Abstract
Tumor Mutational Burden (TMB) is a widely used biomarker for selecting cancer patients for immune checkpoint inhibitor (ICI) therapy. However, TMB alone has limited predictive power, as it fails to account for the functional impact of mutations. We introduce AlphaTMB, a composite biomarker that integrates the quantity of mutations (TMB) with the qualitative assessment of their pathogenicity using AlphaMissense, a deep learning model that predicts the deleteriousness of missense variants. Using a pan-cancer cohort of 1,662 patients from the MSK-IMPACT study who received ICI therapy, we computed three scores per patient: TMB, Alpha (sum of AlphaMissense scores), and AlphaTMB (product of TMB and Alpha). Patients were stratified using both cancer-specific and pan-cancer quantiles. Survival outcomes were evaluated using Kaplan-Meier and multivariate Cox proportional hazards models, controlling for cancer type, age, and ICI regimen. AlphaTMB showed strong correlation with TMB (Spearman ρ = 0.866, p < 0.001), but offered improved prognostic accuracy. Patients in the bottom 80% AlphaTMB group had significantly poorer survival than those in the top 10% (HR < 2.51, p < 0.001), outperforming TMB and Alpha alone. AlphaTMB reclassified borderline cases, identifying subsets with low TMB but high deleterious mutation load, and vice versa. Gene mutation heatmaps and co-occurrence analysis confirmed that to 10% AlphaTMB-high tumors were enriched in mismatch repair and POLE mutations, reflecting a neoantigen-rich, immunotherapy-responsive phenotype. AlphaTMB improves survival prediction beyond TMB alone, better captures immunogenic tumor profiles, and reflects more accurate patient stratification. This AI derived somatic mutations pathogenicity scoring represents a step toward personalized immuno-oncology and merits further validation in prospective studies.