Driver mutation impact in Chinese hepatocellular carcinoma.

Authors

Ruiheng Wu, Houmin Xing, Jeffrey P Townsend

Published in

PloS one. Volume 21. Issue 8. Pages e0355717. Epub Aug 24, 2026.

Abstract

Hepatocellular carcinoma (HCC) remains a leading cause of cancer mortality in China. The Chinese Liver Cancer Atlas (CLCA) recently reported whole-genome sequencing of 494 HCC tumors, nominating 23 coding and 31 non-coding driver candidates based on combined P values across multiple algorithms. However, the statistical significance of recurrence alone does not distinguish functionally impactful drivers from neutral passengers. We therefore analyzed cancer effects in the CLCA dataset, quantifying the selective advantage conferred by each mutation. Whole-genome mutation calls from CLCA were analyzed. Gene- and trinucleotide context-specific neutral mutation rates were estimated for all sites. Scaled selection coefficients for every recurrent variant were quantified. Pairwise selective epistasis between genes was quantified and tested. Associations between selected driver mutations and clinical outcomes were examined in the TCGA-LIHC cohort, which included censoring information. We identified canonical drivers CTNNB1, TP53, and the TERT-promoter hotspot as subject to intense positive selection in the CLCA cohort. We also prioritized several lower-prevalence candidate genes and variants with high inferred cancer effect sizes, including P4HA1, STAT3, and IL6ST. These lower-prevalence events should be interpreted as computationally prioritized candidates requiring variant-level functional validation rather than as established novel HCC drivers. Conversely, some genes previously nominated as driver candidates exhibited cancer effects that were indistinguishable from neutrality. These findings show that not all recurrently mutated genes exert equivalent inferred selective effects. Such evolutionary prioritization complements recurrence based driver discovery by ranking somatic variants according to inferred selective effect, while highlighting the need for larger cohorts and functional studies to validate rare high-effect candidates.

PMID:
42636226
Bibliographic data and abstract were imported from PubMed on 25 Aug 2026.

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