RT Journal Article
SR Electronic
T1 Metabolomics of Prostate Cancer Gleason Score in Tumor Tissue and Serum
JF Molecular Cancer Research
JO Mol Cancer Res
FD American Association for Cancer Research
SP molcanres.0548.2020
DO 10.1158/1541-7786.MCR-20-0548
A1 Penney, Kathryn L.
A1 Tyekucheva, Svitlana
A1 Rosenthal, Jacob
A1 El Fandy, Habiba
A1 Carelli, Ryan
A1 Borgstein, Stephanie
A1 Zadra, Giorgia
A1 Fanelli, Giuseppe Nicolo
A1 Stefanizzi, Lavinia
A1 Giunchi, Francesca
A1 Pomerantz, Mark
A1 Peisch, Samuel
A1 Coulson, Hannah
A1 Lis, Rosina T
A1 Kibel, Adam S
A1 Fiorentino, Michelangelo
A1 Umeton, Renato
A1 Loda, Massimo
YR 2020
UL http://mcr.aacrjournals.org/content/early/2020/11/13/1541-7786.MCR-20-0548.abstract
AB 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.