Somatic mutation landscape reveals differential variability of cell-of-origin for primary liver cancer

Kyungsik Ha, Masashi Fujita, Rosa Karlić, Sungmin Yang, Ruidong Xue, Chong Zhang, Fan Bai, Ning Zhang, Yujin Hoshida, Paz Polak, Hidewaki Nakagawa, Hong Gee Kim, Hwajin Lee

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Primary liver tissue cancer types are renowned to display a consistent increase in global disease burden and mortality, thus needing more effective diagnostics and treatments. Yet, integrative research efforts to identify cell-of-origin for these cancers by utilizing human specimen data were poorly established. To this end, we analyzed previously published whole-genome sequencing data for 384 tumor and progenitor tissues along with 423 publicly available normal tissue epigenomic features and single cell RNA-seq data from human livers to assess correlation patterns and extended this information to conduct in-silico prediction of the cell-of-origin for primary liver cancer subtypes. Despite mixed histological features, the cell-of-origin for mixed hepatocellular carcinoma/intrahepatic cholangiocarcinoma subtype was predominantly predicted to be hepatocytic origin. Individual sample-level predictions also revealed hepatocytes as one of the major predicted cell-of-origin for intrahepatic cholangiocarcinoma, thus implying trans-differentiation process during cancer progression. Additional analyses on the whole genome sequencing data of hepatic progenitor cells suggest these cells may not be a direct cell-of-origin for liver cancers. These results provide novel insights on the nature and potential contributors of cell-of-origins for primary liver cancers. Systems biology; Biocomputational method; Gene mutation; Genomics; Cancer research; Bioinformatics-based prediction of cell-of-origin; Primary liver cancers; Integration of epigenome, Genome and single-cell RNA-Seq data.

Original languageEnglish (US)
Article numbere03350
Issue number2
StatePublished - Feb 2020


  • Biocomputational method
  • Bioinformatics-based prediction of cell-of-origin
  • Cancer research
  • Gene mutation
  • Genome and single-cell RNA-Seq data
  • Genomics
  • Integration of epigenome
  • Primary liver cancers
  • Systems biology

ASJC Scopus subject areas

  • General


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