Overexpression of CCL-20 and CXCL-8 genes enhances tumor escape and resistance to cemiplimab, a programmed cell death protein-1 (PD-1) inhibitor, in patients with locally advanced and metastatic cutaneous squamous cell carcinoma

Abstract

Cemiplimab has demonstrated relevant clinical activity in cutaneous squamous cell carcinoma (cSCC) but mechanisms of primary and acquired resistance to immunotherapy are still unknown. We collected clinical data from locally advanced and/or metastatic cSSC patients treated with cemiplimab in two Italian University centers. In addition, gene expression analysis by using Nanostring Technologies platform to evaluate 770 cancer- and immune-related genes on 20 tumor tissue samples (9 responders and 11 non-responders to cemiplimab) was performed. We enrolled 81 patients with a median age of 82 years. After 16.4 months of median follow-up, 12- and 24-months PFS were 53% and 42%, respectively; while 12- and 24-months OS were 71% and 61%, respectively. Treatment was well tolerated. Overall response rate (ORR) was 58%, with a disease control rate (DCR) of 77.8%. The difference between genes expressed in responder versus non-responder patient samples was substantial, particularly for genes involved in immune system regulation. Cemiplimab-resistant tumors were associated with over-expression of CCL-20 and CXCL-8. Cemiplimab confirmed efficacy and safety data in real-life cSCC patients. Overexpression of CCL-20 and CXCL-8 could represent biomarkers of lack of response to immunotherapy.

Keywords: Cemiplimab; cSCC; cutaneous squamous cell carcinoma; gene expression profiling; immunotherapy; nanostring; real-world data.

Figure 1.

Kaplan–Meier curves: progression free survival (a) and overall survival (b).

Figure 2.

Bar graph for patients who experienced an objective response. Light yellow bars are for cemiplimab beyond-progression. Grey bars are for best supportive care (BSC).

Figure 3.

Heatmap showing unsupervised hierarchical clustering of all differentially expressed genes (a) or genes of lymphoid compartment (b) in 21 tumor samples of pre-treated patients, classified as responders (R; green line) and non-responders (NR = gray line), based on clinical outcomes measured after 3 months, and in healthy donors (H; n = 3 orange line). All the heatmaps show z-scores of differentially expressed genes. Heatmaps were generated using unsupervised clustering. Orange indicates high expression; blue indicates low expression.

Figure 4.

Venn-diagram of top up-regulated (a) and down-regulated (b) genes (FDR < 0.05) of responders and non-responders samples. STRING DB analysis of up-regulated (c) and down-regulated (d) genes common to responders and non-responders patients with associated (below) METASCAPE analysis and table with gene ontology (GO) annotations.

Figure 5.

Heatmap displaying each sample’s directed global significance scores, measuring up- and down-regulated gene sets. Red denotes gene sets whose genes exhibit extensive over-expression with the covariate, blue denotes gene sets with extensive under-expression. The directed global significance score is determined as the square root of the average of the squared signed t-statistics for the genes within a gene set. These t-statistics are derived from the linear regression that forms the basis of our differential expression analysis (a). Volcano plot showing top up-regulated genes encoding for cytokines and chemokines (b), cell proliferation pathway (c), apoptosis pathway (d) in non-responders samples compared to healthy donors.