Development and validation of a genomic mutation signature to predict response to PD-1 inhibitors in non-squamous NSCLC: a multicohort study. Issue 1 (30th June 2020)

• Record Type: Journal Article
• Authors: Bai, Xue; Wu, De-Hua; Ma, Si-Cong; Wang, Jian; Tang, Xin-Ran; Kang, Shuai; Fu, Qiang John; Cao, Chuan-Hui; Luo, He-San; Chen, Yu-Han; Zhu, Hong-Bo; Yan, Hong-Hong; Wu, Yi-Long; Dong, Zhong-Yi
• Is Part Of: Journal for immunotherapy of cancer. Volume 8:Issue 1(2020)
• Publication Date: 2020-06-30
• Subjects: oncology -- biomarkers, tumor -- genetic markers -- immunotherapy -- lung neoplasms; Cancer -- Immunotherapy -- Periodicals; Cancer -- Immunological aspects -- Periodicals; Tumors -- Immunological aspects -- Periodicals; Immunotherapy -- Periodicals; 616.99406105
• DOI: 10.1136/jitc-2019-000381
• Languages: English
• ISSNs: 2051-1426

Abstract

Abstract : Background: Genetic 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. Methods: In 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. Results: A GMS risk model consisting of eight genes ( TP53, KRAS, STK11, EGFR, PTPRD, KMT2C, SMAD4, and HGF ) 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. Conclusions: Our 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.