TMBclaw: tumor clone-aware graph learning improves immunotherapy response prediction across heterogeneous cohorts.

Immune checkpoint inhibitors (ICIs) have emerged as a cornerstone of modern oncology, necessitating the development of robust biomarkers for optimizing patient stratification and treatment selection. While tumor mutation burden (TMB) has demonstrated prognostic value, conventional quantification methods based on mutation counts fail to reflect immunogenic neoantigen presentation due to intratumoral clonal heterogeneity. Recent efforts have focused on mutation subsets derived from tumor clonality, yet the complex interactions among clones remain a significant obstacle to accurate prognosis. This challenge is further exacerbated by the inherent constraints of limited cohort sizes in clinical studies, which severely compromise model generalizability across heterogeneous cohorts. Therefore, we propose TMBclaw (Tumor Mutation Burden-based Clonal attention with Laplacian Adaptive Weighting), a graph-regularized multi-task learning framework for immunotherapy response prediction. TMBclaw establishes unified integration of group-structured cohorts while enabling cross-cohort knowledge transfer and clonal relationship exploration. For clinical validation, we utilized four cohorts of 238 patients with non-small-cell lung cancer (NSCLC), melanoma, or nasopharyngeal carcinoma treated with ICIs, along with external multicenter validation cohorts (N = 1433) of melanoma and NSCLC patients from public datasets. Comparative analyses demonstrate that TMBclaw significantly outperforms conventional methods in prognostic accuracy and risk stratification. Through systematic quantification of clonal dynamics and discriminative identification of driver clones, TMBclaw shows potential to improve understanding of tumor heterogeneity and provides interpretable insights into the immunotherapy process.