TMBocelot: an omnibus statistical control model optimizing the TMB thresholds with systematic measurement errors

作者：Xin Lai, Shaoliang Wang, Xuanping Zhang, Xiaoyan Zhu, Yuqian Liu, Zhili Chang, Xiaonan Wang, Yang Shao, Jiayin Wang, Yixuan Wang · 发表于：Frontiers in Immunology · 年份：2025 · DOI：10.3389/fimmu.2024.1514295 · 被引用次数：2 · 研究领域：Cancer Immunotherapy and Biomarkers、Cancer Genomics and Diagnostics、Lung Cancer Treatments and Mutations

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.