Systematic assessment and optimizing algorithm of tumor mutational burden calculation and their implications in...

Area of Science:

• Genomics and Bioinformatics
• Cancer Research
• Immunotherapy Biomarkers

Background:

• Tumor mutation burden (TMB) is a validated biomarker for predicting immune checkpoint inhibitor (ICI) treatment response across various cancers.
• However, variations in sequencing platforms, cancer types, and calculation algorithms, particularly in the East Asian population, necessitate further investigation for accurate TMB assessment and its predictive cut-off value.

Purpose of the Study:

• To assess the impact of different sequencing platforms and methods on TMB calculation.
• To analyze the influence of sequencing depth and tumor purity on TMB distribution.
• To optimize the somatic-germline-zygosity (SGZ) algorithm for TMB calculation in East Asian populations and determine the optimal TMB cut-off for ICI treatment efficacy in lung cancer.

Main Methods:

• Analysis of 4126 targeted panel sequencing or whole-exome sequencing (WES) samples and 3680 public sequencing data (targeted panel, WES, whole-genome sequencing [WGS]).
• Assessment of TMB calculation biases across platforms and evaluation of sequencing depth and tumor purity effects.
• Optimization of the SGZ algorithm for tumor-only sequencing and determination of TMB cut-off values using ROC curve and Log-rank analysis in training and test cohorts.

Main Results:

• No significant bias in TMB calculation was found across different sequencing platforms, though WGS yielded lower TMB values compared to targeted panel sequencing and WES.
• TMB distribution remained consistent with sequencing depth > 500 and tumor purity > 0.4 (NGS estimate) or 0.1-1.0 (HE staining estimate).
• The optimized SGZ algorithm showed high correlation (0.951) with paired normal-tumor sequencing. The optimal TMB cut-off for East Asian lung cancer patients treated with ICIs was identified as 7 mut/Mb, lower than the conventional 10 mut/Mb.

Conclusions:

• This study systematically analyzed factors influencing TMB calculation and optimized the SGZ algorithm for tumor-only samples in East Asian populations.
• The identified TMB cut-off of 7 mut/Mb for East Asian lung cancer patients offers a more precise biomarker for predicting immunotherapy efficacy, aiding clinical decision-making.