CHARACTERIZING LUNG CANCER USING A HIGH THROUGHPUT METABOLOMICS SCREEN

Importance of the Topic

Lung cancer remains one of the leading causes of cancer mortality worldwide, driven in part by late‐stage detection and limited understanding of its biological mechanisms. Detailed metabolic profiling of patient plasma can reveal dysregulated pathways and biomarker candidates, supporting earlier diagnosis, personalized treatment decisions, and improved survival rates.

Study Objectives and Overview

This study introduces MetaboQuan-R, a unified high-throughput metabolomics and proteomics platform designed to measure multiple assay types in under three minutes per plasma sample. The primary aims were:

• To develop a single-platform workflow capable of quantifying small molecules and proteins simultaneously.
• To apply this workflow to plasma from lung cancer patients and healthy controls.
• To identify statistically significant differences in acylcarnitines, bile acids, and protein abundance linked to lung cancer subtypes.

Methodology and Instrumentation

Sample Preparation and Analysis Workflow:

• Plasma samples from lung cancer patients (adenocarcinoma and squamous cell carcinoma) and matched healthy individuals.
• MetaboQuan-R platform employing rapid chromatography and high-resolution mass spectrometry.
• Parallel assays targeting acylcarnitines, bile acids, and proteins with minimal method optimization.
• Data processed using statistical tests (ANOVA/t-test, PCA) with p-value and fold-change thresholding.

Used Instrumentation:

• Waters MetaboQuan-R high-throughput LC-MS system.
• Automated sample injector configured for 3-minute cycle times.
• Data analysis software for multivariate statistics and pathway mapping.

Main Results and Discussion

Acylcarnitines:
Nine acylcarnitine species (e.g., C14:2, C8:1, C16:1) were significantly elevated in lung cancer cohorts (ANOVA/t-test, FDR p<0.01) across a linear quantification range of 4.8–625 ng/mL.

Bile Acids:
Most bile acids were downregulated in cancer patients, except deoxycholic acid (DCA) and taurochenodeoxycholic acid (TCDCA), which showed elevated levels (156–2500 ng/mL quantification range). Dysregulation aligns with known involvement of bile acid receptors (TGR5) and the JAK2/STAT3 pathway.

Proteins:
A total of 73 proteins were quantified; multivariate PCA distinguished cancer patients from controls and separated adenocarcinoma from squamous cell carcinoma. Ten proteins met significance criteria (p<0.1, fold-change >2), including acute-phase and immune-related glycoproteins.

Pathway Analysis:
Molecular functions implicated metabolic and immune system processes, connecting small-molecule changes with protein network alterations.

Practical Benefits and Applications

This unified platform offers:

• Rapid turnaround (3 min/sample) supporting large cohort screening.
• Multiplex detection of metabolites and proteins without bespoke assay development.
• High sensitivity and reproducibility for biomarker discovery and clinical research.

Future Trends and Opportunities

• Expansion of assay panels to include lipidomics and glycomics.
• Integration of machine learning for improved pattern recognition and biomarker validation.
• Translation into clinical diagnostics and monitoring of treatment response.
• Longitudinal studies to track disease progression and therapeutic outcomes.

Conclusion

MetaboQuan-R demonstrates a robust, high-throughput approach for combined metabolomic and proteomic profiling in lung cancer research. The platform’s speed, sensitivity, and multiplexing capability facilitate comprehensive biomarker discovery and have potential to accelerate early detection and personalized medicine strategies.

Reference

• Cancer Research UK. Lung cancer survival statistics. 2021.
• Liu Y, et al. Dysregulation of bile acid receptors in non-small cell lung cancer. Cancer Lett. 2018;412:194–207.
• Melone MAB, et al. The role of acylcarnitines in cancer metabolism. Cell Death Dis. 2018;9:228.