Tumor Mutational Burden as a Predictive Biomarker in Solid Tumors.

Area of Science:

• Oncology
• Genomics
• Immunotherapy

Background:

• Tumor mutational burden (TMB) quantifies somatic mutations per megabase.
• Panel sequencing is now standard for TMB assessment in clinical settings.
• TMB shows potential in predicting immune checkpoint inhibitor (ICI) efficacy.

Purpose of the Study:

• To address the need for prospective validation of TMB cutoffs across tumor types.
• To emphasize the importance of standardizing TMB measurement across platforms.
• To highlight TMB's role in improving predictive accuracy for immunotherapy outcomes.

Main Methods:

• Retrospective analysis of TMB data from clinical trials (e.g., KEYNOTE-158).
• Evaluation of panel sequencing versus whole-exome sequencing for TMB estimation.
• Focus on TMB as a predictive biomarker for ICI therapy.

Main Results:

• TMB levels vary significantly among different cancer types.
• FDA approval of pembrolizumab for TMB-high tumors (KEYNOTE-158) demonstrates clinical utility.
• Retrospective data support TMB as a predictor of ICI response.

Conclusions:

• Harmonization and standardization of TMB estimation and reporting are essential for clinical implementation.
• Validated TMB cutoffs are needed for different tumor types and patient outcomes.
• TMB has the potential to broaden patient selection for ICI therapy.