Tumor Mutational Burden Analysis in ICI Therapy

We collected and analyzed the TMB data from the MSKCC ICI therapy cohort and the CheckMate ICI therapy cohort. TMB data from the MSKCC ICI therapy cohort were generated from the Memorial Sloan Kettering Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT). TMB data from the CheckMate ICI therapy cohort were calculated as the sum of all nonsynonymous mutations in a sample according to Braun et al. (21 (link)). TMB in the 2 cohorts was further stratified by the R package “survminer” (version 0.4.8; The R Foundation for Statistical Computing) as low-TMB and high-TMB.

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Chen M., Li P., Yao H., Liu F., Fu L., Wang Y., Zhu J., Xu Q., Liang H., Zhou Y., Wang Z., Deng Q., Chen W., Cao J., Chen X, & Luo J. (2023). Development and validation of a novel defined mutation classifier based on Lasso logistic regression for predicting the overall survival of immune checkpoint inhibitor therapy in renal cell carcinoma. Translational Andrology and Urology, 12(3), 406-424.

Publication 2023

survminer

Cancer Mutations Therapy

Corresponding Organization : Jiangmen Central Hospital

Other organizations : Chinese Academy of Medical Sciences & Peking Union Medical College, Southern Medical University

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Variable analysis

independent variables

TMB (Tumor Mutational Burden) data

dependent variables

Stratification of TMB into low-TMB and high-TMB

control variables

The Memorial Sloan Kettering Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) used to generate TMB data for the MSKCC ICI therapy cohort
The method described by Braun et al. (21) used to calculate TMB data for the CheckMate ICI therapy cohort
The R package "survminer" (version 0.4.8; The R Foundation for Statistical Computing) used to stratify TMB into low-TMB and high-TMB

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