CD4 T cell surface architecture is associated with response to anti-PD-1 therapy in non-small cell lung cancer

Authors

Cinotti, R., Yang, Y., van Hoef, V., van Ooijen, H., Parling, A., Geindreau, M., Bigdeli, N., Cruz de Los Santos, M., Baldran-Groves, L., Yang, Y., Liu, L., Johansson, M., Karre, K., Fredriksson, S., de Petris, L., Lundqvist, A.

Abstract

Anti-PD-1 therapy has substantially improved outcomes for patients with advanced non-small cell lung cancer (NSCLC), but clinical responses remain highly heterogeneous and are difficult to predict. Current immune profiling approaches primarily characterize immune cell composition and protein abundance, whereas the spatial organization of proteins at the cell surface has remained largely unexplored. Here, we demonstrate for the first time that cell surface protein organization is associated with clinical response to immune checkpoint inhibition therapy. Using the Proximity Network Assay (PNA), we simultaneously quantified cell frequencies, protein abundance, and protein organization in pretreatment peripheral blood mononuclear cells from 62 patients with advanced NSCLC treated with anti-PD-1 agents. While most cell populations were unchanged between responders and non-responders, pronounced response-associated alterations were identified within the CD4 T-cell compartment. Responders exhibited elevated expression of adhesion-associated proteins, and remodeling of a CD5-containing membrane domain. Increased colocalization within this domain was associated with poor clinical outcome and reduced survival. Machine learning analysis further identified CD4 T cell features as the strongest predictors of treatment response and demonstrated that protein organization provided greater predictive power than protein abundance alone. Together, these findings identify cell surface protein organization as a previously unexplored source of biomarkers and highlight its potential to improve immune profiling and biomarker discovery in cancer immunotherapy.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 31 Oct 2026.

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