Periodic Reporting for period 1 - ONCOLIPID (Oncolipidomics: Why is lipidomic dysregulation pattern in blood similar for various cancers?)
Période du rapport: 2023-08-01 au 2026-01-31
Lipids play a crucial role in human metabolism and are involved in numerous biological pathways related to pathological states, including cancer. Alterations in blood lipid profiles have been observed in cancer patients, yet the underlying biological mechanisms remain unknown. The ERC Advanced Grant project ONCOLIPID aims to elucidate the mechanisms behind lipidomic dysregulation in cancer, addressing one of the major unmet needs in oncology—early cancer diagnosis. Understanding why lipidomic alterations follow a similar pattern across different cancer types could pave the way for groundbreaking advancements in cancer detection and personalized treatment strategies.
This project seeks to overcome current limitations in lipidomics by developing novel analytical workflows that enable accurate molar quantitation of over 2,000 lipids across more than 80 classes. Using 13C stable isotope labeled internal standards, ultrahigh-resolution chromatography, mass spectrometry, and ion mobility, ONCOLIPID will construct the Cancer Lipidome Atlas (WP1). A newly developed Bayesian-based software platform for automated data processing (WP2) will facilitate statistical evaluation of lipidomic and metabolomic data. Furthermore, this project integrates multiomics approaches (WP3), correlating lipidomics with metabolomics, proteomics, and transcriptomics to uncover the molecular mechanisms driving lipid dysregulation in cancer. By examining these alterations in cell lines, animal models, human tissues, plasma samples, and extracellular vesicles, this research has the potential to identify novel cancer biomarkers and therapeutic targets.
This project seeks to overcome current limitations in lipidomics by developing novel analytical workflows that enable accurate molar quantitation of over 2,000 lipids across more than 80 classes. Using 13C stable isotope labeled internal standards, ultrahigh-resolution chromatography, mass spectrometry, and ion mobility, ONCOLIPID will construct the Cancer Lipidome Atlas (WP1). A newly developed Bayesian-based software platform for automated data processing (WP2) will facilitate statistical evaluation of lipidomic and metabolomic data. Furthermore, this project integrates multiomics approaches (WP3), correlating lipidomics with metabolomics, proteomics, and transcriptomics to uncover the molecular mechanisms driving lipid dysregulation in cancer. By examining these alterations in cell lines, animal models, human tissues, plasma samples, and extracellular vesicles, this research has the potential to identify novel cancer biomarkers and therapeutic targets.
• Analytical Workflow for Comprehensive Lipidomic Quantitation
New methodologies for targeted lipidomic quantitation using derivatization agents were established and successfully applied to clinical samples, such as benzoyl chloride derivatization and charge-switch derivatization using 3-(chlorosulfonyl)benzoic acid. Workflows for the biosynthesis of ¹³C-labeled standards of lipids, metabolites, and proteins were also developed, and a patent application is now pending. The construction of a lipidome atlas was initiated using multiple lipidomic, glycolipidomic, and metabolomic methods in healthy controls. These datasets form the basis for further comparative analyses and data implementation for cancer patients.
• Bioinformatics and Statistical Workflow
The LipidQuant software has been converted from Matlab to Python and extended with new modules, and the advanced prototype version is currently under internal testing. Its finalization is expected within the next months. A review in Nature Communications “Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics data” has been published, which includes an extensive collection of selected R and Python tools for data processing and visualization available in the associated GitHub library.
• Biological Mechanism of Lipid Dysregulation
The multi-omics experiments on two orthotopic murine PDAC models were conducted, generating comprehensive datasets including lipidomics, metabolomics, proteomics, and transcriptomics of plasma, tumor, adjacent pancreas, and liver tissues. The multi-omics data integration is underway.
New methodologies for targeted lipidomic quantitation using derivatization agents were established and successfully applied to clinical samples, such as benzoyl chloride derivatization and charge-switch derivatization using 3-(chlorosulfonyl)benzoic acid. Workflows for the biosynthesis of ¹³C-labeled standards of lipids, metabolites, and proteins were also developed, and a patent application is now pending. The construction of a lipidome atlas was initiated using multiple lipidomic, glycolipidomic, and metabolomic methods in healthy controls. These datasets form the basis for further comparative analyses and data implementation for cancer patients.
• Bioinformatics and Statistical Workflow
The LipidQuant software has been converted from Matlab to Python and extended with new modules, and the advanced prototype version is currently under internal testing. Its finalization is expected within the next months. A review in Nature Communications “Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics data” has been published, which includes an extensive collection of selected R and Python tools for data processing and visualization available in the associated GitHub library.
• Biological Mechanism of Lipid Dysregulation
The multi-omics experiments on two orthotopic murine PDAC models were conducted, generating comprehensive datasets including lipidomics, metabolomics, proteomics, and transcriptomics of plasma, tumor, adjacent pancreas, and liver tissues. The multi-omics data integration is underway.
• Comprehensive Lipidome Characterization
The data integration from ultrahigh-performance liquid chromatography (UHPLC), ultrahigh-performance supercritical fluid chromatography (UHPSFC), tandem mass spectrometry (both high-resolution and low-resolution), and ion mobility spectrometry provides an unprecedented level of confidence in lipid annotation as well as significantly expanded lipidomic coverage. The developed and validated analytical methods enable the comprehensive molar quantitation for a broad range of lipid classes at the fatty acyl/alkyl level of structural annotation, providing substantially improved molecular resolution that is essential for investigating metabolic pathways and the biological mechanisms of lipid dysregulation.
• Samples
The unique prospective collection of human plasma and tissue samples from cooperating hospitals and biobanks, covering 10 cancer types, has been completed. All samples have been transferred to our laboratory for the multi-omics analyses. The completion of detailed clinical information is currently in progress.
• Lipidomic Community Leadership and Data Harmonization
The PI played an important role in major international consortia aimed at standardizing lipidomic methods, such as International Lipidomics Society (ILS) and Lipid MAPS. This resulted in publications providing technical recommendations for oxylipin analysis, establishing concordant inter-laboratory concentrations for ceramides in human plasma reference materials, and introducing a lipidomics scoring system for data quality assessment. The PI leads the Clinical Lipidomics Interest Group (CLIG) interlaboratory study on the harmonization of lipid concentrations in human plasma, which involves the participation of 24 leading lipidomic laboratories. The data have been processed, the results have been visualized, and the publication of this study is expected next year.
The data integration from ultrahigh-performance liquid chromatography (UHPLC), ultrahigh-performance supercritical fluid chromatography (UHPSFC), tandem mass spectrometry (both high-resolution and low-resolution), and ion mobility spectrometry provides an unprecedented level of confidence in lipid annotation as well as significantly expanded lipidomic coverage. The developed and validated analytical methods enable the comprehensive molar quantitation for a broad range of lipid classes at the fatty acyl/alkyl level of structural annotation, providing substantially improved molecular resolution that is essential for investigating metabolic pathways and the biological mechanisms of lipid dysregulation.
• Samples
The unique prospective collection of human plasma and tissue samples from cooperating hospitals and biobanks, covering 10 cancer types, has been completed. All samples have been transferred to our laboratory for the multi-omics analyses. The completion of detailed clinical information is currently in progress.
• Lipidomic Community Leadership and Data Harmonization
The PI played an important role in major international consortia aimed at standardizing lipidomic methods, such as International Lipidomics Society (ILS) and Lipid MAPS. This resulted in publications providing technical recommendations for oxylipin analysis, establishing concordant inter-laboratory concentrations for ceramides in human plasma reference materials, and introducing a lipidomics scoring system for data quality assessment. The PI leads the Clinical Lipidomics Interest Group (CLIG) interlaboratory study on the harmonization of lipid concentrations in human plasma, which involves the participation of 24 leading lipidomic laboratories. The data have been processed, the results have been visualized, and the publication of this study is expected next year.