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

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.

Keywords: biomarkers, tumor; genetic markers; immunotherapy; lung neoplasms; oncology.

Figure 1

Summary of clinical and molecular features associated with response of anti-PD-(L)1 based therapy in three cohorts with non-squamous NSCLC. Individual patients are represented in each column, sorted by progression-free survival time and treatment response (CR/PR or SD/PD). PD-L1 expression is stratified as 0%, 1%–49%, or ≥50%. NsM or mutations/megabase, and GMS score from each cohort are shown in histograms. Categories of smoking status (never or ever) and clinical benefit (DCB or NCB) are characterized. The occurrences of selected genes in each case are represented in the OncoPrint. CR, complete response; DCB, durable clinical benefit; GMS, genomic mutation signature; NDB, no durable benefit; NSCLC, non-small cell lung cancer; NsM, nonsynonymous mutations; PD-(L)1, programmed cell death (ligand)1; PR, partial response; SD, stable disease; TMB, tumor mutation burden.

Figure 2

GMS in MSK training and internal validation cohort of patients with non-squamous NSCLC treated with anti-PD-(L)1 based immunotherapy. (A) Survminer R package determine the optimal cut-point to separate patients into GMS-high and GMS-low groups based on GMS score and progression-free survival in the training cohort. Kaplan-Meier estimates of (B) progression-free survival and (C) overall survival according to GMS status in MSK training cohort. Kaplan-Meier estimates of (D) progression-free survival and (E) overall survival according to GMS status in MSK internal validation cohort. GMS, genomic mutation signature; MSK, Memorial Sloan Kettering; NSCLC, non-small cell lung cancer.

Figure 3

GMS analysis in external validation cohort of patients with non-squamous NSCLC treated with anti-PD-(L)1 therapy or without the treatment of immune checkpoint inhibitors. (A) The association of GMS score with PFS time and objective response rate in external validation cohort. Vertical and horizontal dashed lines were indicated as GMS cut-point (0.565) and PFS≥6 months (durable clinical benefit), respectively. (B) Kaplan-Meier estimates of PFS by GMS in external validation cohorts treated with anti-PD-(L)1. Kaplan-Meier estimates of overall survival by GMS in (C) MSK and (D) TCGA lung adenocarcinoma cohorts without the treatment of immune checkpoint inhibitors. GMS, genomic mutation signature; MSK, Memorial Sloan Kettering; NSCLC, non-small cell lung cancer; PFS, progression-free survival; TCGA, The Cancer Genome Atlas.

Figure 4

Subgroup analysis of GMS for progression-free survival from three cohorts according to baseline clinicopathological characteristics. HR of each subgroup was obtained from univariate analysis. Pooled HRs for each subgroup was computed using fixed-effects model. The bars indicate 95% CI. GMS, genomic mutation signature; PD-(L)1, PD-L1, programmed cell death ligand-1.

Figure 5

Association of a combination of GMS and PD-L1 with response to anti-PD-(L)1 therapy. Kaplan-Meier estimates of progression-free survival classified by the status of GMS and PD-L1 in (A) MSK cohort and (B) external validation cohorts. Proportional representation of objective response rate among subgroups categorized by GMS and PD-L1 in (C) MSK cohort and (D) combined external validation cohorts. GMS, genomic mutation signature; MSK, Memorial Sloan Kettering; PD-L1, programmed cell death ligand-1.