TMBleR: a bioinformatic tool to optimize TMB estimation and predictive power

Area of Science:

• Oncology
• Bioinformatics
• Genomics

Background:

• Tumor mutational burden (TMB) is a proposed biomarker for predicting immunotherapy response in cancer patients.
• High TMB is thought to correlate with increased neoantigen load, enhancing treatment efficacy.
• Whole-exome sequencing (WES) is the gold standard for TMB assessment but is limited to research settings.

Purpose of the Study:

• To introduce TMBleR, a novel tool designed to evaluate the clinical impact of different strategies for measuring TMB using targeted gene panels.
• To address the lack of standardization in clinical TMB estimation methods.

Main Methods:

• Development of TMBleR, an R package and Docker container for TMB measurement.
• Provision of a user-friendly graphical-user interface (GUI) website for accessibility.

Main Results:

• TMBleR enables the measurement and comparison of various panel-based TMB assessment strategies.
• The tool facilitates the evaluation of TMB's clinical utility in diverse genomic contexts.

Conclusions:

• TMBleR offers a standardized approach to TMB measurement, crucial for clinical decision-making in cancer immunotherapy.
• The tool supports the advancement of TMB as a reliable predictive biomarker in oncology.