TMBocelot: an omnibus statistical control model optimizing the TMB thresholds with systematic measurement errors
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 assessme...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1514295/full |
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| author | Xin Lai Shaoliang Wang Xuanping Zhang Xiaoyan Zhu Yuqian Liu Zhili Chang Zhili Chang Xiaonan Wang Xiaonan Wang Yang Shao Yang Shao Jiayin Wang Yixuan Wang |
| author_facet | Xin Lai Shaoliang Wang Xuanping Zhang Xiaoyan Zhu Yuqian Liu Zhili Chang Zhili Chang Xiaonan Wang Xiaonan Wang Yang Shao Yang Shao Jiayin Wang Yixuan Wang |
| author_sort | Xin Lai |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-511dd7570ff14c22bb510509d4bc692d |
| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-511dd7570ff14c22bb510509d4bc692d2025-01-20T07:20:16ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-01-011510.3389/fimmu.2024.15142951514295TMBocelot: an omnibus statistical control model optimizing the TMB thresholds with systematic measurement errorsXin Lai0Shaoliang Wang1Xuanping Zhang2Xiaoyan Zhu3Yuqian Liu4Zhili Chang5Zhili Chang6Xiaonan Wang7Xiaonan Wang8Yang Shao9Yang Shao10Jiayin Wang11Yixuan Wang12School of Computer Science and Technology, Faculty of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaSchool of Computer Science and Technology, Faculty of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaSchool of Computer Science and Technology, Faculty of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaSchool of Computer Science and Technology, Faculty of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaSchool of Computer Science and Technology, Faculty of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaSchool of Computer Science and Technology, Faculty of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaGeneseeq Research Institute, Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu, ChinaSchool of Computer Science and Technology, Faculty of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaGeneseeq Research Institute, Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu, ChinaGeneseeq Research Institute, Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu, ChinaSchool of Public Health, Nanjing Medical University, Nanjing, Jiangsu, ChinaSchool of Computer Science and Technology, Faculty of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaDepartment of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaTumor 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.https://www.frontiersin.org/articles/10.3389/fimmu.2024.1514295/fulltumor mutation burdenimmunotherapy endpointspairwise error controlpositive-threshold optimizationBayesian framework |
| spellingShingle | Xin Lai Shaoliang Wang Xuanping Zhang Xiaoyan Zhu Yuqian Liu Zhili Chang Zhili Chang Xiaonan Wang Xiaonan Wang Yang Shao Yang Shao Jiayin Wang Yixuan Wang TMBocelot: an omnibus statistical control model optimizing the TMB thresholds with systematic measurement errors Frontiers in Immunology tumor mutation burden immunotherapy endpoints pairwise error control positive-threshold optimization Bayesian framework |
| title | TMBocelot: an omnibus statistical control model optimizing the TMB thresholds with systematic measurement errors |
| title_full | TMBocelot: an omnibus statistical control model optimizing the TMB thresholds with systematic measurement errors |
| title_fullStr | TMBocelot: an omnibus statistical control model optimizing the TMB thresholds with systematic measurement errors |
| title_full_unstemmed | TMBocelot: an omnibus statistical control model optimizing the TMB thresholds with systematic measurement errors |
| title_short | TMBocelot: an omnibus statistical control model optimizing the TMB thresholds with systematic measurement errors |
| title_sort | tmbocelot an omnibus statistical control model optimizing the tmb thresholds with systematic measurement errors |
| topic | tumor mutation burden immunotherapy endpoints pairwise error control positive-threshold optimization Bayesian framework |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1514295/full |
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