Hellmuth-A. Meyer

Comparative Assessment of Urinary Prostate Cancer Antigen 3 and TMPRSS2:ERG Gene Fusion with the Serum [−2]Proprostate-Specific Antigen–Based Prostate Health Index for Detection of Prostate Cancer

BACKGROUND We compared urinary prostate cancer antigen 3 (PCA3), transmembrane protease, serine 2 (TMPRSS2):v-ets erythroblastosis virus E26 oncogene homolog (avian) (ERG) gene fusion (T2:ERG), and the serum [-2]proprostate-specific antigen ([-2]proPSA)-based prostate health index (Phi) for predicting biopsy outcome. METHODS Serum samples and first-catch urine samples were collected after digital rectal examination (DRE) from consented outpatients with PSA 0.5-20 μg/L who were scheduled for prostate biopsy. The PCA3 score (PROGENSA PCA3, Hologic Gen-Probe) and T2:ERG score (Hologic Gen-Probe) were determined. Measurements of serum PSA, free PSA, and [-2]proPSA (Beckman Coulter) were performed, and the percentages of free PSA (%fPSA) and Phi ([-2]proPSA/fPSA × √PSA) were determined. RESULTS Of 246 enrolled men, prostate cancer (PCa) was diagnosed in 110 (45%) and there was no evidence of malignancy (NEM) in 136 (55%). A first set of biopsies was performed in 136 (55%) of all men, and 110 (45%) had ≥1 repeat biopsies. PCA3, Phi, and T2:ERG differed significantly between men with PCa and NEM, and these markers showed the largest areas under the ROC curve (AUCs) (0.74, 0.68, and 0.63, respectively). PCA3 had the largest AUC of all parameters, albeit not statistically different from Phi. Phi showed somewhat lower specificities than PCA3 at 90% sensitivity. Combination of both markers enhanced diagnostic power with modest AUC gains of 0.01-0.04. Although PCA3 had the highest AUC in the repeat-biopsy cohort, the highest AUC for Phi was observed in DRE-negative patients with PSA in the 2-10 μg/L range. CONCLUSIONS PCA3 and Phi were superior to the other evaluated parameters but their combination gave only moderate enhancements in diagnostic accuracy for PCa at first or repeat prostate biopsy.

Identification of Stanniocalcin 2 as Prognostic Marker in Renal Cell Carcinoma

BACKGROUND For an individualized therapy in renal cell carcinoma (RCC), there is a clear need for novel prognostic biomarkers to ensure adequate risk stratification and help with the choice of therapy options. OBJECTIVE To identify new secreted biomarkers for diagnosis and estimation of prognosis in RCC. DESIGN, SETTING, AND PARTICIPANTS A meta-analysis of published microarray data was performed. Stanniocalcin 2 (STC2), a glycoprotein hormone that is involved in regulatory effects on calcium and phosphate transport in the kidney, was found overexpressed in tumors and hence analyzed in detail. Kidney tissue samples derived from 108 patients with RCC undergoing radical nephrectomy between July 2003 and January 2006 were used to validate and estimate the potential of STC2 as a biomarker for RCC. MEASUREMENTS STC2, found upregulated in clear cell RCC, was analyzed in detail using real-time reverse transcription-polymerase chain reaction (RT-PCR), western blotting, and immunohistochemistry. Furthermore, STC2 protein expression determined on a tissue microarray was correlated to clinical pathologic parameters, including patient survival. RESULTS AND LIMITATIONS STC2 was upregulated at the mRNA and protein levels in RCC. In normal renal tissue, STC2 expression was limited to distal tubuli and glomeruli, whereas in tumor a strong cytoplasmic and also membranous staining was detected. STC2 expression was found in clear cell, chromophobe, and papillary RCC. Strong cytoplasmic STC2 expression was significantly associated with shorter patient survival in Kaplan-Meier analyses. In the group of patients without metastases, cytoplasmic STC2 expression was also found as a significant independent risk factor in multivariate analysis. A limitation of the study is the small number of patients. CONCLUSIONS Increased cytoplasmic STC2 expression correlated with conventional indicators of aggressiveness of RCC and shorter overall patient survival times. STC2 could become an adjunct tissue biomarker that may be useful in the postoperative risk stratification of RCC patients.

A New Algorithm for Integrated Analysis of miRNA-mRNA Interactions Based on Individual Classification Reveals Insights into Bladder Cancer

Background MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression. It has been proposed that miRNAs play an important role in cancer development and progression. Their ability to affect multiple gene pathways by targeting various mRNAs makes them an interesting class of regulators. Methodology/Principal Findings We have developed an algorithm, Classification based Analysis of Paired Expression data of RNA (CAPE RNA), which is capable of identifying altered miRNA-mRNA regulation between tissues samples that assigns interaction states to each sample without preexisting stratification of groups. The distribution of the assigned interaction states compared to given experimental groups is used to assess the quality of a predicted interaction. We demonstrate the applicability of our approach by analyzing urothelial carcinoma and normal bladder tissue samples derived from 24 patients. Using our approach, normal and tumor tissue samples as well as different stages of tumor progression were successfully stratified. Also, our results suggest interesting differentially regulated miRNA-mRNA interactions associated with bladder tumor progression. Conclusions/Significance The need for tools that allow an integrative analysis of microRNA and mRNA expression data has been addressed. With this study, we provide an algorithm that emphasizes on the distribution of samples to rank differentially regulated miRNA-mRNA interactions. This is a new point of view compared to current approaches. From bootstrapping analysis, our ranking yields features that build strong classifiers. Further analysis reveals genes identified as differentially regulated by miRNAs to be enriched in cancer pathways, thus suggesting biologically interesting interactions.

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.

Internal validation of an artificial neural network for prostate biopsy outcome.

Objectives:  To carry out an internal validation of the retrospectively trained artificial neural network (ANN) ‘ProstataClass’.

Avoiding Pitfalls in Applying Prediction Models, As Illustrated by the Example of Prostate Cancer Diagnosis

BACKGROUND The use of different mathematical models to support medical decisions is accompanied by increasing uncertainties when they are applied in practice. Using prostate cancer (PCa) risk models as an example, we recommend requirements for model development and draw attention to possible pitfalls so as to avoid the uncritical use of these models. CONTENT We conducted MEDLINE searches for applications of multivariate models supporting the prediction of PCa risk. We critically reviewed the methodological aspects of model development and the biological and analytical variability of the parameters used for model development. In addition, we reviewed the role of prostate biopsy as the gold standard for confirming diagnoses. In addition, we analyzed different methods of model evaluation with respect to their application to different populations. When using models in clinical practice, one must validate the results with a population from the application field. Typical model characteristics (such as discrimination performance and calibration) and methods for assessing the risk of a decision should be used when evaluating a models output. The choice of a model should be based on these results and on the practicality of its use. SUMMARY To avoid possible errors in applying prediction models (the risk of PCa, for example) requires examining the possible pitfalls of the underlying mathematical models in the context of the individual case. The main tools for this purpose are discrimination, calibration, and decision curve analysis.

Effect of mutations of the human serpin protein corticosteroid-binding globulin on cortisol-binding, thermal and protease sensitivity

Corticosteroid-binding globulin (CBG, transcortin) belongs to the serpin family of serine protease inhibitors (SERPINA6) and is mainly secreted by the liver. The negative acute phase protein CBG regulates free cortisol levels in the blood and distributes cortisol to its target tissues. So far no CBG serpin partner protease has been identified. However, its cleavage by human neutrophil elastase destroys ligand binding capacity and supposedly liberates cortisol at sites of inflammation. Here we report on the recombinant expression and secretion of human wild-type CBG and several novel mutants by human 293-EBNA cells. Functional characterization of wild-type and mutant CBG revealed distinct differences in ligand binding sensitivity to heat or elastase. Certain mutants are almost devoid of cortisol binding activity (Q232R and CBG Lyon), some display higher sensitivity for heat inactivation (G335V, Q232R and CBG Lyon) or for elastase cleavage (G335V). CBG mutant T342A is more resistant to elastase cleavage. Our data support the validity of the serpin structural concept. The expression system used provides functionally active human recombinant transcortin for further functional characterization of wild-type and human CBG mutant variants, which have been associated with altered serum free cortisol levels or pathophysiological constellations such as increased body weight, fatigue or hypotension.

Risk prediction models for biochemical recurrence after radical prostatectomy using prostate-specific antigen and Gleason score

Many computer models for predicting the risk of prostate cancer have been developed including for prediction of biochemical recurrence (BCR). However, models for individual BCR free probability at individual time-points after a BCR free period are rare. Follow-up data from 1656 patients who underwent laparoscopic radical prostatectomy (LRP) were used to develop an artificial neural network (ANN) to predict BCR and to compare it with a logistic regression (LR) model using clinical and pathologic parameters, prostate-specific antigen (PSA), margin status (R0/1), pathological stage (pT), and Gleason Score (GS). For individual BCR prediction at any given time after operation, additional ANN, and LR models were calculated every 6 months for up to 7.5 years of follow-up. The areas under the receiver operating characteristic (ROC) curve (AUC) for the ANN (0.754) and LR models (0.755) calculated immediately following LRP, were larger than that for GS (AUC: 0.715; P = 0.0015 and 0.001), pT or PSA (AUC: 0.619; P always <0.0001) alone. The GS predicted the BCR better than PSA (P = 0.0001), but there was no difference between the ANN and LR models (P = 0.39). Our ANN and LR models predicted individual BCR risk from radical prostatectomy for up to 10 years postoperative. ANN and LR models equally and significantly improved the prediction of BCR compared with PSA and GS alone. When the GS and ANN output values are combined, a more accurate BCR prediction is possible, especially in high-risk patients with GS ≥7.

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, CA Background Metabolite 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 Methods A 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. The 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.Results Differentially 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. Conclusion Our integrated analysis of transcription, metabolite and proteomics data confirms and extends the role of several biological pathways which are involved in PCa progression Citation 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