsen, R., Chuah, E., Clarke, A., Dhalla, N., Holt, R. A., Jones, S. J. M., Lee, D., Ma, Y., Marra, M. A., Mayo, M., Moore, R. A., Mungall, A. J., Schein, J. E., Sipahimalani, P., Tam, A., Thiessen, N., Cheung, D., Wong, T., Brooks, D., Robertson, A. G., Bowlby, R., Mungall, K., Sadeghi, S., Xi, L., Covington, K., Shinbrot, E., Wheeler, D. A., Gibbs, R. A., Donehower, L. A., Wang, L., Bowen, J., Gastier-Foster, J. M., Gerken, M., Helsel, C., Leraas, K. M., Lichtenberg, T. M., Ramirez, N. C., Wise, L., Zmuda, E., Gabriel, S. B., Meyerson, M., Cibulskis, C., Murray, B. A., Shih, J., Beroukhim, R., Cherniack, A. D., Schumacher, S. E., Saksena, G., Pedamallu, C. S., Chin, L., Getz, G., Noble, M., Zhang, H., Heiman, D., Cho, J., Gehlenborg, N., Saksena, G., Voet, D., Lin, P., Frazer, S., Defreitas, T., Meier, S., Lawrence, M., Kim, J., Creighton, C. J., Muzny, D., Doddapaneni, H., Hu, J., Wang, M., Morton, D., Korchina, V., Han, Y., Dinh, H., Lewis, L., Bellair, M., Liu, X., Santibanez, J., Glenn, R., Lee, S., Hale, W., Parker, J. S., Wilkerson, M. D., Hayes, D. N., Reynolds, S. M., Shmulevich, I., Zhang, W., Liu, Y., Iype, L., Makhlouf, H., Torbenson, M. S., Kakar, S., Yeh, M. M., Jain, D., Kleiner, D. E., Jain, D., Dhanasekaran, R., El-Serag, H. B., Yim, S. Y., Weinstein, J. N., Mishra, L., Zhang, J., Akbani, R., Ling, S., Ju, Z., Su, X., Hegde, A. M., Mills, G. B., Lu, Y., Chen, J., Lee, J.-S., Sohn, B. H., Shim, J. J., Tong, P., Aburatani, H., Yamamoto, S., Tatsuno, K., Li, W., Xia, Z., Stransky, N., Seiser, E., Innocenti, F., Gao, J., Kundra, R., Zhang, H., Heins, Z., Ochoa, A., Sander, C., Ladanyi, M., Shen, R., Arora, A., Sanchez-Vega, F., Schultz, N., Kasaian, K., Radenbaugh, A., Bissig, K.-D., Moore, D. D., Totoki, Y., Nakamura, H., Shibata, T., Yau, C., Graim, K., Stuart, J., Haussler, D., Slagle, B. L., Ojesina, A. I., Katsonis, P., Koire, A., Lichtarge, O., Hsu, T.-K., Ferguson, M. L., Demchok, J. A., Felau, I., Sheth, M., Tarnuzzer, R., Wang, Z., Yang, L., Zenklusen, J. C., Zhang, J., Hutter, C. M., Sofia, H. J., Verhaak, R. G. W., Zheng, S., Lang, F., Chudamani, S., Liu, J., Lolla, L., Wu, Y., Naresh, R., Pihl, T., Sun, C., Wan, Y., Benz, C., Perou, A. H., Thorne, L. B., Boice, L., Huang, M., Rathmell, W. K., Noushmehr, H., Saggioro, F. P., Tirapelli, D. P. D. C., Junior, C. G. C., Mente, E. D., Silva, O. D. C., Trevisan, F. A., Kang, K. J., Ahn, K. S., Giama, N. H., Moser, C. D., Giordano, T. J., Vinco, M., Welling, T. H., Crain, D., Curley, E., Gardner, J., Mallery, D., Morris, S., Paulauskis, J., Penny, R., Shelton, C., Shelton, T., Kelley, R., Park, J.-W., Chandan, V. S., Roberts, L. R., Bathe, O. F., Hagedorn, C. H., Auman, J. T., O'Brien, D. R., Kocher, J.-P. A., Jones, C. D., Mieczkowski, P. A., Perou, C. M., Skelly, T., Tan, D., Veluvolu, U., Balu, S., Bodenheimer, T., Hoyle, A. P., Jefferys, S. R., Meng, S., Mose, L. E., Shi, Y., Simons, J. V., Soloway, M. G., Roach, J., Hoadley, K. A., Baylin, S. B., Shen, H., Hinoue, T., Bootwalla, M. S., Van Den Berg, D. J., Weisenberger, D. J., Lai, P. H., Holbrook, A., Berrios, M., & Laird, P. W.
ORCID
Venue
Cell
Publisher
Elsevier BV
Published
2017-06
Volume
169
Issue
7
Pages
1327-1341.e23
ISSN
0092-8674 (ISSN-L)
OpenAlex
W2625559053
Cited by
2,490
References
99
Citations by year
2026: 163 · 2025: 206 · 2024: 282 · 2023: 320 · 2022: 329 · 2021: 384 · 2020: 341 · 2019: 252 · 2018: 177 · 2017: 32 · 2014: 1
FWCI
74.05
Type
journal article
Language
en
Open access
Locations
  • Cell (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
  • UCL Discovery (University College London) (submittedVersion)
  • KUMeL Repository (Keimyung University) (submittedVersion, cc-by-nc-nd)
Funders
NIH (5U24CA143799, 5U24CA143835, 5U24CA143840, 5U24CA143843, 5U24CA143845, 5U24CA143848, 5U24CA143858, 5U24CA143866, 5U24CA143867, 5U24CA143882, 5U24CA143883, 5U24CA144025, U54HG003067, U54HG003079, U54HG003273, P30CA16672)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
  • Health Sciences › Medicine › Hepatology › Hepatocellular Carcinoma Treatment and Prognosis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
Keywords
hepatocellular carcinoma, genomic profiling, DNA methylation, metabolic reprogramming, molecular subtyping, prognostic factors, therapeutic target, Wnt signaling, MDM4, MET, VEGF-A, Mcl-1, IDH1, telomerase reverse transcriptase, immune checkpoint molecules, LZTR1, eEF1A1, SF3B1, SMARCA4, albumin, apolipoprotein B, CPS1
MeSH terms
Carcinoma, Hepatocellular, Humans, Isocitrate Dehydrogenase, Liver Neoplasms, Mutation, DNA Methylation, Genomics, MicroRNAs

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