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.
Highlights
Prostate cancer alters cellular metabolism through events potentially preceding cancer morphological formation
Positing tumor metabolomic microenvironments to be sensitive to prostate cancer characteristics, we investigated the clinical potential of tissue magnetic resonance spectroscopy (MRS) analysis of human histologically-benign (Hb; no histologically identifiable prostate cancer cells or glands) samples that were obtained from cancerous prostates
To discover metabolomic profiles of prostate cancer disease, we used high-resolution magic angle spinning (HRMAS) Magnetic resonance spectroscopy (MRS), which we developed for intact tissue metabolic analysis[18,19], and the 2016 five-step, International Society of Urological Pathology prostate cancer Prognostic Grade Group (PGG) system[20], based on clinical data obtained across previous decades and representing the most up-to-date prostate cancer pathology scale evolved from the original Gleason scores
Summary
Prostate cancer alters cellular metabolism through events potentially preceding cancer morphological formation. 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 KaplanMeier recurrence groups, thereby identifying patients more at-risk for recurrence. Such findings, achievable by biopsy or prostatectomy tissue measurement, could inform treatment strategies. One of these areas is the increasingly recognized cancer research focus of tumor-stroma interactions and tumor microenvironments, as presented in the context of cancer genomics, proteomics, and transcriptomics[12,13,14,15,16,17]
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