Prediction performance comparison of biomarkers for response to immune checkpoint inhibitors in advanced non-small cell lung cancer.

Abstract

The aim of the present study was to compare the predictive accuracy of PD-L1 immunohistochemistry (IHC), tissue or blood tumor mutation burden (tTMB, bTMB), gene expression profile (GEP), driver gene mutation, and combined biomarkers for immunotherapy response of advanced non-small cell lung cancer (NSCLC). In part 1, clinical trials involved with predictive biomarker exploration for immunotherapy in advanced NSCLC were included. The area under the curve (AUC) of the summary receiver operating characteristic (SROC), sensitivity, specificity, likelihood ratio and predictive value of the biomarkers were evaluated. In part 2, public datasets of immune checkpoint inhibitor (ICI)-treated NSCLC involved with biomarkers were curated (N = 871). Odds ratio (OR) of the positive versus negative biomarker group for objective response rate (ORR) was measured. In part 1, the AUC of combined biomarkers (0.75) was higher than PD-L1 (0.64), tTMB (0.64), bTMB (0.68), GEP (0.67), and driver gene mutation (0.51). Combined biomarkers also had higher specificity, positive likelihood ratio and positive predictive value than single biomarkers. In part 2, the OR of combined biomarkers of PD-L1 plus TMB (PD-L1 cutoff 1%, 0.14; cutoff 50% 0.13) was lower than that of PD-L1 (cutoff 1%, 0.33; cutoff 50% 0.24), tTMB (0.28), bTMB (0.48), EGFR mutation (0.17) and KRAS mutation (0.47), for distinguishing ORR of patients after immunotherapy. Furthermore, positive PD-L1, tTMB-high, wild-type EGFR, and positive PD-L1 plus TMB were associated with prolonged progression-free survival (PFS). Combined biomarkers have superior predictive accuracy than single biomarkers for immunotherapy response of NSCLC. Further investigation is warranted to select optimal biomarkers for various clinical settings.