Purpose: Immune checkpoint inhibitors revolutionized the treatment of non-small cell lung cancer (NSCLC). However, only one-quarter of patients benefit from these new therapies. PD-L1 assessment and tumor mutational burden (TMB) are available tools to optimize use of checkpoint inhibitors but novel tools are needed. Exome sequencing could generate many variables but their role in identifying predictors of response is unknown.
Experimental design: We performed somatic and constitutional exome analyses for 77 patients with NSCLC treated with nivolumab. We studied: one-tumor-related characteristics: aneuploidy, CNA clonality, mutational signatures, TMB, mutations in WNT, AKT, MAPK, and DNA repair pathways, and two-immunologic characteristics: number of intratumoral TCR clones, HLA types, and number of neoantigens; and six clinical parameters.
Results: A high TMB per Mb, a high number of neoantigens, mutational signatures 1A and 1B, mutations in DNA repair pathways, and a low number of TCR clones are associated with greater PFS. Using a LASSO method, we established an exome-based model with nine exome parameters that could discriminate patients with good or poor PFS (P < 0.0001) and overall survival (P = 0.002). This model shows better ability to predict outcomes compared with a PD-L1 clinical model with or without TMB. It was externally validated on two cohorts of patients with NSCLC treated with pembrolizumab or with nivolumab and ipilimumab as well as in urothelial tumors treated with atezolizumab.
Conclusions: Altogether, these data provide a validated biomarker that predicts the efficacy of nivolumab or pembrolizumab in patients with NSCLC. Our biomarker seems to be superior to PD-L1 labeling and TMB models.
©2018 American Association for Cancer Research.