Targeted Multi-OMICS: Rapid Plasma Profiling of a Bladder and Lung Cancer Human Cohort

Importance of the Topic

The integration of targeted metabolomics and proteomics into a single analytical workflow enables rapid and comprehensive plasma profiling for biomarker discovery in oncology. Such multi-OMICS approaches improve sensitivity, reproducibility, and throughput when assessing metabolic and protein alterations in disease states.

Objectives and Study Overview

This study aimed to demonstrate a streamlined targeted multi-OMICS workflow (MetaboQuan-R) for rapid profiling of amino acids, acylcarnitines, and selected proteins in plasma samples from bladder and lung cancer patients. The goals were to evaluate method robustness, quantify reproducibility, and identify potential disease biomarkers.

Methodology and Instrumentation

Sample Preparation and Analysis Workflow:

• Plasma samples processed with standardized extraction protocols.
• Consecutive analysis of metabolites and proteins on a single UPLC-MS platform.
• Data processing using Skyline and TargetLynx for semi-quantitative peak integration.
• Statistical analysis performed in MetaboAnalyst with t-tests and false discovery rate (FDR) correction.

Instrumentation Used

Key instruments and software:

• Ultra-Performance Liquid Chromatography system with reversed-phase column.
• Triple quadrupole mass spectrometer for targeted MS/MS detection.
• Skyline for chromatogram review and integration.
• TargetLynx for batch data processing.
• MetaboAnalyst for statistical evaluation.

Main Results and Discussion

Reproducibility and Sensitivity:

• Representative chromatograms showed efficient separation of amino acids, acylcarnitines, and peptides using a unified mobile phase and column.
• Quality control samples exhibited median coefficient of variation of 5% for a spiked valine-d8 internal standard and overall %CV below 15% across 128 analytes.

Biomarker Differentiation:

• Pairwise comparisons (t-test, FDR < 0.01) highlighted sarcosine as significantly overexpressed in both bladder and lung cancer cohorts.
• Octanoyl carnitine (C8:1) was significantly under-expressed in bladder cancer; acylcarnitine alterations in lung cancer were less pronounced.
• Volcano plot analysis identified several protein targets showing differential expression between cancer and control groups, suggesting novel candidate biomarkers.

Benefits and Practical Applications

This targeted multi-OMICS workflow offers:

• High throughput analysis of diverse molecular classes on a single platform.
• Robust quantitative performance with low variability, suitable for clinical study designs.
• Streamlined data processing pipeline facilitating rapid biomarker identification.

Future Trends and Applications

Potential developments include expanding analyte panels to additional metabolite and protein classes, integrating lipidomics, applying machine learning for pattern recognition, and validating identified biomarkers in larger, multi-center cohorts. The modular workflow may also be adapted for other disease areas and companion diagnostics.

Conclusion

The presented single-platform targeted multi-OMICS workflow delivers rapid, reproducible plasma profiling for simultaneous quantitation of metabolites and proteins. It effectively distinguishes metabolic signatures in bladder and lung cancer cohorts, demonstrating its utility for biomarker discovery in translational research.

Reference

Sarah Lennon, Billy Joe Molloy. Targeted Multi-OMICS: Rapid Plasma Profiling of a Bladder and Lung Cancer Human Cohort. Waters Corporation, 2019.