Pre-treatment T cell phospho-signalling identifies clinical pembrolizumab response in non-small cell lung cancer...

Immune checkpoint inhibitors (ICIs), targeting the programmed death (ligand)-1 (PD-1/PD-L1) axis, have significantly improved survival in non-small cell lung cancer (NSCLC). Identifying early-response biomarkers is crucial to optimize therapy. We applied a novel ex vivo immunopharmacological bioassay to assess pembrolizumab-dependent T cell signalling in peripheral blood mononuclear cells (PBMCs) from 64 NSCLC patients. PBMCs were stimulated with anti-CD3/CD28 with or without pembrolizumab, and phosphorylation states of PD-1-dependent T cell receptor (TCR) signalling pathways were measured by spectral flow cytometry. A composite signalling score was calculated representing the net pembrolizumab-induced phosphorylation response. Associations with survival outcomes were evaluated using univariate Cox regression. At baseline, hierarchical clustering of phosphorylation profiles identified two distinct response patterns: low and optimal modulation by pembrolizumab. Patients with optimal pembrolizumab-dependent phosphorylation exhibited higher phospho-signalling score outcomes than those with low pathway modulation (p < 0.0001), and had longer overall survival (HR = 2.83, p = 0.013). Conventional pharmacodynamic parameters, including half-maximal effective concentration (EC₅₀) for PD-1 receptor occupancy and maximum IL-2 production (E_max), were not associated with clinical outcomes. In vivo, patterns of differentiation from naive to terminal effector memory T cells, positive TCR signalling, and controlled activation early on-treatment associated with longer survival (HRs = 0.53-0.70), consistent with our ex vivo findings. We demonstrate that pre-treatment phospho-signalling, in patient T cells ex vivo treated with pembrolizumab, may be associated with clinical outcomes in NSCLC. This functional bioassay represents a potential approach for biomarker development that requires validation in independent cohorts before clinical application and may ultimately contribute to personalised treatment decisions before therapy initiation.