Proton HR-MAS spectroscopy and quantitative pathologic analysis of MRI/3D-MRSI-targeted postsurgical prostate tissues

Metabolic analysis using HR-MAS in prostate tissue for prostate cancer diagnosis

INTRODUCTION

In this study we used nuclear magnetic resonance spectroscopy in prostate tissue to provide new data on potential biomarkers of prostate cancer in patients eligible for prostate biopsy.

MATERIAL AND METHODS

Core needle prostate tissue samples were obtained. After acquiring all the spectra using a Bruker Avance III DRX 600 spectrometer, tissue samples were subjected to routine histology to confirm presence or absence of prostate cancer. Univariate and multivariate analyses with metabolic and clinical variables were performed to predict the occurrence of prostate cancer.

RESULTS

A total of 201 patients, were included in the study. Of all cores subjected to high-resolution magic angle spinning (HR-MAS) followed by standard histological study, 56 (27.8%) tested positive for carcinoma. According to HR-MAS probe analysis, metabolic pathways such as glycolysis, the Krebs cycle, and the metabolism of different amino acids were associated with presence of prostate cancer. Metabolites detected in tissue such as citrate or glycerol-3-phosphocholine, together with prostate volume and suspicious rectal examination, formed a predictive model for prostate cancer in tissue with an area under the curve of 0.87, a specificity of 94%, a positive predictive value of 80% and a negative predictive value of 84%.

CONCLUSIONS

Metabolomics using HR-MAS analysis can uncover a specific metabolic fingerprint of prostate cancer in prostate tissue, using a tissue core obtained by transrectal biopsy. This specific fingerprint is based on levels of citrate, glycerol-3-phosphocholine, glycine, carnitine, and 0-phosphocholine. Several clinical variables, such as suspicious digital rectal examination and prostate volume, combined with these metabolites, form a predictive model to diagnose prostate cancer that has shown encouraging results.

High resolution magic angle spinning MRS in prostate cancer

INTRODUCTION

Prostate cancer (PCa) is one of the leading causes of death among men worldwide. The current methods utilized to screen for prostate cancer may not have sufficient sensitivity in distinguishing aggressive from indolent diseases, which affect the quality of life of patients in the short and long term. The overdiagnosis of cases and overtreatment are prevalent due to the heterogeneity of the disease in terms of latent and progressive variants, as well as in the tissue types present in biopsy samples.

METHODS

The purpose of this review is to discuss the potential clinical benefits of incorporating high-resolution magic angle spinning (HRMAS) magnetic resonance spectroscopy (MRS) modalities to overcome the current challenges in the diagnosis, prognostication, and monitoring of PCa.

Developments in proton MR spectroscopic imaging of prostate cancer

In this paper, we review the developments of 1H-MR spectroscopic imaging (MRSI) methods designed to investigate prostate cancer, covering key aspects such as specific hardware, dedicated pulse sequences for data acquisition and data processing and quantification techniques. Emphasis is given to recent advancements in MRSI methodologies, as well as future developments, which can lead to overcome difficulties associated with commonly employed MRSI approaches applied in clinical routine. This includes the replacement of standard PRESS sequences for volume selection, which we identified as inadequate for clinical applications, by sLASER sequences and implementation of 1H MRSI without water signal suppression. These may enable a new evaluation of the complementary role and significance of MRSI in prostate cancer management.

Ex Vivo High-Resolution Magic Angle Spinning (HRMAS) 1H NMR Spectroscopy for Early Prostate Cancer Detection

Simple Summary

Prostate cancer is the second leading cancer diagnosed in men worldwide. Current diagnostic standards lack sufficient reliability in detecting and characterizing prostate cancer. Due to the cancer’s multifocality, prostate biopsies are associated with high numbers of false negatives. Whereas several studies have already shown the potential of metabolomic information for PCa detection and characterization, in this study, we focused on evaluating its predictive power for future PCa diagnosis. In our study, metabolomic information differed substantially between histobenign patients based on their risk for receiving a future PCa diagnosis, making metabolomic information highly valuable for the individualization of active surveillance strategies.

Abstract

The aim of our study was to assess ex vivo HRMAS (high-resolution magic angle spinning) ¹H NMR spectroscopy as a diagnostic tool for early PCa detection by testing whether metabolomic alterations in prostate biopsy samples can predict future PCa diagnosis. In a primary prospective study (04/2006–10/2018), fresh biopsy samples of 351 prostate biopsy patients were NMR spectroscopically analyzed (Bruker 14.1 Tesla, Billerica, MA, USA) and histopathologically evaluated. Three groups of 16 patients were compared: group 1 and 2 represented patients whose NMR scanned biopsy was histobenign, but patients in group 1 were diagnosed with cancer before the end of the study period, whereas patients in group 2 remained histobenign. Group 3 included cancer patients. Single-metabolite concentrations and metabolomic profiles were not only able to separate histobenign and malignant prostate tissue but also to differentiate between samples of histobenign patients who received a PCa diagnosis in the following years and those who remained histobenign. Our results support the hypothesis that metabolomic alterations significantly precede histologically visible changes, making metabolomic information highly beneficial for early PCa detection. Thanks to its predictive power, metabolomic information can be very valuable for the individualization of PCa active surveillance strategies.

The Emerging Clinical Role of Spermine in Prostate Cancer

Spermine, a member of polyamines, exists in all organisms and is essential for normal cell growth and function. It is highly expressed in the prostate compared with other organs and is detectable in urine, tissue, expressed prostatic secretions, and erythrocyte. A significant reduction of spermine level was observed in prostate cancer (PCa) tissue compared with benign prostate tissue, and the level of urinary spermine was also significantly lower in men with PCa. Decreased spermine level may be used as an indicator of malignant phenotype transformation from normal to malignant tissue in prostate. Studies targeting polyamines and key rate-limiting enzymes associated with spermine metabolism as a tool for PCa therapy and chemoprevention have been conducted with various polyamine biosynthesis inhibitors and polyamine analogues. The mechanism between spermine and PCa development are possibly related to the regulation of polyamine metabolism, cancer-driving pathways, oxidative stress, anticancer immunosurveillance, and apoptosis regulation. Although the specific mechanism of spermine in PCa development is still unclear, ongoing research in spermine metabolism and its association with PCa pathophysiology opens up new opportunities in the diagnostic and therapeutic roles of spermine in PCa management.

Advances and Perspectives in Prostate Cancer Biomarker Discovery in the Last 5 Years through Tissue and Urine Metabolomics

Prostate cancer (PCa) is the second most diagnosed cancer in men worldwide. For its screening, serum prostate specific antigen (PSA) test has been largely performed over the past decade, despite its lack of accuracy and inability to distinguish indolent from aggressive disease. Metabolomics has been widely applied in cancer biomarker discovery due to the well-known metabolic reprogramming characteristic of cancer cells. Most of the metabolomic studies have reported alterations in urine of PCa patients due its noninvasive collection, but the analysis of prostate tissue metabolome is an ideal approach to disclose specific modifications in PCa development. This review aims to summarize and discuss the most recent findings from tissue and urine metabolomic studies applied to PCa biomarker discovery. Eighteen metabolites were found consistently altered in PCa tissue among different studies, including alanine, arginine, uracil, glutamate, fumarate, and citrate. Urine metabolomic studies also showed consistency in the dysregulation of 15 metabolites and, interestingly, alterations in the levels of valine, taurine, leucine and citrate were found in common between urine and tissue studies. These findings unveil that the impact of PCa development in human metabolome may offer a promising strategy to find novel biomarkers for PCa diagnosis.

Sample Preparation and Data Analysis for NMR-Based Metabolomics

NMR spectroscopy has become one of the preferred analytical techniques for metabolomics studies due to its inherent nondestructive nature, ability to identify and quantify metabolites simultaneously in a complex mixture, minimal sample preparation requirement, and high degree of experimental reproducibility. NMR-based metabolomics studies involve the measurement and multivariate statistical analysis of metabolites present in biological samples such as biofluids, stool/feces, intestinal content, tissue, and cell extracts by high-resolution NMR spectroscopy-the goal then is to identify and quantify metabolites and evaluate changes of metabolite concentrations in response to some perturbation. Here we describe methodologies for NMR sample preparation of biofluids (serum, saliva, and urine) and stool/feces, intestinal content, and tissues for NMR experiments including extraction of polar metabolites and application of NMR in metabolomics studies. One dimensional (1D) ¹H NMR experiments with different variations such as pre-saturation, relaxation-edited, and diffusion-edited are routinely acquired for profiling and metabolite identification and quantification. 2D homonuclear ¹H-¹H TOCSY and COSY, 2D J-resolved, and heteronuclear ¹H-¹³C HSQC and HMBC are also performed to assist with metabolite identification and quantification. The NMR data are then subjected to targeted and/or untargeted multivariate statistical analysis for biomarker discovery, clinical diagnosis, toxicological studies, molecular phenotyping, and functional genomics.

Exercise May Affect Metabolism in Cancer-Related Cognitive Impairment

Cancer-related cognitive impairment (CRCI) is a significant comorbidity for cancer patients and survivors. Physical activity (PA) has been found to be a strong gene modulator that can induce structural and functional changes in the brain. PA and exercise reduce the risk of cancer development and progression and has been shown to help in overcoming post-treatment syndromes. Exercise plays a role in controlling cancer progression through direct effects on cancer metabolism. In this review, we highlight several priorities for improving studies on CRCI in patients and its underlying potential metabolic mechanisms.

Human Prostate Cancer is Characterized by an Increase in Urea Cycle Metabolites

Despite it being the most common incident of cancer among men, the pathophysiological mechanisms contributing to prostate cancer (PCa) are still poorly understood. Altered mitochondrial metabolism is postulated to play a role in the development of PCa. To determine the key metabolites (which included mitochondrial oncometabolites), benign prostatic and cancer tissues of patients with PCa were analyzed using capillary electrophoresis and liquid chromatography coupled with mass spectrometry. Gene expression was studied using real-time PCR. In PCa tissues, we found reduced levels of early tricarboxylic acid cycle metabolites, whereas the contents of urea cycle metabolites including aspartate, argininosuccinate, arginine, proline, and the oncometabolite fumarate were higher than that in benign controls. Fumarate content correlated positively with the gene expression of oncogenic HIF1α and NFκB pathways, which were significantly higher in the PCa samples than in the benign controls. Furthermore, data from the TCGA database demonstrated that prostate cancer patients with activated NFκB pathway had a lower survival rate. In summary, our data showed that fumarate content was positively associated with carcinogenic genes.

OMICS insights into cancer histology; Metabolomics and proteomics approach

Metabolomics as a post-genomic research area comprising different analytical methods for small molecules analysis. One of the underlying applications of metabolomics technology for better disease diagnosis and prognosis is discovering the metabolic pathway differences between healthy individuals and patients. On the other hand, the other noteworthy applications of metabolomics include its effective role in biomarker screening for cancer detection, monitoring, and prediction. In other words, emerging of the metabolomics field can be hopeful to provide a suitable alternative for the common current cancer diagnostic methods especially histopathological tests. Indeed, cancer as a major global issue places a substantial burden on the health care system. Hence, proper management can be beneficial. In this respect, formalin-fixed paraffin-embedded tissue specimens (in histopathological tests) are considered as a valuable source for metabolomics investigations. Interestingly, formalin-fixed paraffin-embedded tissue specimens can provide informative data for cancer management. In general, using these specimens, determining the cancer stage, individual response to the different therapies, personalized risk prediction are possible and high-quality clinical services are the promise of OMICS technologies for cancer disease. However, considering all of these beneficial characteristics, there are still some limitations in this area that need to be addressed in order to optimize the metabolomics utilizations and advancement.

Pre-diagnostic Serum Metabolomic Profiling of Prostate Cancer Survival

Impaired metabolism may play a role in the development and lethality of prostate cancer, yet a comprehensive analysis of the interrelationships appears lacking. We measured 625 metabolites using ultrahigh performance liquid chromatography/mass spectrometry (LC-MS) and gas chromatography/mass spectrometry (GC-MS) of prediagnostic serum from 197 prostate cancer cases in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study (ages at diagnosis, 55-86 years). Cox proportional hazards models estimated associations between circulating metabolites and prostate cancer mortality for 1 SD differences (log-metabolite scale), adjusted for age, year of diagnosis, and disease stage. Associations between metabolite chemical classes and survival were examined through pathway analysis, and Cox models assessed the relationship with a sterol/steroid metabolite principal component analysis factor score. Elevated serum N-oleoyl taurine was significantly associated with prostate cancer-specific mortality (hazard ratios [HR] = 1.72 per 1 SD, p < .00008, Bonferroni-corrected threshold = 0.05/625; HR = 3.6 for highest vs lowest tertile, p < .001). Pathway analyses revealed a statistically significant association between lipids and prostate cancer death (p < .006, Bonferroni-corrected threshold = 0.05/8), and sterol/steroid metabolites showed the strongest chemical sub-class association (p = .0014, Bonferroni-corrected threshold = 0.05/45). In the principal component analysis, a 1-SD increment in the sterol/steroid metabolite score increased the risk of prostate cancer death by 46%. Prediagnostic serum N-oleoyl taurine and sterol/steroid metabolites were associated with prostate cancer survival.

Nuclear magnetic resonance spectroscopy of human body fluids and in vivo magnetic resonance spectroscopy: Potential role in the diagnosis and management of prostate cancer

Prostate cancer is the most common solid organ cancer in men, and the second most common cause of male cancer-related mortality. It has few effective therapies, and is difficult to diagnose accurately. Prostate-specific antigen (PSA), which is currently the most effective diagnostic tool available, cannot reliably discriminate between different pathologies, and in fact only around 30% of patients found to have elevated levels of PSA are subsequently confirmed to actually have prostate cancer. As such, there is a desperate need for more reliable diagnostic tools that will allow the early detection of prostate cancer so that the appropriate interventions can be applied. Nuclear magnetic resonance (NMR) spectroscopy and magnetic resonance spectroscopy (MRS) are 2 high throughput, noninvasive analytical procedures that have the potential to enable differentiation of prostate cancer from other pathologies using metabolomics, by focusing specifically on certain metabolites which are associated with the development of prostate cancer cells and its progression. The value that this type of approach has for the early detection, diagnosis, prognosis, and personalized treatment of prostate cancer is becoming increasingly apparent. Recent years have seen many promising developments in the fields of NMR spectroscopy and MRS, with improvements having been made to hardware as well as to techniques associated with the acquisition, processing, and analysis of related data. This review focuses firstly on proton NMR spectroscopy of blood serum, urine, and expressed prostatic secretions in vitro, and then on 1- and 2-dimensional proton MRS of the prostate in vivo. Major advances in these fields and methodological principles of data collection, acquisition, processing, and analysis are described along with some discussion of related challenges, before prospects that proton MRS has for future improvements to the clinical management of prostate cancer are considered.

Metabolomic prostate cancer fields in HRMAS MRS-profiled histologically benign tissue vary with cancer status and distance from cancer

In this article, we review the state of the field of high resolution magic angle spinning MRS (HRMAS MRS)-based cancer metabolomics since its beginning in 2004; discuss the concept of cancer metabolomic fields, where metabolomic profiles measured from histologically benign tissues reflect patient cancer status; and report our HRMAS MRS metabolomic results, which characterize metabolomic fields in prostatectomy-removed cancerous prostates. Three-dimensional mapping of cancer lesions throughout each prostate enabled multiple benign tissue samples per organ to be classified based on distance from and extent of the closest cancer lesion as well as the Gleason score (GS) of the entire prostate. Cross-validated partial least squares-discriminant analysis separations were achieved between cancer and benign tissue, and between cancer tissue from prostates with high (≥4 + 3) and low (≤3 + 4) GS. Metabolomic field effects enabled histologically benign tissue adjacent to cancer to distinguish the GS and extent of the cancer lesion itself. Benign samples close to either low GS cancer or extensive cancer lesions could be distinguished from those far from cancer. Furthermore, a successfully cross-validated multivariate model for three benign tissue groups with varying distances from cancer lesions within one prostate indicates the scale of prostate cancer metabolomic fields. While these findings could, at present, be potentially useful in the prostate cancer clinic for analysis of biopsy or surgical specimens to complement current diagnostics, the confirmation of metabolomic fields should encourage further examination of cancer fields and can also enhance understanding of the metabolomic characteristics of cancer in myriad organ systems. Our results together with the success of HRMAS MRS-based cancer metabolomics presented in our literature review demonstrate the potential of cancer metabolomics to provide supplementary information for cancer diagnosis, staging, and patient prognostication.

Evolution of UHF Body Imaging in the Human Torso at 7T: Technology, Applications, and Future Directions

The potential value of ultrahigh field (UHF) magnetic resonance imaging (MRI) and spectroscopy to biomedical research and in clinical applications drives the development of technologies to overcome its many challenges. The increased difficulties of imaging the human torso compared with the head include its overall size, the dimensions and location of its anatomic targets, the increased prevalence and magnitude of physiologic effects, the limited availability of tailored RF coils, and the necessary transmit chain hardware. Tackling these issues involves addressing notoriously inhomogeneous transmit B1 (B1) fields, limitations in peak B1, larger spatial variations of the static magnetic field B0, and patient safety issues related to implants and local RF power deposition. However, as research institutions and vendors continue to innovate, the potential gains are beginning to be realized. Solutions overcoming the unique challenges associated with imaging the human torso are reviewed as are current studies capitalizing on the benefits of UHF in several anatomies and applications. As the field progresses, strategies associated with the RF system architecture, calibration methods, RF pulse optimization, and power monitoring need to be further integrated into the MRI systems making what are currently complex processes more streamlined. Meanwhile, the UHF MRI community must seize the opportunity to build upon what have been so far proof of principle and feasibility studies and begin to further explore the true impact in both research and the clinic.

NMR-Based Metabolomic Profiling of Urine: Evaluation for Application in Prostate Cancer Detection

Detection of prostate cancer (PCa) and distinguishing indolent versus aggressive forms of the disease is a critical clinical challenge. The current clinical test is circulating prostate-specific antigen levels, which faces particular challenges in cancer diagnosis in the range of 4 to 10 ng/mL. Thus, a concerted effort toward building a noninvasive biomarker panel has developed. In this report, the hypothesis that nuclear magnetic resonance (NMR)-derived metabolomic profiles measured in the urine of biopsy-negative versus biopsy-positive individuals would nominate a selection of potential biomarker signals was investigated. 1H NMR spectra of urine samples from 317 individuals (111 biopsy-negative, 206 biopsy-positive) were analyzed. A double cross-validation partial least squares-discriminant analysis modeling technique was utilized to nominate signals capable of distinguishing the two classes. It was observed that after variable selection protocols were applied, a subset of 29 variables produced an area under the curve (AUC) value of 0.94 after logistic regression analysis, whereas a “master list” of 18 variables produced a receiver operating characteristic ROC) AUC of 0.80. As proof of principle, this study demonstrates the utility of NMR-based metabolomic profiling of urine biospecimens in the nomination of PCa-specific biomarker signals and suggests that further investigation is certainly warranted.

Preoperative Metabolic Signatures of Prostate Cancer Recurrence Following Radical Prostatectomy

Technological advances in mass spectrometry (MS), liquid chromatography (LC) separations, nuclear magnetic resonance (NMR) spectroscopy, and big data analytics have made possible studying metabolism at an "omics" or systems level. Here, we applied a multiplatform (NMR + LC-MS) metabolomics approach to the study of preoperative metabolic alterations associated with prostate cancer recurrence. Thus far, predicting which patients will recur even after radical prostatectomy has not been possible. Correlation analysis on metabolite abundances detected on serum samples collected prior to surgery from prostate cancer patients ( n = 40 remission vs n = 40 recurrence) showed significant alterations in a number of pathways, including amino acid metabolism, purine and pyrimidine synthesis, tricarboxylic acid cycle, tryptophan catabolism, glucose, and lactate. Lipidomics experiments indicated higher lipid abundances on recurrent patients for a number of classes that included triglycerides, lysophosphatidylcholines, phosphatidylethanolamines, phosphatidylinositols, diglycerides, acyl carnitines, and ceramides. Machine learning approaches led to the selection of a 20-metabolite panel from a single preoperative blood sample that enabled prediction of recurrence with 92.6% accuracy, 94.4% sensitivity, and 91.9% specificity under cross-validation conditions.

Characterization of Brain Metabolism by Nuclear Magnetic Resonance

The noninvasive, quantitative ability of nuclear magnetic resonance (NMR) spectroscopy to characterize small molecule metabolites has long been recognized as a major strength of its application in biology. Numerous techniques exist for characterizing metabolism in living, excised, or extracted tissue, with a particular focus on 1 H-based methods due to the high sensitivity and natural abundance of protons. With the increasing use of high magnetic fields, the utility of in vivo 1 H magnetic resonance spectroscopy (MRS) has markedly improved for measuring specific metabolite concentrations in biological tissues. Higher fields, coupled with recent developments in hyperpolarization, also enable techniques for complimenting 1 H measurements with spectroscopy of other nuclei, such as 31 P and 13 C, and for combining measurements of metabolite pools with metabolic flux measurements. We compare ex vivo and in vivo methods for studying metabolism in the brain using NMR and highlight insights gained through using higher magnetic fields, the advent of dissolution dynamic nuclear polarization, and combining in vivo MRS and ex vivo NMR approaches.

Metabolite Profile of Alzheimer's Disease in the Frontal Cortex as Analyzed by HRMAS 1H NMR

Background: Investigation on neurochemical changes in the frontal cortex in individuals with Alzheimer’s disease (AD) and different Apolipoprotein E (APOE) genotypes, using ex vivo solid-state high-resolution NMR analysis, may lead to a better understanding of the neurochemistry associated with AD as well as new AD-specific metabolite biomarkers that might potentially improve the clinical diagnosis of AD.

Methods: Intact tissue samples of the frontal cortex were obtained from 11 patients and 11 age-matched non-demented controls. Metabolite profiles in all samples were analyzed ex vivo, using solid-state high-resolution magic angle spinning (HRMAS) 600 MHz ¹H nuclear magnetic resonance (NMR). A logistic regression analysis was used to rank metabolites based on their level of contribution in differentiating the AD patient tissues and the controls, and different AD-associated APOE genotypes (APOE ε4 vs. APOE ε3).

Results: Tissue samples from the AD patients showed significantly lower NAA/Cr (p = 0.011), Ace/Cr (p = 0.027), GABA/Cr (p = 0.005), Asp/Cr (p < 0.0001), mI/Cr (p < 0.0001), and Tau/Cr (p = 0.021), and higher PCho/Cr (p < 0.0001), GPCho/Cr (p < 0.0001), and α&β-Glc/Cr (p < 0.0001) than the controls did. Specifically, a newly observed resonance at 3.71 ppm, referred to as α&β-Glc, was observed in 90.9% of the AD samples (10/11). Samples with APOE ε4 also exhibited higher PCho/Cr (p = 0.0002), GPCho/Cr (p = 0.0001), α&β-Glc/Cr (p < 0.0001), and lower Asp/Cr (p = 0.004) and GABA/Cr (p = 0.04) than the samples with APOE ε3 did. In the logistic regression analysis, PCho, GPCho, ASP, and α&β-Glc were found to be the most relevant metabolites for differentiating the AD patient tissues and the controls, and different APOE genotypes.

Conclusion: HRMAS ¹H NMR with high spectral resolution and sensitivity offers a powerful tool to gain quantitative information on AD associated neurochemical changes. There are important neurochemical differences in the frontal cortex between the AD patient tissues and the controls, and between those with different APOE genotypes. The resonance (α&β-Glc) found at 3.71 ppm in the AD patient tissues may be further investigated for its potential in the diagnosis and monitoring of AD.

Integrative metabolic and transcriptomic profiling of prostate cancer tissue containing reactive stroma

Reactive stroma is a tissue feature commonly observed in the tumor microenvironment of prostate cancer and has previously been associated with more aggressive tumors. The aim of this study was to detect differentially expressed genes and metabolites according to reactive stroma content measured on the exact same prostate cancer tissue sample. Reactive stroma was evaluated using histopathology from 108 fresh frozen prostate cancer samples gathered from 43 patients after prostatectomy (Biobank1). A subset of the samples was analyzed both for metabolic (n = 85) and transcriptomic alterations (n = 78) using high resolution magic angle spinning magnetic resonance spectroscopy (HR-MAS MRS) and RNA microarray, respectively. Recurrence-free survival was assessed in patients with clinical follow-up of minimum five years (n = 38) using biochemical recurrence (BCR) as endpoint. Multivariate metabolomics and gene expression analysis compared low (≤15%) against high reactive stroma content (≥16%). High reactive stroma content was associated with BCR in prostate cancer patients even when accounting for the influence of Grade Group (Cox hazard proportional analysis, p = 0.013). In samples with high reactive stroma content, metabolites and genes linked to immune functions and extracellular matrix (ECM) remodeling were significantly upregulated. Future validation of these findings is important to reveal novel biomarkers and drug targets connected to immune mechanisms and ECM in prostate cancer. The fact that high reactive stroma grading is connected to BCR adds further support for the clinical integration of this histopathological evaluation.

Applications of metabolomics to study cancer metabolism

Reprogrammed metabolism supports tumor growth and provides a potential source of therapeutic targets and disease biomarkers. Mass spectrometry-based metabolomics has emerged as a broadly informative technique for profiling metabolic features associated with specific oncogenotypes, disease progression, therapeutic liabilities and other clinically relevant aspects of tumor biology. In this review, we introduce the applications of metabolomics to study deregulated metabolism and metabolic vulnerabilities in cancer. We provide examples of studies that used metabolomics to discover novel metabolic regulatory mechanisms, including processes that link metabolic alterations with gene expression, protein function, and other aspects of systems biology. Finally, we discuss emerging applications of metabolomics for in vivo isotope tracing and metabolite imaging, both of which hold promise to advance our understanding of the role of metabolic reprogramming in cancer.

Metabolomics reveals perturbations in endometrium and serum of minimal and mild endometriosis

Endometriosis is a common benign gynecological disease, characterized by growth and proliferation of endometrial glands and stroma outside the uterus. With studies showing metabolic changes in various biofluids of endometriosis women, we have set upon to investigate whether endometrial tissue show differences in their metabolic profiles. ¹H NMR analysis was performed on eutopic endometrial tissue of women with endometriosis and controls. Analysis was performed on spectral data and on relative concentrations of metabolites obtained from spectra using multivariate and univariate data analysis. Analysis shows that various energy, ketogenic and glucogenic metabolites have significant altered concentrations in various stages of endometriosis. In addition, altered tissue metabolites in minimal and mild stages of endometriosis were explored in serum of these patients to assess their role in disease diagnosis. For Stage I diagnosis alanine was found to have 90% sensitivity (true positives) and 58% specificity (true negatives). For Stage II diagnosis alanine, leucine, lysine, proline and phenylalanine showed significant altered levels in serum. While sensitivity of these serum metabolites varied between 69.2–100% the specificity values ranged between 58.3–91.7%. Further, a regression model generated with this panel of serum markers showed an improved sensitivity and specificity of 100% and 83%, respectively for Stage II diagnosis.

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

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.

Evaluation of Tissue Metabolites with High Resolution Magic Angle Spinning MR Spectroscopy Human Prostate Samples after Three-Year Storage at –80 °C

Accurate interpretation and correlation of tissue spectroscopy with pathological conditions requires disease-specific tissue metabolite databases; however, specimens for research are often kept in frozen storage for various lengths of time. Whether such frozen storage results in alterations to the measured metabolites is a critical but largely unknown issue. In this study, human prostate tissues from specimens that had been stored at –80 °C for 32 months were analyzed with high resolution magic angle spinning (HRMAS) magnetic resonance (MR) spectroscopy, and compared with the initial measurements of the adjacent specimens from the same cases when snap frozen in the operation room and kept frozen for less than 24 hours. Results of the current study indicate that that the storage-induced metabolite alterations are below the limits that tissue MR spectroscopy can discriminate. Furthermore, quantitative pathology evaluations suggest the observed alterations in metabolite profiles measured from the adjacent specimens of the same prostates may be accounted for by tissue pathological heterogeneities and are not a result of storage conditions. Hence, these results indicate that long-term frozen storage of prostate specimens can be quantitatively analyzed by HRMAS MR spectroscopy without concerns regarding significant metabolic degradation or alteration.

Applications of high-resolution magic angle spinning MRS in biomedical studies II-Human diseases

High-resolution magic angle spinning (HRMAS) MRS is a powerful method for gaining insight into the physiological and pathological processes of cellular metabolism. Given its ability to obtain high-resolution spectra of non-liquid biological samples, while preserving tissue architecture for subsequent histopathological analysis, the technique has become invaluable for biochemical and biomedical studies. Using HRMAS MRS, alterations in measured metabolites, metabolic ratios, and metabolomic profiles present the possibility to improve identification and prognostication of various diseases and decipher the metabolomic impact of drug therapies. In this review, we evaluate HRMAS MRS results on human tissue specimens from malignancies and non-localized diseases reported in the literature since the inception of the technique in 1996. We present the diverse applications of the technique in understanding pathological processes of different anatomical origins, correlations with in vivo imaging, effectiveness of therapies, and progress in the HRMAS methodology.

Ex vivo metabolic fingerprinting identifies biomarkers predictive of prostate cancer recurrence following radical prostatectomy

Background:

Robust biomarkers that identify prostate cancer patients with high risk of recurrence will improve personalised cancer care. In this study, we investigated whether tissue metabolites detectable by high-resolution magic angle spinning magnetic resonance spectroscopy (HR-MAS MRS) were associated with recurrence following radical prostatectomy.

Methods:

We performed a retrospective ex vivo study using HR-MAS MRS on tissue samples from 110 radical prostatectomy specimens obtained from three different Norwegian cohorts collected between 2002 and 2010. At the time of analysis, 50 patients had experienced prostate cancer recurrence. Associations between metabolites, clinicopathological variables, and recurrence-free survival were evaluated using Cox proportional hazards regression modelling, Kaplan–Meier survival analyses and concordance index (C-index).

Results:

High intratumoural spermine and citrate concentrations were associated with longer recurrence-free survival, whereas high (total-choline+creatine)/spermine (tChoCre/Spm) and higher (total-choline+creatine)/citrate (tChoCre/Cit) ratios were associated with shorter time to recurrence. Spermine concentration and tChoCre/Spm were independently associated with recurrence in multivariate Cox proportional hazards modelling after adjusting for clinically relevant risk factors (C-index: 0.769; HR: 0.72; P=0.016 and C-index: 0.765; HR: 1.43; P=0.014, respectively).

Conclusions:

Spermine concentration and tChoCre/Spm ratio in prostatectomy specimens were independent prognostic markers of recurrence. These metabolites can be noninvasively measured in vivo and may thus offer predictive value to establish preoperative risk assessment nomograms.

Differentiation Diagnosis of Hypo-Intense T2 Area in Unilateral Peripheral Zone of Prostate Using Magnetic Resonance Spectroscopy (MRS): Prostate Carcinoma versus Prostatitis

Background

To determine whether magnetic resonance spectroscopy (MRS) can be used as a reliable denominator for the differentiation of prostatitis and prostate cancer (PCa) in the peripheral zone.

Material/Methods

Forty-three patients with unilateral peripheral zone PCa and 35 patients with unilateral peripheral zone prostatitis were recruited for this study. Magnetic resonance imaging (MRI) and MRS were acquired on a 1.5T MR scanner. The ratios of (Cho+Cr)/Cit of hypo-intense T2 area were calculated. The mean ratios of (Cho+Cr)/Cit in hypo-intense T2 area of PCa and that of prostatitis were compared retrospectively by t-test. The citrate and choline amplitudes in the hypo-intense T2 area were compared with that in the contralateral normal peripheral zone tissue.

Results

The mean ratios of (Cho+Cr)/Cit in the hypo-intense T2 area of PCa was 3.0±2.48, whereas that of prostatitis was 5.2±7.08, without significant statistical difference (p=0.306). A reduction in citrate was seen in both PCa and prostatitis tissue, however, choline was elevated in PCa tissue, whereas on the contrary, choline had no significant change in cases of prostatitis.

Conclusions

The mean ratios of (Cho+Cr)/Cit had no specificity in differentiation of PCa and prostatitis in the peripheral zone, however, the metabolic pattern showed promise as an adjunct to conventional imaging in differentiating prostatitis from PCa in the peripheral zone.

Integration of tissue metabolomics, transcriptomics and immunohistochemistry reveals ERG- and gleason score-specific metabolomic alterations in prostate cancer

Integrated analysis of metabolomics, transcriptomics and immunohistochemistry can contribute to a deeper understanding of biological processes altered in cancer and possibly enable improved diagnostic or prognostic tests. In this study, a set of 254 metabolites was determined by gas-chromatography/liquid chromatography-mass spectrometry in matched malignant and non-malignant prostatectomy samples of 106 prostate cancer (PCa) patients. Transcription analysis of matched samples was performed on a set of 15 PCa patients using Affymetrix U133 Plus 2.0 arrays. Expression of several proteins was immunohistochemically determined in 41 matched patient samples and the association with clinico-pathological parameters was analyzed by an integrated data analysis. These results further outline the highly deregulated metabolism of fatty acids, sphingolipids and polyamines in PCa. For the first time, the impact of the ERG translocation on the metabolome was demonstrated, highlighting an altered fatty acid oxidation in TMPRSS2-ERG translocation positive PCa specimens. Furthermore, alterations in cholesterol metabolism were found preferentially in high grade tumors, enabling the cells to create energy storage. With this integrated analysis we could not only confirm several findings from previous metabolomic studies, but also contradict others and finally expand our concepts of deregulated biological pathways in PCa.

Metabolic Imaging in Humans

Metabolic imaging enhances understanding of disease metabolisms and holds great potential as a measurement tool for evaluating disease prognosis and treatment effectiveness. Advancement of techniques, such as magnetic resonance spectroscopy, positron emission tomography, and mass spectrometry, allows for improved accuracy for quantification of metabolites and present unique possibilities for use in clinic. This article reviews and discusses literature reports of metabolic imaging in humans published since 2010 according to disease type, including cancer, degenerative disorders, psychiatric disorders, and others, as well as the current application of the various related techniques.

Urinary Polyamines: A Pilot Study on Their Roles as Prostate Cancer Detection Biomarkers

Current screening methods towards prostate cancer (PCa) are not without limitations. Research work has been on-going to assess if there are other better tests suitable for primary or secondary screening of PCa to supplement the serum prostate specific antigen (PSA) test, which fails to work accurately in a grey zone of 4-10ng/ml. In this pilot study, the potential roles of urinary polyamines as prostate cancer biomarkers were evaluated. PCa, benign prostatic hyperplasia (BPH) patients and healthy controls (HC) showing PSA>4.0ng/ml were enrolled in the study. Their urine samples were obtained, and the urinary levels of putrescine (Put), spermidine (Spd) and spermine (Spm) were determined by ultra-high performance liquid chromatography coupled with triple quadrupole mass spectrometer (UPLC-MS/MS). Receiver operating characteristics (ROC) curve and Student’s t-test were used to evaluate their diagnostic accuracies. Among the three biogenic polyamines, Spm had demonstrated a good diagnostic performance when comparing their levels in PCa patients with BPH patients (1.47 in PCa vs 5.87 in BPH; p<0.0001). Results are in accordance with transrectal ultrasound prostatic biopsy (TRUSPB) results, with an area under curve (AUC) value of 0.83±0.03. Therefore urinary Spm shows potential to serve as a novel PCa diagnostic biomarker, which in turn can help to address the limited sensitivity and specificity problem of serum PSA test.

Determining the chemical exchange saturation transfer (CEST) behavior of citrate and spermine under in vivo conditions

PURPOSE

To estimate the exchange rates of labile (1) H in citrate and spermine, metabolites present in prostatic secretions, to predict the size of the citrate and spermine CEST effects in vivo.

METHODS

CEST z-spectra were acquired at high-field [11.7 Tesla (T)] from citrate and spermine solutions at physiological pH (6.5) using saturation power 6 μT. CEST was performed at different temperatures to determine exchange regimes (slow, intermediate or fast). For low pH solutions of spermine, exchange rates were estimated from resonance line width, fitting z-spectra using the Bloch equations incorporating exchange, and using quantifying exchange using saturation time experiments (QUEST). These rates were extrapolated to physiological pH.

RESULTS

Citrate showed little CEST effect at pH 6.5 and temperature (T) = 310 K (maximum 0.001% mM(-1) ), indicating fast exchange, whereas spermine showed greater CEST effects (maximum 0.2% mM(-1) ) indicating intermediate-to-fast exchange. Extrapolating data acquired from low pH spermine solutions predicts exchange rates at pH 6.5 and T of 310 K of at least 2 × 10(4) s(-1) .

CONCLUSION

Citrate and spermine show minimal CEST effects at 11.7T even using high saturation power. These effects would be much less than 2% at clinical field-strengths due to relatively faster exchange and would be masked by CEST from proteins. Magn Reson Med 76:742-746, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Biomarker Discovery in Human Prostate Cancer: an Update in Metabolomics Studies

Prostate cancer (PCa) is the most frequently diagnosed cancer and the second leading cause of cancer death among men in Western countries. Current screening techniques are based on the measurement of serum prostate specific antigen (PSA) levels and digital rectal examination. A decisive diagnosis of PCa is based on prostate biopsies; however, this approach can lead to false-positive and false-negative results. Therefore, it is important to discover new biomarkers for the diagnosis of PCa, preferably noninvasive ones. Metabolomics is an approach that allows the analysis of the entire metabolic profile of a biological system. As neoplastic cells have a unique metabolic phenotype related to cancer development and progression, the identification of dysfunctional metabolic pathways using metabolomics can be used to discover cancer biomarkers and therapeutic targets. In this study, we review several metabolomics studies performed in prostatic fluid, blood plasma/serum, urine, tissues and immortalized cultured cell lines with the objective of discovering alterations in the metabolic phenotype of PCa and thus discovering new biomarkers for the diagnosis of PCa. Encouraging results using metabolomics have been reported for PCa, with sarcosine being one of the most promising biomarkers identified to date. However, the use of sarcosine as a PCa biomarker in the clinic remains a controversial issue within the scientific community. Beyond sarcosine, other metabolites are considered to be biomarkers for PCa, but they still need clinical validation. Despite the lack of metabolomics biomarkers reaching clinical practice, metabolomics proved to be a powerful tool in the discovery of new biomarkers for PCa detection.

Androgen deprivation leads to increased carbohydrate metabolism and hexokinase 2-mediated survival in Pten/Tp53-deficient prostate cancer

Prostate cancer is characterized by a dependence upon androgen receptor (AR) signaling, and androgen deprivation therapy (ADT) is the accepted treatment for progressive prostate cancer. Although ADT is usually initially effective, acquired resistance termed castrate-resistant prostate cancer (CRPC) develops. PTEN and TP53 are two of the most commonly deleted or mutated genes in prostate cancer, the compound loss of which is enriched in CRPC. To interrogate the metabolic alterations associated with survival following ADT, we used an orthotopic model of Pten/Tp53 null prostate cancer. Metabolite profiles and associated regulators were compared in tumors from androgen-intact mice and in tumors surviving castration. AR inhibition led to changes in the levels of glycolysis and tricarboxylic acid (TCA) cycle pathway intermediates. As anticipated for inhibitory reciprocal feedback between AR and PI3K/AKT signaling pathways, pAKT levels were increased in androgen-deprived tumors. Elevated mitochondrial hexokinase 2 (HK2) levels and enzyme activities also were observed in androgen-deprived tumors, consistent with pAKT-dependent HK2 protein induction and mitochondrial association. Competitive inhibition of HK2-mitochondrial binding in prostate cancer cells led to decreased viability. These data argue for AKT-associated HK2-mediated metabolic reprogramming and mitochondrial association in PI3K-driven prostate cancer as one survival mechanism downstream of AR inhibition.

Quantitative Pathology in Tissue MR Spectroscopy Based Human Prostate Metabolomics

At present, the clinical utility of metabolomic profiles of human prostate tissue relies on the establishment of correlations between metabolite data and clinical measurements, particularly pathological findings. Because metabolomics is a quantitative study, its clinical value can be rigorously investigated by determining its association with other quantitative measures. The human visual assessment of prostate tissue, however, introduces both inter- and intra-observer biases that may limit the reliability of its quantitations, and therefore, the strength of its correlations with metabolomic profiles. The aim of this study was to develop a simple, feasible protocol for the computer-aided image analysis (CAIA) of prostate pathology slides in order to achieve quantitative pathology from tissue samples, following metabolomic measurement with high-resolution magic angle spinning (HRMAS) magnetic resonance spectroscopy (MRS). Thirty-eight samples from 29 prostatectomy cases were studied with HRMAS MRS. After spectroscopy analysis, samples were serial-sectioned, stained and visually assessed by pathologists. Cross-sections from these samples were then measured with the CAIA protocol. Results showed a two-fold difference between human visual assessments of the area percentages of tissue pathologies and CAIA area percentages obtained for the same features. Linear correlations were found between both metabolites indicative of normal epithelium and those indicative of prostate cancer, and the CAIA quantitative results. CAIA based quantitative pathology is more reliable than human visual assessment in establishing correlations useful for disease diagnosis between prostate pathology and metabolite concentrations.

Metabolomics in rheumatic diseases: desperately seeking biomarkers

Metabolomics enables the profiling of large numbers of small molecules in cells, tissues and biological fluids. These molecules, which include amino acids, carbohydrates, lipids, nucleotides and their metabolites, can be detected quantitatively. Metabolomic methods, often focused on the information-rich analytical techniques of NMR spectroscopy and mass spectrometry, have potential for early diagnosis, monitoring therapy and defining disease pathogenesis in many therapeutic areas, including rheumatic diseases. By performing global metabolite profiling, also known as untargeted metabolomics, new discoveries linking cellular pathways to biological mechanisms are being revealed and are shaping our understanding of cell biology, physiology and medicine. These pathways can potentially be targeted to diagnose and treat patients with immune-mediated diseases.

A Balanced Tissue Composition Reveals New Metabolic and Gene Expression Markers in Prostate Cancer

Molecular analysis of patient tissue samples is essential to characterize the in vivo variability in human cancers which are not accessible in cell-lines or animal models. This applies particularly to studies of tumor metabolism. The challenge is, however, the complex mixture of various tissue types within each sample, such as benign epithelium, stroma and cancer tissue, which can introduce systematic biases when cancers are compared to normal samples. In this study we apply a simple strategy to remove such biases using sample selections where the average content of stroma tissue is balanced between the sample groups. The strategy is applied to a prostate cancer patient cohort where data from MR spectroscopy and gene expression have been collected from and integrated on the exact same tissue samples. We reveal in vivo changes in cancer-relevant metabolic pathways which are otherwise hidden in the data due to tissue confounding. In particular, lowered levels of putrescine are connected to increased expression of SRM, reduced levels of citrate are attributed to upregulation of genes promoting fatty acid synthesis, and increased succinate levels coincide with reduced expression of SUCLA2 and SDHD. In addition, the strategy also highlights important metabolic differences between the stroma, epithelium and prostate cancer. These results show that important in vivo metabolic features of cancer can be revealed from patient data only if the heterogeneous tissue composition is properly accounted for in the analysis.

Metabolomic Biomarkers of Prostate Cancer: Prediction, Diagnosis, Progression, Prognosis, and Recurrence

Metabolite profiling is being increasing employed in the study of prostate cancer as a means of identifying predictive, diagnostic, and prognostic biomarkers. This review provides a summary and critique of the current literature. Thirty-three human case-control studies of prostate cancer exploring disease prediction, diagnosis, progression, or treatment response were identified. All but one demonstrated the ability of metabolite profiling to distinguish cancer from benign, tumor aggressiveness, cases who recurred, and those who responded well to therapy. In the subset of studies where biomarker discriminatory ability was quantified, high AUCs were reported that would potentially outperform the current gold standards in diagnosis, prognosis, and disease recurrence, including PSA testing. There were substantial similarities between the metabolites and the associated pathways reported as significant by independent studies, and important roles for abnormal cell growth, intensive cell proliferation, and dysregulation of lipid metabolism were highlighted. The weight of the evidence therefore suggests metabolic alterations specific to prostate carcinogenesis and progression that may represent potential metabolic biomarkers. However, replication and validation of the most promising biomarkers is currently lacking and a number of outstanding methodologic issues remain to be addressed to maximize the utility of metabolomics in the study of prostate cancer. Cancer Epidemiol Biomarkers Prev; 25(6); 887-906. ©2016 AACR.

Prostate MRSI predicts outcome in radical prostatectomy patients

BACKGROUND

New non-invasive methods are needed for sub-stratifying high-risk prostate cancer patients. Magnetic resonance spectroscopic imaging (MRSI) maps metabolites in prostate cancer, providing information on tumor aggressiveness and volume.

PURPOSE

To investigate the correlation between MRSI and treatment failure (TF) after radical prostatectomy (RP).

METHODS

Two-hundred sixty-two patients who underwent endorectal MRI/MRSI followed by RP at our institution from 2003 to 2007 were studied. MRI stage, number of voxels in the MRSI index lesion (NILV), number of high-grade voxels (NHGV), and number of voxels containing undetectable polyamines (NUPV) were derived. Clinical outcome was followed until August, 2014. Treatment failure was defined as 1) biochemical recurrence (BCR), 2) persistently detectable PSA after RP, or 3) adjuvant therapy initiated in the absence of BCR. MRI/MRSI features and clinical parameters were compared to TF by univariate Cox Proportional Hazards Regression. After backward selection, each MRSI parameter was included in a separate regression model adjusted for NCCN-based clinical risk score (CRS), number of biopsy cores positive (NPC), and MRI stage.

RESULTS

In univariate analysis, all clinical variables were associated with TF in addition to MRI stage, NILV, NHGV, and NUPV. In multivariate analysis, NILV, NHGV, and NUPV were also significant risk factors for TF (p=0.016, p=0.002, p=0.006, respectively). The association between the number of tumor voxels with undetectable polyamines and the probability of treatment failure has not been previously reported. The number of MRSI cancer voxels correlated with extracapsular extension (ECE) (p<0.0001).

CONCLUSIONS

MRSI was associated with post-radical prostatectomy treatment failure in models adjusted for the number of positive biopsy cores and clinical risk score. This is the first report that in radical prostatectomy patients MRSI has an association with treatment failure independent of the number of positive biopsy cores. MRSI may help the clinician determine whether patients with high risk disease who undergo RP are candidates for specialized additional treatment.

Metabolite profiling in retinoblastoma identifies novel clinicopathological subgroups

BACKGROUND

Tumour classification, based on histopathology or molecular pathology, is of value to predict tumour behaviour and to select appropriate treatment. In retinoblastoma, pathology information is not available at diagnosis and only exists for enucleated tumours. Alternative methods of tumour classification, using noninvasive techniques such as magnetic resonance spectroscopy, are urgently required to guide treatment decisions at the time of diagnosis.

METHODS

High-resolution magic-angle spinning magnetic resonance spectroscopy (HR-MAS MRS) was undertaken on enucleated retinoblastomas. Principal component analysis and cluster analysis of the HR-MAS MRS data was used to identify tumour subgroups. Individual metabolite concentrations were determined and were correlated with histopathological risk factors for each group.

RESULTS

Multivariate analysis identified three metabolic subgroups of retinoblastoma, with the most discriminatory metabolites being taurine, hypotaurine, total-choline and creatine. Metabolite concentrations correlated with specific histopathological features: taurine was correlated with differentiation, total-choline and phosphocholine with retrolaminar optic nerve invasion, and total lipids with necrosis.

CONCLUSIONS

We have demonstrated that a metabolite-based classification of retinoblastoma can be obtained using ex vivo magnetic resonance spectroscopy, and that the subgroups identified correlate with histopathological features. This result justifies future studies to validate the clinical relevance of these subgroups and highlights the potential of in vivo MRS as a noninvasive diagnostic tool for retinoblastoma patient stratification.

TE = 32 ms vs TE = 100 ms echo-time (1)H-magnetic resonance spectroscopy in prostate cancer: Tumor metabolite depiction and absolute concentrations in tumors and adjacent tissues

PURPOSE

To compare the depiction of metabolite signals in short and long echo time (TE) prostate cancer spectra at 3T, and to quantify their concentrations in tumors of different stage and grade, and tissues adjacent to tumor.

MATERIALS AND METHODS

First, single-voxel magnetic resonance imaging (MRI) spectra were acquired from voxels consisting entirely of tumor, as defined on T2-weighted and diffusion-weighted (DW)-MRI and from a biopsy-positive octant, at TEs of 32 msec and 100 msec in 26 prostate cancer patients. Then, in a separate cohort of 26 patients, single-voxel TE = 32 msec MR spectroscopy (MRS) was performed over a partial-tumor region and a matching, contralateral normal-appearing region, defined similarly. Metabolite depiction was compared between TEs using Cramér-Rao lower bounds (CRLB), and absolute metabolite concentrations were calculated from TE = 32 msec spectra referenced to unsuppressed water spectra.

RESULTS

Citrate and spermine resonances in tumor were better depicted (had significantly lower CRLB) at TE = 32 msec, while the choline resonance was better depicted at TE = 100 msec. Citrate and spermine concentrations were significantly lower in patients of more advanced stage, significantly lower in Gleason grade 3+4 than 3+3 tumors, and significantly lower than expected from the tumor fraction in partial-tumor voxels (by 14 mM and 4 mM, respectively, P < 0.05).

CONCLUSION

Citrate and spermine resonances are better depicted at short TE than long TE in tumors. Reduction in these concentrations is related to increasing tumor stage and grade in vivo, while reductions in the normal-appearing tissues immediately adjacent to tumor likely reflect tumor field effects.

A prospective study of the efficacy of magnetic resonance spectroscopy imaging for predicting locally advanced prostate cancer

OBJECTIVE

To evaluate the efficacy of magnetic resonance spectroscopy imaging (MRSI) for predicting locally advanced prostate cancer (PC).

MATERIALS AND METHODS

Between April 2009 and July 2012, 80 consecutive patients with clinically localized PC had undergone endorectal MRSI before radical retropubic prostatectomy. Clinicopathological parameters, including age, preoperative prostate-specific antigen (PSA), Gleason score (GS) at biopsy, perinural invasion at biopsy, prostate weight at surgery, GS of surgical specimen, and pathological staging were recorded. The MRSI findings were compared with the histopathological findings of the radical prostatectomy. The diagnostic accuracy measures consisting of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) of MRSI, and other variables in the diagnosis of locally advanced PC (Pathology Stages pT3a, pT3b, or pT4) were evaluated.

RESULTS

Sensitivity, specificity, PPV, and NPV of MRSI in detecting locally advanced PC is 42.4%, 93.6%, 82.3%, and 69.8%, respectively [area under the receiver operating characteristic (ROC) curve=0.658, p value <0.0001]. MRSI, cancer-positive core percentage at biopsy, and GS at biopsy are more accurate factors among all the predictive variables in predicting locally advanced PC.

CONCLUSION

MRSI may be considered as a complementary diagnostic modality with high specificity and moderate sensitivity in predicting locally advanced PC. Combination of this modality with other predictive factors helps the surgeon and patient to select an appropriate treatment strategy.

Characterization of metabolites determined by means of 1H HR MAS NMR in intervertebral disc degeneration

Object

The objective of this study is the identification of metabolites by means of ¹H high resolution magic angle spinning nuclear magnetic resonance (¹H HR MAS NMR) spectroscopy and the evaluation of their applicability in distinguishing between healthy and degenerated disc tissues.

Materials and methods

Differences between the metabolic profiles of healthy and degenerated disc tissues were studied by means of ¹H HR MAS NMR. Analysis was performed for 81 disc tissue samples (control samples n = 21, degenerated disc tissue samples n = 60). Twenty six metabolites (amino acids, carbohydrates, and alcohols) were identified and quantified.

Results

The results indicate that the metabolic profile of degenerated discs is characterized by the presence of 2-propanol and the absence of scyllo-inositol and taurine. The concentrations of 2-propanol and lactate increase with age.

Conclusion

PCA analysis of ex vivo ¹H HR MAS NMR data revealed the occurrence of two groups: healthy and degenerative disc tissues. The effects of insufficient nutrient supply of discs, leading to their degeneration and back pain, are discussed.

Electronic supplementary material

The online version of this article (doi:10.1007/s10334-014-0457-0) contains supplementary material, which is available to authorized users.

Feasibility of detecting prostate cancer by ultraperformance liquid chromatography-mass spectrometry serum metabolomics

Prostate cancer (PCa) is the second leading cause of cancer-related mortality in men. The prevalent diagnosis method is based on the serum prostate-specific antigen (PSA) screening test, which suffers from low specificity, overdiagnosis, and overtreatment. In this work, untargeted metabolomic profiling of age-matched serum samples from prostate cancer patients and healthy individuals was performed using ultraperformance liquid chromatography coupled to high-resolution tandem mass spectrometry (UPLC-MS/MS) and machine learning methods. A metabolite-based in vitro diagnostic multivariate index assay (IVDMIA) was developed to predict the presence of PCa in serum samples with high classification sensitivity, specificity, and accuracy. A panel of 40 metabolic spectral features was found to be differential with 92.1% sensitivity, 94.3% specificity, and 93.0% accuracy. The performance of the IVDMIA was higher than the prevalent PSA test. Within the discriminant panel, 31 metabolites were identified by MS and MS/MS, with 10 further confirmed chromatographically by standards. Numerous discriminant metabolites were mapped in the steroid hormone biosynthesis pathway. The identification of fatty acids, amino acids, lysophospholipids, and bile acids provided further insights into the metabolic alterations associated with the disease. With additional work, the results presented here show great potential toward implementation in clinical settings.

Novel tools for prostate cancer prognosis, diagnosis, and follow-up

Prostate-specific antigen (PSA) is the main diagnostic tool when it comes to prostate cancer but it possesses serious limitations. Therefore, there is an urgent need for more sensitive and specific biomarkers for prostate cancer prognosis and patient follow-up. Recent advances led to the discovery of many novel diagnostic/prognostic techniques and provided us with many worthwhile candidates. This paper briefly reviews the most promising biomarkers with respect to their implementation in screening, early detection, diagnostic confirmation, prognosis, and prediction of therapeutic response or monitoring disease and recurrence; and their use as possible therapeutic targets. This review also examines the possible future directions in the field of prostate cancer marker research.