1229 Pre-treatment plasma proteomics-based predictive biomarkers for immune related adverse events in non-small cell lung cancer

BackgroundImmune-related adverse events (irAEs) resulting from immune checkpoint inhibitors (ICIs) can substantially affect patient quality of life and treatment trajectory. Currently, there are no reliable pre-treatment biomarkers for predicting the development of irAEs; hence, there is a clinical need for irAE predictive biomarkers.MethodsPlasma samples were obtained at baseline from 426 non-small cell lung cancer (NSCLC) patients treated with ICIs as part of an ongoing multi-center clinical trial (NCT04056247; approved by local IRB committees from each site) with irAE-related information. Proteomic profiling of plasma samples was performed using the SomaScan® assay (SomaLogic Inc.), enabling deep coverage of approximately 7000 proteins in each sample. A machine learning-based model was developed to predict significant irAEs arising up to 3 months from treatment initiation; significant irAEs were defined as irAEs with CTCAE grade ≥3 or irAEs that induced treatment discontinuation. Using the model, we identified a set of plasma proteins, termed Toxicity Associated Proteins (TAPs), that serve as indicators of irAEs depending on their plasma level in the individual patient. Bioinformatic analysis was performed to decipher the biology underlying immune-related toxicity implied by the TAPs.ResultsOverall, 60 patients experienced significant irAEs at early onset; 197 patients had low grade irAEs, irAEs at late onset or AEs that are not immune-related; and 169 patients did not display any adverse event. A computational model was generated to predict significant irAEs, showing a strong correlation between the predicted probability of significant irAEs and the observed rate of such events (R2= 0.92; p-value