Immune profile analysis of peripheral blood and tumors of lung cancer patients treated with immune checkpoint inhibitors

Background: Immune checkpoint inhibitors (ICIs) have become central to lung cancer drug therapy, and establishing biomarkers that can predict effects and adverse events (AEs) is awaited. We prospectively analyzed the association between the immune-related molecular expression in peripheral blood mononuclear cells (PBMCs) and lung cancer tissues, and the effects of ICI monotherapy. Methods: Twenty-one patients with advanced non-small cell lung cancer (NSCLC) who received ICI monotherapy were included. Changes in the expression of immune-related molecules in PBMCs before and after the administration of ICI were analyzed by flow cytometry. The major histocompatibility complex (MHC) class I and programmed cell death-ligand 1 (PD-L1) expression of cancer cells, and the PD-L1, CD8 and CD103 expression of tumor infiltrating immune cells in lung cancer tissue before the administration of ICI were confirmed by immunohistochemistry (IHC).Results: Twenty-one patients were investigated, including 11 adenocarcinoma and 10 squamous cell carcinoma cases. Anti-programmed cell death protein-1 (PD-1) antibody (n=18) and anti-PD-L1 antibody (n=3) were administered. The clinical responses were graded as follows: complete response (CR) (n=1), partial response (PR) (n=7), stable disease (SD) (n=10) and progressive disease (PD) (n=3). Among immune-related molecules expressed in PBMCs, the CD103+ CD39+ CD8+ T cell change after administration closely correlated with the clinical response. In the univariate analyses of the factors associated with progression-free survival (PFS), CD103+ CD39+ CD8+ cell change after administration was identified as a significant prognostic factor, while the CD103+ CD39+ CD8+ cell change after administration and Brinkman index were independent prognostic factors in a multivariate analysis of the factors associated with PFS.Conclusions: The CD103+ CD39+ CD8+ cell change after administration may predict the efficacy of ICIs.