Biological Interpretation of Breast Cancer Using Rapid Multi-omic Profiling Methods

Significance of the Topic

In breast cancer research high throughput multiomic profiling of plasma enables deeper biological insights while supporting large cohort studies and clinical translation. Combining proteomics and lipidomics with data independent acquisition strategies enhances confidence in molecular identification and accelerates analysis workflows.

Objectives and Study Overview

This application note illustrates a rapid multiomic workflow applied to plasma samples from breast cancer patients and healthy controls. The goals are to demonstrate DIA based lipidomic and proteomic analyses using SYNAPT XS mass spectrometry and to integrate results for biological pathway interpretation.

Methodology and Instrumentation

Sample preparation included protein precipitation for lipidomics and surfactant aided digestion for proteomics. Lipid extracts were analyzed using ACQUITY UPLC I Class PLUS with a BEH C8 column in HDMSE mode. Proteomic samples were separated on an ACQUITY UPLC CSH column and acquired with SONAR. Data processing was performed using Progenesis QI for lipidomics and Progenesis QI for Proteomics followed by statistical analysis in EZinfo and MetaboAnalyst. Pathway integration used Metacore Cortellis solution.

Results and Discussion

• Lipidomic profiling revealed clear separation between cancer and control groups in PCA models with elevated phospholipids and triglycerides driving differences.
• Key lipid markers included increased species of PS and TG along with altered neutral lipids as determined by multivariate statistics and anova metrics.
• Proteomic analysis quantified over 170 proteins across more than three orders of magnitude. PCA and heatmap visualizations highlighted under expression of ApoA2 and over expression of LRG1 in breast cancer samples.
• Integrated pathway analysis identified inflammation IL6 signalling among top enriched pathways. Network mapping showed key interactions mediated by STAT3 linking the observed proteomic and lipidomic changes.

Benefits and Practical Applications

• High throughput DIA methods enable rapid screening of large cohorts with consistent data quality.
• Multiomic integration supports comprehensive molecular characterization and biomarker discovery.
• Streamlined workflows from sample preparation to pathway analysis facilitate translational research in oncology.

Future Trends and Applications

Advances in mass spectrometry throughput and data processing algorithms will further accelerate multiomic studies. Integration with artificial intelligence and cloud based analytics is expected to improve biomarker validation and personalized medicine. Expansion of DIA strategies to additional omics layers such as glycomics and metabolomics will enrich disease understanding.

Conclusion

The presented multiomic workflow demonstrates the feasibility of rapid, high confidence lipidomic and proteomic profiling of plasma in breast cancer research. Integration of DIA data with pathway analysis offers valuable biological insights and supports scalable studies for biomarker development.

Instrumentation Used

• SYNAPT XS High Resolution Mass Spectrometer
• ACQUITY UPLC I Class PLUS System
• BEH C8 and CSH Columns
• RapiGest SF Surfactant
• Progenesis QI and Progenesis QI for Proteomics Software
• MassLynx MS Software

References

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