Marie Breeur
A5025158551- Metabolomics and Mass Spectrometry Studies
- Gene expression and cancer classification
- Advanced Proteomics Techniques and Applications
- Cancer, Lipids, and Metabolism
- Diet and metabolism studies
- Gut microbiota and health
- Nutritional Studies and Diet
- Genetics, Bioinformatics, and Biomedical Research
- Obesity, Physical Activity, Diet
- Adipose Tissue and Metabolism
- Cardiovascular Disease and Adiposity
- Epigenetics and DNA Methylation
- Body Composition Measurement Techniques
- RNA modifications and cancer
- Liver Disease Diagnosis and Treatment
Centre International de Recherche sur le Cancer
2021-2024
Massachusetts Institute of Technology
2023
Abstract Background Amino acid metabolism is dysregulated in colorectal cancer patients; however, it not clear whether pre-diagnostic levels of amino acids are associated with subsequent risk cancer. We investigated circulating relation to the European Prospective Investigation into Cancer and Nutrition (EPIC) UK Biobank cohorts. Methods Concentrations 13-21 were determined baseline fasting plasma or serum samples 654 incident cases matched controls EPIC. following adjustment for false...
Abstract Background Epidemiological studies of associations between metabolites and cancer risk have typically focused on specific types separately. Here, we designed a multivariate pan-cancer analysis to identify potentially associated with multiple types, while also allowing the investigation type-specific associations. Methods We analysed targeted metabolomics data available for 5828 matched case-control pairs from cancer-specific breast, colorectal, endometrial, gallbladder, kidney,...
Pooling metabolomics data across studies is often desirable to increase the statistical power of analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations variability between datasets. Specifically, different may use variable sample types (e.g., serum versus plasma) collected, treated, stored according protocols, assayed laboratories using instruments. To address these issues, a new...
Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput LC-MS poses major challenge for biomarker discovery, annotation, experimental comparison, necessitating merging multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability variations hyperparameter...
Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput LC-MS poses major challenge for biomarker discovery, annotation, experimental comparison, necessitating merging multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability variations hyperparameter...
Abstract Background Metabolomics studies in cancer epidemiology have mostly focused on single metabolite-cancer site associations. Pan-cancer analyses may larger statistical power when identifying metabolites showing consistent associations across sites, while allowing the identification of site-specific Methods Data from seven cancer-specific case-control nested within European Prospective Investigation into Cancer and Nutrition Cohort (EPIC) were pooled, resulting a total sample 7,957...
Abstract Background Epidemiological studies of associations between metabolites and cancer risk have typically focused on specific types separately. Here, we designed a multivariate pan-cancer analysis to identify potentially associated with multiple types, while also allowing the investigation type-specific associations. Methods We analyzed targeted metabolomics data available for 5,828 matched case-control pairs from cancer-specific breast, colorectal, endometrial, gallbladder, kidney,...
Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput LC-MS poses major challenge for biomarker discovery, annotation, experimental comparison, necessitating merging multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability variations hyperparameter...
Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput LC-MS poses major challenge for biomarker discovery, annotation, experimental comparison, necessitating merging multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability variations hyperparameter...
Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput LC-MS poses major challenge for biomarker discovery, annotation, experimental comparison, necessitating merging multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability variations hyperparameter...
Abstract Pooling metabolomics data across studies is often desirable to increase the statistical power of analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations variability between datasets. Specifically, different may use variable sample types (e.g., serum versus plasma) collected, treated stored according protocols, assayed laboratories using instruments. To address these issues, a...