Beate Kamlage

Quality Markers Addressing Preanalytical Variations of Blood and Plasma Processing Identified by Broad and Targeted Metabolite Profiling

BACKGROUNDnMetabolomics is a valuable tool with applications in almost all life science areas. There is an increasing awareness of the essential need for high-quality biospecimens in studies applying omics technologies and biomarker research. Tools to detect effects of both blood and plasma processing are a key for assuring reproducible and credible results. We report on the response of the human plasma metabolome to common preanalytical variations in a comprehensive metabolomics analysis to reveal such high-quality markers.nnnMETHODSnHuman EDTA blood was subjected to preanalytical variations while being processed to plasma: microclotting, prolonged processing times at different temperatures, hemolysis, and contamination with buffy layer. In a second experiment, EDTA plasma was incubated at different temperatures for up to 16 h. Samples were subjected to GC-MS and liquid chromatography-tandem mass spectrometry-based metabolite profiling (MxP™ Broad Profiling) complemented by targeted methods, i.e., sphingoids (as part of MxP™ Lipids), MxP™ Catecholamines, and MxP™ Eicosanoids.nnnRESULTSnShort-term storage of blood, hemolysis, and short-term storage of noncooled plasma resulted in statistically significant increases of 4% to 19% and decreases of 8% to 12% of the metabolites. Microclotting, contamination of plasma with buffy layer, and short-term storage of cooled plasma were of less impact on the metabolome (0% to 11% of metabolites increased, 0% to 8% decreased).nnnCONCLUSIONSnThe response of the human plasma metabolome to preanalytical variation demands implementation of thorough quality assurance and QC measures to obtain reproducible and credible results from metabolomics studies. Metabolites identified as sensitive to preanalytics can be used to control for sample quality.

Sarcosine in Prostate Cancer Tissue is Not a Differential Metabolite for Prostate Cancer Aggressiveness and Biochemical Progression

PURPOSEnSarcosine in prostate cancer tissue samples was recently reported to be increased during prostate cancer progression to metastasis and suggested to be a key metabolite of cancer cell invasion and aggressiveness. We reevaluated sarcosine in prostate cancer tissue samples as a potential indicator of tumor aggressiveness, and as a predictor of recurrence-free survival.nnnMATERIALS AND METHODSnSarcosine in matched samples of malignant and nonmalignant tissue from 92 patients with prostate cancer after radical prostatectomy was measured in the framework of a global metabolite profiling study of prostate cancer by gas chromatography/mass spectrometry. We related results to age, prostate volume, tumor stage, Gleason score, preoperative prostate specific antigen and biochemical recurrence, defined as a persistent prostate specific antigen increase of greater than 0.2 ng/ml. Nonparametric statistical tests, ROC curves and Kaplan-Meier analyses were done.nnnRESULTSnMedian sarcosine content in tissue was about 7% higher in matched malignant vs nonmalignant samples, which was significantly. Sarcosine values were not associated with tumor stage (pT2 vs pT3), tumor grade (Gleason score less than 7 vs 7 or greater) or biochemical recurrence. The lack of metastatic tissue samples was a study limitation.nnnCONCLUSIONSnSarcosine in prostate cancer tissue samples cannot be considered a suitable predictor of tumor aggressiveness or biochemical recurrence.

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.

Metabolic biomarker signature to differentiate pancreatic ductal adenocarcinoma from chronic pancreatitis

Objective Current non-invasive diagnostic tests can distinguish between pancreatic cancer (pancreatic ductal adenocarcinoma (PDAC)) and chronic pancreatitis (CP) in only about two thirds of patients. We have searched for blood-derived metabolite biomarkers for this diagnostic purpose. Design For a case–control study in three tertiary referral centres, 914 subjects were prospectively recruited with PDAC (n=271), CP (n=282), liver cirrhosis (n=100) or healthy as well as non-pancreatic disease controls (n=261) in three consecutive studies. Metabolomic profiles of plasma and serum samples were generated from 477 metabolites identified by gas chromatography–mass spectrometry and liquid chromatography–tandem mass spectrometry. Results A biomarker signature (nine metabolites and additionally CA19-9) was identified for the differential diagnosis between PDAC and CP. The biomarker signature distinguished PDAC from CP in the training set with an area under the curve (AUC) of 0.96 (95% CI 0.93–0.98). The biomarker signature cut-off of 0.384 at 85% fixed specificity showed a sensitivity of 94.9% (95% CI 87.0%–97.0%). In the test set, an AUC of 0.94 (95% CI 0.91–0.97) and, using the same cut-off, a sensitivity of 89.9% (95% CI 81.0%–95.5%) and a specificity of 91.3% (95% CI 82.8%–96.4%) were achieved, successfully validating the biomarker signature. Conclusions In patients with CP with an increased risk for pancreatic cancer (cumulative incidence 1.95%), the performance of this biomarker signature results in a negative predictive value of 99.9% (95% CI 99.7%–99.9%) (training set) and 99.8% (95% CI 99.6%–99.9%) (test set). In one third of our patients, the clinical use of this biomarker signature would have improved diagnosis and treatment stratification in comparison to CA19-9.

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 biomarkers for the differential diagnosis of pancreatic ductal adenocarcinoma vs. chronic pancreatitis

Background The incidence of chronic pancreatitis (CP) varies between 4 and 23/100.000 in different populations and a tenfold higher prevalence. Current diagnostic tests such as transabdominal ultrasound and CA 19-9 can distinguish between pancreatic cancer (PDAC) and chronic pancreatitis (CP) in only about two thirds of patients. CA19-9 has been reported to discriminate between pancreatic cancer patients and healthy controls with a sensitivity of 0.80 (95 % CI 0.787-0.83) and a specificity of 0.80 (95 % CI 0.78-0.82). Therefore more sensitive biomarkers for the early detection of pancreatic cancer would be urgently needed. Our aim was to identify a panel of plasma metabolite biomarkers for this diagnostic purpose.

Abstract 1415: Prostate cancer: An integrated evaluation of metabolomics, transcriptomics, and proteomics expression data

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CAnnBackgroundnnMetabolite profiling research offers a deeper insight into biochemical changes in cancer metabolism. Moreover the integrated analysis of transcription, metabolomics and proteomics data can improve the understanding of the underlying biological processes.Material and MethodsnnA set of 254 metabolites was determined by gas chromatography/liquid chromatography-mass spectrometry in matched malignant and non-malignant prostatectomy samples from 95 prostate cancer (PCa) patients. Transcription profiling data obtained from 15 PCa patients by means of Affymetrix U133 arrays was analysed together with public GEO expression data. Expression levels of selected proteins were determined by means of immunohistochemistry and tissue micro array technology in 41 matched frozen tissue samples.nnThe association with clinicopathological variables and clinical outcome was tested. Transcription and metabolomics data were statistically analysed (ANOVA, Mann-Whitney U test) and significant differentially regulated metabolites/genes/proteins were selected.ResultsnnDifferentially regulated metabolites/genes discrimination between malignant and non-malignant tissues was used for network analysis. Enriched pathways which are involved in PCa progression or recurrence such as carbohydrate and fatty acid metabolism were identified. The role of fatty acid metabolism in PCa was analysed in more detail. Several fatty acids such as cerebronic acid, 2-hydroxybehenic acid, tricosanoic acid showed higher concentrations in malignant than in non-malignant tissues. This finding is in concordance to the observed higher mRNA and protein expression level of fatty acid synthase (FASN) in PCa. In contrast to normal prostate tissue, where protein expression level of FASN was correlated to the level of measured metabolites we found in malignant tissues a deregulation of the corresponding pathway. ConclusionnnOur integrated analysis of transcription, metabolite and proteomics data confirms and extends the role of several biological pathways which are involved in PCa progressionnnCitation Format: Ulrike Rennefahrt, Hellmuth-A. Meyer, Beate Kamlage, Regina Reszka, Philipp Schatz, Carsten Stephan, Klaus Jung, Dimo Dietrich, Glen Kristiansen. Prostate cancer: An integrated evaluation of metabolomics, transcriptomics, and proteomics expression data. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1415. doi:10.1158/1538-7445.AM2014-1415

Metabolic biomarkers for the diagnosis of pancreatic ductal adenocarcinoma

s / Pancreatology 14 (2014) S1eS129 S19 clinically relevant concentrations of ethanol 10mM (E10) ±cigarette smoke extract (CSE, 4 and 40ng/mL) or nicotine (0.1, 0.5 and 1mM) or nicotinederived nitrosamine ketone (NNK, 100mM and 1mM) and proliferation as well as migration assessed. Results: 1. hPSCs express the nAChR isoforms (a3, a7, b and e). Data below are expressed as % of relevant control. 2. Proliferation was increased by: i) E10 + CSE 40ng/mL (125±5.8%, p<0.05); ii) NNK 100nM (118±2.7%, p<0.02), iii) E10 + NNK 100nM or 1mM (131±10.8%, p<0.05; 130±11.5%, p<0.05). 3. Migration was increased by: i) CSE 4 or 40ng/mL (174±17.4%, p<0.05; 210±29.5%, p<0.05) ii) E10 + CSE 4 or 40ng/mL (204±14.5%, p<0.01; 211±17.5%, p<0.01); iii) NNK 100nM or 1mM alone (150±13%, p<0.05; 170±14.3%, p<0.03); iv) E10 + NNK 100nM or 1mM (206±15.7%, p<0.01; 185±15.5%, p<0.02). Conclusion: PSCs are activated by clinically relevant concentrations of CSE and NNK, alone or in combination with ethanol, providing a possible mechanism for smoking induced progression of alcoholic pancreatic fibrosis.

Abstract 3501: Identification of plasma metabolites as biomarker candidates for the diagnosis of pancreatic ductal adenocarcinoma (PDAC).

Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DCnnBackground and objective: Pancreatic cancer (PDAC) is characterized by very poor prognosis mostly caused by late diagnosis. Although modern imaging techniques have pushed the detection limit to lesions below 10mm. The combination of diagnostic imaging and biomarkers can distinguish between PDAC and chronic pancreatitis in only 67% (Carriere et al. J Eval Clin Pract. 2009). We have therefore conducted discovery and confirmation studies to identify metabolite plasma biomakers for the detection of pancreatic cancer and the differentiation from chronic pancreatitis. Material and method:The retrospective study was conducted in three phases: An initial pilot study on plasma samples was followed by analysis of a second plasma sample collection and a serum sample collection from pancreatic cancer, chronic pancreatitis and liver cirrhosis patients as well as blood donors. Metabolomic profiles of plasma and serum samples were generated applying a high throughput polar and lipid GC-MS and LC-MS/MS technology (MxP™ Broad Profiling). In addition, targeted platforms for steroids and lipids (MxP™ Steroid, MxP™ Lipids) were applied. Up to 477 metabolites were analyzed semi-quantitatively or quantitatively per study, 90% of them with an identified chemical structure. Statistical data analysis was done by linear models (ANOVA) on log10 transformed data considering age, gender, BMI and sample storage time as fixed effects. A panel of metabolites was selected for the creation of a diagnosis biomarker. The predictive ability of the biomarker was evaluated through the estimation of ROC characteristics and AUC values from Bootstrap-based Cross-Validation. Result within the three consecutive studies, sphingolipids (sphingomyelins and ceramides) were consistently and significantly increased in the pancreatic cancer group relative to the corresponding pancreatitis group whereas certain amino acids, amino acid related metabolites and coenzyme Q9 were consistently and significantly decreased. A multi-marker panel consisted of 10 metabolites and provided an AUC=0.85 when discriminating between pancreatic cancer and pancreatitis. When the CA19-9 data was included in the analysis, an AUC= 0.92 was reached. Conclusions: The results from the metabolomics study indicate that a plasma metabolite biomarker panel can be used to distinguish between pancreatic cancer and chronic pancreatitis with a high degree of accuracy. The most discriminating metabolites showed robustness with respect to transferability of their diagnostic potential from plasma to serum. A multicenter validation study has now been initiated to establish a diagnostic assay with a targeted negative predictive value (NPV) > 95%. Such a test would allow the exclusion of suspicious patients from further more invasive diagnostic procedures.nnCitation Format: Julia Mayerle, Holger Kalthoff, Regina Reszka, Beate Kamlage, Christian Pilarsky, Robert Grutzmann, Bodo Schniewind, F. Ulrich Weiss, Markus M. Lerch. Identification of plasma metabolites as biomarker candidates for the diagnosis of pancreatic ductal adenocarcinoma (PDAC). [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3501. doi:10.1158/1538-7445.AM2013-3501

Abstract 3224: Metabolite profiling as tool for the identification of differentiating and prognostic markers of prostate carcinoma.

Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DCnnBackground and objective: 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.nnMaterial and methods: 107 matched-paired tissue samples collected after radical prostatectomy were subjected to the MxPTM Broad Profiling by gas chromatography- mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (Patent WO 2010/139711 A1: Means and methods for diagnosing prostate carcinomas). Aminoadipic acid, cerebronic acid, gluconic acid, glycerophosphoethanolamine, 2- hydroxybehenic acid, isopentenyl pyrophosphate, maltotriose, 7-methylguanine, and tricosanoic acid were related to clinicopathological variables like prostate volume, tumor stage, Gleason score, preoperative prostate-specific antigen (PSA), and disease recurrence in the follow-up. Non-parametric statistical tests, receiver- operating characteristics (ROC) and univariate and multivariate analyses (Kaplan- Meier curve; Cox regression) were performed.nnResults: All metabolites showed higher concentrations in malignant than in non- malignant samples except for gluconic acid and maltotriose, which had lower levels in tumors. ROC analyses showed a clear differentiation for all metabolites with a maximal area under the curve of 0.86 for tricosanoic acid. However, the metabolites were not related to tumor stage and Gleason grade. Aminoadipic acid, gluconic acid, and maltotriose levels were associated with tumor recurrence (Kaplan-Meier analysis) and were, together with tumor stage and Gleason score, a successful metabolite combination in the multivariate Cox regression model for the prediction of tumor recurrence.nnConclusions: This exemplary study performed with selected metabolites from a global metabolic profiling investigation proves that metabolites in prostate carcinoma tissue can be used, in combination with traditional pathological and histomorphological parameters, as promising diagnostic and prognostic tools.nnCitation Format: Glen Kristiansen, Regina Reszka, Beate Kamlage, Bianca Bethan, Michael Lein, Carsten Stephan, Klaus Jung. Metabolite profiling as tool for the identification of differentiating and prognostic markers of prostate carcinoma. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3224. doi:10.1158/1538-7445.AM2013-3224