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

ABSTRACT: 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

SUBMITTER: Wang Y 

PROVIDER: S-EPMC10208409 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature