Abstract 5314: A peripheral blood-based glycoproteomic predictor of checkpoint inhibitor treatment benefit in advanced non-small cell lung cancer

Abstract

Abstract Background: Protein glycosylation is the most common and complex form of post-translational protein modification. Glycosylation profoundly affects protein structure, conformation, and function. The elucidation of the potential role of differential protein glycosylation as biomarkers has been limited by the technical complexity of generating and interpreting this information. We have recently established a novel, powerful platform that combines liquid chromatography-mass spectrometry with a proprietary artificial-intelligence-based data processing engine that allows, for the first time, highly scalable interrogation of the glycoproteome. Here we report the performance of this platform to predict likely benefit from immune-checkpoint inhibitor (ICI) therapy in advanced non-small cell lung cancer (NSCLC). Methods: Our platform was utilized to assess 532 glycopeptide (GP) and peptide signatures representing 75 serum proteins in pretreatment blood samples from a cohort of 123 individuals (54 females, 69 males, age range 30 to 88 years). Inclusion criteria were a diagnosis of unresectable stage 3 or 4 NSCLC, treatment with pembrolizumab monotherapy (26 patients), or treatment with combination pembrolizumab-chemotherapy (97 patients). Overall survival (OS) data were available for all patients. Results: An ensemble multivariable-model-based glycoproteomic classifier consisting of 7 GP and non-glycosylated peptide biomarker features selected from a generalized additive model for OS was developed using ≈2/3rds of the full cohort (n=88) and validated in the remainder of patients (n=35). The classifier yielded similar statistical significance in Cox regression analysis for separating patients who are likely to benefit from ICI therapy from those who are not, to accurately predict likely ICI benefit with a sensitivity of >95% while performing at a specificity of 33% to predict those who are unlikely to benefit. Results were further analyzed in patients with either non-squamous or squamous NSCLC with first-line therapy (n=98). The classifier yielded a hazard ratio (HR) for prediction of likely ICI benefit of 3.6 with median OS of 13.9 vs. 4.2 months, and of 3.5 with median OS of 13.5 vs. 4.5 months in the entire cohort and the first-line treated patients, respectively. Conclusions: The glycoproteomic classifier described here predicts with high sensitivity which patients are likely to benefit from ICI therapy. In addition to potentially reducing the use of ICIs in a safe manner in patients who would be unnecessarily subjected to possible adverse drug reactions, our classifier simultaneously has the potential of reducing the burden of health care expenditures. Our results indicate that glycoproteomics holds a strong promise as a predictor for ICI treatment benefit which appears to significantly outperform other currently pursued biomarker approaches. Citation Format: Klaus Lindpaintner, Chad Pickering, Alan Mitchell, Gege Xu, Xin Cong, Daniel Serie. A peripheral blood-based glycoproteomic predictor of checkpoint inhibitor treatment benefit in advanced non-small cell lung cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5314.