Literature DB >> 33307194

A Gene Mutation Signature Predicting Immunotherapy Benefits in Patients With NSCLC.

Dong Pan1, Angela Y Hu2, Scott J Antonia3, Chuan-Yuan Li4.   

Abstract

INTRODUCTION: Identification of patients who can benefit from immune checkpoint blockade (ICB) therapy is key for improved clinical outcome. Recently, U.S. Food and Drug Administration approved tumor mutational burden (TMB) high (TMB-H or TMB ≥ 10) as a biomarker for pembrolizumab treatment of solid tumors. We intend to test the hypothesis that mutations in select genes may be a better predictor of NSCLC response to ICB therapy than TMB-H.
METHODS: We compiled a list of candidate genes that may predict for benefits from ICB treatment by use of data from a recently published cohort of 350 patients with NSCLC. We then evaluated the influences of different mutation signatures in the candidate genes on ICB efficacy. They were also compared with TMB-H. The predictive powers of different mutation signatures were then evaluated in an independent cohort of patients with NSCLC treated with ICB.
RESULTS: A compound mutation signature, in which two or more of the 52 candidate genes were mutated, accounted for 145 of 350 patients with NSCLC and was associated with considerable ICB treatment benefits. Specifically, the median duration of overall survival was 36 versus 8 months in NSCLC in those with two or more versus none of the 52 genes mutated. Moreover, those patients with the compound mutation signature but had low TMB (<10) achieved significant overall survival benefits when compared with those without the signature but had TMB-H (≥10). Finally, in an independent cohort of 156 patients with ICB-treated NSCLC, the median duration of progression-free survival was 8.3 months versus 3.5 months in those with the compound mutation signature versus those with none mutated in the 52 genes.
CONCLUSIONS: A genetic signature with mutations in at least two of 52 candidate genes was superior than TMB-H in predicting clinical benefits for ICB therapy in patients with NSCLC.
Copyright © 2020 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Gene mutation signature; Immunotherapy; NSCLC; Predictive biomarker; Tumor mutational load

Mesh:

Substances:

Year:  2020        PMID: 33307194      PMCID: PMC7920921          DOI: 10.1016/j.jtho.2020.11.021

Source DB:  PubMed          Journal:  J Thorac Oncol        ISSN: 1556-0864            Impact factor:   15.609


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