Precision medicine research on chemo-immunotherapy combination treatment for locally advanced or metastatic non-small cell lung cancer based on deep plasma proteomics.

Abstract

2552 Background: For locally advanced or metastatic non-small cell lung cancer (NSCLC) patients lacking specific genetic mutations, chemotherapy combined with anti-programmed death-1/programmed death-ligand 1 immunotherapy has become standard first-line treatment with enhanced therapeutic efficacy and prolonged survival. However, 40-50% of patients do not benefit from chemo-immunotherapy and develop resistance. Currently, there is a lack of predictive biomarkers for the efficacy of combined therapy in NSCLC, and research on the regulatory mechanisms and drug targets is insufficient, either. We leveraged an advanced proteomics platform to profile serum in NSCLC patients, aiming to identify chemo-immunotherapy biomarkers and uncover resistance mechanisms. Methods: This study collected pre-treatment plasma samples from 103 patients with locally advanced or advanced NSCLC receiving chemo-immunotherapy. These samples were analyzed using a deep proteomics platform that integrates antibody arrays and mass spectrometry. Patients were classified into "responders" (R, complete/partial response or stable disease > 6 months) and "non-responders" (NR, progressive disease or stable disease ≤6 months) based on treatment efficacy. Differentially expressed serum proteins were identified between the groups, and weighted gene co-expression network analysis (WGCNA) was applied. Cox survival analysis was conducted on prognosis-related modules, leading to the identification of key proteins associated with treatment efficacy and survival. Results: Through our high throughput blood proteomics platform, a total of 1,397 proteins were detected. The median progression-free survival was 9 months, and the median overall survival was 32 months. A total of 175 differentially expressed proteins were identified between the R and NR groups. WGCNA identified 12 distinct modules, with ME4 associated with poor prognosis, enriched in inflammation, gene activation, and apoptosis suppression pathways, while ME8 correlated with favorable prognosis and ERK1/ERK2 cascade regulation. In the NR group, upregulated proteins associated with poor prognosis included erythropoietin receptor (HR: 1.41, p < 0.01), fibrinogen gamma chain (HR: 1.90, p: 0.03), Fc alpha receptor (HR: 2.63, p < 0.01), and prion protein (HR: 1.30, p: 0.04). In contrast, upregulated proteins in the R group linked to favorable prognosis were insulin-like growth factor-binding protein 2 (HR: 0.77, p: 0.02), keratin 19 (HR: 0.61, p: 0.02), and retinol-binding protein 4 (HR: 0.74, p: 0.03). Conclusions: Through in-depth proteomics analysis, this study systematically characterized the plasma proteomic landscape of patients undergoing chemo-immunotherapy, identifying potential novel biomarkers, and providing new insights to optimize clinical decision-making.