整合的血浆多组学分析识别与NSCLC治疗反应相关的循环预测性生物标志物和生物学通路

In non-small cell lung cancer (NSCLC), identifying circulating predictive biomarkers is critical to optimize patient selection and improve therapeutic outcomes. Plasma-based biomarkers offer a minimally invasive and serially accessible approach for monitoring treatment response; however, the low abundance of disease-relevant analytes within a background of highly abundant plasma proteins presents significant analytical challenges. Highly sensitive and reproducible multi-omics assays are therefore required to detect subtle but biologically relevant changes associated with therapy. To address this, we implemented a plasma multi-omics workflow integrating unbiased mass spectrometry (P2 DIA-MS), the NULISA inflammation panel, and targeted metabolomics using the Biocrates MxP Quant 1000 kit. This workflow was applied to longitudinal plasma 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 1,200 metabolites and lipids, enabling cross-platform integration of proteomic, cytokine, and metabolic signatures. For predictive biomarker discovery, we applied a machine learning framework to integrate proteomic, inflammatory, and metabolic data. Incorporation of multi-omics features improved the stratification of responders versus non-responders compared with single-omic models, underscoring the complementary nature of each dataset. To further investigate biological relationships among omics layers, we evaluated several data integration methods, including Multi-Omics Factor Analysis (MOFA), to identify shared sources of variation and linked biological pathways. This analysis revealed coordinated processes connecting plasma proteomic changes with inflammatory and metabolic networks. For example, immune activation signatures arising from the integration of unbiased proteomics with inflammation markers, and metabolic stress adaptation reflected in proteomic-metabolomic associations. In conclusion, this plasma multi-omics approach demonstrates the potential of integrated proteomic, inflammatory, and metabolic profiling to identify predictive circulating biomarkers and to elucidate biological mechanisms of treatment response in NSCLC. The workflow provides a scalable and clinically applicable strategy for biomarker discovery and response monitoring in oncology.