Gleason score, a measure of prostate tumor differentiation, is the strongest predictor of lethal prostate cancer at the time of diagnosis. Metabolomic profiling of tumor and of patient serum could identify biomarkers of aggressive disease and lead to the development of a less-invasive assay to perform active surveillance monitoring. Metabolomic profiling of prostate tissue and serum samples was performed. Metabolite levels and metabolite-set were compared pathways across Gleason scores. Machine learning algorithms were trained and tuned to predict transformation or differentiation status from metabolite data. 135 metabolites were significantly different (adjusted p<0.05) in tumor vs normal tissue, and pathway analysis identified one sugar metabolism pathway (adjusted p=0.03). Machine learning identified profiles that predicted tumor versus normal tissue (AUC of 0.82 ± 0.08). In tumor tissue, 25 metabolites were associated with Gleason score (unadjusted p<0.05), 4 increased in high grade while the remainder were enriched in low grade. While pyroglutamine and 1,5-anhydroglucitol were correlated (0.73 and 0.72, respectively) between tissue and serum from the same patient, no metabolites were consistently associated with Gleason score in serum. Previously reported as well as novel metabolites with differing abundance were identified across tumor tissue. However, a "metabolite signature" for Gleason score was not obtained. This may be due to study design and analytical challenges that future studies should consider. Implications: Metabolic profiling can distinguish benign and neoplastic tissues. A novel unsupervised machine learning method can be utilized to achieve this distinction.
Molecular cancer research : MCR. 2020 Nov 09 [Epub ahead of print]
Kathryn L Penney, Svitlana Tyekucheva, Jacob Rosenthal, Habiba El Fandy, Ryan Carelli, Stephanie Borgstein, Giorgia Zadra, Giuseppe Nicolo Fanelli, Lavinia Stefanizzi, Francesca Giunchi, Mark Pomerantz, Samuel Peisch, Hannah Coulson, Rosina Lis, Michelangelo Fiorentino, Renato Umeton, Massimo Loda
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital/Harvard Medical School., Dana-Farber Cancer Institute., Informatics and Analytics, Dana-Farber Cancer Institute., National Cancer Instiute, Cairo University., Weill Cornell Medicine., Oncologic Pathology, Dana-Farber Cancer Institute., Division of Pathology, University of Pisa, Italy, University of Pisa., University of Verona., Pathology Unit, Addarii Institute, S. Orsola-Malpighi Hospital., Medical Oncology, Dana-Farber Cancer Institute., Department of Epidemiology, Harvard T.H. Chan School of Public Health., Brigham and Women's Hospital., Specialistic, Diagnostic and Experimental Medicine, University of Bologna., Pathologist-in-Chief, Weill Cornell Medicine .