Dataset Information

Genomic and epigenomic architecture of hepatocellular carcinoma

ABSTRACT: Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide. Understanding of the intratumoral heterogeneity of HCC is instructive for developing personalized therapy and molecular biomarkers. We applied whole exome sequencing to a total of 63 samples from 11 patients to resolve the genetic architecture and subclonal diversification. Notably, spatial genomic diversity was found in all 11 HCC cases, with 33% of the driver mutations being heterogeneous. Temporal dissection of mutational signatures suggested that exogenous and endogenous factors have different contributions in shaping the mutational spectrum during the development of HCC. Moreover, we observed extensive intratumoral epigenetic heterogeneity in HCC, and showed that integrative analysis of both genetic and epigenetic phylogenies could generate more robust resolution of clonal architecture than single profiling approach. Our results also demonstrated prominent epigenetic subclonal diversification even in a stable HCC genome. Together, these findings highlight the widespread intratumoral heterogeneity at both genomic and epigenomic levels in HCC, and provide important molecular foundation for better understanding of the biology and pathogenesis of this malignancy.

ORGANISM(S): Homo sapiens

PROVIDER: GSE83691 | GEO | 2017/03/03

SECONDARY ACCESSION(S): PRJNA326729

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Intratumoral heterogeneity (ITH) arises from distinct subclonal expansion following genetic or epigenetic alterations, and profoundly influences how tumors response to their immune microenvironment. Tumor progression trees based on single-cell mutational profiles have made it possible to trace subclonal evolution; however, conventional tree-building methods can incorporate only a limited number of cells and mutations, restricting their application to larger single-cell data. To investigate the effect of ITH on the therapeutic response of melanoma, we have developed Trisicell (https://trisicell.rtfd.io), a computational toolkit for scalable inference of mutational ITH through assessment of single-cell genomic variant data. By applying Trisicell to genetically matched mouse melanoma datasets, we found that expressed mutations are sufficient to effectively drive subclonal evolution. On single-cell, full-length RNA sequencing data of mouse melanoma from preclinical immune checkpoint blockade (ICB) studies, the analysis showed that the subtree-seeding mutations in the trees identified distinct subclones associated with a specific developmental state and neural crest lineage markers. Using the tree to trace cell lineages, we found that neoantigens depleted by ICB were predominantly expressed in minor subclones, suggesting that post-treatment recurrence is driven by immunoediting. Moreover, these neoantigens were enriched with those derived from frameshift mutations and mutated nuclear genes. Importantly, recurrently mutated genes in ICB-responding human melanoma exhibited the same features. We next used Trisicell to analyze single-cell, full-length RNA data of brain metastases (BM) from melanoma patients treated with ICB, and discovered that relapsing BM from ICB-responding patients exhibited subclones that also expressed a higher fraction of frameshift mutations and were associated with elevated levels of infiltrated T cells. Notably, they also exhibited more mutated HLA genes and expressed high level of the novel immune checkpoint HLA-G, a putative local immunosuppressive mechanism. In summary, applying Trisicell to single-cell transcriptomic analyses allowed us to identify novel features of ICB-responding melanoma neoantigens and distinct mechanisms for adapting to immunotherapy at different sites. These results have important implications for both melanoma evolution and target identification for immunotherapies, including ICB and cancer vaccines. This is the first study to trace subclonal evolution of neoantigens at single-cell resolution, demonstrating that Trisicell and our datasets represent important resources for the field.

Dataset Information

Genomic and epigenomic architecture of hepatocellular carcinoma

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets