Tumor Mutational BurdenEdit

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Tumor Mutational Burden (TMB) is a quantitative metric used in oncology to describe the total number of somatic mutations per coding area of a tumor genome. Expressed as mutations per megabase (mut/Mb), TMB is intended to reflect the mutational landscape of a tumor and, by extension, its potential to generate neoantigens that could be recognized by the immune system. The concept has become prominent in the discussion of cancer immunotherapy, particularly with immune checkpoint inhibitors, as higher mutational loads are often associated with greater neoantigen loads and, in some contexts, improved response rates. See also Neoantigen and Immunotherapy.

Biological basis - Neoantigen generation. Tumor cells accrue somatic mutations that can alter protein sequences, producing neoantigens that may be presented by major histocompatibility complex molecules and recognized by T cells. In theory, more mutations increase the chances of generating immunogenic neoantigens, potentially making a tumor more visible to the immune system. For background on the immune recognition framework, see Neoantigen and T-cell biology. - Tumor evolution and mutational processes. TMB is influenced by the underlying mutational processes within a tumor, including exposure to carcinogens, defective DNA repair pathways, and intrinsic replication errors. For examples of mutational signatures and underlying biology, see Mutational signature and Mismatch repair deficiency. - Distinction from other biomarkers. TMB is conceptually distinct from biomarkers such as PD-L1 expression and the presence of Microsatellite instability; together, these markers can inform decisions about therapy, but each has its own limitations and context of use. See also Biomarker.

Measurement and methodology - Whole-exome sequencing (WES). WES surveys coding regions across the genome to estimate TMB, typically expressed as mut/Mb. WES provides a broad view of mutational burden but can be resource-intensive and may require substantial tumor DNA. See Whole-exome sequencing. - Targeted gene panels. Many clinical assays use targeted panels that cover a subset of genes to infer TMB, extrapolating mutations to a per-megabase rate. Examples include platforms such as FoundationOne and MSK-IMPACT. Panel-based TMB estimates can vary due to panel size, gene content, and bioinformatic pipelines. - Standardization and thresholds. Unlike a single universal standard, TMB cutoffs vary by assay, cancer type, and clinical context. Ongoing efforts aim to harmonize measurement, reporting, and interpretation across platforms. See also Clinical assay and Bioinformatics. - Pre-analytic considerations. Factors such as tumor purity, sequencing depth, and sample quality influence TMB estimates. Accurate estimation often requires careful quality control and standardized processing. See Tumor purity and Sequencing depth. - Relationship to other measures. There is ongoing discussion about how best to compare TMB across platforms and how to integrate TMB with other biomarkers to guide treatment decisions. See Biomarker integration.

Clinical relevance - Predictive value for immunotherapy. In several cancer types, higher TMB has been associated with better responses to immune checkpoint inhibitors (e.g., anti–PD-1/PD-L1 or anti–CTLA-4 therapies) and may correlate with higher objective response rates and longer progression-free survival in some settings. See also Checkpoint inhibitor and cancer-specific literature such as Non-small cell lung cancer and Melanoma. - Prognostic considerations. In some tumors, TMB has been linked to prognosis independent of therapy, but this relationship is not uniform across cancers, and prognostic significance can be context-dependent. See Prognostic biomarker for general framing. - Cancer-type differences. The strength of the association between TMB and therapeutic response varies by tumor type. For example, certain cancers with inherently high TMB due to etiologic factors (like smoking-related lung cancers) may show stronger associations in some studies, while others display weaker or inconsistent correlations. See Lung cancer and Melanoma. - MSI/MMR context. Tumors with high microsatellite instability (MSI-H) or known mismatch repair deficiency (dMMR) often exhibit elevated TMB, which can complicate interpretation since MSI status and TMB can both reflect underlying genomic instability. See Microsatellite instability and Mismatch repair deficiency.

Controversies and debates - Standardization and clinical utility. A central debate centers on whether TMB can be standardized with reliable cutoffs across diverse sequencing platforms and tumor types to guide routine clinical decisions. Critics point to assay variability, differing gene content, and analytic pipelines that yield discordant TMB estimates. See Standardization and Clinical utility. - Predictive value limits. While high TMB has shown predictive value for some patients receiving checkpoint inhibitors, it is not universally predictive. Some patients with high TMB do not respond, and some with low TMB do respond, underscoring the need for integrative biomarkers and caution against overreliance on TMB alone. See Biomarker and Clinical trial discussions. - Regulatory and reimbursement considerations. Regulatory agencies have at times debated the evidentiary standard for using TMB to select therapies, and reimbursement decisions hinge on demonstrated clinical benefit, reproducibility, and cost-effectiveness. Historical regulatory actions around tissue-agnostic TMB-directed approvals illustrate the evolving landscape. See Regulatory approval and Health economics. - Practical and ethical implications. The push to sequence tumors for TMB touches on issues of access, cost, and the potential to influence treatment choices with incomplete certainty. Proponents emphasize personalized medicine and targeted therapy opportunities, while critics warn against over-treatment or misallocation of resources in cases where benefit is uncertain. See Healthcare policy.

See also - Neoantigen - Immunotherapy - Checkpoint inhibitor - Non-small cell lung cancer - Melanoma - FoundationOne - MSK-IMPACT - Whole-exome sequencing - Microsatellite instability - Mismatch repair deficiency - Biomarker - Cancer genomics - Clinical trial