Philipp Schatz

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

BACKGROUND Metabolomics 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. METHODS Human 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. RESULTS Short-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). CONCLUSIONS The 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.

Novel method for high throughput DNA methylation marker evaluation using PNA-probe library hybridization and MALDI-TOF detection

The methylation of CpG dinucleotides has become a topic of great interest in cancer research, and the methylation of promoter regions of several tumor suppressor genes has been identified as a marker of tumorigenesis. Evaluation of DNA methylation markers in tumor tissue requires hundreds of samples, which must be analyzed quantitatively due to the heterogeneous composition of biological material. Therefore novel, fast and inexpensive methods for high throughput analysis are needed. Here we introduce a new assay based on peptide nucleic acid (PNA)-library hybridization and subsequent MALDI-TOF analysis. This method is multiplexable, allows the use of standard 384 well automated pipetting, and is more specific and flexible than established methods, such as microarrays and MS-SNuPE. The approach was used to evaluate three candidate colon cancer methylation markers previously identified in a microarray study. The methylation of the genes Ade-nomatous polyposis coli (APC), glycogen synthase kinase-β-3 (GSK3β) and eyes absent 4 (EYA4) was analyzed in 12 colon cancer and 12 normal tissues. APC and EYA4 were confirmed as being differentially methylated in colon cancer patients whereas GSK3β did not show differential methylation.

Development and clinical validation of a real-time PCR assay for PITX2 DNA methylation to predict prostate-specific antigen recurrence in prostate cancer patients following radical prostatectomy

Prostate cancer is the most common cancer among men. The prospective discrimination of aggressive and clinically insignificant tumors still poses a significant and, as yet, unsolved problem. PITX2 DNA methylation is a strong prognostic biomarker in prostate cancer. Recently, a diagnostic microarray for prostate cancer prognosis based on PITX2 methylation has been developed and validated. Because this microarray requires nonstandard laboratory equipment, its use in a diagnostic setting is limited. This study aimed to develop and validate an alternative quantitative real-time PCR assay for measuring PITX2 methylation that can easily be established in clinical laboratories, thereby facilitating the implementation of this biomarker in clinical practice. A methylation cut-off for patient stratification was established in a training cohort (n = 157) and validated in an independent test set (n = 523) of men treated with radical prostatectomy. In univariate Cox proportional hazards analysis, PITX2 hypermethylation was a significant predictor for biochemical recurrence (P < 0.001, hazard ratio = 2.614). Moreover, PITX2 hypermethylation added significant prognostic information (P = 0.003, hazard ratio = 1.814) to the Gleason score, pathological T stage, prostate-specific antigen, and surgical margins in a multivariate analysis. The clinical performance was particularly high in patients at intermediate risk (Gleason score of 7) and in samples containing high tumor cell content. This assay might aid in risk stratification and support the decision-making process when determining whether a patient might benefit from adjuvant treatment after radical prostatectomy.

A New Metabolomic Signature in Type-2 Diabetes Mellitus and Its Pathophysiology

Objective The objective of the current study was to find a metabolic signature associated with the early manifestations of type-2 diabetes mellitus. Research Design and Method Modern metabolic profiling technology (MxP™ Broad Profiling) was applied to find early alterations in the plasma metabolome of type-2 diabetic patients. The results were validated in an independent study. Eicosanoid and single inon monitoring analysis (MxP™ Eicosanoid and MxP™ SIM analysis) were performed in subsets of samples. Results A metabolic signature including significantly increased levels of glyoxylate as a potential novel marker for early detection of type-2 diabetes mellitus was identified in an initial study (Study1). The signature was significantly altered in fasted diabetic and pre-diabetic subjects and in non-fasted subjects up to three years prior to the diagnosis of type-2 diabetes; most alterations were also consistently found in an independent patient group (Study 2). In Study 2 diabetic and most control subjects suffered from heart failure. In Study 1 a subgroup of diabetic subjects, with a history of use of anti-hypertensive medication further showed a more pronounced increase of glyoxylate levels, compared to a non-diabetic control group when tested in a hyperglycemic state. In the context of a prior history of anti-hypertensive medication, alterations in hexosamine and eicosanoid levels were also found. Conclusion A metabolic signature including glyoxylate was associated with type-2 diabetes mellitus, independent of the fasting status and of occurrence of another major disease. The same signature was also found to be associated with pre-diabetic subjects. Glyoxylate levels further showed a specifically strong increase in a subgroup of diabetic subjects. It could represent a new marker for the detection of medical subgroups of diabetic subjects.

Development of a Diagnostic Microarray Assay to Assess the Risk of Recurrence of Prostate Cancer Based on PITX2 DNA Methylation

Prostate cancer is among the most common cancers. Although it has a relatively good prognosis, 15 to 30% of men with prostate cancer experience a relapse after radical prostatectomy. Identifying patients with an aggressive tumor will therefore help to improve prostate cancer management. DNA methylation of PITX2 has been established in several studies as a prognostic biomarker for breast and prostate cancer. These case control studies were conducted using research assay components; to facilitate its use in a diagnostic setting, the PITX2 biomarker was transferred to a validated diagnostic platform, the Affymetrix GeneChip System. A customized microarray (Epichip PITX2) was designed using features in high redundancy to ensure a robust determination of the methylation state of the PITX2 promoter. The developed method allowed for accurate assessment of prognosis in prostate cancer patients who underwent radical prostatectomy. Determination of PITX2 methylation in formalin-fixed and paraffin-embedded tissue samples from a cohort of 157 prostatectomy patients resulted in an excellent level of concordance of the clinical classification, as well as the measured output between the research assay and the Epichip PITX2. These analytical performance results describe the Epichip PITX2 as a robust and reliable diagnostic tool for assessing the methylation status of PITX2, enabling an improved outcome prediction in cancer patients following radical prostatectomy.

Gene Promoter Methylation and Its Potential Relevance in Early Prostate Cancer Diagnosis

Aims: We investigated hypermethylation of the glutathione S-transferase pi (GSTP1), retinoic acid receptor β2 (RARβ2), adenomatous polyposis coli (APC) and paired-like homeodomain transcription factor 2 (PITX2) gene promoters which could serve as a sensitive tool to indicate a risk of prostate cancer even in histologically tumor-free tissues. Methods: Tumor tissues and non-neoplastic tissues at variable distances from the tumor foci were retrieved from 25 formalin-fixed and paraffin-embedded prostatectomy specimens and subjected to DNA extraction. The methylation levels were assessed by means of different assay technologies. Results: Significantly increased methylation levels in cancer specimens were found for all promoter regions (GSTP1: 21/25, 84%; RARβ2: 24/25, 96%; APC: 21/25, 84%; PITX2: 20/25, 80%) and in most samples containing prostatic intraepithelial neoplasia. Several samples showed increased RARβ2 and APC methylation in adjacent non-neoplastic tissue. An association between the methylation extent of GSTP1, APC and RARβ2, respectively, and primary Gleason grade was detectable. GSTP1 methylation was also associated with extraprostatic tumor extension. Conclusion: GSTP1, APC, RARβ2 and PITX2 methylation occur frequently in prostate cancer, making these markers sensitive tools for the detection of neoplastic lesions in the prostate. For RARβ2, the results suggest a kind of methylation field effect which could be helpful for the detection of prostate cancer. Larger studies are necessary to investigate a potential correlation of GSTP1, RARβ2 and APC hypermethylation with tumor aggressiveness.

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.

Validation of a metabolite panel for early diagnosis of type 2 diabetes.

BACKGROUND Accurate, early diagnosis of type 2 diabetes (T2D) would enable more effective clinical management and a reduction in T2D complications. Therefore, we sought to identify plasma metabolite and protein biomarkers that, in combination with glucose, can better predict future T2D compared with glucose alone. METHODS In this case-control study, we used plasma samples from the Bavarian Red Cross Blood Transfusion Center study (61 T2D cases and 78 non-diabetic controls) for discovering T2D-associated metabolites, and plasma samples from the Personalized Medicine Research Project in Wisconsin (56 T2D cases and 445 non-diabetic controls) for validation. All samples were obtained before or at T2D diagnosis. We tested whether the T2D-associated metabolites could distinguish incident T2D cases from controls, as measured by the area under the receiver operating characteristic curve (AUC). Additionally, we tested six metabolic/pro-inflammatory proteins for their potential to augment the ability of the metabolites to distinguish cases from controls. RESULTS A panel of 10 metabolites discriminated better between T2D cases and controls than glucose alone (AUCs: 0.90 vs 0.87; p=2.08×10(-5)) in Bavarian samples, and associations between these metabolites and T2D were confirmed in Wisconsin samples. With use of either a Bayesian network classifier or ridge logistic regression, the metabolites, with or without the proteins, discriminated incident T2D cases from controls marginally better than glucose in the Wisconsin samples, although the difference in AUCs was not statistically significant. However, when the metabolites and proteins were added to two previously reported T2D prediction models, the AUCs were higher than those of each prediction model alone (AUCs: 0.92 vs 0.87; p=3.96×10(-2) and AUCs: 0.91 vs 0.71; p=1.03×10(-5), for each model, respectively). CONCLUSIONS Compared with glucose alone or with previously described T2D prediction models, a panel of plasma biomarkers showed promise for improved discrimination of incident T2D, but more investigation is needed to develop an early diagnostic marker.

A Novel Lipid Biomarker Panel for the Detection of Heart Failure with Reduced Ejection Fraction.

OBJECTIVES In this study we aimed to identify novel metabolomic biomarkers suitable for improved diagnosis of heart failure with reduced ejection fraction (HFrEF). METHODS We prospectively recruited 887 individuals consisting of HFrEF patients with either ischemic (ICMP, n = 257) or nonischemic cardiomyopathy (NICMP, n = 269), healthy controls (n = 327), and patients with pulmonary diseases (n = 34). A single-center identification (n = 238) was followed by a multicenter confirmation study (n = 649). Plasma samples from the single-center study were subjected to metabolite profiling analysis to identify metabolomic features with potential as HFrEF biomarkers. A dedicated analytical protocol was developed for the routine analysis of selected metabolic features in the multicenter cohort. RESULTS In the single-center study, 92 of 181 metabolomic features with known chemical identity (51%) were significantly changed in HFrEF patients compared to healthy controls (P <0.05). Three specific metabolomic features belonging to the lipid classes of sphingomyelins, triglycerides, and phosphatidylcholines were selected as the cardiac lipid panel (CLP) and analyzed in the multicenter study using the dedicated analytical protocol. The combination of the CLP with N-terminal pro-B-type natriuretic peptide (NT-proBNP) distinguished HFrEF patients from healthy controls with an area under the curve (AUC) of 0.97 (sensitivity 80.2%, specificity 97.6%) and was significantly superior compared to NT-proBNP alone (AUC = 0.93, sensitivity 81.7%, specificity 88.1%, P <0.001), even in the subgroups with mildly reduced left ventricular EF (0.94 vs 0.87; P <0.001) and asymptomatic patients (0.95 vs 0.91; P <0.05). CONCLUSIONS The new metabolomic biomarker panel has the potential to improve HFrEF detection, even in mild and asymptomatic stages. The observed changes further indicate lipid alterations in the setting of HFrEF.

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.