Blood based kinase activity profiling to predict response to immune checkpoint inhibitors in patients with advanced stage non-small cell lung cancer: The IOpener study.
Authors: Karlijn de Joode, Harry J.M. Groen, Michel M. van den Heuvel, Jeroen S. Kloover, Femke van der Meer, Cor H. van der Leest, Ben E.E.M. van den Borne, Rik De Wijn, Daan P. Hurkmans, Dianne M.A. van den Heuvel, H. M. Pinedo, Ellen Kapiteijn, Reno Debets, Els M.E. Verdegaal, S. H. van der Burg, John P. Groten, Joachim Aerts, Ron H.J. Mathijssen
Journal: Journal of Clinical Oncology (2024)

e20576 Background: Treatment with immune checkpoint inhibitors (ICI) has become part of the standard of care for patients with advanced stage non-small cell lung cancer (NSCLC). Unfortunately, its success is limited to a subset of treated patients, while ICI treatment is associated with immune-related adverse events. The predictive value of kinase activity profiling of peripheral blood mononuclear cells (PBMCs) was previously shown in a discovery study (Hurkmans et al., JITC 2020). The aim of the current study was to perform a prospective and blinded validation of the predictive value of kinase activity profiles of PBMCs for response to ICI treatment in patients with NSCLC in a standardized setting. Preliminary results for this study were presented before (De Joode et al. 2022), here we present results for a significantly extended validation cohort with a follow-up of at least 1 year. Methods: PBMCs from patients with advanced stage NSCLC included in this observational multicenter study were collected prior to ICI treatment. Tyrosine kinase activity of PBMCs was profiled using a micro-array with 144 different peptide-substrates (Pamchip). Classification analysis was performed to discriminate between patients with or without progressive disease (RECIST v1.1) within 24 weeks after start of treatment. Only patients treated with first-line pembrolizumab ± chemotherapy were included. Results: A predictive model was first established based on kinase activity profiles, determined in a calibration cohort (N = 61) and subsequently applied to an independent validation cohort (N = 136). The predictive accuracy of the model in the validation cohort was 62% (CI 95 = 53-70%). Interestingly, the accuracy improved when the model was combined with PD-L1 expression: for patients with a PD-L1 score &lt; 50%, the accuracy for predicting progressive disease at 24 weeks was 68% (CI 95 = 57-78%). Here, significant lower progression free survival (PFS) rates were observed for patients for whom progression was predicted compared to those for whom no progression was predicted (p = 0.0003, hazard ratio = 2.6, CI 95 = 1.6-4.3).The 1-year PFS for patients with predicted progression was only 13% (CI 95 = 5-31%) which implies a NPV of 87%. PFS rates were also lower for patients with predicted progression in the subgroups with PD-L1 score &lt; 1% (p = 0.03, hazard ratio = 2.2, CI 95 = 1.1-4.3) and PD-L1 score 1-49% (p = 0.002, hazard ratio = 5.0, CI 95 = 1.8-14). The predictive kinase activity signature appears to be T-cell related, which will be further investigated to improve understanding of the underlying biology. Conclusions: In this prospective validation study the predictive value of kinase activity profiling of PBMCs for response to first-line ICI therapy of patients with advanced NSCLC was confirmed. The final aim is to apply the kinome analysis in clinical practice.