Elevated PD-L1 Expression on Circulating Classical Monocytes Upon Checkpoint Inhibitor Therapy Is Associated With
Eva Probst1, Christian Idel2, Jonas Fleckner2
1Department of Medical Clinic III, University of Luebeck, Luebeck, Germany.
Background/Aim:
Lung cancer remains one of the most prevalent cancers worldwide, with high morbidity and mortality rates. Besides established treatment options such as surgery and radio(chemo)therapy, advanced approaches such as anti-programmed death 1 (PD-1)/anti-programmed death ligand 1 (PD-L1) immune checkpoint inhibitors (ICIs) have been introduced as effective therapeutic options. In this context, PD-L1 expression on myeloid cells has been correlated with poor clinical outcomes in patients with cancer. This study aimed to investigate the influence of ICI treatment on the immunologic alterations of circulating monocyte subsets as potential bioliquid indicators for therapy response assessment in patients with lung cancer.
Materials And Methods:
Flow cytometry was employed to analyze the distribution of circulating CD14/CD16 monocyte subsets and the expression of adhesion molecules CD11a (integrin-α L; LFA-1), CD11b (integrin-α M; Mac-1), CD11c (integrin-α X), CX3CR1 (CX3CL1 receptor) and the checkpoint molecule PD-L1 in 22 patients with lung cancer. Furthermore, plasma concentrations of ICI-associated cytokine interleukin (IL-5) were quantified using ELISA the course of therapy.
Results:
The study revealed a significant increase in intermediate monocyte abundance, coupled with altered adhesion molecule expression and elevated PD-L1 levels in patients with lung cancer. Moreover, non-responders exhibited significantly increased plasma IL-5 levels after one month of ICI therapy. A significant positive correlation was also observed between plasma IL-5 concentrations and PD-L1 expression on peripheral blood classical monocytes.
Conclusion:
The identification of liquid biomarkers, such as peripheral blood monocyte subsets and plasma IL-5 levels, could serve as valuable indicators for ICI therapy decision-making and response prediction in lung cancer patients.
