TMBserval: a statistical explainable learning model reveals weighted tumor mutation burden better categorizing therapeutic benefits

Frontiers in Immunology

Immunology

Yixuan Wang¹  Jian Zhao¹  Quan Wang¹  Xiaofeng Song¹  Jingjing Liu¹  Shuanying Yang²  Xiao Xiao³  Jiayin Wang⁴  Yuqian Liu⁴  Xin Lai⁴  Wenfeng Fang⁵ 

[1] Department of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China;Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China;Genomics Institute, Geneplus-Shenzhen, Shenzhen, China;School of Computer Science and Technology, Faculty of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, China;State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China;

关键词: clinical immunology;    multidimensional tumor mutation burden;    multiple instance learning;    categorical decision-making;    statistical interpretability;    model calibration;   

DOI  :  10.3389/fimmu.2023.1151755

 received in 2023-01-26, accepted in 2023-04-24,  发布年份 2023

来源: Frontiers

【 摘 要 】

A high tumor mutation burden (TMB) is known to drive the response to immune checkpoint inhibitors (ICI) and is associated with favorable prognoses. However, because it is a one-dimensional numerical representation of non-synonymous genetic alterations, TMB suffers from clinical challenges due to its equal quantification. Since not all mutations elicit the same antitumor rejection, the effect on immunity of neoantigens encoded by different types or locations of somatic mutations may vary. In addition, other typical genomic features, including complex structural variants, are not captured by the conventional TMB metric. Given the diversity of cancer subtypes and the complexity of treatment regimens, this paper proposes that tumor mutations capable of causing various degrees of immunogenicity should be calculated separately. TMB should therefore, be segmented into more exact, higher dimensional feature vectors to exhaustively measure the foreignness of tumors. We systematically reviewed patients’ multifaceted efficacy based on a refined TMB metric, investigated the association between multidimensional mutations and integrative immunotherapy outcomes, and developed a convergent categorical decision-making framework, TMBserval (Statistical Explainable machine learning with Regression-based VALidation). TMBserval integrates a multiple-instance learning concept with statistics to create a statistically interpretable model that addresses the broad interdependencies between multidimensional mutation burdens and decision endpoints. TMBserval is a pan-cancer-oriented many-to-many nonlinear regression model with discrimination and calibration power. Simulations and experimental analyses using data from 137 actual patients both demonstrated that our method could discriminate between patient groups in a high-dimensional feature space, thereby rationally expanding the beneficiary population of immunotherapy.

【 授权许可】

Unknown   
Copyright © 2023 Wang, Wang, Fang, Xiao, Wang, Zhao, Liu, Yang, Liu, Lai and Song