What makes TMB an ambivalent biomarker for immunotherapy? A subtle mismatch between the sample-based design of variant callers and real clinical cohort

Yuqian Liu; Shenjie Wang; Yixuan Wang; Yifei Li; Xiaoyan Zhu; Xin Lai; Xuanping Zhang; Xuqi Li; Xiao Xiao; Jiayin Wang

doi:10.3389/fimmu.2023.1151224

What makes TMB an ambivalent biomarker for immunotherapy? A subtle mismatch between the sample-based design of variant callers and real clinical cohort

Front Immunol. 2023 May 25:14:1151224. doi: 10.3389/fimmu.2023.1151224. eCollection 2023.

Authors

Yuqian Liu^{1

2}, Shenjie Wang^{1

2}, Yixuan Wang³, Yifei Li¹, Xiaoyan Zhu^{1

2}, Xin Lai^{1

2}, Xuanping Zhang^{1

2}, Xuqi Li⁴, Xiao Xiao^{2

5}, Jiayin Wang^{1

2}

Affiliations

¹ School of Computer Science and Technology, Faculty of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
² Shaanxi Engineering Research Center of Medical and Health Big Data, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
³ Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
⁴ Department of General Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
⁵ Geneplus Shenzhen, Shenzhen, China.

Abstract

Tumor mutation burden (TMB) is a widely recognized biomarker for predicting the efficacy of immunotherapy. However, its use still remains highly controversial. In this study, we examine the underlying causes of this controversy based on clinical needs. By tracing the source of the TMB errors and analyzing the design philosophy behind variant callers, we identify the conflict between the incompleteness of biostatistics rules and the variety of clinical samples as the critical issue that renders TMB an ambivalent biomarker. A series of experiments were conducted to illustrate the challenges of mutation detection in clinical practice. Additionally, we also discuss potential strategies for overcoming these conflict issues to enable the application of TMB in guiding decision-making in real clinical settings.

Keywords: bioinformatics tool; categorization thresholds; clinical immunology; measurement error; sequencing data analysis; tumor mutation burden.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Affect*
Biomarkers
Humans
Immunotherapy*

Substances

Biomarkers

Grants and funding

This work was funded by the National Natural Science Foundation of China, grant numbers 72293581, 72293580, 72274152. This work was also supported by the Natural Science Basic Research Program of Shaanxi, grant number 2020JC-01. The article processing charge was funded by the Natural Science Basic Research Program of Shaanxi, grant number 2020JC-01.