The identification of circulating predictive biomarkers for treatment response in non-small cell lung cancer (NSCLC) is critical to optimize patient selection and improve therapeutic outcomes. However, while plasma-based biomarkers offer a minimally invasive and accessible approach, low abundance of disease-relevant analytes remains a major analytical and computational challenge to detect subtle but biologically relevant changes associated with therapy response.
We used plasma multi-omics, integrating targeted metabolomic profiling using the biocrates MxP Quant 1000 assay, unbiased mass spectrometry proteomics (TrueDiscovery P2 DIA-MS), and the NULISAseq inflammation panel to analyze samples from patients with NSCLC enrolled in the multicenter phase II clinical trial SAKK 17/18. Parallel profiling quantified approximately 6,000 plasma proteins, 250 inflammation-related markers, and more than 1,100 metabolites and lipids, enabling cross-platform integration of proteomic, cytokine, and metabolic signatures.
To identify predictive biomarkers of therapeutic response, we implemented and compared machine learning frameworks that integrate metabolic, proteomic, and inflammatory features. Multi-omics models outperformed single-omic approaches in stratifying responders and non responders based on samples obtained before therapy start, highlighting the complementary information captured by each dataset. Feature-level integration further enabled the identification of coordinated molecular signatures associated with treatment response. The best results were obtained with Multi-Omics Factor Analysis (MOFA)-based biomarker identification.
Overall, this study demonstrates the value of integrated plasma multi-omics data and computational modeling for predictive biomarker discovery in NSCLC. The presented workflow is scalable and clinically applicable, providing a foundation for response monitoring and systems-level characterization of treatment effects in oncology.

Co-authors: Laura Heeb, Polina Shichkova, Gordian Adam, Anurag Gupta, Yuehan Feng, Alessandra Curioni-Fontecedro