Profiling the Lipidome of Adrenal Cancer Tissues using Fast Chromatography Coupled with the Xevo™ MRT Mass Spectrometer

Importance of the topic

Adrenocortical carcinoma (ACC) is a rare but aggressive cancer with limited therapeutic options and poor prognosis. Alterations in lipid metabolism are increasingly recognized as hallmarks of cancer, offering potential biomarkers for diagnosis and targets for intervention. Comprehensive lipid profiling of ACC versus benign adrenal tissues can reveal dysregulated lipid species and metabolic pathways, deepening our understanding of tumor biology and guiding future clinical strategies.

Objectives and study overview

This study aimed to develop and demonstrate a rapid, high-resolution lipidomics workflow for adrenal tissue samples to:

• Differentiate ACC from benign adrenal nodules by identifying statistically significant lipid markers.
• Achieve confident lipid identifications using high-accuracy mass spectrometry.
• Combine qualitative identification and relative quantification in a single data acquisition.
• Integrate lipidomics data with pathway analysis tools to uncover affected metabolic networks in ACC.

Methodology and Instrumentation

Sample preparation followed a two-step solvent extraction protocol:

1. Tissue homogenization in chilled methanol:water (1:1) using a QIAGEN® TissueLyser, followed by centrifugation to separate aqueous extract.
2. Further extraction of lipid fraction with dichloromethane:methanol (3:1), drying, and reconstitution in methanol:water (1:1).
3. Removal of particulates by centrifugation and transfer to LC-MS vials.

Instrumental setup:

• Chromatography: Waters ACQUITY™ Premier UPLC System with a charged surface hybrid C18 column (1.7 µm, 2.1 × 50 mm); 6.5 min gradient from 50% to 99% organic phase at 0.4 mL/min.
• Mobile phases: A – 60:40 acetonitrile:water, 10 mM ammonium formate, 0.1% formic acid; B – 90:10 isopropanol:acetonitrile, same additives.
• Mass spectrometry: Xevo™ MRT platform using MSE data-independent acquisition (10 Hz low energy, 10 Hz high energy) in positive and negative modes; sub-1 ppm mass accuracy and >67,000 resolution.
• Data processing: Conversion to mzML, peak picking and normalization in UNIFI™ and Lipostar2; statistical analysis with MetaboAnalyst; pathway enrichment via LIPEA linked to KEGG.

Main Results and Discussion

Multivariate statistics including principal component analysis (PCA), correlation analysis, and variable importance in projection (VIP) clearly separated ACC and benign groups, with tight clustering of quality controls. Key findings:

• Lipid identification spanned major classes; ceramides and long-chain fatty acids showed the most significant upregulation in ACC.
• Representative MSE spectra demonstrated high spectral quality and consistent data points across chromatographic peaks.
• Pathway enrichment highlighted three dysregulated routes in ACC: glycerophospholipid metabolism, choline metabolism, and fatty acid biosynthesis—pathways linked to membrane remodeling, signaling, and energy homeostasis in tumors.

Benefits and Practical Applications

This workflow delivers:

• Rapid analysis (under 7 minutes per sample) without compromising resolution or identification confidence.
• Simultaneous qualitative and relative quantitative lipid profiling in a single run.
• Integration with third-party software for robust statistical and pathway interpretation.
• Potential biomarker discovery for ACC diagnostics and treatment monitoring.

Future Trends and Potential Applications

Advances and opportunities include:

• Expansion to other cancer types and tissue matrices to uncover common and unique lipid signatures.
• Development of targeted assays for panels of ACC-associated lipids to support clinical screening.
• Integration with proteomic and transcriptomic data for multi-omics modeling of adrenal cancer metabolism.
• Exploration of high-throughput microflow or chip-based LC-MS to further reduce analysis time and sample consumption.

Conclusion

The presented fast UPLC–MSE lipidomics workflow on the Xevo MRT platform effectively distinguishes ACC from benign adrenal tissues, identifying key dysregulated lipid species and pathways. High mass accuracy and rapid acquisition facilitate comprehensive profiling in a single run, while downstream informatics integration unlocks biological insights with implications for biomarker development and therapeutic targeting.

References

1. Want et al. Global metabolic profiling of animal and human tissues via UPLC-MS. Nature Protocols, 8, 17-32 (2013).
2. Pang et al. MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics. Nature Communications. doi:10.1038/s41467-024-48009-6.
3. Glunde et al. Choline metabolism in malignant transformation. Nat Rev Cancer, 11, 835-848 (2011).
4. Koundouros et al. Reprogramming of fatty acid metabolism in cancer. Br J Cancer, 122, 4-22 (2020).
5. Chen et al. Lipid pathway modulation in platinum-resistant gastric cancer. J Transl Med, 22, 249 (2024).