Search

Background: Early detection of aggressive prostate cancer (PCa) remains crucial for effective treatment of patients. However, PCa screening remains controversial due to a high rate of overdiagnosis and overtreatment. To better reconcile both objectives, more effective methods for assessing disease severity at the time of diagnosis are needed., Methods: The relationship between DNA-methylation and high-grade PCa was examined in a cohort of 102 prospectively enrolled men who received standard 12-core prostate biopsies. EpiScore, an algorithm that quantifies the relative DNA methylation intensities of GSTP1, RASSF1, and APC in prostate biopsy tissue, was evaluated as a method to compensate for biopsy under-sampling and improve risk stratification at the time of diagnosis., Results: DNA-methylation intensities of GSTP1, RASSF1, and APC were higher in biopsy cores from men diagnosed with GS ≥ 7 cancer compared to men with diagnosed GS 6 disease. This was confirmed by EpiScore, which was significantly higher for subjects with high-grade biopsies and higher NCCN risk categories (both P < 0.001). In patients diagnosed with GS ≥ 7, increased levels of DNA-methylation were present, not only in the high-grade biopsy cores, but also in other cores with no or low-grade disease (P < 0.001). By combining EpiScore with traditional clinical risk factors into a logistic regression model, the prediction of high GS reached an AUC of 0.82 (95%CI: 0.73-0.91) with EpiScore, DRE, and atypical histological findings as most important contributors., Conclusions: In men diagnosed with PCa, DNA-methylation profiling can detect under-sampled high-risk PCa in prostate biopsy specimens through a field effect. Predictive accuracy increased when EpiScore was combined with other clinical risk factors. These results suggest that EpiScore could aid in the detection of occult high-grade disease at the time of diagnosis, thereby improving the selection of candidates for Active Surveillance., (© 2017 Wiley Periodicals, Inc.)

Background: Prostate cancer (PCa) diagnosis is challenging because efforts for effective, timely treatment of men with significant cancer typically result in over-diagnosis and repeat biopsies. The presence or absence of epigenetic aberrations, more specifically DNA-methylation of GSTP1, RASSF1, and APC in histopathologically negative prostate core biopsies has resulted in an increased negative predictive value (NPV) of ∼90% and thus could lead to a reduction of unnecessary repeat biopsies. Here, it is investigated whether, in methylation-positive men, DNA-methylation intensities could help to identify those men harboring high-grade (Gleason score ≥7) PCa, resulting in an improved positive predictive value., Methods: Two cohorts, consisting of men with histopathologically negative index biopsies, followed by a positive or negative repeat biopsy, were combined. EpiScore, a methylation intensity algorithm was developed in methylation-positive men, using area under the curve of the receiver operating characteristic as metric for performance. Next, a risk score was developed combining EpiScore with traditional clinical risk factors to further improve the identification of high-grade (Gleason Score ≥7) cancer., Results: Compared to other risk factors, detection of DNA-methylation in histopathologically negative biopsies was the most significant and important predictor of high-grade cancer, resulting in a NPV of 96%. In methylation-positive men, EpiScore was significantly higher for those with high-grade cancer detected upon repeat biopsy, compared to those with either no or low-grade cancer. The risk score resulted in further improvement of patient risk stratification and was a significantly better predictor compared to currently used metrics as PSA and the prostate cancer prevention trial (PCPT) risk calculator (RC). A decision curve analysis indicated strong clinical utility for the risk score as decision-making tool for repeat biopsy., Conclusions: Low DNA-methylation levels in PCa-negative biopsies led to a NPV of 96% for high-grade cancer. The risk score, comprising DNA-methylation intensity and traditional clinical risk factors, improved the identification of men with high-grade cancer, with a maximum avoidance of unnecessary repeat biopsies. This risk score resulted in better patient risk stratification and significantly outperformed current risk prediction models such as PCPTRC and PSA. The risk score could help to identify patients with histopathologically negative biopsies harboring high-grade PCa. Prostate 76:1078-1087, 2016. © 2016 The Authors. The Prostate Published by Wiley Periodicals, Inc., (© 2016 The Authors. The Prostate Published by Wiley Periodicals, Inc.)

Background: Prostate cancer is the most common cancer diagnosis in men and a leading cause of death. Improvements in disease management would have a significant impact and could be facilitated by the development of biomarkers, whether for diagnostic, prognostic, or predictive purposes. The blood-based prostate biomarker PSA has been part of clinical practice for over two decades, although it is surrounded by controversy. While debates of usefulness are ongoing, alternatives should be explored. Particularly with recent recommendations against routine PSA-testing, the time is ripe to explore promising biomarkers to yield a more efficient and accurate screening for detection and management of prostate cancer. Epigenetic changes, more specifically DNA methylation, are amongst the most common alterations in human cancer. These changes are associated with transcriptional silencing of genes, leading to an altered cellular biology., Methods: One gene in particular, GSTP1, has been widely studied in prostate cancer. Therefore a meta-analysis has been conducted to examine the role of this and other genes and the potential contribution to prostate cancer management and screening refinement., Results: More than 30 independent, peer reviewed studies have reported a consistently high sensitivity and specificity of GSTP1 hypermethylation in prostatectomy or biopsy tissue. The meta-analysis combined and compared these results., Conclusions: GSTP1 methylation detection can serve an important role in prostate cancer managment. The meta-analysis clearly confirmed a link between tissue DNA hypermethylation of this and other genes and prostate cancer. Detection of DNA methylation in genes, including GSTP1, could serve an important role in clinical practice., (Copyright © 2011 Wiley Periodicals, Inc.)

The diagnosis of sessile serrated adenomas (SSAs) is challenging, and there is a great deal of interobserver variability amongst pathologists in differentiating SSAs from hyperplastic polyps (HPPs). The aim of this study was (i) to assess the utility of epigenetic changes such as DNA methylation in differentiating SSAs from HPPs and (ii) to identify common methylation based molecular markers potentially useful for early detection of premalignant neoplastic lesions of gastrointestinal tract. A total of 97 primary patient adenoma samples were obtained from The Johns Hopkins Hospital pathology archive with IRB approval and HIPAA compliance. We analyzed the promoter associated CpG island methylation status of 17 genes using nested multiplex methylation specific PCR (MSP). Methylation of CDX2, hMLH1 and TLR2 was detected in SSAs and SSAs with dysplasia but not in HPPs. A subset of genes including EVL, GATAs (4 and 5), HIN-1, SFRPs (1, 2, 4 and 5), SOX17 and SYNE1 were methylated frequently in all premalignant gastrointestinal adenomas including tubular adenomas, villous adenomas, SSAs and SSAs with dysplasia but infrequently in non-premalignant polyps such as HPPs. Methylation of CDX2, hMLH1 and TLR2 may be of diagnostic utility in differentiating, histologically challenging cases of SSAs from HPPs. Genes such as EVL, GATAs, HIN-1, SFRPs, SOX17 and SYNE1, which are frequently methylated in all types of tested premalignant adenomas, may be useful as biomarkers in stool-based strategies for early detection of these adenomas and CRCs in future., (Copyright © 2010 UICC.)

The organization of proteins in space and time is essential to their function. To accurately quantify subcellular protein characteristics in a population of cells with regard for the stochasticity of events in a natural context, there is a fast-growing need for image-based cytometry. Simultaneously, the massive amount of data that is generated by image-cytometric analyses, calls for tools that enable pattern recognition and automated classification. In this article, we present a general approach for multivariate phenotypic profiling of individual cell nuclei and quantification of subnuclear spots using automated fluorescence mosaic microscopy, optimized image processing tools, and supervised classification. We demonstrate the efficiency of our analysis by determination of differential DNA damage repair patterns in response to genotoxic stress and radiation, and we show the potential of data mining in pinpointing specific phenotypes after transient transfection. The presented approach allowed for systematic analysis of subnuclear features in large image data sets and accurate classification of phenotypes at the level of the single cell. Consequently, this type of nuclear fingerprinting shows potential for high-throughput applications, such as functional protein assays or drug compound screening.