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

Abstract

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 cellof-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-oforigin for liver cancers. These results provide novel insights on the nature and potential contributors of cell-oforigins for primary liver cancers.

COOBoostR: an extreme gradient boosting-based tool for robust tissue or cell-of-origin prediction of tumors

“…2c). In the case of liver adult stem cells, none of the samples were predicted as the expected TOO (liver tissue), which is in line with the previous result based on the random forest-based algorithm 28 .…”

“…Our TOO/COO predictive analysis based on random forest regression was performed by reflecting and modifying previous research 25,26,28 . Once the training sets of each tree were constructed, the mean squared errors were then measured from out-of-bag data to determine the importance of each variable.…”

“…The somatic mutation landscape from these data revealed various genotypic alterations ranging from driver and passenger somatic mutations for cancer initiation and progression [14][15][16][17] , mutation signatures 18 , clonal and subclonal evolutions 19 , to the findings on novel structural variations including kategis 20,21 , chromothripsis 22 , and wholegenome doubling 23,24 . Although mechanisms on how these aberrations arise have not been fully defined, a number of statistical and machine-learning approaches have shown that the somatic point mutation landscapes of multiple cancer types and precancerous lesions correlated with the chromatin mark landscape 25,26 , and tissue-of-origin (TOO) or cell-oforigin (COO) predictions for different cancer types are possible by leveraging such information [27][28][29][30][31] .…”

An Organoid Biobank of Neuroendocrine Neoplasms Enables Genotype-Phenotype Mapping

“…Given the prevalence of TP53 and RB1 mutations in GEP-NECs, these genetic aberrations are critical but appear to be insufficient for the development of NEC. Somatic mutation densities in the cancer genome are associated with chromatin marks such as regional histone modifications, DNA accessibility, and DNA replication timing (Polak et al, 2015), and we applied our previous method (Ha et al, 2020;Polak et al, 2015) to identify the likely cell-of-origin (COO) across NECs in multiple tissues and asked whether the NEC origins estimated from the distribution of somatic mutations fit with the common epithelial cells in NEC-developed tissues. The analysis assigned most G-NEC and C-NEC organoids to the tissues or cells of origin (Figures S3F and S3G; Data S2).…”

Re-Recognizing the Cellular Origin of the Primary Epithelial Tumors of the Liver

“…Recent few years, advances in new technology, such as high-density single-cell mRNA sequencing, have made it possible to demonstrate cellular origin and helps detect known oncogenic drivers of liver malignancies. [106][107][108][109] In future, an indepth understanding of the epigenetic landscapes underlining global patterns of gene expression in different kind of liver parenchymal cells will enable us to accurately depict the essential characteristics of tumors, which is expected to open a new path for tumor classification, prognosis assessment and new target discovery.…”