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: de Joode Karlijn , Groen Harry J.M. , van den Heuvel Michel M. , Kloover Jeroen S. , van der Meer Femke , van der Leest Cor H. , van den Borne Ben E.E.M. , De Wijn Rik , Hurkmans Daan P. , van den Heuvel Dianne M.A. , Pinedo H. M. , Kapiteijn Ellen , Debets Reno , Verdegaal Els M.E. , van der Burg S. H. , Groten John P. , Aerts Joachim , Mathijssen Ron H.J. 
Published: July 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% (CI95 = 53-70%). Interestingly, the accuracy improved when the model was combined with PD-L1 expression: for patients with a PD-L1 score < 50%, the accuracy for predicting progressive disease at 24 weeks was 68% (CI95 = 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, CI95 = 1.6-4.3).The 1-year PFS for patients with predicted progression was only 13% (CI95 = 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 < 1% (p = 0.03, hazard ratio = 2.2, CI95 = 1.1-4.3) and PD-L1 score 1-49% (p = 0.002, hazard ratio = 5.0, CI95 = 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.