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Sci Rep. 2018 Mar 26;8(1):4997. doi: 10.1038/s41598-018-23177-w.

Metabolomic Prediction of Human Prostate Cancer Aggressiveness: Magnetic Resonance Spectroscopy of Histologically Benign Tissue.

Author information

1
Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, 02114, USA.
2
Department of Radiology, Charité Medical University of Berlin, Charitéplatz 1, 10117, Berlin, Germany.
3
Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, 02114, USA.
4
Department of Urology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, 02114, USA.
5
Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, 02114, USA. cwu2@mgh.harvard.edu.
6
Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, 02114, USA. cheng@nmr.mgh.harvard.edu.
7
Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, 02114, USA. cheng@nmr.mgh.harvard.edu.

Abstract

Prostate cancer alters cellular metabolism through events potentially preceding cancer morphological formation. Magnetic resonance spectroscopy (MRS)-based metabolomics of histologically-benign tissues from cancerous prostates can predict disease aggressiveness, offering clinically-translatable prognostic information. This retrospective study of 185 patients (2002-2009) included prostate tissues from prostatectomies (n = 365), benign prostatic hyperplasia (BPH) (n = 15), and biopsy cores from cancer-negative patients (n = 14). Tissues were measured with high resolution magic angle spinning (HRMAS) MRS, followed by quantitative histology using the Prognostic Grade Group (PGG) system. Metabolic profiles, measured solely from 338 of 365 histologically-benign tissues from cancerous prostates and divided into training-testing cohorts, could identify tumor grade and stage, and predict recurrence. Specifically, metabolic profiles: (1) show elevated myo-inositol, an endogenous tumor suppressor and potential mechanistic therapy target, in patients with highly-aggressive cancer, (2) identify a patient sub-group with less aggressive prostate cancer to avoid overtreatment if analysed at biopsy; and (3) subdivide the clinicopathologically indivisible PGG2 group into two distinct Kaplan-Meier recurrence groups, thereby identifying patients more at-risk for recurrence. Such findings, achievable by biopsy or prostatectomy tissue measurement, could inform treatment strategies. Metabolomics information can help transform a morphology-based diagnostic system by invoking cancer biology to improve evaluation of histologically-benign tissues in cancer environments.

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