Preoperative Metabolic Signatures of Prostate Cancer Recurrence Following Radical Prostatectomy.
Chaevien S ClendinenDavid A GaulMaría Eugenia MongeRebecca S ArnoldArthur S EdisonJohn A PetrosFacundo M FernándezPublished in: Journal of proteome research (2019)
Technological advances in mass spectrometry (MS), liquid chromatography (LC) separations, nuclear magnetic resonance (NMR) spectroscopy, and big data analytics have made possible studying metabolism at an "omics" or systems level. Here, we applied a multiplatform (NMR + LC-MS) metabolomics approach to the study of preoperative metabolic alterations associated with prostate cancer recurrence. Thus far, predicting which patients will recur even after radical prostatectomy has not been possible. Correlation analysis on metabolite abundances detected on serum samples collected prior to surgery from prostate cancer patients ( n = 40 remission vs n = 40 recurrence) showed significant alterations in a number of pathways, including amino acid metabolism, purine and pyrimidine synthesis, tricarboxylic acid cycle, tryptophan catabolism, glucose, and lactate. Lipidomics experiments indicated higher lipid abundances on recurrent patients for a number of classes that included triglycerides, lysophosphatidylcholines, phosphatidylethanolamines, phosphatidylinositols, diglycerides, acyl carnitines, and ceramides. Machine learning approaches led to the selection of a 20-metabolite panel from a single preoperative blood sample that enabled prediction of recurrence with 92.6% accuracy, 94.4% sensitivity, and 91.9% specificity under cross-validation conditions.
Keyphrases
- prostate cancer
- radical prostatectomy
- mass spectrometry
- big data
- liquid chromatography
- magnetic resonance
- machine learning
- end stage renal disease
- newly diagnosed
- ejection fraction
- patients undergoing
- free survival
- artificial intelligence
- minimally invasive
- high resolution
- prognostic factors
- high resolution mass spectrometry
- acute coronary syndrome
- single cell
- atrial fibrillation
- insulin resistance
- skeletal muscle
- computed tomography
- systemic lupus erythematosus
- simultaneous determination
- coronary artery disease
- fatty acid
- gene expression
- patient reported
- weight loss
- solid state