Abstract 3306: Comprehensive plasma profiling in NSCLC patients undergoing IO- and chemotherapy identifies potential proteomic signatures for progression-free survival

Abstract In NSCLC, the identification of circulating predictive biomarkers is critical to improve patient selection. Circulating biomarkers have the advantage of accessibility of samples but challenges arise in detecting disease-relevant biomarkers in plasma, where low-abundance circulating analytes are often obscured by highly abundant proteins. This underlines the need for highly sensitive, reproducible assays to detect subtle changes. Therefore, developing a sensitive, reproducible method for biomarker discovery in plasma could significantly enhance clinical outcomes for patients with NSCLC. To address this need, we developed a novel workflow combining particle enrichment (P2) and quantitative mass spectrometry (DIA-MS). We deployed this method on plasma samples from a multicentric phase II clinical trial (SAKK17/18)1 in patients with NSCLC. Longitudinal plasma samples were collected at baseline (n=33) and at two post-dose time points (C1D8, n=31 and C2D1, n=31). The samples were subjected to the developed P2 workflow and unbiased mass spectrometry analysis, leading to the quantification of 9283 proteins across all 95 samples. With this depth at plasma proteome profiling, we could cover up to 12 orders of magnitude, detecting low-abundance tissue/cell proteins from leakage events as well as proteins from extracellular vesicles2. We conducted weighted gene correlation network analysis (WGCNA) incorporating plasma protein expression and clinical outcome measures. This analysis identified a cluster of key proteins significantly associated with progression-free survival (PFS), including DERL1 and SEPRB, both known to be upregulated in NSCLC, as well as MOSPD1, which has been previously reported to promote epithelial-mesenchymal transition (EMT). To enhance our understanding of the biological context surrounding these plasma proteomic findings, we also incorporated data from two orthogonal methods: 1) the characterization of circulating immune cells from blood samples taken from patients at the same time points, performed using high-dimensional spectral flow cytometry; and 2) the integration of information on cell type markers from published single-cell datasets to delineate potential sources of plasma proteomic signatures. In conclusion, this integrated proteomics approach provides a powerful platform for identifying predictive biomarkers, enabling more precise patient stratification and potentially improving clinical outcomes in NSCLC treatment. 1 Rigutto et al. WCLC 2024 abstract MA09.05 2 Lattmann et al. Cell Mol Life Sci. 2024 Citation Format: Angelica Rigutto, Sandra Schaer, Polina Shichkova, Luca Raess, Arthur Viodé, Christopher Below, Roland Bruderer, Markus Joerger, Jana Musilova, Stefanie Hayoz, Vithersan Somasundaram, Nicolas Nuñez, Nadine Fournier, Joao Lourenço, Raphael Gottardo, Burkhard Becher, Alfredo Addeo, Anurag Gupta, Yuehan Feng, Alessandra Curioni-Fontecedro. Comprehensive plasma profiling in NSCLC patients undergoing IO- and chemotherapy identifies potential proteomic signatures for progression-free survival [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3306.