Risk score predicts high-grade prostate cancer in DNA-methylation positive, histopathologically negative biopsies.

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.MDxHealthThis is the author accepted manuscript. It is currently under an indefinite embargo pending publication by Wiley

Relation between telomerase activity, hTERT and telomere length for intracranial tumours

Human linear chromosomes are capped by specialized DNA-protein structures called telomeres. The present study analysed the telomerase activity, hTERT protein and telomere length in meningiomas and gliomas in relation to their WHO grading. Fifty-three freshly dissected tumour biopsies were analysed for telomerase activity, hTERT protein expression and telomere length. Telomerase activity was examined in 41 of the 53 biopsies. Telomerase activity was detected in 3 of 35 (8.6%) screened meningiomas (I benign, 1 atypical and I malignant meningioma). For hTERT expression, 56.4% of meningiomas were positive with a mean labelling index (hTERT LI) of 31.3% (SD=26.5) for the hTERT positive meningiomas. The mean telomere length for meningiomas was 6.983 kb (SD=1.969). For gliomas, no active telomerase was detected in 2 low-grade astrocytomas, whereas three of the four screened glioblastomas were positive for telomerase activity. The only hTERT protein positive astrocytoma had a mean labelling index of 9.0%. On the other hand, the hTERT LI for glioblastomas was 53.6% (SD=28.0). The two low-grade astrocytomas had a telomere length of 14.310 and 9.236 kb. The anaplastic astrocytoma had a telomere length of 4.903 kb and the glioblastomas 5.767 kb (SD=2.042). The normal meningeal and neuronal tissue is negative for telomerase activity and hTERT. The length was +/- 10.000 kb. These results indicate that telomere shortening may be a critical step in pathogenesis of atypical and malignant meningiomas and gliomas. Critical telomere shortening in vitro was shown to activate telomerase

Array-Based DNA Methylation Profiling for Breast Cancer Subtype Discrimination

BACKGROUND: Abnormal DNA methylation is well established for breast cancer and contributes to its progression by silencing tumor suppressor genes. DNA methylation profiling platforms might provide an alternative approach to expression microarrays for accurate breast tumor subtyping. We sought to determine whether the distinction of the inflammatory breast cancer (IBC) phenotype from the non-IBC phenotype by transcriptomics could be sustained by methylomics. METHODOLOGY/PRINCIPAL FINDINGS: We performed methylation profiling on a cohort of IBC (N = 19) and non-IBC (N = 43) samples using the Illumina Infinium Methylation Assay. These results were correlated with gene expression profiles. Methylation values allowed separation of breast tumor samples into high and low methylation groups. This separation was significantly related to DNMT3B mRNA levels. The high methylation group was enriched for breast tumor samples from patients with distant metastasis and poor prognosis, as predicted by the 70-gene prognostic signature. Furthermore, this tumor group tended to be enriched for IBC samples (54% vs. 24%) and samples with a high genomic grade index (67% vs. 38%). A set of 16 CpG loci (14 genes) correctly classified 97% of samples into the low or high methylation group. Differentially methylated genes appeared to be mainly related to focal adhesion, cytokine-cytokine receptor interactions, Wnt signaling pathway, chemokine signaling pathways and metabolic processes. Comparison of IBC with non-IBC led to the identification of only four differentially methylated genes (TJP3, MOGAT2, NTSR2 and AGT). A significant correlation between methylation values and gene expression was shown for 4,981 of 6,605 (75%) genes. CONCLUSIONS/SIGNIFICANCE: A subset of clinical samples of breast cancer was characterized by high methylation levels, which coincided with increased DNMT3B expression. Furthermore, an association was observed with molecular signatures indicative of poor patient prognosis. The results of the current study also suggest that aberrant DNA methylation is not the main force driving the molecular biology of IBC

Comparing the DNA Hypermethylome with Gene Mutations in Human Colorectal Cancer

We have developed a transcriptome-wide approach to identify genes affected by promoter CpG island DNA hypermethylation and transcriptional silencing in colorectal cancer. By screening cell lines and validating tumor-specific hypermethylation in a panel of primary human colorectal cancer samples, we estimate that nearly 5% or more of all known genes may be promoter methylated in an individual tumor. When directly compared to gene mutations, we find larger numbers of genes hypermethylated in individual tumors, and a higher frequency of hypermethylation within individual genes harboring either genetic or epigenetic changes. Thus, to enumerate the full spectrum of alterations in the human cancer genome, and to facilitate the most efficacious grouping of tumors to identify cancer biomarkers and tailor therapeutic approaches, both genetic and epigenetic screens should be undertaken

Convergence of Mutation and Epigenetic Alterations Identifies Common Genes in Cancer That Predict for Poor Prognosis

Stephen Baylin and colleagues show that a combined genetic and epigenetic analysis of breast and colon cancers identifies a number of clinically significant genes targeted by multiple modes of inactivation

We are all individuals...: bioinformatics in the personalized medicine era

The medical landscape is evolving at a rapid pace, creating the opportunity for more personalized patient treatment and shifting the way healthcare is approached and thought about. With the availability of (epi)genome-wide, transcriptomic and proteogenomic profiling techniques detailed characterization of a disease at the level of the individual is now possible, offering the opportunity for truly tailored approaches for treatment and patient care. While improvements are still expected, the techniques and the basic analytical tools have reached a state that these can be efficiently deployed in both routine research and clinical practice. Still, some major challenges remain. Notably, holistic approaches, integrating data from several sources, e.g. genomic and epigenomic, will increase the understanding of the underlying biological concepts and provide insight into the causes, effects and effective solutions. However, creating and validating such a knowledge base, potentially for different levels of expertise, and integrating several data points into meaningful information is not trivial

Evaluation of an epigenetic profile for the detection of bladder cancer in patients with hematuria

Purpose: Many patients enter the care cycle with gross or microscopic hematuria and undergo cystoscopy to rule out bladder cancer. Sensitivity of this invasive examination is limited, leaving many patients at risk for undetected cancer. To improve current clinical practice more sensitive and noninvasive screening methods should be applied. Materials and Methods: A total of 154 urine samples were collected from patients with hematuria, including 80 without and 74 with bladder cancer. DNA from cells in the urine was epigenetically profiled using 2 independent assays. Methylation specific polymerase chain reaction was performed on TWIST1. SNaPshot (TM) methylation analysis was done for different loci of OTX1 and ONECUT2. Additionally all samples were analyzed for mutation status of TERT (telomerase reverse transcriptase), PIK3CA, FGFR3 (fibroblast growth factor receptor 3), HRAS, KRAS and NRAS. Results: The combination of TWIST1, ONECUT2 (2 loci) and OTX1 resulted in the best overall performing panel. Logistic regression analysis on these methylation markers, mutation status of FGFR3, TERT and HRAS, and patient age resulted in an accurate model with 97% sensitivity, 83% specificity and an AUC of 0.93 (95% CI 0.88-0.98). Internal validation led to an optimism corrected AUC of 0.92. With an estimated bladder cancer prevalence of 5% to 10% in a hematuria cohort the assay resulted in a 99.6% to 99.9% negative predictive value. Conclusions: Epigenetic profiling using TWIST1, ONECUT2 and OTX1 results in a high sensitivity and specificity. Accurate risk prediction might result in less extensive and invasive examination of patients at low risk, thereby reducing unnecessary patient burden and health care costs