NSCLC Serum Lipidomics and Proteomics for Nivolumab Response Prediction

Hiroaki Hase published integrated lipidomic and proteomic data from pre-treatment serum samples of non-small cell lung cancer patients treated with nivolumab. The dataset includes phospholipid profiles and proteomic analyses of whole serum and extracellular vesicles, used to identify predictive biomarkers like lysophosphatidylcholine (LPC). It was last updated on April 14, 2026.

Use Cases

• Train classification models to predict nivolumab response based on pre-treatment serum phospholipid profiles.
• Perform multi-omics integration analysis using Weighted Gene Co-expression Network Analysis (WGCNA) and Multi-Omics Factor Analysis (MOFA) methods described.
• Identify biomarker signatures, such as LPC(20:0) levels, associated with neutrophil and platelet activation pathways.
• Validate proteomic findings related to complement components and acute-phase protein expression in non-responders.

Strengths

• Integrated multi-omics data covering serum lipidomics (phospholipids) and proteomics (whole serum and extracellular vesicles).
• Includes reported predictive performance metrics for key biomarkers (e.g., LPC(20:0) with AUC = 0.782).
• Dataset is openly available under a CC-BY-4.0 license.

Limitations

• Row count and column-level documentation are unknown, limiting suitability assessment.
• The dataset is small (1.5 MB), which may constrain the complexity of models that can be trained.
• Data may reflect bias inherent to the specific patient cohort and study design described.

Provenance

Source

Hiroaki Hase via figshare.

Collection Method

Integrated lipidomic and proteomic analysis of pre-treatment serum samples from NSCLC patients receiving nivolumab.

Freshness

Last updated 2026-04-14 04:21:45.