Of Context, Quality, and Complexity: Fine-Combing Tumor Mutational Burden in Immunotherapy-Treated Cancers

背景(考古学) 计算生物学 免疫疗法 突变 癌症 微卫星不稳定性 生物标志物 临床试验 生物 生物信息学 遗传学 微卫星 基因 古生物学 等位基因
作者
Noushin Niknafs,Mimi Najjar,Colum Dennehy,Ioannis Stouras,Valsamo Anagnostou
出处
期刊:Clinical Cancer Research [American Association for Cancer Research]
卷期号:31 (14): 2850-2863 被引量:5
标识
DOI:10.1158/1078-0432.ccr-23-0824
摘要

Tumor mutational burden (TMB) is considered a prototypic feature of tumor foreignness and has been established as a tumor-agnostic FDA-approved biomarker at a threshold of 10 mut/Mb for immune checkpoint inhibitors (ICI). Despite its clinical utility as a companion diagnostic for ICI across cancers, a high TMB does not consistently predict response due to technical and biological limitations. Tumor heterogeneity and purity, blood versus tissue sampling, variation in next-generation sequencing, and algorithmic evaluation attenuate the predictive value of TMB. In addition to technical standardization and moving beyond TMB as a numeric or binarized value, it is of paramount importance to consider the underlying biology and the differential contribution of mutation subsets to tumor foreignness and immunogenicity. The importance of consideration of mutations within the overall TMB that are unlikely to be immunoedited together with the density of immunogenic "quality" mutation-associated neoantigens introduces the concept of biological calibration of TMB that may enhance its clinical utility. Mutagenic processes such as microsatellite instability and ultra-mutation and cancer lineage-dependent co-mutation patterns also represent biological modifiers that enable the interpretation of the overall TMB in different contexts. In this perspective, we dissect TMB on a biological and technical level, followed by a critical assessment of the predictive role of TMB in capturing ICI response in the setting of clinical trials across human cancers. The standardization of technical methodologies, together with the interpretation of TMB on the basis of the tumor genomic landscape, represents key steps toward maximizing the predictive value of TMB for cancer immunotherapy.
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