School of Computer Science and Technology, Faculty of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Shaanxi Engineering Research Center of Medical and Health Big Data, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Front Immunol. 2023 May 25;14:1151224. doi: 10.3389/fimmu.2023.1151224. eCollection 2023.
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.
肿瘤突变负荷(TMB)是一种广泛认可的预测免疫治疗疗效的生物标志物。然而,其应用仍然存在很大的争议。在本研究中,我们根据临床需求探讨了这种争议的根本原因。通过追踪 TMB 错误的来源,并分析变异调用者背后的设计理念,我们发现生物统计学规则的不完整性与临床样本多样性之间的冲突是导致 TMB 成为一个矛盾的生物标志物的关键问题。进行了一系列实验来阐明临床实践中突变检测所面临的挑战。此外,我们还讨论了克服这些冲突问题的潜在策略,以实现 TMB 在指导真实临床环境下决策中的应用。