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

ABSTRACT: 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

SUBMITTER: Wang Y 

PROVIDER: S-EPMC12596255 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature