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Metabolism

Metabolomics of Prostate Cancer Gleason Score in Tumor Tissue and Serum

Kathryn L. Penney, Svitlana Tyekucheva, Jacob Rosenthal, Habiba El Fandy, Ryan Carelli, Stephanie Borgstein, Giorgia Zadra, Giuseppe Nicolò Fanelli, Lavinia Stefanizzi, Francesca Giunchi, Mark Pomerantz, Samuel Peisch, Hannah Coulson, Rosina Lis, Adam S. Kibel, Michelangelo Fiorentino, Renato Umeton and Massimo Loda
Kathryn L. Penney
1Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
2Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
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Svitlana Tyekucheva
3Department of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts.
4Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
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  • ORCID record for Svitlana Tyekucheva
Jacob Rosenthal
4Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
5Department of Informatics and Analytics, Dana-Farber Cancer Institute, Boston, Massachusetts.
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Habiba El Fandy
6Department of Pathology, Dana-Farber Cancer Institute, Boston, Massachusetts.
7Department of Pathology, NCI, Cairo University, Giza, Egypt.
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Ryan Carelli
8Department of Pathology and Laboratory Medicine, Weill Cornell Medicine and the New York Genome Center, New York, New York.
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Stephanie Borgstein
6Department of Pathology, Dana-Farber Cancer Institute, Boston, Massachusetts.
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Giorgia Zadra
6Department of Pathology, Dana-Farber Cancer Institute, Boston, Massachusetts.
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Giuseppe Nicolò Fanelli
9Division of Pathology, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy.
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Lavinia Stefanizzi
10Department of Diagnostics and Public Health, University and Hospital Trust of Verona, Verona, Italy.
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Francesca Giunchi
11Metropolitan Department of Pathology, University of Bologna, Bologna, Italy.
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Mark Pomerantz
12Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.
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Samuel Peisch
2Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
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Hannah Coulson
13Department of Pathology, Brigham & Women's Hospital, Boston, Massachusetts.
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Rosina Lis
12Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.
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Adam S. Kibel
14Division of Urology, Department of Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
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Michelangelo Fiorentino
11Metropolitan Department of Pathology, University of Bologna, Bologna, Italy.
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Renato Umeton
4Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
5Department of Informatics and Analytics, Dana-Farber Cancer Institute, Boston, Massachusetts.
15Massachusetts Institute of Technology, Cambridge, Massachusetts.
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Massimo Loda
8Department of Pathology and Laboratory Medicine, Weill Cornell Medicine and the New York Genome Center, New York, New York.
16The Broad Institute, Cambridge, Massachusetts.
17Harvard Medical School, Boston, Massachusetts.
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  • For correspondence: mloda@med.cornell.edu
DOI: 10.1158/1541-7786.MCR-20-0548 Published March 2021
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Abstract

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 sets were compared across Gleason scores. Machine learning algorithms were trained and tuned to predict transformation or differentiation status from metabolite data. A total of 135 metabolites were significantly different (Padjusted < 0.05) in tumor versus normal tissue, and pathway analysis identified one sugar metabolism pathway (Padjusted = 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 analytic 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.

This article is featured in Highlights of This Issue, p. 359

Footnotes

  • Note: Supplementary data for this article are available at Molecular Cancer Research Online (http://mcr.aacrjournals.org/).

  • Mol Cancer Res 2021;19:475–84

  • Received June 18, 2020.
  • Revision received September 2, 2020.
  • Accepted November 3, 2020.
  • Published first November 9, 2020.
  • ©2020 American Association for Cancer Research.
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Molecular Cancer Research: 19 (3)
March 2021
Volume 19, Issue 3
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Metabolomics of Prostate Cancer Gleason Score in Tumor Tissue and Serum
Kathryn L. Penney, Svitlana Tyekucheva, Jacob Rosenthal, Habiba El Fandy, Ryan Carelli, Stephanie Borgstein, Giorgia Zadra, Giuseppe Nicolò Fanelli, Lavinia Stefanizzi, Francesca Giunchi, Mark Pomerantz, Samuel Peisch, Hannah Coulson, Rosina Lis, Adam S. Kibel, Michelangelo Fiorentino, Renato Umeton and Massimo Loda
Mol Cancer Res March 1 2021 (19) (3) 475-484; DOI: 10.1158/1541-7786.MCR-20-0548

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Metabolomics of Prostate Cancer Gleason Score in Tumor Tissue and Serum
Kathryn L. Penney, Svitlana Tyekucheva, Jacob Rosenthal, Habiba El Fandy, Ryan Carelli, Stephanie Borgstein, Giorgia Zadra, Giuseppe Nicolò Fanelli, Lavinia Stefanizzi, Francesca Giunchi, Mark Pomerantz, Samuel Peisch, Hannah Coulson, Rosina Lis, Adam S. Kibel, Michelangelo Fiorentino, Renato Umeton and Massimo Loda
Mol Cancer Res March 1 2021 (19) (3) 475-484; DOI: 10.1158/1541-7786.MCR-20-0548
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