A novel tumor mutational burden estimation model as a predictive and prognostic biomarker in NSCLC patients.

ABSTRACT:

Background

Tumor mutational burden (TMB) has both prognostic value in resected non-small cell lung cancer (NSCLC) patients and predictive value for immunotherapy response. However, TMB evaluation by whole-exome sequencing (WES) is expensive and time-consuming, hampering its application in clinical practice. In our study, we aimed to construct a mutational burden estimation model, with a small set of genes, that could precisely estimate WES-TMB and, at the same time, has prognostic and predictive value for NSCLC patients.

Methods

TMB estimation model was trained based on genomic data from 1056 NSCLC samples from The Cancer Genome Atlas (TCGA). Validation was performed using three independent cohorts, including Rizvi cohort and our own Asian cohorts, including 89 early-stage a

SUBMITTER: Tian Y 

PROVIDER: S-EPMC7448445 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature