Immunologic constant of rejection as a predictive biomarker of immune checkpoint inhibitors efficacy in non-small cell lung cancer

Abstract

Background: Anti-PD1/PDL1 immune checkpoint inhibitors (ICI) transformed the prognosis of patients with advanced non-small cell lung cancer (NSCLC). However, the response rate remains disappointing and toxicity may be life-threatening, making urgent identification of biomarkers predictive for efficacy. Immunologic Constant of Rejection signature (ICR) is a 20-gene expression signature of cytotoxic immune response with prognostic value in some solid cancers. Our objective was to assess its predictive value for benefit from anti-PD1/PDL1 in patients with advanced NSCLC.

Methods: We retrospectively profiled 44 primary tumors derived from NSCLC patients treated with ICI as single-agent in at least the second-line metastatic setting. Transcriptomic analysis was performed using the nCounter^® analysis system and the PanCancer Immune Profiling Panel. We then pooled our data with clinico-biological data from four public gene expression data sets, leading to a total of 162 NSCLC patients treated with single-agent anti-PD1/PDL1. ICR was applied to all samples and correlation was searched between ICR classes and the Durable Clinical Benefit (DCB), defined as stable disease or objective response according to RECIST 1.1 for a minimum of 6 months after the start of ICI.

Results: The DCB rate was 29%; 22% of samples were classified as ICR1, 30% ICR2, 22% ICR3, and 26% ICR4. These classes were not associated with the clinico-pathological variables, but showed enrichment from ICR1 to ICR4 in quantitative/qualitative markers of immune response. ICR2-4 class was associated with a 5.65-fold DCB rate when compared with ICR1 class. In multivariate analysis, ICR classification remained associated with DCB, independently from PDL1 expression and other predictive immune signatures. By contrast, it was not associated with disease-free survival in 556 NSCLC TCGA patients untreated with ICI.

Conclusion: The 20-gene ICR signature was independently associated with benefit from anti-PD1/PDL1 ICI in patients with advanced NSCLC. Validation in larger retrospective and prospective series is warranted.

Keywords: Biomarkers; ICR signature; Immune checkpoints inhibitors; Immune therapy; Lung cancer; Transcriptomics.

Fig. 1

ICR classification of 162 NSCLC samples treated with anti-PD1/PDL1 ICI and correlations with immune variables. A Expression heatmap of the 20 ICR genes in 162 tumor samples. The samples (columns) are ordered from left to right according to their increasing ICR score. The 20 genes (raws) are ordered from top to bottom according to hierarchical clustering with uncentered Pearson correlation distance and centroid agglomerative method as parameters. The expression levels are color-coded according to the indicated color scale. Above the heatmap, the four ICR classes are indicated. B Correlation of ICR classes with DCB. The percentage of patients with DCB is indicated for each class. C Heatmap representation of expression scores of several immune-related variables and non-immune related variables in the four ICR classes. The mean scores are shown as median-centered according to the colored scale shown at the bottom. The p-values of comparison between the four classes (one-way ANOVA test) are shown on the right (NS not significant; * < 0.05; **, < 0.01; ***, < 0,001)

Fig. 2

Uni- and multivariate analyses for DCB. Forest plots of univariate (A) and multivariate (B) analyses for DCB after ICI. The Odds Ratios are log₁₀-transformed

Fig. 3

Disease-free survival according to the ICR classification in NSCLC untreated with ICI. Kaplan–Meier DFS curves in early-stage patients with lung adenocarcinoma and squamous cell carcinoma (TCGA dataset) according to the two ICR classes (ICR1 versus ICR2-4). The p-value indicated is for the log-rank test