Metabolomic profiles of intact tissues reflect clinically relevant prostate cancer subtypes

Abstract

Background Prostate cancer (PC) is a heterogenous multifocal disease ranging from indolent to lethal states. For improved treatment-stratification, reliable approaches are needed to faithfully differentiate between high- and low-risk tumors and to predict therapy response at diagnosis. Methods A metabolomic approach based on high resolution magic angle spinning nuclear magnetic resonance (HR MAS NMR) analysis was applied on intact biopsies samples (n = 111) obtained from patients (n = 31) treated by prostatectomy, and combined with advanced multi- and univariate statistical analysis methods to identify metabolomic profiles reflecting tumor differentiation (Gleason scores and the International Society of Urological Pathology (ISUP) grade) and subtypes based on tumor immunoreactivity for Ki67 (cell proliferation) and prostate specific antigen (PSA, marker for androgen receptor activity). Results Validated metabolic profiles were obtained that clearly distinguished cancer tissues from benign prostate tissues. Subsequently, metabolic signatures were identified that further divided cancer tissues into two clinically relevant groups, namely ISUP Grade 2 (n = 29) and ISUP Grade 3 (n = 17) tumors. Furthermore, metabolic profiles associated with different tumor subtypes were identified. Tumors with low Ki67 and high PSA (subtype A, n = 21) displayed metabolite patterns significantly different from tumors with high Ki67 and low PSA (subtype B, n = 28). In total, seven metabolites; choline, peak for combined phosphocholine/glycerophosphocholine metabolites (PC + GPC), glycine, creatine, combined signal of glutamate/glutamine (Glx), taurine and lactate, showed significant alterations between PC subtypes A and B. Conclusions The metabolic profiles of intact biopsies obtained by our non-invasive HR MAS NMR approach together with advanced chemometric tools reliably identified PC and specifically differentiated highly aggressive tumors from less aggressive ones. Thus, this approach has proven the potential of exploiting cancer-specific metabolites in clinical settings for obtaining personalized treatment strategies in PC.

Metabolic readouts of tumor instructed normal tissues (TINT) identify aggressive prostate cancer subgroups for tailored therapy

“…Second, we wanted to explore variations in metabolic levels in those benign samples as a function of their distance from malignant tissue. To obtain highly reliable findings we used a well-characterized study cohort and previously obtained tissue metabolomics profiles (20). With the application of HR MAS NMR, 39 metabolites could be identified and quantified in the main NMR spectral region (0.7–8.5 1 H ppm).…”

“…Comparisons between cancer and benign samples and among different types of PC subtypes were presented in a previous paper (20). Table 1 summarizes the histological characteristics of the samples used in our final analysis.…”

“…In this study we used a well-characterized patient cohort as described previously (20). The study was conducted in accordance with the Declaration of Helsinki, and the study protocol was approved by the research ethical committee at Umeå University Hospital (Regional Ethical Review Board in Umeå).…”

“…Biopsy prostate samples were obtained from 31 patients treated by prostatectomy at the Urology Clinic, Umeå University Hospital, between 2009-2012. A detailed description of sample collection is described in (20). From the metabolomics profiles of 104 samples (48 benign and 56 cancer) analysed in the previous study, we selected 57 samples (27 benign and 30 cancer) from patients with unifocal cancer and additionally 35 samples (17 benign and 18 cancer) from patients with multifocal cancer, with the following conditions fulfilled: the tumor is the index tumor (highest grade and largest), the normal sample is taken from the same side, and the other tumors are located on the contralateral side and at even larger distances.…”

“…Metabolomic analysis of prostate tissue samples was carried out as described previously (20). Brefly, NMR experiments were carried out on a 500 MHz NMR spectrometer (Bruker Biospin, GmbH, Germany) at 277 K. We used a Carr-Purcell-Meiboom-Gill (CPMG) NMR pulse sequence with a spectral width of 20 ppm, 1024 scans, echo time of 0.2 ms, a total acquisition time of 1.64 s, a recycle delay of 1.5 s and 32 K data points.…”