Application of metabolomics to prostate cancer

Metabolite Analysis and Histology on the Exact Same Tissue: Comprehensive Metabolomic Profiling and Metabolic Classification of Prostate Cancer

We report a method of metabolomic profiling of intact tissue based on molecular preservation by extraction and fixation (mPREF) and high-performance chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS). mPREF extracts metabolites by aqueous methanol from tissue biopsies without altering tissue architecture and thus conventional histology can be performed on the same tissue. In a proof-of-principle study, we applied dansylation LC-MS to profile the amine/phenol submetabolome of prostate needle biopsies from 25 patient samples derived from 16 subjects. 2900 metabolites were consistently detected in more than 50% of the samples. This unprecedented coverage allowed us to identify significant metabolites for differentiating tumor and normal tissues. The panel of significant metabolites was refined using 36 additional samples from 18 subjects. Receiver Operating Characteristic (ROC) analysis showed area-under-the-curve (AUC) of 0.896 with sensitivity of 84.6% and specificity of 83.3% using 7 metabolites. A blind study of 24 additional validation samples gave a specificity of 90.9% at the same sensitivity of 84.6%. The mPREF extraction can be readily implemented into the existing clinical workflow. Our method of combining mPREF with CIL LC-MS offers a powerful and convenient means of performing histopathology and discovering or detecting metabolite biomarkers in the same tissue biopsy.

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.

Detection of prostate cancer using a voltammetric electronic tongue

A simple method based on the multivariate analysis of data from urine using an electronic voltammetric tongue is used to detect patients with prostate cancer. A sensitivity of 91% and a specificity of 73% were obtained to distinguish the urine from cancer patients and the urine from non-cancer patients.

SERUM metabolomics of acute lymphoblastic leukaemia and acute myeloid leukaemia for probing biomarker molecules

Acute leukaemia (AL) is a critical neoplasm of white blood cells. Diagnosing AL requires bone marrow puncture procedure, which many patients do not consent to for it is invasive. Hence sensitive and specific early diagnostic biomarkers are essential for non-invasive diagnosis, new therapeutics and improving the disease prognosis. To differentiate the metabolic alterations associated with acute lymphoblastic leukaemia (ALL) and acute myeloid leukaemia (AML), we investigated serum of ALL and AML patients in comparison with two controls using gas chromatography coupled with triple quadrupole tandem mass spectrometry and multivariate statistical analysis. Twenty seven out of 1425 metabolites were found differentiative among ALL, AML, aplastic anaemia (APA) patients and healthy control using p-value ≤ 0.001. ALL is the most dissimilar group from other three groups as in hierarchical clustering showed 72.1% dissimilarity. Model generation using PLSDA gave an overall accuracy of 91.9%. This study helps in metabolic fingerprinting of control and disease serum at high significance levels and could be used for early diagnosing of AL. Based on pathways analysis, fatty acid metabolism is deregulated in patients with AL and may represent an underlying metabolic pathway associated with disease progression. Copyright © 2016 John Wiley & Sons, Ltd.

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.

Integration of lipidomics and transcriptomics unravels aberrant lipid metabolism and defines cholesteryl oleate as potential biomarker of prostate cancer

In-depth delineation of lipid metabolism in prostate cancer (PCa) is significant to open new insights into prostate tumorigenesis and progression, and provide potential biomarkers with greater accuracy for improved diagnosis. Here, we performed lipidomics and transcriptomics in paired prostate cancer tumor (PCT) and adjacent nontumor (ANT) tissues, followed by external validation of biomarker candidates. We identified major dysregulated pathways involving lipogenesis, lipid uptake and phospholipids remodeling, correlated with widespread lipid accumulation and lipid compositional reprogramming in PCa. Specifically, cholesteryl esters (CEs) were most prominently accumulated in PCa, and significantly associated with cancer progression and metastasis. We showed that overexpressed scavenger receptor class B type I (SR-BI) may contribute to CEs accumulation. In discovery set, CEs robustly differentiated PCa from nontumor (area under curve (AUC) of receiver operating characteristics (ROC), 0.90–0.94). In validation set, CEs potently distinguished PCa and non-malignance (AUC, 0.84–0.91), and discriminated PCa and benign prostatic hyperplasia (BPH) (AUC, 0.90–0.96), superior to serum prostate-specific antigen (PSA) (AUC = 0.83). Cholesteryl oleate showed highest AUCs in distinguishing PCa from non-malignance or BPH (AUC = 0.91 and 0.96). Collectively, our results unravel the major lipid metabolic aberrations in PCa and imply the potential role of CEs, particularly, cholesteryl oleate, as molecular biomarker for PCa detection.

Higher plasma levels of lysophosphatidylcholine 18:0 are related to a lower risk of common cancers in a prospective metabolomics study

BACKGROUND

First metabolomics studies have indicated that metabolic fingerprints from accessible tissues might be useful to better understand the etiological links between metabolism and cancer. However, there is still a lack of prospective metabolomics studies on pre-diagnostic metabolic alterations and cancer risk.

METHODS

Associations between pre-diagnostic levels of 120 circulating metabolites (acylcarnitines, amino acids, biogenic amines, phosphatidylcholines, sphingolipids, and hexoses) and the risks of breast, prostate, and colorectal cancer were evaluated by Cox regression analyses using data of a prospective case-cohort study including 835 incident cancer cases.

RESULTS

The median follow-up duration was 8.3 years among non-cases and 6.5 years among incident cases of cancer. Higher levels of lysophosphatidylcholines (lysoPCs), and especially lysoPC a C18:0, were consistently related to lower risks of breast, prostate, and colorectal cancer, independent of background factors. In contrast, higher levels of phosphatidylcholine PC ae C30:0 were associated with increased cancer risk. There was no heterogeneity in the observed associations by lag time between blood draw and cancer diagnosis.

CONCLUSION

Changes in blood lipid composition precede the diagnosis of common malignancies by several years. Considering the consistency of the present results across three cancer types the observed alterations point to a global metabolic shift in phosphatidylcholine metabolism that may drive tumorigenesis.

Metabolomic analysis of prostate cancer risk in a prospective cohort: The alpha-tocolpherol, beta-carotene cancer prevention (ATBC) study

Despite decades of concerted epidemiological research, relatively little is known about the etiology of prostate cancer. As genome-wide association studies have identified numerous genetic variants, so metabolomic profiling of blood and other tissues represents an agnostic, "broad-spectrum" approach for examining potential metabolic biomarkers of prostate cancer risk. To this end, we conducted a prospective analysis of prostate cancer within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study cohort based on 200 cases (100 aggressive) and 200 controls (age- and blood collection date-matched) with fasting serum collected up to 20 years prior to case diagnoses. Ultrahigh performance liquid chromatography/mass spectroscopy and gas chromatography/mass spectroscopy identified 626 compounds detected in >95% of the men and the odds ratio per 1-standard deviation increase in log-metabolite levels and risk were estimated using conditional logistic regression. We observed strong inverse associations between energy and lipid metabolites and aggressive cancer (p = 0.018 and p = 0.041, respectively, for chemical class over-representation). Inositol-1-phosphate showed the strongest association (OR = 0.56, 95% CI = 0.39-0.81, p = 0.002) and glycerophospholipids and fatty acids were heavily represented; e.g., oleoyl-linoleoyl-glycerophosphoinositol (OR = 0.64, p = 0.004), 1-stearoylglycerophosphoglycerol (OR=0.65, p = 0.025), stearate (OR=0.65, p = 0.010) and docosadienoate (OR = 0.66, p = 0.014). Both alpha-ketoglutarate and citrate were associated with aggressive disease risk (OR = 0.69, 95% CI = 0.51-0.94, p = 0.02; OR = 0.69, 95% CI = 0.50-0.95, p = 0.02), as were elevated thyroxine and trimethylamine oxide (OR = 1.65, 95% CI = 1.08-2.54, p = 0.021; and OR = 1.36, 95% CI = 1.02-1.81, p = 0.039). Serum PSA adjustment did not alter the findings. Our data reveal several metabolomic leads that may have pathophysiological relevance to prostate carcinogenesis and should be examined through additional research.

Translational metabolomics in cancer research

Over the last decade there has been a bottleneck in the introduction of new validated cancer metabolic biomarkers into clinical practice. Unfortunately, there are no biomarkers with adequate sensitivity for the early detection of cancer, and there remain a reliance on cancer antigens for monitoring treatment. The need for new diagnostics has led to the exploration of untargeted metabolomics for discovery of early biomarkers of specific cancers and targeted metabolomics to elucidate mechanistic aspects of tumor progression. The successful translation of such strategies to the treatment of cancer would allow earlier intervention to improve survival. We have reviewed the methodology that is being used to achieve these goals together with recent advances in implementing translational metabolomics in cancer.

Metabolomic profiling for the identification of novel diagnostic markers in prostate cancer

Metabolomic profiling offers a powerful methodology for understanding the perturbations of biochemical systems occurring during a disease process. During neoplastic transformation, prostate cells undergo metabolic reprogramming to satisfy the demands of growth and proliferation. An early event in prostate cell transformation is the loss of capacity to accumulate zinc. This change is associated with a higher energy efficiency and increased lipid biosynthesis for cellular proliferation, membrane formation and cell signaling. Moreover, recent studies have shown that sarcosine, an N-methyl derivative of glycine, was significantly increased during disease progression from normal to localized to metastatic prostate cancer. Mapping the metabolomic profiles to their respective biochemical pathways showed an upregulation of androgen-induced protein synthesis, an increased amino acid metabolism and a perturbation of nitrogen breakdown pathways, along with high total choline-containing compounds and phosphocholine levels. In this review, the role of emerging biomarkers is summarized, based on the current understanding of the prostate cancer metabolome.

Overview of mass spectrometry-based metabolomics: opportunities and challenges

The field of metabolomics has witnessed an exponential growth in the last decade driven by important applications spanning a wide range of areas in the basic and life sciences and beyond. Mass spectrometry in combination with chromatography and nuclear magnetic resonance are the two major analytical avenues for the analysis of metabolic species in complex biological mixtures. Owing to its inherent significantly higher sensitivity and fast data acquisition, MS plays an increasingly dominant role in the metabolomics field. Propelled by the need to develop simple methods to diagnose and manage the numerous and widespread human diseases, mass spectrometry has witnessed tremendous growth with advances in instrumentation, experimental methods, software, and databases. In response, the metabolomics field has moved far beyond qualitative methods and simple pattern recognition approaches to a range of global and targeted quantitative approaches that are now routinely used and provide reliable data, which instill greater confidence in the derived inferences. Powerful isotope labeling and tracing methods have become very popular. The newly emerging ambient ionization techniques such as desorption ionization and rapid evaporative ionization have allowed direct MS analysis in real time, as well as new MS imaging approaches. While the MS-based metabolomics has provided insights into metabolic pathways and fluxes, and metabolite biomarkers associated with numerous diseases, the increasing realization of the extremely high complexity of biological mixtures underscores numerous challenges including unknown metabolite identification, biomarker validation, and interlaboratory reproducibility that need to be dealt with for realization of the full potential of MS-based metabolomics. This chapter provides a glimpse at the current status of the mass spectrometry-based metabolomics field highlighting the opportunities and challenges.

Metabolomics and its Application to the Development of Clinical Laboratory Tests for Prostate Cancer

INTRODUCTION

There is a critical need to develop clinical laboratory assays that provide risk assessment for men at elevated risk for prostate cancer, and once diagnosed, could further identify those men with clinically significant disease.

METHODS

Recent advancements in analytical instrumentation have enabled mass spectrometry-based metabolomics methodologies. Further advancements in chromatographic techniques have facilitated high throughput, quantitative assays for a broad spectrum of biochemicals.

RESULTS

Screening metabolomics techniques have been applied to biospecimens from large cohorts of men comparing those individuals with prostate cancer to those with no evidence of malignancy. Work beginning in tissues has identified biochemical profiles that correlate with disease and disease severity, including tumor grade and stage. Some of these metabolic abnormalities, such as dramatic elevations in sarcosine, have been found to translate into biological fluids, especially blood and urine, which can be sampled in a minimally invasive manner.

DISCUSSION

The differential abundances of these tumor-associated metabolites have been found to improve the performance of clinical prognostic/diagnostic tools.

CONCLUSION

The outlook is bright for metabolomic technology to address clinical diagnostic needs for prostate cancer patient management. Early validation of specific clinical tests provides a preview of further successes in this area. Metabolomics has shown its utility to complement and augment traditional clinical approaches as well as emerging genomic, transcriptomic and proteomic methodologies, and is expected to play a key role in the precision medicine-based management of the prostate cancer patient.

Distinct serum metabolomics profiles associated with malignant progression in the KrasG12D mouse model of pancreatic ductal adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer deaths worldwide with less than a 6% 5-year survival rate. PDAC is associated with poor prognosis based on the late stage diagnosis of the disease. Current diagnostic tests lack the sensitivity and specificity to identify markers of early staging. Metabolomics has provided biomarkers for various diseases, stressors, and environmental exposures. In this study we utilized the p48-Cre/LSL-Kras^G12D mouse model with age-matched wild type mice. This model shows malignant progression to PDAC analogous to the human disease stages via early and late pancreatic intra-epithelial neoplasia (PanIN) lesions.

Results

Serum was collected from mice with early PanIN lesions (at 3-5 months) and with late PanIN or invasive PDAC lesions (13-16 months), as determined by histopathology. Metabolomics analysis of the serum samples was conducted through UPLC-TOFMS (Ultra Performance Liquid Chromatography coupled to Time-of-flight Mass Spectrometry). Multivariate data analysis revealed distinct metabolic patterns in serum samples collected during malignant progression towards invasive PDAC. Animals with early or late stage lesions were distinguished from their respective controls with 82.1% and 81.5% accuracy, respectively. This also held up for randomly selected subgroups in the late stage lesion group that showed less variability between animals. One of the metabolites, citrate, was validated through tandem mass spectrometry and showed increased levels in serum with disease progression. Furthermore, serum metabolite signatures from animals with early stage lesions identified controls and animals with late stage lesions with 81.5% accuracy (p<0.01) and vice-versa with 73.2% accuracy (p<0.01).

Conclusions

We conclude that metabolomics analysis of serum samples can identify the presence of early and late stage pancreatic cancer.

Metabonomic profiling of bladder cancer

Early diagnosis and life-long surveillance are clinically important to improve the long-term survival of bladder cancer patients. Currently, a noninvasive biomarker that is as sensitive and specific as cystoscopy in detecting bladder tumors is lacking. Metabonomics is a complementary approach for identifying perturbed metabolic pathways in bladder cancer. Significant progress has been made using modern metabonomic techniques to characterize and distinguish bladder cancer patients from control subjects, identify marker metabolites, and shed insights on the disease biology and potential therapeutic targets. With its rapid development, metabonomics has the potential to impact the clinical management of bladder cancer patients in the future by revolutionizing the diagnosis and life-long surveillance strategies and stratifying patients for diagnostic, surgical, and therapeutic clinical trials. An introduction to metabonomics, typical metabonomic workflow, and critical evaluation of metabonomic investigations in identifying biomarkers for the diagnosis of bladder cancer are presented.

Molecular profiling and companion diagnostics: where is personalized medicine in cancer heading?

The goal of personalized medicine is to use the right drug at the right dose - with minimal or no toxicity - for the right patient at the right time. Recent advances in understanding cell biology and pathways, and in using molecular 'omics' technologies to diagnose cancer, offer a strategic bridge to personalized medicine in cancer. Modern personalized medicine takes into account an individual's genetic makeup and disease history before developing a treatment regimen. The future of clinical oncology will be based on the use of predictive and prognostic biomarkers in patient management. Once implemented widely, personalized medicine will benefit patients and the healthcare system greatly.

AKT1 and MYC induce distinctive metabolic fingerprints in human prostate cancer

Cancer cells may overcome growth factor dependence by deregulating oncogenic and/or tumor-suppressor pathways that affect their metabolism, or by activating metabolic pathways de novo with targeted mutations in critical metabolic enzymes. It is unknown whether human prostate tumors develop a similar metabolic response to different oncogenic drivers or a particular oncogenic event results in its own metabolic reprogramming. Akt and Myc are arguably the most prevalent driving oncogenes in prostate cancer. Mass spectrometry-based metabolite profiling was performed on immortalized human prostate epithelial cells transformed by AKT1 or MYC, transgenic mice driven by the same oncogenes under the control of a prostate-specific promoter, and human prostate specimens characterized for the expression and activation of these oncoproteins. Integrative analysis of these metabolomic datasets revealed that AKT1 activation was associated with accumulation of aerobic glycolysis metabolites, whereas MYC overexpression was associated with dysregulated lipid metabolism. Selected metabolites that differentially accumulated in the MYC-high versus AKT1-high tumors, or in normal versus tumor prostate tissue by untargeted metabolomics, were validated using absolute quantitation assays. Importantly, the AKT1/MYC status was independent of Gleason grade and pathologic staging. Our findings show how prostate tumors undergo a metabolic reprogramming that reflects their molecular phenotypes, with implications for the development of metabolic diagnostics and targeted therapeutics.

1-stearoylglycerol is associated with risk of prostate cancer: results from serum metabolomic profiling

Although prostate cancer is the most commonly diagnosed cancer among men in developed populations, recent recommendations against routine prostate-specific antigen screening have cast doubt on its utility for early detection. We compared the metabolomic profiles of prospectively collected fasting serum from 74 prostate cancer cases and 74 controls selected from the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study cohort of male smokers. Circulating 1-stearoylglycerol (1-SG, or 1-monostearin) was statistically significantly inversely associated with risk of prostate cancer after Bonferroni correction for multiple comparisons (i.e., 420 identified metabolites) (OR=0.34, 95% CI=0.20 - 0.58, p=6.3 × 10-5). The magnitude of this association did not differ by disease aggressiveness and was observed for cases diagnosed up to 23 years after blood collection. Similar but somewhat weaker prostate cancer risk signals were also evident for glycerol and alpha-ketoglutarate. In this population, men with higher serum 1-SG were less likely to develop prostate cancer, supporting a role for dysregulation of lipid metabolism in this malignancy. Additional studies are needed to retest the association and to examine 1-SG for its potential as a prostate cancer early detection marker.

Prioritizing candidate disease metabolites based on global functional relationships between metabolites in the context of metabolic pathways

Identification of key metabolites for complex diseases is a challenging task in today's medicine and biology. A special disease is usually caused by the alteration of a series of functional related metabolites having a global influence on the metabolic network. Moreover, the metabolites in the same metabolic pathway are often associated with the same or similar disease. Based on these functional relationships between metabolites in the context of metabolic pathways, we here presented a pathway-based random walk method called PROFANCY for prioritization of candidate disease metabolites. Our strategy not only takes advantage of the global functional relationships between metabolites but also sufficiently exploits the functionally modular nature of metabolic networks. Our approach proved successful in prioritizing known metabolites for 71 diseases with an AUC value of 0.895. We also assessed the performance of PROFANCY on 16 disease classes and found that 4 classes achieved an AUC value over 0.95. To investigate the robustness of the PROFANCY, we repeated all the analyses in two metabolic networks and obtained similar results. Then we applied our approach to Alzheimer's disease (AD) and found that a top ranked candidate was potentially related to AD but had not been reported previously. Furthermore, our method was applicable to prioritize the metabolites from metabolomic profiles of prostate cancer. The PROFANCY could identify prostate cancer related-metabolites that are supported by literatures but not considered to be significantly differential by traditional differential analysis. We also developed a freely accessible web-based and R-based tool at http://bioinfo.hrbmu.edu.cn/PROFANCY.

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.

Detection of prostate cancer by an electronic nose: a proof of principle study

PURPOSE

We evaluate the ability of an electronic nose to discriminate prostate cancer from benign prostatic hyperplasia using urine headspace, potentially offering a clinically applicable noninvasive and rapid diagnostic method.

MATERIALS AND METHODS

The ChemPro® 100-eNose was used to discriminate prostate cancer from benign prostatic hyperplasia using urine sample headspace. Its performance was tested with 50 patients with confirmed prostate cancer and 24 samples from 15 patients with benign prostatic hyperplasia (15 patients provided urine preoperatively and 9 patients provided samples 3 months postoperatively) scheduled to undergo robotic assisted laparoscopic radical prostatectomy or transurethral resection of prostate, respectively. The patients provided urine sample preoperatively and those with benign prostatic hyperplasia also provided samples 3 months postoperatively to be used as a pooled control sample population. A discrimination classifier was identified for eNose and subsequently, sensitivity and specificity values were determined. Leave-one-out cross-validation was performed.

RESULTS

Using leave-one-out cross-validation the eNose reached a sensitivity of 78%, a specificity of 67% and AUC 0.77.

CONCLUSIONS

The electronic nose is capable of rapidly and noninvasively discriminating prostate cancer and benign prostatic hyperplasia using urine headspace in patients undergoing surgery.

Towards the potential use of1H NMR spectroscopy in urine samples for prostate cancer detection

An multivariate approach based on¹H NMR spectra profiles of urine samples to detect patients with prostate cancer.

Overview of mass spectrometry-based metabolomics: opportunities and challenges

The field of metabolomics has witnessed an exponential growth in the last decade driven by important applications spanning a wide range of areas in the basic and life sciences and beyond. Mass spectrometry in combination with chromatography and nuclear magnetic resonance are the two major analytical avenues for the analysis of metabolic species in complex biological mixtures. Owing to its inherent significantly higher sensitivity and fast data acquisition, MS plays an increasingly dominant role in the metabolomics field. Propelled by the need to develop simple methods to diagnose and manage the numerous and widespread human diseases, mass spectrometry has witnessed tremendous growth with advances in instrumentation, experimental methods, software, and databases. In response, the metabolomics field has moved far beyond qualitative methods and simple pattern recognition approaches to a range of global and targeted quantitative approaches that are now routinely used and provide reliable data, which instill greater confidence in the derived inferences. Powerful isotope labeling and tracing methods have become very popular. The newly emerging ambient ionization techniques such as desorption ionization and rapid evaporative ionization have allowed direct MS analysis in real time, as well as new MS imaging approaches. While the MS-based metabolomics has provided insights into metabolic pathways and fluxes, and metabolite biomarkers associated with numerous diseases, the increasing realization of the extremely high complexity of biological mixtures underscores numerous challenges including unknown metabolite identification, biomarker validation, and interlaboratory reproducibility that need to be dealt with for realization of the full potential of MS-based metabolomics. This chapter provides a glimpse at the current status of the mass spectrometry-based metabolomics field highlighting the opportunities and challenges.

Identification of biomarkers for apoptosis in cancer cell lines using metabolomics: tools for individualized medicine

BACKGROUND

Metabolomics is a versatile unbiased method to search for biomarkers of human disease. In particular, one approach in cancer therapy is to promote apoptosis in tumour cells; this could be improved with specific biomarkers of apoptosis for monitoring treatment. We recently observed specific metabolic patterns in apoptotic cell lines; however, in that study, apoptosis was only induced with one pro-apoptotic agent, staurosporine.

OBJECTIVE

The aim of this study was to find novel biomarkers of apoptosis by verifying our previous findings using two further pro-apoptotic agents, 5-fluorouracil and etoposide, that are commonly used in anticancer treatment.

METHODS

Metabolic parameters were assessed in HepG2 and HEK293 cells using the newborn screening assay adapted for cell culture approaches, quantifying the levels of amino acids and acylcarnitines with mass spectrometry.

RESULTS

We were able to identify apoptosis-specific changes in the metabolite profile. Moreover, the amino acids alanine and glutamate were both significantly up-regulated in apoptotic HepG2 and HEK293 cells irrespective of the apoptosis inducer.

CONCLUSION

Our observations clearly indicate the potential of metabolomics in detecting metabolic biomarkers applicable in theranostics and for monitoring drug efficacy.

Serum sarcosine is a risk factor for progression and survival in patients with metastatic castration-resistant prostate cancer

AIM

Sarcosine has been identified as a differential metabolite that is greatly increased during progression from normal tissue to prostate cancer and metastatic disease. In this study we assessed the role of serum sarcosine in metastatic castration-resistant prostate cancer (mCRPC) patients.

PATIENTS & METHODS

Data from 52 mCRPC patients treated with docetaxel-based chemotherapy were retrospectively analyzed. Receiver operating characteristic curves, and Kaplan-Meier and Cox multivariate analyses were performed.

RESULTS

Median sarcosine values were significantly higher in mCRPC versus non-mCRPC patients (0.81 vs 0.52 nmol/µl; p < 0.0001). A significant correlation resulted between serum sarcosine levels and the duration of hormone sensitivity (Spearman's correlation coefficient: -0.51; p = 0.001). At multivariate analysis sarcosine was an independent prognostic factor of outcome in terms of overall and progression-free survival.

CONCLUSION

Serum sarcosine values were significantly increased in patients with metastatic disease. Moreover, this biomarker is a risk factor for progression and survival in chemotherapy-treated mCRPC patients.

Metabolomic signatures of aggressive prostate cancer

BACKGROUND

Current diagnostic techniques have increased the detection of prostate cancer; however, these tools inadequately stratify patients to minimize mortality. Recent studies have identified a biochemical signature of prostate cancer metastasis, including increased sarcosine abundance. This study examined the association of tissue metabolites with other clinically significant findings.

METHODS

A state of the art metabolomics platform analyzed prostatectomy tissues (331 prostate tumor, 178 cancer-free prostate tissues) from two independent sites. Biochemicals were analyzed by gas chromatography-mass spectrometry and ultrahigh performance liquid chromatography-tandem mass spectrometry. Statistical analyses identified metabolites associated with cancer aggressiveness: Gleason score, extracapsular extension, and seminal vesicle and lymph node involvement.

RESULTS

Prostate tumors had significantly altered metabolite profiles compared to cancer-free prostate tissues, including biochemicals associated with cell growth, energetics, stress, and loss of prostate-specific biochemistry. Many metabolites were further associated with clinical findings of aggressive disease. Aggressiveness-associated metabolites stratified prostate tumor tissues with high abundances of compounds associated with normal prostate function (e.g., citrate and polyamines) from more clinically advanced prostate tumors. These aggressive prostate tumors were further subdivided by abundance profiles of metabolites including NAD+ and kynurenine. When added to multiparametric nomograms, metabolites improved prediction of organ confinement (AUROC from 0.53 to 0.62) and 5-year recurrence (AUROC from 0.53 to 0.64).

CONCLUSIONS

These findings support and extend earlier metabolomic studies in prostate cancer and studies where metabolic enzymes have been associated with carcinogenesis and/or outcome. Furthermore, these data suggest that panels of analytes may be valuable to translate metabolomic findings to clinically useful diagnostic tests.

Tumor necrosis factor alpha increases aerobic glycolysis and reduces oxidative metabolism in prostate epithelial cells

BACKGROUND

Chronic inflammation promotes prostate cancer formation and progression. Furthermore, alterations in energy metabolism are a hallmark of prostate cancer cells. However, the actions of inflammatory factors on the energy metabolism of prostate epithelial cells have not been previously investigated. This is the first study to report on the effect of the inflammatory cytokine tumor necrosis factor alpha (TNFα) on the glycolytic and oxidative metabolism, and the mitochondrial function of widely used prostate epithelial cells.

METHODS

Pre-malignant RWPE-1 and cancerous LNCaP and PC-3 cells were treated with low-dose TNFα. Glycolytic and oxidative metabolism was quantified by measuring extracellular acidification and oxygen consumption rates, respectively. ATP content and lactate export were measured by luminescence and fluorescence, respectively. Mitochondrial content and the expression of glucose transporter 1 (GLUT1), peroxisome proliferator-activated receptor co-activator 1 alpha (PGC-1α), and Cytochrome C were measured by flow cytometry.

RESULTS

Our data suggest that TNFα increases glycolysis, ATP production, and lactate export, while it reduces oxidative metabolism and mitochondrial function in prostate epithelial cells. The highly aggressive PC-3 cells tend to be less responsive to the actions of TNFα than the pre-malignant RWPE-1 and the non-aggressive LNCaP cells.

CONCLUSIONS

Cellular energetics, that is, glycolytic and oxidative metabolism is significantly influenced by low-level inflammation in prostate epithelial cells. In widely used prostate epithelial cell models, the micro-environmental inflammatory cytokine TNFα induces aerobic glycolysis while inhibiting oxidative metabolism. This supports the hypothesis that low-level inflammation can induce Warburg metabolism in prostate epithelial cells, which may promote cancer formation and progression.

Biomarker Discovery and Translation in Metabolomics

The multifaceted field of metabolomics has witnessed exponential growth in both methods development and applications. Owing to the urgent need, a significant fraction of research investigations in the field is focused on understanding, diagnosing and preventing human diseases; hence, the field of biomedicine has been the major beneficiary of metabolomics research. A large body of literature now documents the discovery of numerous potential biomarkers and provides greater insights into pathogeneses of numerous human diseases. A sizable number of findings have been tested for translational applications focusing on disease diagnostics ranging from early detection, to therapy prediction and prognosis, monitoring treatment and recurrence detection, as well as the important area of therapeutic target discovery. Current advances in analytical technologies promise quantitation of biomarkers from even small amounts of bio-specimens using non-invasive or minimally invasive approaches, and facilitate high-throughput analysis required for real time applications in clinical settings. Nevertheless, a number of challenges exist that have thus far delayed the translation of a majority of promising biomarker discoveries to the clinic. This article presents advances in the field of metabolomics with emphasis on biomarker discovery and translational efforts, highlighting the current status, challenges and future directions.

Sarcosine as a potential prostate cancer biomarker--a review

Prostate cancer (CaP) is the most common type of tumour disease in men. Early diagnosis of cancer of the prostate is very important, because the sooner the cancer is detected, the better it is treated. According to that fact, there is great interest in the finding of new markers including amino acids, proteins or nucleic acids. Prostate specific antigen (PSA) is commonly used and is the most important biomarker of CaP. This marker can only be detected in blood and its sensitivity is approximately 80%. Moreover, early stages cannot be diagnosed using this protein. Currently, there does not exist a test for diagnosis of early stages of prostate cancer. This fact motivates us to find markers sensitive to the early stages of CaP, which are easily detected in body fluids including urine. A potential is therefore attributed to the non-protein amino acid sarcosine, which is generated by glycine-N-methyltransferase in its biochemical cycle. In this review, we summarize analytical methods for quantification of sarcosine as a CaP marker. Moreover, pathways of the connection of synthesis of sarcosine and CaP development are discussed.

Tissue metabolite profiling identifies differentiating and prognostic biomarkers for prostate carcinoma

Metabolomic research offers a deeper insight into biochemical changes in cancer metabolism and is a promising tool for identifying novel biomarkers. We aimed to evaluate the diagnostic and prognostic potential of metabolites in prostate cancer (PCa) tissue after radical prostatectomy. In matched malignant and nonmalignant prostatectomy samples from 95 PCa patients, aminoadipic acid, cerebronic acid, gluconic acid, glycerophosphoethanolamine, 2-hydroxybehenic acid, isopentenyl pyrophosphate, maltotriose, 7-methylguanine and tricosanoic acid were determined within a global metabolite profiling study using gas chromatography/liquid chromatography-mass spectrometry. The data were related to clinicopathological variables like prostate volume, tumor stage, Gleason score, preoperative prostate-specific antigen and disease recurrence in the follow-up. All nine metabolites showed higher concentrations in malignant than in nonmalignant samples except for gluconic acid and maltotriose, which had lower levels in tumors. Receiver -operating characteristics analysis demonstrated a significant discrimination for all metabolites between malignant and nonmalignant tissue with a maximal area under the curve of 0.86 for tricosanoic acid, whereas no correlation was observed between the metabolite levels and the Gleason score or tumor stage except for gluconic acid. Univariate Cox regression and Kaplan-Meier analyses showed that levels of aminoadipic acid, gluconic acid and maltotriose were associated with the biochemical tumor recurrence (prostate-specific antigen > 0.2 ng/mL). In multivariate Cox regression analyses, aminoadipic acid together with tumor stage and Gleason score remained in a model as independent marker for prediction of biochemical recurrence. This study proved that metabolites in PCa tissue can be used, in combination with traditional clinicopathological factors, as promising diagnostic and prognostic tools.

Application of Holistic Liquid Chromatography-High Resolution Mass Spectrometry Based Urinary Metabolomics for Prostate Cancer Detection and Biomarker Discovery

Human exhibit wide variations in their metabolic profiles because of differences in genetic factors, diet and lifestyle. Therefore in order to detect metabolic differences between individuals robust analytical methods are required. A protocol was produced based on the use of Liquid Chromatography- High Resolution Mass Spectrometry (LC-HRMS) in combination with orthogonal Hydrophilic Interaction (HILIC) and Reversed Phase (RP) liquid chromatography methods for the analysis of the urinary metabolome, which was then evaluated as a diagnostic tool for prostate cancer (a common but highly heterogeneous condition). The LC-HRMS method was found to be robust and exhibited excellent repeatability for retention times (<±1%), and mass accuracy (<±1 ppm). Based on normalised data (against creatinine levels, osmolality or MS total useful signals/MSTUS) coupled with supervised multivariate analysis using Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA), we were able to discriminate urine samples from men with or without prostate cancer with R2Y(cum) >0.9. In addition, using the receiver operator characteristics (ROC) test, the area under curve (AUC) for the combination of the four best characterised biomarker compounds was 0.896. The four biomarker compounds were also found to differ significantly (P<0.05) between an independent patient cohort and controls. This is the first time such a rigorous test has been applied to this type of model. If validated, the established protocol provides a robust approach with a potentially wide application to metabolite profiling of human biofluids in health and disease.

Metabolomic imaging of prostate cancer with magnetic resonance spectroscopy and mass spectrometry

Metabolomic imaging of prostate cancer (PCa) aims to improve in vivo imaging capability so that PCa tumors can be localized noninvasively to guide biopsy and evaluated for aggressiveness prior to prostatectomy, as well as to assess and monitor PCa growth in patients with asymptomatic PCa newly diagnosed by biopsy. Metabolomics studies global variations of metabolites with which malignancy conditions can be evaluated by profiling the entire measurable metabolome, instead of focusing only on certain metabolites or isolated metabolic pathways. At present, PCa metabolomics is mainly studied by magnetic resonance spectroscopy (MRS) and mass spectrometry (MS). With MRS imaging, the anatomic image, obtained from magnetic resonance imaging, is mapped with values of disease condition-specific metabolomic profiles calculated from MRS of each location. For example, imaging of removed whole prostates has demonstrated the ability of metabolomic profiles to differentiate cancerous foci from histologically benign regions. Additionally, MS metabolomic imaging of prostate biopsies has uncovered metabolomic expression patterns that could discriminate between PCa and benign tissue. Metabolomic imaging offers the potential to identify cancer lesions to guide prostate biopsy and evaluate PCa aggressiveness noninvasively in vivo, or ex vivo to increase the power of pathology analysis. Potentially, this imaging ability could be applied not only to PCa, but also to different tissues and organs to evaluate other human malignancies and metabolic diseases.

Current metabolomics: practical applications

The field of metabolomics continues to grow rapidly over the last decade and has been proven to be a powerful technology in predicting and explaining complex phenotypes in diverse biological systems. Metabolomics complements other omics, such as transcriptomics and proteomics and since it is a 'downstream' result of gene expression, changes in the metabolome is considered to best reflect the activities of the cell at a functional level. Thus far, metabolomics might be the sole technology capable of detecting complex, biologically essential changes. As one of the omics technology, metabolomics has exciting applications in varied fields, including medical science, synthetic biology, medicine, and predictive modeling of plant, animal and microbial systems. In addition, integrated applications with genomics, transcriptomics, and proteomics provide greater understanding of global system biology. In this review, we discuss recent applications of metabolomics in microbiology, plant, animal, food, and medical science.

Texaphyrins and water-soluble zinc(II) ionophores: development, mechanism of anticancer activity, and synergistic effects

Texaphyrins, first prepared by Sessler and coworkers in the 1980s, represent early examples of expanded porphyrins. This class of pentaaza, oligopyrrolic macrocycles demonstrates excellent tumor localization and metal-chelating properties. In biological milieus, texaphyrins act as redox mediators and are able to produce reactive oxygen species. Furthermore, texaphyrins have been shown to upregulate zinc in vivo, an important feature that inspired us to develop new zinc ionophores that might allow the same function to be elicited but via a simpler chemical means. In this review, the basic properties of texaphyrins and the zinc ionophores they helped spawn will be discussed in the cadre of developing an understanding that could lead to the preparation of new, redox-active anticancer agents.

Serum sarcosine increases the accuracy of prostate cancer detection in patients with total serum PSA less than 4.0 ng/ml

BACKGROUND

Sarcosine is reported to be a differential metabolite that is greatly increased during prostate cancer (PCa) progression. In this study, we assessed the role of serum sarcosine as a biomarker for PCa, as well as any association between sarcosine levels and clinical-pathological parameters.

METHODS

Sarcosine was measured by fluorometric assay in serum samples from 290 PCa patients and 312 patients with no evidence of malignancy (NEM), confirmed by 8-12 core prostate biopsies. Nonparametric statistical tests and receiver operating characteristics (ROC) analyses were performed to assess the diagnostic performance of sarcosine in different (prostate-specific antigen) PSA ranges.

RESULTS

ROC analyses in subjects with PSA < 4 ng/ml showed a higher predictive value of sarcosine (AUC = 0.668) versus total PSA (AUC = 0.535) (P = 0.03), whereas for the other two PSA ranges (4-10 ng/ml and >10 ng/ml), percent ratio of free to total PSA (%fPSA) showed a predictive superiority over sarcosine. Moreover, in patients with a PSA < 4 ng/ml, the percentage of low/intermediate-grade cancers was positively associated with sarcosine levels (P = 0.005). The specificities for serum sarcosine, %fPSA, PSA, and the logistic regression model at 95% sensitivity were 24.4, 3.41, 2.22, and 28.4%, respectively.

CONCLUSIONS

We provide evidence that serum sarcosine has a higher predictive value than tPSA and %fPSA in patients with PSA < 4 ng/ml. Moreover, sarcosine levels were significantly different in low grade versus high grade cancers in this subset of patients, suggesting that this marker may be a further tool not only for diagnosing PCa in normal PSA and abnormal DRE/TRUS patients but also for selecting candidates for non-aggressive therapies and active surveillance.

Serum glutamate levels correlate with Gleason score and glutamate blockade decreases proliferation, migration, and invasion and induces apoptosis in prostate cancer cells

PURPOSE

During glutaminolysis, glutamine is catabolized to glutamate and incorporated into citric acid cycle and lipogenesis. Serum glutamate levels were measured in patients with primary prostate cancer or metastatic castrate-resistant prostate cancer (mCRPCa) to establish clinical relevance. The effect of glutamate deprivation or blockade by metabotropic glutamate receptor 1 (GRM1) antagonists was investigated on prostate cancer cells' growth, migration, and invasion to establish biologic relevance.

EXPERIMENTAL DESIGN

Serum glutamate levels were measured in normal men (n = 60) and patients with primary prostate cancer (n = 197) or mCRPCa (n = 109). GRM1 expression in prostatic tissues was examined using immunohistochemistry (IHC). Cell growth, migration, and invasion were determined using cell cytotoxicity and modified Boyden chamber assays, respectively. Apoptosis was detected using immunoblotting against cleaved caspases, PARP, and γ-H2AX.

RESULTS

Univariate and multivariate analyses showed significantly higher serum glutamate levels in Gleason score ≥ 8 than in the Gleason score ≤ 7 and in African Americans than in the Caucasian Americans. African Americans with mCRPCa had significantly higher serum glutamate levels than those with primary prostate cancer or benign prostate. However, in Caucasian Americans, serum glutamate levels were similar in normal research subjects and patients with mCRPC. IHC showed weak or no expression of GRM1 in luminal acinar epithelial cells of normal or hyperplastic glands but high expression in primary or metastatic prostate cancer tissues. Glutamate deprivation or blockade decreased prostate cancer cells' proliferation, migration, and invasion and led to apoptotic cell death.

CONCLUSIONS

Glutamate expression is mechanistically associated with and may provide a biomarker of prostate cancer aggressiveness.

Pathway-directed weighted testing procedures for the integrative analysis of gene expression and metabolomic data

We explore the utility of p-value weighting for enhancing the power to detect differential metabolites in a two-sample setting. Related gene expression information is used to assign an a priori importance level to each metabolite being tested. We map the gene expression to a metabolite through pathways and then gene expression information is summarized per-pathway using gene set enrichment tests. Through simulation we explore four styles of enrichment tests and four weight functions to convert the gene information into a meaningful p-value weight. We implement the p-value weighting on a prostate cancer metabolomic dataset. Gene expression on matched samples is used to construct the weights. Under certain regulatory conditions, the use of weighted p-values does not inflate the type I error above what we see for the un-weighted tests except in high correlation situations. The power to detect differential metabolites is notably increased in situations with disjoint pathways and shows moderate improvement, relative to the proportion of enriched pathways, when pathway membership overlaps.