Decoding tumor mutation burden: A predictor of immunotherapy efficacy across diverse cancer types

Abstract

Tumor Mutation Burden (TMB) has become one of the most popular predictive biomarkers to determine the application of immune checkpoint inhibitors (ICIs) in cancer treatment, on the grounds that a higher somatic mutation burden has a higher neoantigen load and, as such, is more easily recognized by the immune system. TMB has become a solid part of tumor-agnostic clinical decision-making, with the tumor-agnostic approval of pembrolizumab on solid tumors with TMB of ten or more mutations per megabase by the United States Food and Drug Administration, but its predictive capabilities across cancer types, sequencing systems, and pipelines can vary significantly. This study discusses TMB's biological nature, estimation methods, and clinical results in diverse cancers using peer-reviewed literature, breakthrough clinical studies, and regulatory data. The results reveal that TMB is a context-specific but significant biomarker. Its predictive capacity in classifications with mutational load as the leading driver of immunogenicity such as melanoma, non-small cell lung cancer, and microsatellite instability-high tumors, but is diluted in histologies with low mutational loads or in tumors that contain immunosuppressive microenvironment, intra-tumoral heterogeneity, and other immune-evasion strategies. The lack of a universally validated cut-off, variability based on platforms and inequities in access to genomic testing are the main obstacles to wider adoption. TMB is always better predicted with complementary biomarkers including PD-L1, microsatellite instability, and tumor-infiltrating lymphocyte signals. It concludes that TMB is part of a multi-biomarker system to select immunotherapy patients and that future developments will rely on harmonized measurement standards, histology-specific thresholds, prospective real-world validation, artificial intelligence, and liquid-biopsy systems.