Clonal evolution in liver cancer at single-cell and single-variant resolution

Abstract

Genetic heterogeneity of tumor is closely related to its clonal evolution, phenotypic diversity and treatment resistance, and such heterogeneity has only been characterized at single-cell sub-chromosomal scale in liver cancer. Here we reconstructed the single-variant resolution clonal evolution in human liver cancer based on single-cell mutational profiles. The results indicated that key genetic events occurred early during tumorigenesis, and an early metastasis followed by independent evolution was observed in primary liver tumor and intrahepatic metastatic portal vein tumor thrombus. By parallel single-cell RNA-Seq, the transcriptomic phenotype of HCC was found to be related with genetic heterogeneity. For the first time we reconstructed the single-cell and single-variant clonal evolution in human liver cancer, and dissection of both genetic and phenotypic heterogeneity will facilitate better understanding of their relationship.

Keywords: Clonal structure; Genetic heterogeneity; Hepatocellular carcinoma; Somatic mutation; Tumor evolution.

Fig. 1

Single-cell analysis revealed a common origin but independent evolution of primary and metastatic liver tumors. a Overview of the single-cell analysis strategy of human liver cancer. b Mutational status of SNV/INDEL sites in single cells from paratumor, primary tumor and PVTT tissues in HCC8. c Clonal evolution in HCC8 with genes mutated at each step shown. Dashed circle: virtual ancestor clone in PVTT. *COSMIC Cancer Gene Census catalogued driver genes. d Statistics of nucleotide substitution types for clone-specific point mutations newly acquired from the most recent ancestor in HCC8 as shown in (c). e Maximum parsimony tree of single cells from HCC8 based on nucleotide sequences at the target sites. Scale bar: nucleotide substitution rate

Fig. 2

Genetic and phenotypic heterogeneity are showing consistence in liver cancer. a Cell clusters in scRNA-Seq analysis. b Heatmap showing patient-specific tumor marker genes. c The expression patterns of representative markers. d Copy number changes inferred from global transcriptomic profile of single cells in HCC9, with boxes highlighting sub-populations. e Heatmap (left) and t-SNE plot (right) showing the sub-populations in HCC9 based on differentially expressed genes. f The expression patterns of genes mutated in each HCC sample in scRNA-Seq data. Genes were grouped by tissue origin in the upper panel, and clustered by expression patterns in the lower panel