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METHODS article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1514295
This article is part of the Research Topic Community Series in Novel Biomarkers for Predicting Response to Cancer Immunotherapy: Volume III View all 3 articles

TMBocelot: an Omnibus statistical Control modeL Optimizing the TMB Thresholds with systematic measurement errors

Provisionally accepted
Xin Lai Xin Lai 1Shaoliang Wang Shaoliang Wang 1Xuanping Zhang Xuanping Zhang 1Xiaoyan Zhu Xiaoyan Zhu 1Yuqian Liu Yuqian Liu 1Zhili Chang Zhili Chang 1,2Xiaonan Wang Xiaonan Wang 1,2Yang W. Shao Yang W. Shao 2,3Jiayin Wang Jiayin Wang 1*Yixuan Wang Yixuan Wang 4*
  • 1 School of Computer Science and Technology, Faculty of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an, China
  • 2 Geneseeq Technology Inc., Nanjing, Liaoning Province, China
  • 3 School of Public Health, Nanjing Medical University, Nanjing, Jiangsu Province, China
  • 4 Department of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China

The final, formatted version of the article will be published soon.

    Tumor mutation burden (TMB), defined as the number of somatic mutations of tumor DNA, is a well-recognized immunotherapy biomarker endorsed by regulatory agencies and pivotal in stratifying patients for clinical decision-making. However, measurement errors can compromise the accuracy of TMB assessments and the reliability of clinical outcomes, introducing bias into statistical inferences and adversely affecting TMB thresholds through cumulative and magnified effects. Given the unavoidable errors with current technologies, it is essential to adopt modeling methods to determine the optimal TMB-positive threshold. Therefore, we proposed a universal framework, TMBocelot, which accounts for pairwise measurement errors in clinical data to stabilize the determination of hierarchical thresholds. TMBocelot utilizes a Bayesian approach based on the stationarity principle of Markov chains to implement an enhanced error control mechanism, utilizing moderately informative priors. Simulations and retrospective data from 438 patients reveal that TMBocelot outperforms conventional methods in terms of accuracy, consistency of parameter estimations, and threshold determination. TMBocelot enables precise and reliable delineation of TMB-positive thresholds, facilitating the implementation of immunotherapy. The source code for TMBocelot is publicly available at https://github.com/YixuanWang1120/TMBocelot.

    Keywords: Tumor mutation burden, Immunotherapy endpoints, Pairwise error control, Positive-threshold optimization, Bayesian framework

    Received: 20 Oct 2024; Accepted: 24 Dec 2024.

    Copyright: © 2024 Lai, Wang, Zhang, Zhu, Liu, Chang, Wang, Shao, Wang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Jiayin Wang, School of Computer Science and Technology, Faculty of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
    Yixuan Wang, Department of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.