Methylation of PITX2, HOXD3, RASSF1 and TDRD1 predicts biochemical recurrence in high-risk prostate cancer

Significance of HOXD transcription factors family in progression, migration and angiogenesis of cancer

The transcription factors (TFs) of the HOX family play significant roles during early embryonic development and cellular processes. They also play a key role in tumorigenesis as tumor oncogenes or suppressors. Furthermore, TFs of the HOXD geFIne cluster affect proliferation, migration, and invasion of tumors. Consequently, dysregulated activity of HOXD TFs has been linked to clinicopathological characteristics of cancer. HOXD TFs are regulated by non-coding RNAs and methylation of DNA on promoter and enhancer regions. In addition, HOXD genes modulate the biological function of cancer cells via the MEK and AKT signaling pathways, thus, making HOXD TFs, a suitable molecular marker for cancer prognosis and therapy. In this review, we summarized the roles of HOXD TFs in different cancers and highlighted its potential as a diagnostic and therapeutic target.

Systematic Elucidation of the Aneuploidy Landscape and Identification of Aneuploidy Driver Genes in Prostate Cancer

Aneuploidy is widely identified as a remarkable feature of malignancy genomes. Increasing evidences suggested aneuploidy was involved in the progression and metastasis of prostate cancer (PCa). Nevertheless, no comprehensive analysis was conducted in PCa about the effects of aneuploidy on different omics and, especially, about the driver genes of aneuploidy. Here, we validated the association of aneuploidy with the progression and prognosis of PCa and performed a systematic analysis in mutation profile, methylation profile, and gene expression profile, which detailed the molecular process aneuploidy implicated. By multi-omics analysis, we managed to identify 11 potential aneuploidy driver genes (GSTM2, HAAO, C2orf88, CYP27A1, FAXDC2, HFE, C8orf88, GSTP1, EFS, HIF3A, and WFDC2), all of which were related to the development and metastasis of PCa. Meanwhile, we also found aneuploidy and its driver genes were correlated with the immune microenvironment of PCa. Our findings could shed light on the tumorigenesis of PCa and provide a better understanding of the development and metastasis of PCa; additionally, the driver genes could be promising and actionable therapeutic targets pointing to aneuploidy.

DNA Promoter Methylation and ERG Regulate the Expression of CD24 in Prostate Cancer

CD24 is overexpressed in many human cancers and is a driver of tumor progression. Herein, molecular mechanisms leading to up-regulation of CD24 in prostate cancer were studied. DNA methylation of the CD24 gene promoter at four loci using quantitative methylation-specific PCR was evaluated. Expression of CD24 in tumor tissues was studied by immunohistochemistry. To corroborate the results in vitro, ERG-inducible LNCaP TMPRSS2:ERG (T2E) cells and luciferase promoter assays were used. DNA methylation of the CD24 promoter was significantly higher in tumors than in benign tissue and was associated with biochemical recurrence-free survival, tumor grade, and stage. CD24 mRNA and protein expression were significantly higher in T2E-positive, ERG-overexpressing, and/or PTEN-deficient cases. Higher levels of CD24 protein expression conferred shorter biochemical recurrence-free survival, and these observations were confirmed using The Cancer Genome Atlas prostate adenocarcinoma data. In silico analysis of the CD24 promoter revealed an ERG binding site in between the DNA methylation sites. ERG overexpression led to a strong induction of CD24 mRNA and protein expression. Luciferase promoter assays using the wild-type and mutated ERG binding site within the CD24 promoter showed ERG-dependent activation. Collectively, our results suggest that promoter DNA methylation of the CD24 gene and T2E fusion status are factors involved in the up-regulation of CD24 in patients with prostate cancer.

A novel CpG island methylation panel predicts survival in lung adenocarcinomas

The lack of clinically useful biomarkers compromise the personalized management of lung adenocarcinomas (ADCs); epigenetic events and DNA methylation in particular have exhibited potential value as biomarkers. By comparing genome-wide DNA methylation data of paired lung ADCs and normal tissues from 6 public datasets, cancer-specific CpG island (CGI) methylation changes were identified with a pre-specified criterion. Correlations between DNA methylation and expression data for each gene were assessed by Pearson correlation analysis. A prognostically relevant CGI methylation signature was constructed by risk-score analysis, and was validated using a training-validation approach. Survival data were analyzed by log-rank test and Cox regression model. In total, 134 lung ADC-specific CGI CpGs were identified, among which, a panel of 9 CGI loci were selected as prognostic candidates, and were used to construct a risk-score signature. The novel CGI methylation signature was identified to classify distinct prognostic subgroups across different datasets, and was demonstrated to be a potent independent prognostic factor for overall survival time of patients with lung ADCs. In addition, it was identified that cancer-specific CGI hypomethylation of RPL39L, along with the corresponding gene expression, provided optimized prognostication of lung ADCs. In summary, cancer-specific CGI methylation aberrations are optimal candidates for novel biomarkers of lung ADCs; the 9-CpG methylation panel and hypomethylation of RPL39L exhibited particularly promising significance.

Clinical protein science in translational medicine targeting malignant melanoma

Melanoma of the skin is the sixth most common type of cancer in Europe and accounts for 3.4% of all diagnosed cancers. More alarming is the degree of recurrence that occurs with approximately 20% of patients lethally relapsing following treatment. Malignant melanoma is a highly aggressive skin cancer and metastases rapidly extend to the regional lymph nodes (stage 3) and to distal organs (stage 4). Targeted oncotherapy is one of the standard treatment for progressive stage 4 melanoma, and BRAF inhibitors (e.g. vemurafenib, dabrafenib) combined with MEK inhibitor (e.g. trametinib) can effectively counter BRAFV600E-mutated melanomas. Compared to conventional chemotherapy, targeted BRAFV600E inhibition achieves a significantly higher response rate. After a period of cancer control, however, most responsive patients develop resistance to the therapy and lethal progression. The many underlying factors potentially causing resistance to BRAF inhibitors have been extensively studied. Nevertheless, the remaining unsolved clinical questions necessitate alternative research approaches to address the molecular mechanisms underlying metastatic and treatment-resistant melanoma. In broader terms, proteomics can address clinical questions far beyond the reach of genomics, by measuring, i.e. the relative abundance of protein products, post-translational modifications (PTMs), protein localisation, turnover, protein interactions and protein function. More specifically, proteomic analysis of body fluids and tissues in a given medical and clinical setting can aid in the identification of cancer biomarkers and novel therapeutic targets. Achieving this goal requires the development of a robust and reproducible clinical proteomic platform that encompasses automated biobanking of patient samples, tissue sectioning and histological examination, efficient protein extraction, enzymatic digestion, mass spectrometry-based quantitative protein analysis by label-free or labelling technologies and/or enrichment of peptides with specific PTMs. By combining data from, e.g. phosphoproteomics and acetylomics, the protein expression profiles of different melanoma stages can provide a solid framework for understanding the biology and progression of the disease. When complemented by proteogenomics, customised protein sequence databases generated from patient-specific genomic and transcriptomic data aid in interpreting clinical proteomic biomarker data to provide a deeper and more comprehensive molecular characterisation of cellular functions underlying disease progression. In parallel to a streamlined, patient-centric, clinical proteomic pipeline, mass spectrometry-based imaging can aid in interrogating the spatial distribution of drugs and drug metabolites within tissues at single-cell resolution. These developments are an important advancement in studying drug action and efficacy in vivo and will aid in the development of more effective and safer strategies for the treatment of melanoma. A collaborative effort of gargantuan proportions between academia and healthcare professionals has led to the initiation, establishment and development of a cutting-edge cancer research centre with a specialisation in melanoma and lung cancer. The primary research focus of the European Cancer Moonshot Lund Center is to understand the impact that drugs have on cancer at an individualised and personalised level. Simultaneously, the centre increases awareness of the relentless battle against cancer and attracts global interest in the exceptional research performed at the centre.

The impact of DNA methylation on the cancer proteome

Aberrant DNA methylation disrupts normal gene expression in cancer and broadly contributes to oncogenesis. We previously developed MethylMix, a model-based algorithmic approach to identify epigenetically regulated driver genes. MethylMix identifies genes where methylation likely executes a functional role by using transcriptomic data to select only methylation events that can be linked to changes in gene expression. However, given that proteins more closely link genotype to phenotype recent high-throughput proteomic data provides an opportunity to more accurately identify functionally relevant abnormal methylation events. Here we present a MethylMix analysis that refines nominations for epigenetic driver genes by leveraging quantitative high-throughput proteomic data to select only genes where DNA methylation is predictive of protein abundance. Applying our algorithm across three cancer cohorts we find that using protein abundance data narrows candidate nominations, where the effect of DNA methylation is often buffered at the protein level. Next, we find that MethylMix genes predictive of protein abundance are enriched for biological processes involved in cancer including functions involved in epithelial and mesenchymal transition. Moreover, our results are also enriched for tumor markers which are predictive of clinical features like tumor stage and we find clustering using MethylMix genes predictive of protein abundance captures cancer subtypes.

To elucidate the molecular basis of cancer we examine the variation and dynamics characterizing the flow of information from epigenome to the transcriptome and proteome. Conducting the first genome wide analysis of epigenome-proteome associations, we present a MethylMix analysis that leverages protein abundance data taking advantage of recent high-throughput proteomic data generated using mass-spectrometry technology to elucidate the role of DNA methylation in cancer. By integrating across molecular data types, we confirm the benefit of using protein abundance data to provide additional insights into pathways and processes involved in oncogenesis and how they manifest as clinical phenotypes. Applying our method across three large cancer cohorts including breast cancer, ovarian cancer and colorectal cancer, MethylMix identifies key genes and describes molecular features and subtypes in these cancers.

Deep learning with multimodal representation for pancancer prognosis prediction

MOTIVATION

Estimating the future course of patients with cancer lesions is invaluable to physicians; however, current clinical methods fail to effectively use the vast amount of multimodal data that is available for cancer patients. To tackle this problem, we constructed a multimodal neural network-based model to predict the survival of patients for 20 different cancer types using clinical data, mRNA expression data, microRNA expression data and histopathology whole slide images (WSIs). We developed an unsupervised encoder to compress these four data modalities into a single feature vector for each patient, handling missing data through a resilient, multimodal dropout method. Encoding methods were tailored to each data type-using deep highway networks to extract features from clinical and genomic data, and convolutional neural networks to extract features from WSIs.

RESULTS

We used pancancer data to train these feature encodings and predict single cancer and pancancer overall survival, achieving a C-index of 0.78 overall. This work shows that it is possible to build a pancancer model for prognosis that also predicts prognosis in single cancer sites. Furthermore, our model handles multiple data modalities, efficiently analyzes WSIs and represents patient multimodal data flexibly into an unsupervised, informative representation. We thus present a powerful automated tool to accurately determine prognosis, a key step towards personalized treatment for cancer patients.

AVAILABILITY AND IMPLEMENTATION

https://github.com/gevaertlab/MultimodalPrognosis.

Where is the limit of prostate cancer biomarker research? Systematic investigation of potential prognostic and diagnostic biomarkers

BACKGROUND

The identification of appropriate biomarkers is essential to support important clinical decisions in patients with prostate cancer. The aim of our study was a systematic bioinformatical analysis of the mRNA expression of all genes available for the prostate adenocarcinoma cohort of The Cancer Genome Atlas (TCGA), regarding their potential prognostic and diagnostic role.

METHODS

The study cohort comprises 499 patients (TCGA prostate cancer cohort). mRNA expression data were available for approx. 20,000 genes. The bioinformatical statistical pipeline addressed gene expression differences in tumor vs. benign prostate tissue (including gene set enrichment analysis, GSEA) in samples from tumors with different aggressivenesses (Gleason score), as well as prognostic values in multistep survival analyses.

RESULTS

Among all genes analyzed, 1754 were significantly downregulated and 1553 genes were significantly upregulated in tumor tissue. In GSEA, 16 of 30 top enriched biological processes were alterations of epigenetic regulation at different levels. Significant correlation with Gleason Score was evident for 8724 genes (range of Pearson r-values 0.09-0.43; all p < 0.05). In univariate Cox regression analyses, mRNA expression of 3571 genes showed statistically significant association with biochemical recurrence-free survival with a range of hazard ratios 0.3-3.8 (p-value 7.4e- 07 to 0.05). Among these, 571 genes were independently associated with biochemical recurrence in multivariate analysis. Access to the full database including results is provided as supplement.

CONCLUSIONS

In our systematic analysis we found a big number of genes of potential diagnostic and prognostic value, many of which have not been studied in prostate cancer to date. Due to the comprehensive nature of this analysis and free access to the results, this study represents a reference database for prostate cancer researchers which can be used as a powerful tool for validation purposes and planning of new studies.

PITX2 methylation: a novel and effective biomarker for monitoring biochemical recurrence risk of prostate cancer

AIMS

Prostate cancer is one of the most common malignancies in men. Biochemical recurrence (BCR) and progression following curative treatment pose a significant public health challenge. Thus, it is essential to explore effective biomarkers for disease progression monitoring and risk stratification. The promoter region of the paired-like homeodomain transcription factor 2 (PITX2) gene has been found to be frequently methylated in prostate cancer. However, the prognostic role of PITX2 methylation in prostate cancer and which patients most likely to be recommended for PITX2 methylation tests to assess BCR risk remain controversial. Therefore, a systematic review was performed to explore the relationship of PITX2 methylation with the BCR risk of prostate cancer.

METHODS

The PubMed, EMBASE, and Cochrane Library databases were systematically searched for eligible studies. Seven studies with a total of 2185 patients were included. Pooled hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) were calculated.

RESULTS

The overall HR was 2.71 (95% CI, 2.21-3.31), suggesting that PITX2 methylation has an adverse impact on BCR of prostate cancer. The pooled estimate of 5-year BCR-free survival for patients with a high methylation status was significantly lower than that for patients with a low methylation status (71% vs 90%; odds ratio [OR] = 3.50; 95% CI, 2.67-4.60, P = .000). A subgroup analysis was conducted according to detection method; the combined HRs were 2.68 (95% CI, 2.02-3.55) for quantitative methylation-specific PCR (qMSP) and 3.29 (95% CI, 2.31-4.68) for microarray EpiChip. In subgroups defined by region, Gleason score, pathological stage, surgical margin status and ethnicity, high methylation status was also associated with BCR of prostate cancer.

CONCLUSIONS

As an effective biomarker, PITX2 methylation is feasible for individualized BCR risk assessment of prostate cancer following radical prostatectomy.

DNA methylation profiles in cancer diagnosis and therapeutics

Cancer initiation and proliferation is regulated by both epigenetic and genetic events with epigenetic modifications being increasingly identified as important targets for cancer research. DNA methylation catalyzed by DNA methyltransferases (DNMTs) is one of the essential epigenetic mechanisms that control cell proliferation, apoptosis, differentiation, cell cycle, and transformation in eukaryotes. Recent progress in epigenetics revealed a deeper understanding of the mechanisms of tumorigenesis and provided biomarkers for early detection, diagnosis, and prognosis in cancer patients. Although DNA methylation biomarker possesses potential contributing to precision medicine, there are still limitations to be overcome before it reaches clinical setting. Hence, the current status of DNA methylation biomarkers was reviewed and the future use in clinic was also predicted.

Markers of clinical utility in the differential diagnosis and prognosis of prostate cancer

Molecular diagnostics is a rapidly evolving area of surgical pathology, that is gradually beginning to transform our diagnostical procedures for a variety of tumors. Next to molecular prognostication that has begun to complement our histological diagnosis in breast cancer, additional testing to detect targets and to predict therapy response has become common practice in breast and lung cancer. Prostate cancer is a bit slower in this respect, as it is still largely diagnosed and classified on morphological grounds. Our diagnostic immunohistochemical armamentarium of basal cell markers and positive markers of malignancy now allows to clarify the majority of lesions, if applied to the appropriate morphological context (and step sections). Prognostic immunohistochemistry remains a problematic and erratic yet tempting research field that provides information on tumor relevance of proteins, but little hard data to integrate into our diagnostic workflow. Main reasons are various issues of standardization that hamper the reproducibility of cut-off values to delineate risk categories. Molecular testing of DNA-methylation or transcript profiling may be much better standardized and this review discusses a couple of commercially available tests: The ConfirmDX test measures DNA-methylation to estimate the likelihood of cancer detection on a repeat biopsy and may help to reduce unnecessary biopsies. The tests Prolaris, OncotypeDX Prostate, and Decipher all are transcript tests that have shown to provide prognostic data independent of clinico-pathological parameters and that may aid in therapy planning. However, further validation and more comparative studies will be needed to clarify the many open questions concerning sampling bias and tumor heterogeneity.

DNA Methylation and Urological Cancer, a Step Towards Personalized Medicine: Current and Future Prospects

Urologic malignancies are some of the commonest tumors often curable when diagnosed at early stage. However, accurate diagnostic markers and faithful predictors of prognosis are needed to avoid over-diagnosis leading to overtreatment. Many promising exploratory studies have identified epigenetic markers in urinary malignancies based on DNA methylation, histone modification and non-coding ribonucleic acid (ncRNA) expression that epigenetically regulate gene expression. We review and discuss the current state of development and the future potential of epigenetic biomarkers for more accurate and less invasive detection of urological cancer, tumor recurrence and progression of disease serving to establish diagnosis and monitor treatment efficacies. The specific clinical implications of such methylation tests on therapeutic decisions and patient outcome and current limitations are also discussed.

The Predictive Value of PITX2 DNA Methylation for High-Risk Breast Cancer Therapy: Current Guidelines, Medical Needs, and Challenges

High-risk breast cancer comprises distinct tumor entities such as triple-negative breast cancer (TNBC) which is characterized by lack of estrogen (ER) and progesterone (PR) and the HER2 receptor and breast malignancies which have spread to more than three lymph nodes. For such patients, current (inter)national guidelines recommend anthracycline-based chemotherapy as the standard of care, but not all patients do equally benefit from such a chemotherapy. To further improve therapy decision-making, predictive biomarkers are of high, so far unmet, medical need. In this respect, predictive biomarkers would permit patient selection for a particular kind of chemotherapy and, by this, guide physicians to optimize the treatment plan for each patient individually. Besides DNA mutations, DNA methylation as a patient selection marker has received increasing clinical attention. For instance, significant evidence has accumulated that methylation of the PITX2 (paired-like homeodomain transcription factor 2) gene might serve as a novel predictive and prognostic biomarker, for a variety of cancer diseases. This review highlights the current understanding of treatment modalities of high-risk breast cancer patients with a focus on recommended treatment options, with special attention on the future clinical application of PITX2 as a predictive biomarker to personalize breast cancer management.

Integrative modelling of tumour DNA methylation quantifies the contribution of metabolism

Altered DNA methylation is common in cancer and often considered an early event in tumorigenesis. However, the sources of heterogeneity of DNA methylation among tumours remain poorly defined. Here we capitalize on the availability of multi-platform data on thousands of human tumours to build integrative models of DNA methylation. We quantify the contribution of clinical and molecular factors in explaining intertumoral variability in DNA methylation. We show that the levels of a set of metabolic genes involved in the methionine cycle is predictive of several features of DNA methylation in tumours, including the methylation of cancer genes. Finally, we demonstrate that patients whose DNA methylation can be predicted from the methionine cycle exhibited improved survival over cases where this regulation is disrupted. This study represents a comprehensive analysis of the determinants of methylation and demonstrates the surprisingly large interaction between metabolism and DNA methylation variation. Together, our results quantify links between tumour metabolism and epigenetics and outline clinical implications.

Altered DNA methylation is a feature of cancer and between-patient variability is prevalent. Here, the authors integrate data on thousands of human tumours, and find that expression levels of methionine metabolism genes are predictive of methylation features, and that the breakdown of this relationship is a negative prognostic marker.

LINE-1 methylation status in prostate cancer and non-neoplastic tissue adjacent to tumor in association with mortality

Aberrant DNA methylation seems to be associated with prostate cancer behavior. We investigated LINE-1 methylation in prostate cancer and non-neoplastic tissue adjacent to tumor (NTAT) in association with mortality from prostate cancer. We selected 157 prostate cancer patients with available NTAT from 2 cohorts of patients diagnosed between 1982-1988 and 1993-1996, followed up until 2010. An association between LINE-1 hypomethylation and prostate cancer mortality in tumor was suggested [hazard ratio per 5% decrease in LINE-1 methylation levels: 1.40, 95% confidence interval (CI): 0.95-2.01]. After stratification of the patients for Gleason score, the association was present only for those with a Gleason score of at least 8. Among these, low (<75%) vs. high (>80%) LINE-1 methylation was associated with a hazard ratio of 4.68 (95% CI: 1.03-21.34). LINE-1 methylation in the NTAT was not associated with prostate cancer mortality. Our results are consistent with the hypothesis that tumor tissue global hypomethylation may be a late event in prostate cancerogenesis and is associated with tumor progression.

Methylation status of homeobox genes in common human cancers

Approximately 300 homeobox loci were identified in the euchromatic regions of the human genome, of which 235 are probable functional genes and 65 are likely pseudogenes. Many of these genes play important roles in embryonic development and cell differentiation. Dysregulation of homeobox gene expression is a frequent occurrence in cancer. Accumulating evidence suggests that as genetics disorders, epigenetic modifications alter the expression of oncogenes and tumor suppressor genes driving tumorigenesis and perhaps play a more central role in the evolution and progression of this disease. Here, we described the current knowledge regarding homeobox gene DNA methylation in human cancer and describe its relevance in the diagnosis, therapeutic response and prognosis of different types of human cancers.

DNA Methylation of PITX2 and PANCR Is Prognostic for Overall Survival in Patients with Resected Adenocarcinomas of the Biliary Tract

Biliary tract cancers (BTC) are rare but highly aggressive malignant epithelial tumors. In order to improve the outcome in this lethal disease, novel biomarkers for diagnosis, prognosis, and therapy response prediction are urgently needed. DNA promoter methylation of PITX2 variants (PITX2ab, PITX2c) and intragenic methylation of the PITX2 adjacent non-coding RNA (PANCR) were investigated by methylations-specific qPCR assays in formalin-fixed paraffin-embedded tissue from 80 patients after resection for BTC. Results were correlated with clinicopathologic data and outcome. PITX2 variants and PANCR showed significant hypermethylation in tumor vs. normal adjacent tissue (p < 0.001 and p = 0.015), respectively. In survival analysis, dichotomized DNA methylation of variant PITX2c and PANCR were significantly associated with overall survival (OS). Patients with high tumor methylation levels of PITX2c had a shorter OS compared to patients with low methylation (12 vs. 40 months OS; HR 2.48 [1.38-4.48], p = 0.002). In contrast, PANCR hypermethylation was associated with prolonged survival (25 vs. 19 months OS; HR 0.54 [0.30-0.94], p = 0.015) and qualified as an independent prognostic factor on multivariate analysis. The biomarkers investigated in this study may help to identify BTC subpopulations at risk for worse survival. Further studies are needed to evaluate if PITX2 might be a clinically useful biomarker for an optimized and individualized treatment.

A cancer specific hypermethylation signature of the TERT promoter predicts biochemical relapse in prostate cancer: a retrospective cohort study

The identification of new biomarkers to differentiate between indolent and aggressive prostate tumors is an important unmet need. We examined the role of THOR (TERT Hypermethylated Oncological Region) as a diagnostic and prognostic biomarker in prostate cancer (PCa).We analyzed THOR in common cancers using genome-wide methylation arrays. Methylation status of the whole TERT gene in benign and malignant prostate samples was determined by MeDIP-Seq. The prognostic role of THOR in PCa was assessed by pyrosequencing on discovery and validation cohorts from patients who underwent radical prostatectomy with long-term follow-up data.Most cancers (n = 3056) including PCa (n = 300) exhibited hypermethylation of THOR. THOR was the only region within the TERT gene that is differentially methylated between normal and malignant prostate tissue (p < 0.0001). Also, THOR was significantly hypermethylated in PCa when compared to paired benign tissues (n = 164, p < 0.0001). THOR hypermethylation correlated with Gleason scores and was associated with tumor invasiveness (p = 0.0147). Five years biochemical progression free survival (BPFS) for PCa patients in the discovery cohort was 87% (95% CI 73-100) and 65% (95% CI 52-78) for THOR non-hypermethylated and hypermethylated cancers respectively (p = 0.01). Similar differences in BPFS were noted in the validation cohort (p = 0.03). Importantly, THOR was able to predict outcome in the challenging (Gleason 6 and 7 (3 + 4)) PCa (p = 0.007). For this group, THOR was an independent risk factor for BPFS with a hazard-ratio of 3.685 (p = 0.0247). Finally, THOR hypermethylation more than doubled the risk of recurrence across all PSA levels (OR 2.5, p = 0.02).

Clinical utility of RASSF1A methylation in human malignancies

The high frequency of RASSF1A methylation has been noted in a vast number of patients in a broad spectrum of malignancies, suggesting that RASSF1A inactivation is associated with cancer pathogenesis. However, whether this recurrent incidence of RASSF1A hypermethylation in human malignancies and its association with more aggressive tumour phenotype is a frequent event across different cancer types has not yet been discussed. In this review, we interrogated existing evidence for association of RASSF1A hypermethylation with clinicopathological characteristics that can indicate more invasive lesions.

DNA Methylation-Guided Prediction of Clinical Failure in High-Risk Prostate Cancer

BACKGROUND

Prostate cancer (PCa) is a very heterogeneous disease with respect to clinical outcome. This study explored differential DNA methylation in a priori selected genes to diagnose PCa and predict clinical failure (CF) in high-risk patients.

METHODS

A quantitative multiplex, methylation-specific PCR assay was developed to assess promoter methylation of the APC, CCND2, GSTP1, PTGS2 and RARB genes in formalin-fixed, paraffin-embedded tissue samples from 42 patients with benign prostatic hyperplasia and radical prostatectomy specimens of patients with high-risk PCa, encompassing training and validation cohorts of 147 and 71 patients, respectively. Log-rank tests, univariate and multivariate Cox models were used to investigate the prognostic value of the DNA methylation.

RESULTS

Hypermethylation of APC, CCND2, GSTP1, PTGS2 and RARB was highly cancer-specific. However, only GSTP1 methylation was significantly associated with CF in both independent high-risk PCa cohorts. Importantly, trichotomization into low, moderate and high GSTP1 methylation level subgroups was highly predictive for CF. Patients with either a low or high GSTP1 methylation level, as compared to the moderate methylation groups, were at a higher risk for CF in both the training (Hazard ratio [HR], 3.65; 95% CI, 1.65 to 8.07) and validation sets (HR, 4.27; 95% CI, 1.03 to 17.72) as well as in the combined cohort (HR, 2.74; 95% CI, 1.42 to 5.27) in multivariate analysis.

CONCLUSIONS

Classification of primary high-risk tumors into three subtypes based on DNA methylation can be combined with clinico-pathological parameters for a more informative risk-stratification of these PCa patients.

The identification of specific methylation patterns across different cancers

Abnormal DNA methylation is known as playing an important role in the tumorgenesis. It is helpful for distinguishing the specificity of diagnosis and therapeutic targets for cancers based on characteristics of DNA methylation patterns across cancers. High throughput DNA methylation analysis provides the possibility to comprehensively filter the epigenetics diversity across various cancers. We integrated whole-genome methylation data detected in 798 samples from seven cancers. The hierarchical clustering revealed the existence of cancer-specific methylation pattern. Then we identified 331 differentially methylated genes across these cancers, most of which (266) were specifically differential methylation in unique cancer. A DNA methylation correlation network (DMCN) was built based on the methylation correlation between these genes. It was shown the hubs in the DMCN were inclined to cancer-specific genes in seven cancers. Further survival analysis using the part of genes in the DMCN revealed high-risk group and low-risk group were distinguished by seven biomarkers (PCDHB15, WBSCR17, IGF1, GYPC, CYGB, ACTG2, and PRRT1) in breast cancer and eight biomarkers (ZBTB32, OR51B4, CCL8, TMEFF2, SALL3, GPSM1, MAGEA8, and SALL1) in colon cancer, respectively. At last, a protein-protein interaction network was introduced to verify the biological function of differentially methylated genes. It was shown that MAP3K14, PTN, ACVR1 and HCK sharing different DNA methylation and gene expression across cancers were relatively high degree distribution in PPI network. The study suggested that not only the identified cancer-specific genes provided reference for individual treatment but also the relationship across cancers could be explained by differential DNA methylation.