Development and validation of a genomic mutation signature to predict response to PD-1 inhibitors in non-squamous NSCLC: a multicohort study

• DOI: 10.1136/jitc-2019-000381
• Publication Date: 2020-06-30T19:52:30Z
• Authors: Xue Bai; De-Hua Wu; Si-Cong Ma; Jian Wang; Xin-Ran Tang; Shuai Kang; Qiang John Fu; Chuan-Hui Cao; He-San Luo; Yu-Han Chen; Hong-Bo Zhu; Hong-Hong Yan; Yi-Long Wu; Zhong-Yi Dong
• Keywords: Male; 0301 basic medicine; 0303 health sciences; Lung Neoplasms; Neoplasms. Tumors. Oncology. Including cancer and carcinogens; Genomics; Progression-Free Survival; 3. Good health; Cohort Studies; 03 medical and health sciences; Carcinoma, Non-Small-Cell Lung; Immunotherapy Biomarkers; Mutation; Humans; Female; Immunotherapy; Immune Checkpoint Inhibitors; RC254-282

BackgroundGenetic variations of some driver genes in non-small cell lung cancer (NSCLC) had shown potential impact on immune microenvironment and associated with response or resistance to programmed cell death protein 1 (PD-1) blockade immunotherapy. We therefore undertook an exploratory analysis to develop a genomic mutation signature (GMS) and predict the response to anti-PD-(L)1 therapy.MethodsIn this multicohort analysis, 316 patients with non-squamous NSCLC treated with anti-PD-(L)1 from three independent cohorts were included in our study. Tumor samples from the patients were molecularly profiled by MSK-IMPACT or whole exome sequencing. We developed a risk model named GMS based on the MSK training cohort (n=123). The predictive model was first validated in the separate internal MSK cohort (n=82) and then validated in an external cohort containing 111 patients from previously published clinical trials.ResultsA GMS risk model consisting of eight genes (TP53,KRAS,STK11,EGFR,PTPRD,KMT2C,SMAD4, andHGF) was generated to classify patients into high and low GMS groups in the training cohort. Patients with high GMS in the training cohort had longer progression-free survival (hazard ratio (HR) 0.41, 0.28–0.61, p<0.0001) and overall survival (HR 0.53, 0.32–0.89, p=0.0275) compared with low GMS. We noted equivalent findings in the internal validation cohort and in the external validation cohort. The GMS was demonstrated as an independent predictive factor for anti-PD-(L)1 therapy comparing with tumor mutational burden. Meanwhile, GMS showed undifferentiated predictive value in patients with different clinicopathological features. Notably, both GMS and PD-L1 were independent predictors and demonstrated poorly correlated; inclusion of PD-L1 with GMS further improved the predictive capacity for PD-1 blockade immunotherapy.ConclusionsOur study highlights the potential predictive value of GMS for immunotherapeutic benefit in non-squamous NSCLC. Besides, the combination of GMS and PD-L1 may serve as an optimal partner in guiding treatment decisions for anti-PD-(L)1 based therapy.