TMBquant: an explainable AI-powered caller advancing tumor mutation burden quantification across heterogeneous samples.

ABSTRACT: Accurate tumor mutation burden (TMB) quantification is critical for immunotherapy stratification, yet remains challenging due to variability across sequencing platforms, tumor heterogeneity, and variant calling pipelines. Here, we introduce TMBquant, an explainable AI-powered caller designed to optimize TMB estimation through dynamic feature selection, ensemble learning, and automated strategy adaptation. Built upon the H2O AutoML framework, TMBquant integrates variant features, minimizes classification errors, and enhances both accuracy and stability across diverse datasets. We benchmarked TMBquant against nine widely used variant callers, including traditional tools (e.g. Mutect2, VarScan2, Strelka2) and recent AI-based methods (DeepSomatic, Octopus), using 706 whole-exome sequencing tum

SUBMITTER: Wang S 

PROVIDER: S-EPMC12415849 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature