Methylome-wide Sequencing Detects DNA Hypermethylation Distinguishing Indolent from Aggressive Prostate Cancer

Molecular heterogeneity in prostate cancer and the role of targeted therapy

Data collected from large-scale studies has shown that the incidence of prostate cancer globally is on the rise, which could be attributed to an overall increase in lifespan. So, the question is how has modern science with all its new technologies and clinical breakthroughs mitigated or managed this disease? The answer is not a simple one as prostate cancer exhibits various subtypes, each with its unique characteristics or signatures which creates challenges in treatment. To understand the complexity of prostate cancer these signatures must be deciphered. Molecular studies of prostate cancer samples have identified certain genetic and epigenetic alterations, which are instrumental in tumorigenesis. Some of these candidates include the androgen receptor (AR), various oncogenes, tumor suppressor genes, and the tumor microenvironment, which serve as major drivers that lead to cancer progression. These aberrant genes and their products can give an insight into prostate cancer development and progression by acting as potent markers to guide future therapeutic approaches. Thus, understanding the complexity of prostate cancer is crucial for targeting specific markers and tailoring treatments accordingly.

Multiomics analysis of adaptation to repeated DNA damage in prostate cancer cells

DNA damage is frequently utilized as the basis for cancer therapies; however, resistance to DNA damage remains one of the biggest challenges for successful treatment outcomes. Critically, the molecular drivers behind resistance are poorly understood. To address this question, we created an isogenic model of prostate cancer exhibiting more aggressive characteristics to better understand the molecular signatures associated with resistance and metastasis. 22Rv1 cells were repeatedly exposed to DNA damage daily for 6 weeks, similar to patient treatment regimes. Using Illumina Methylation EPIC arrays and RNA-seq, we compared DNA methylation and transcriptional profiles between the parental 22Rv1 cell line and the lineage exposed to prolonged DNA damage. Here we show that repeated DNA damage drives the molecular evolution of cancer cells to a more aggressive phenotype and identify molecular candidates behind this process. Total DNA methylation was increased while RNA-seq demonstrated these cells had dysregulated expression of genes involved in metabolism and the unfolded protein response (UPR) with Asparagine synthetase (ASNS) identified as central to this process. Despite the limited overlap between RNA-seq and DNA methylation, oxoglutarate dehydrogenase-like (OGDHL) was identified as altered in both data sets. Utilising a second approach we profiled the proteome in 22Rv1 cells following a single dose of radiotherapy. This analysis also highlighted the UPR in response to DNA damage. Together, these analyses identified dysregulation of metabolism and the UPR and identified ASNS and OGDHL as candidates for resistance to DNA damage. This work provides critical insight into molecular changes which underpin treatment resistance and metastasis.

Integration of gene expression and DNA methylation data across different experiments

Integrative analysis of multi-omic datasets has proven to be extremely valuable in cancer research and precision medicine. However, obtaining multimodal data from the same samples is often difficult. Integrating multiple datasets of different omics remains a challenge, with only a few available algorithms developed to solve it. Here, we present INTEND (IntegratioN of Transcriptomic and EpigeNomic Data), a novel algorithm for integrating gene expression and DNA methylation datasets covering disjoint sets of samples. To enable integration, INTEND learns a predictive model between the two omics by training on multi-omic data measured on the same set of samples. In comprehensive testing on 11 TCGA (The Cancer Genome Atlas) cancer datasets spanning 4329 patients, INTEND achieves significantly superior results compared with four state-of-the-art integration algorithms. We also demonstrate INTEND's ability to uncover connections between DNA methylation and the regulation of gene expression in the joint analysis of two lung adenocarcinoma single-omic datasets from different sources. INTEND's data-driven approach makes it a valuable multi-omic data integration tool. The code for INTEND is available at https://github.com/Shamir-Lab/INTEND.

Recent advances in understanding DNA methylation of prostate cancer

Epigenetic modifications, such as DNA methylation, is widely studied in cancer. DNA methylation patterns have been shown to distinguish between benign and malignant tumors in various cancers, including prostate cancer. It may also contribute to oncogenesis, as it is frequently associated with downregulation of tumor suppressor genes. Aberrant patterns of DNA methylation, in particular the CpG island hypermethylator phenotype (CIMP), have shown associative evidence with distinct clinical features and outcomes, such as aggressive subtypes, higher Gleason score, prostate-specific antigen (PSA), and overall tumor stage, overall worse prognosis, as well as reduced survival. In prostate cancer, hypermethylation of specific genes is significantly different between tumor and normal tissues. Methylation patterns could distinguish between aggressive subtypes of prostate cancer, including neuroendocrine prostate cancer (NEPC) and castration resistant prostate adenocarcinoma. Further, DNA methylation is detectable in cell-free DNA (cfDNA) and is reflective of clinical outcome, making it a potential biomarker for prostate cancer. This review summarizes recent advances in understanding DNA methylation alterations in cancers with the focus on prostate cancer. We discuss the advanced methodology used for evaluating DNA methylation changes and the molecular regulators behind these changes. We also explore the clinical potential of DNA methylation as prostate cancer biomarkers and its potential for developing targeted treatment of CIMP subtype of prostate cancer.

Epigenetic (De)regulation in Prostate Cancer

Prostate cancer (PCa) is a heterogeneous disease exhibiting both genetic and epigenetic deregulations. Epigenetic alterations are defined as changes not based on DNA sequence, which include those of DNA methylation, histone modification, and chromatin remodeling. Androgen receptor (AR) is the main driver for PCa and androgen deprivation therapy (ADT) remains a backbone treatment for patients with PCa; however, ADT resistance almost inevitably occurs and advanced diseases develop termed castration-resistant PCa (CRPC), due to both genetic and epigenetic changes. Due to the reversible nature of epigenetic modifications, inhibitors targeting epigenetic factors have become promising anti-cancer agents. In this chapter, we focus on recent studies about the dysregulation of epigenetic regulators crucially involved in the initiation, development, and progression of PCa and discuss the potential use of inhibitors targeting epigenetic modifiers for treatment of advanced PCa.

Persistent epigenetic alterations in transcription factors after a sustained virological response in hepatocellular carcinoma

Background and Aim

The risk of hepatocellular carcinoma (HCC) persists in a condition of sustained virologic response (SVR) after hepatitis C virus (HCV) eradication. Comprehensive molecular analyses were performed to test the hypothesis that epigenetic abnormalities present after an SVR play a role in hepatocarcinogenesis.

Methods

Whole-genome methylome and RNA sequencing were performed on HCV, SVR, and healthy liver tissue. Integrated analysis of the sequencing data focused on expression changes in transcription factors and their target genes, commonly found in HCV and SVR. Identified expression changes were validated in demethylated cultured HCC cell lines and an independent validation cohort.

Results

The coincidence rates of the differentially methylated regions between the HCV and SVR groups were 91% in the hypomethylated and 71% in the hypermethylated regions in tumorous tissues, and 37% in the hypomethylated and 36% in the hypermethylated regions in non-tumorous tissues. These results indicate that many epigenomic abnormalities persist even after an SVR was achieved. Integrated analysis identified 61 transcription factors and 379 other genes that had methylation abnormalities and gene expression changes in both groups. Validation cohort specified gene expression changes for 14 genes, and gene ontology pathway analysis revealed apoptotic signaling and inflammatory response were associated with these genes.

Conclusion

This study demonstrates that DNA methylation abnormalities, retained after HCV eradication, affect the expression of transcription factors and their target genes. These findings suggest that DNA methylation in SVR patients may be functionally important in carcinogenesis, and could serve as biomarkers to predict HCC occurrence.

Alternations of gene expression in PI3K and AR pathways and DNA methylation features contribute to metastasis of prostate cancer

OBJECTIVE

The molecular heterogeneity of prostate cancer (PCa) gives rise to distinct tumor subclasses based on epigenetic modification and gene expression signatures. Identification of clinically actionable molecular subtypes of PCa is key to improving patient outcome, and the balance between specific pathways may influence PCa outcome. It is also urgent to identify progression-related markers through cytosine-guanine (CpG) methylation in predicting metastasis for patients with PCa.

METHODS

We performed bioinformatics analysis of transcriptomic, and clinical data in an integrated cohort of 551 prostate samples. The datasets included retrospective The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts. Two algorithms, Least Absolute Shrinkage and Selector Operation and Support Vector Machine-Recursive Feature Elimination, were used to select significant CpGs.

RESULTS

We found that PCa progression is more likely to occur after the third year through conditional survival (CS) analysis, and prostate-specific antigen (PSA) was a better predictor of Progression-free survival (PFS) than Gleason score (GS). Our study first demonstrated that PCa tumors have distinct expression profiles based on the expression of genes involved in androgen receptor (AR) and PI3K-AKT, which influence disease outcome. Our results also indicated that there are multiple phenotypes relevant to the AR-PI3K axis in PCa, where tumors with mixed phenotype may be more aggressive or have worse outcome than quiescent phenotype. In terms of epigenetics, we obtained CpG sites and their corresponding genes which have a good predictive value of PFS. However, various evidences showed that the predictive value of CpGs corresponding genes was much lower than GpG sites in Overall survival (OS) and PFS.

CONCLUSIONS

PCa classification specific to AR and PI3K pathways provides novel biological insight into previously established PCa subtypes and may help develop personalized therapies. Our results support the potential clinical utility of DNA methylation signatures to distinguish tumor metastasis and to predict prognosis and outcomes.

Lipid Metabolism and Epigenetics Crosstalk in Prostate Cancer

Prostate cancer (PCa) is the most commonly diagnosed malignant neoplasm in men in the Western world. Localized low-risk PCa has an excellent prognosis thanks to effective local treatments; however, despite the incorporation of new therapeutic strategies, metastatic PCa remains incurable mainly due to disease heterogeneity and the development of resistance to therapy. The mechanisms underlying PCa progression and therapy resistance are multiple and include metabolic reprogramming, especially in relation to lipid metabolism, as well as epigenetic remodelling, both of which enable cancer cells to adapt to dynamic changes in the tumour. Interestingly, metabolism and epigenetics are interconnected. Metabolism can regulate epigenetics through the direct influence of metabolites on epigenetic processes, while epigenetics can control metabolism by directly or indirectly regulating the expression of metabolic genes. Moreover, epidemiological studies suggest an association between a high-fat diet, which can alter the availability of metabolites, and PCa progression. Here, we review the alterations of lipid metabolism and epigenetics in PCa, before focusing on the mechanisms that connect them. We also discuss the influence of diet in this scenario. This information may help to identify prognostic and predictive biomarkers as well as targetable vulnerabilities.

Pan-Cancer DNA Methylation Analysis and Tumor Origin Identification of Carcinoma of Unknown Primary Site Based on Multi-Omics

The metastatic cancer of unknown primary (CUP) sites remains a leading cause of cancer death with few therapeutic options. The aberrant DNA methylation (DNAm) is the most important risk factor for cancer, which has certain tissue specificity. However, how DNAm alterations in tumors differ among the regulatory network of multi-omics remains largely unexplored. Therefore, there is room for improvement in our accuracy in the prediction of tumor origin sites and a need for better understanding of the underlying mechanisms. In our study, an integrative analysis based on multi-omics data and molecular regulatory network uncovered genome-wide methylation mechanism and identified 23 epi-driver genes. Apart from the promoter region, we also found that the aberrant methylation within the gene body or intergenic region was significantly associated with gene expression. Significant enrichment analysis of the epi-driver genes indicated that these genes were highly related to cellular mechanisms of tumorigenesis, including T-cell differentiation, cell proliferation, and signal transduction. Based on the ensemble algorithm, six CpG sites located in five epi-driver genes were selected to construct a tissue-specific classifier with a better accuracy (>95%) using TCGA datasets. In the independent datasets and the metastatic cancer datasets from GEO, the accuracy of distinguishing tumor subtypes or original sites was more than 90%, showing better robustness and stability. In summary, the integration analysis of large-scale omics data revealed complex regulation of DNAm across various cancer types and identified the epi-driver genes participating in tumorigenesis. Based on the aberrant methylation status located in epi-driver genes, a classifier that provided the highest accuracy in tracing back to the primary sites of metastatic cancer was established. Our study provides a comprehensive and multi-omics view of DNAm-associated changes across cancer types and has potential for clinical application.

What Do We Have to Know about PD-L1 Expression in Prostate Cancer? A Systematic Literature Review. Part 1: Focus on Immunohistochemical Results with Discussion of Pre-Analytical and Interpretation Variables

Immunotherapy targeting the PD-1-PD-L1 axis yielded good results in treating different immunologically ''hot'' tumors. A phase II study revealed good therapeutic activity of pembrolizumab in selected prostatic carcinoma (PC)-patients. We performed a systematic literature review (PRISMA guidelines), which analyzes the immunohistochemical expression of PD-L1 in human PC samples and highlights the pre-analytical and interpretation variables. Interestingly, 29% acinar PCs, 7% ductal PCs, and 46% neuroendocrine carcinomas/tumors were PD-L1+ on immunohistochemistry. Different scoring methods or cut-off criteria were applied on variable specimen-types, evaluating tumors showing different clinic-pathologic features. The positivity rate of different PD-L1 antibody clones in tumor cells ranged from 3% (SP142) to 50% (ABM4E54), excluding the single case tested for RM-320. The most tested clone was E1L3N, followed by 22C3 (most used for pembrolizumab eligibility), SP263, SP142, and 28-8, which gave the positivity rates of 35%, 11-41% (depending on different scoring systems), 6%, 3%, and 15%, respectively. Other clones were tested in <200 cases. The PD-L1 positivity rate was usually higher in tumors than benign tissues. It was higher in non-tissue microarray specimens (41-50% vs. 15%), as PC cells frequently showed heterogenous or focal PD-L1-staining. PD-L1 was expressed by immune or stromal cells in 12% and 69% cases, respectively. Tumor heterogeneity, inter-institutional preanalytics, and inter-observer interpretation variability may account for result biases.

SPOP mutation induces DNA methylation via stabilizing GLP/G9a

Mutations in SPOP E3 ligase gene are reportedly associated with genome-wide DNA hypermethylation in prostate cancer (PCa) although the underlying mechanisms remain elusive. Here, we demonstrate that SPOP binds and promotes polyubiquitination and degradation of histone methyltransferase and DNMT interactor GLP. SPOP mutation induces stabilization of GLP and its partner protein G9a and aberrant upregulation of global DNA hypermethylation in cultured PCa cells and primary PCa specimens. Genome-wide DNA methylome analysis shows that a subset of tumor suppressor genes (TSGs) including FOXO3, GATA5, and NDRG1, are hypermethylated and downregulated in SPOP-mutated PCa cells. DNA methylation inhibitor 5-azacytidine effectively reverses expression of the TSGs examined, inhibits SPOP-mutated PCa cell growth in vitro and in mice, and enhances docetaxel anti-cancer efficacy. Our findings reveal the GLP/G9a-DNMT module as a mediator of DNA hypermethylation in SPOP-mutated PCa. They suggest that SPOP mutation could be a biomarker for effective treatment of PCa with DNA methylation inhibitor alone or in combination with taxane chemotherapeutics.

The molecular mechanism underlying the DNA hypermethylation phenotype observed in the SPOP-mutant prostate cancers is unclear. Here, the authors show that mutant SPOP induces global aberrant DNA methylation patterns through GLP/G9a and renders prostate cancer cells susceptible to DNA demethylating agents.

Longitudinal analysis of individual cfDNA methylome patterns in metastatic prostate cancer

Background

Disease progression and therapeutic resistance are hallmarks of advanced stage prostate cancer (PCa), which remains a major cause of cancer-related mortality around the world. Longitudinal studies, coupled with the use of liquid biopsies, offer a potentially new and minimally invasive platform to study the dynamics of tumour progression. Our aim was to investigate the dynamics of personal DNA methylomic profiles of metastatic PCa (mPCa) patients, during disease progression and therapy administration.

Results

Forty-eight plasma samples from 9 mPCa patients were collected, longitudinally, over 13–21 months. After circulating cell-free DNA (cfDNA) isolation, DNA methylation was profiled using the Infinium MethylationEPIC BeadChip. The top 5% most variable probes across time, within each individual, were utilised to study dynamic methylation patterns during disease progression and therapeutic response. Statistical testing was carried out to identify differentially methylated genes (DMGs) in cfDNA, which were subsequently validated in two independent mPCa (cfDNA and FFPE tissue) cohorts. Individual cfDNA global methylation patterns were temporally stable throughout the disease course. However, a proportion of CpG sites presented a dynamic temporal pattern that was consistent with clinical events, including different therapies, and were prominently associated with genes linked to immune response pathways. Additionally, study of the tumour fraction of cfDNA identified > 2000 DMGs with dynamic methylation patterns.

Conclusions

Longitudinal assessment of cfDNA methylation in mPCa patients unveiled dynamic patterns associated with disease progression and therapy administration, thus highlighting the potential of using liquid biopsies to study PCa evolution at a methylomic level.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01155-w.

Elucidation of the Genomic-Epigenomic Interaction Landscape of Aggressive Prostate Cancer

Background

Majority of prostate cancer (PCa) deaths are attributed to localized high-grade aggressive tumours which progress rapidly to metastatic disease. A critical unmet need in clinical management of PCa is discovery and characterization of the molecular drivers of aggressive tumours. The development and progression of aggressive PCa involve genetic and epigenetic alterations occurring in the germline, somatic (tumour), and epigenomes. To date, interactions between genes containing germline, somatic, and epigenetic mutations in aggressive PCa have not been characterized. The objective of this investigation was to elucidate the genomic-epigenomic interaction landscape in aggressive PCa to identify potential drivers aggressive PCa and the pathways they control. We hypothesized that aggressive PCa originates from a complex interplay between genomic (both germline and somatic mutations) and epigenomic alterations. We further hypothesized that these complex arrays of interacting genomic and epigenomic factors affect gene expression, molecular networks, and signaling pathways which in turn drive aggressive PCa.

Methods

We addressed these hypotheses by performing integrative data analysis combining information on germline mutations from genome-wide association studies with somatic and epigenetic mutations from The Cancer Genome Atlas using gene expression as the intermediate phenotype.

Results

The investigation revealed signatures of genes containing germline, somatic, and epigenetic mutations associated with aggressive PCa. Aberrant DNA methylation had effect on gene expression. In addition, the investigation revealed molecular networks and signalling pathways enriched for germline, somatic, and epigenetic mutations including the STAT3, PTEN, PCa, ATM, AR, and P53 signalling pathways implicated in aggressive PCa.

Conclusions

The study demonstrated that integrative analysis combining diverse omics data is a powerful approach for the discovery of potential clinically actionable biomarkers, therapeutic targets, and elucidation of oncogenic interactions between genomic and epigenomic alterations in aggressive PCa.

MYC DNA Methylation in Prostate Tumor Tissue Is Associated with Gleason Score

Increasing evidence suggests a role of epigenetic mechanisms at chromosome 8q24, an important cancer genetic susceptibility region, in prostate cancer. We investigated whether MYC DNA methylation at 8q24 (six CpG sites from exon 3 to the 3′ UTR) in prostate tumor was associated with tumor aggressiveness (based on Gleason score, GS), and we incorporated RNA expression data to investigate the function. We accessed radical prostatectomy tissue for 50 Caucasian and 50 African American prostate cancer patients at the University of Maryland Medical Center, selecting an equal number of GS 6 and GS 7 cases per group. MYC DNA methylation was lower in tumor than paired normal prostate tissue for all six CpG sites (median difference: −14.74 to −0.20 percentage points), and we observed similar results for two nearby sites in The Cancer Genome Atlas (p < 0.0001). We observed significantly lower methylation for more aggressive (GS 7) than less aggressive (GS 6) tumors for three exon 3 sites (for CpG 212 (chr8:128753145), GS 6 median = 89.7%; GS 7 median = 85.8%; p-value = 9.4 × 10⁻⁴). MYC DNA methylation was not associated with MYC expression, but was inversely associated with PRNCR1 expression after multiple comparison adjustment (q-value = 0.04). Findings suggest that prostate tumor MYC exon 3 hypomethylation is associated with increased aggressiveness.

Advances in Prognostic Methylation Biomarkers for Prostate Cancer

There is a major clinical need for accurate biomarkers for prostate cancer prognosis, to better inform treatment strategies and disease monitoring. Current clinically recognised prognostic factors, including prostate-specific antigen (PSA) levels, lack sensitivity and specificity in distinguishing aggressive from indolent disease, particularly in patients with localised intermediate grade prostate cancer. There has therefore been a major focus on identifying molecular biomarkers that can add prognostic value to existing markers, including investigation of DNA methylation, which has a known role in tumorigenesis. In this review, we will provide a comprehensive overview of the current state of DNA methylation biomarker studies in prostate cancer prognosis, and highlight the advances that have been made in this field. We cover the numerous studies into well-established candidate genes, and explore the technological transition that has enabled hypothesis-free genome-wide studies and the subsequent discovery of novel prognostic genes.

Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology

The multitude of multi-omics data generated cost-effectively using advanced high-throughput technologies has imposed challenging domain for research in Artificial Intelligence (AI). Data curation poses a significant challenge as different parameters, instruments, and sample preparations approaches are employed for generating these big data sets. AI could reduce the fuzziness and randomness in data handling and build a platform for the data ecosystem, and thus serve as the primary choice for data mining and big data analysis to make informed decisions. However, AI implication remains intricate for researchers/clinicians lacking specific training in computational tools and informatics. Cancer is a major cause of death worldwide, accounting for an estimated 9.6 million deaths in 2018. Certain cancers, such as pancreatic and gastric cancers, are detected only after they have reached their advanced stages with frequent relapses. Cancer is one of the most complex diseases affecting a range of organs with diverse disease progression mechanisms and the effectors ranging from gene-epigenetics to a wide array of metabolites. Hence a comprehensive study, including genomics, epi-genomics, transcriptomics, proteomics, and metabolomics, along with the medical/mass-spectrometry imaging, patient clinical history, treatments provided, genetics, and disease endemicity, is essential. Cancer Moonshot℠ Research Initiatives by NIH National Cancer Institute aims to collect as much information as possible from different regions of the world and make a cancer data repository. AI could play an immense role in (a) analysis of complex and heterogeneous data sets (multi-omics and/or inter-omics), (b) data integration to provide a holistic disease molecular mechanism, (c) identification of diagnostic and prognostic markers, and (d) monitor patient's response to drugs/treatments and recovery. AI enables precision disease management well beyond the prevalent disease stratification patterns, such as differential expression and supervised classification. This review highlights critical advances and challenges in omics data analysis, dealing with data variability from lab-to-lab, and data integration. We also describe methods used in data mining and AI methods to obtain robust results for precision medicine from "big" data. In the future, AI could be expanded to achieve ground-breaking progress in disease management.

The DNA methylation landscape of advanced prostate cancer

Although DNA methylation is a key regulator of gene expression, the comprehensive methylation landscape of metastatic cancer has never been defined. Through whole-genome bisulfite sequencing paired with deep whole-genome and transcriptome sequencing of 100 castration-resistant prostate metastases, we discovered alterations affecting driver genes only detectable with integrated whole-genome approaches. Notably, we observed that 22% of tumors exhibited a novel epigenomic subtype associated with hyper-methylation and somatic mutations in TET2, DNMT3B, IDH1, and BRAF. We also identified intergenic regions where methylation is associated with RNA expression of the oncogenic driver genes AR, MYC and ERG. Finally, we showed that differential methylation during progression preferentially occurs at somatic mutational hotspots and putative regulatory regions. This study is a large integrated study of whole-genome, whole-methylome and whole-transcriptome sequencing in metastatic cancer and provides a comprehensive overview of the important regulatory role of methylation in metastatic castration-resistant prostate cancer.

Association of DNA-Methylation Profiles With Immune Responses Elicited in Breast Cancer Patients Immunized With a Carbohydrate-Mimicking Peptide: A Pilot Study

Immune response to a given antigen, particularly in cancer patients, is complex and is controlled by various genetic and environmental factors. Identifying biomarkers that can predict robust response to immunization is an urgent need in clinical cancer vaccine development. Given the involvement of DNA methylation in the development of lymphocytes, tumorigenicity and tumor progression, we aimed to analyze pre-vaccination DNA methylation profiles of peripheral blood mononuclear cells (PBMCs) from breast cancer subjects vaccinated with a novel peptide-based vaccine referred to as P10s-PADRE. This pilot study was performed to evaluate whether signatures of differentially methylated (DM) loci can be developed as potential predictive biomarkers for prescreening subjects with cancer who will most likely generate an immune response to the vaccine. Genomic DNA was isolated from PBMCs of eight vaccinated subjects, and their DNA methylation profiles were determined using Infinium^® MethylationEPIC BeadChip array from Illumina. A linear regression model was applied to identify loci that were differentially methylated with respect to anti-peptide antibody titers and with IFN-γ production. The data were summarized using unsupervised-learning methods: hierarchical clustering and principal-component analysis. Pathways and networks involved were predicted by Ingenuity Pathway Analysis. We observed that the profile of DM loci separated subjects in regards to the levels of immune responses. Canonical pathways and networks related to metabolic and immunological functions were found to be involved. The data suggest that it is feasible to correlate methylation signatures in pre-treatment PBMCs with immune responses post-treatment in cancer patients going through standard-of-care chemotherapy. Larger and prospective studies that focus on DM loci in PBMCs is warranted to develop pre-screening biomarkers before BC vaccination.

Clinical Trial Registration:www.ClinicalTrials.gov, Identifier: NCT02229084.

Random forest-based modelling to detect biomarkers for prostate cancer progression

BACKGROUND

The clinical course of prostate cancer (PCa) is highly variable, demanding an individualized approach to therapy. Overtreatment of indolent PCa cases, which likely do not progress to aggressive stages, may be associated with severe side effects and considerable costs. These could be avoided by utilizing robust prognostic markers to guide treatment decisions.

RESULTS

We present a random forest-based classification model to predict aggressive behaviour of prostate cancer. DNA methylation changes between PCa cases with good or poor prognosis (discovery cohort with n = 70) were used as input. DNA was extracted from formalin-fixed tumour tissue, and genome-wide DNA methylation differences between both groups were assessed using Illumina HumanMethylation450 arrays. For the random forest-based modelling, the discovery cohort was randomly split into a training (80%) and a test set (20%). Our methylation-based classifier demonstrated excellent performance in discriminating prognosis subgroups in the test set (Kaplan-Meier survival analyses with log-rank p value < 0.0001). The area under the receiver operating characteristic curve (AUC) for the sensitivity analysis was 95%. Using the ICGC cohort of early- and late-onset prostate cancer (n = 222) and the TCGA PRAD cohort (n = 477) for external validation, AUCs for sensitivity analyses were 77.1% and 68.7%, respectively. Cancer progression-related DNA hypomethylation was frequently located in 'partially methylated domains' (PMDs)-large-scale genomic areas with progressive loss of DNA methylation linked to mitotic cell division. We selected several candidate genes with differential methylation in gene promoter regions for additional validation at the protein expression level by immunohistochemistry in > 12,000 tissue micro-arrayed PCa cases. Loss of ZIC2 protein expression was associated with poor prognosis and correlated with significantly shorter time to biochemical recurrence. The prognostic value of ZIC2 proved to be independent from established clinicopathological variables including Gleason grade, tumour stage, nodal stage and prostate-specific-antigen.

CONCLUSIONS

Our results highlight the prognostic relevance of methylation loss in PMD regions, as well as of several candidate genes not previously associated with PCa progression. Our robust and externally validated PCa classification model either directly or via protein expression analyses of the identified top-ranked candidate genes will support the clinical management of prostate cancer.

A three-gene DNA methylation biomarker accurately classifies early stage prostate cancer

BACKGROUND

We identify and validate accurate diagnostic biomarkers for prostate cancer through a systematic evaluation of DNA methylation alterations.

MATERIALS AND METHODS

We assembled three early prostate cancer cohorts (total patients = 699) from which we collected and processed over 1300 prostatectomy tissue samples for DNA extraction. Using real-time methylation-specific PCR, we measured normalized methylation levels at 15 frequently methylated loci. After partitioning sample sets into independent training and validation cohorts, classifiers were developed using logistic regression, analyzed, and validated.

RESULTS

In the training dataset, DNA methylation levels at 7 of 15 genomic loci (glutathione S-transferase Pi 1 [GSTP1], CCDC181, hyaluronan, and proteoglycan link protein 3 [HAPLN3], GSTM2, growth arrest-specific 6 [GAS6], RASSF1, and APC) showed large differences between cancer and benign samples. The best binary classifier was the GAS6/GSTP1/HAPLN3 logistic regression model, with an area under these curves of 0.97, which showed a sensitivity of 94%, and a specificity of 93% after external validation.

CONCLUSION

We created and validated a multigene model for the classification of benign and malignant prostate tissue. With false positive and negative rates below 7%, this three-gene biomarker represents a promising basis for more accurate prostate cancer diagnosis.

Prostate Cancer Epigenetics: From Basic Mechanisms to Clinical Implications

A level of epigenetic programming, encoded by complex sets of chemical marks on DNA and histones, and by context-specific DNA, RNA, protein interactions, that all regulate the structure, organization, and function of the genome, is critical to establish both normal and neoplastic cell identities and functions. This structure-function relationship of the genome encoded by the epigenetic programming can be thought of as an epigenetic cityscape that is built on the underlying genetic landscape. Alterations in the epigenetic cityscape of prostate cancer cells compared with normal prostate tissues have a complex interplay with genetic alterations to drive prostate cancer initiation and progression. Indeed, mutations in genes encoding epigenetic enzymes are often observed in human cancers including prostate cancer. Interestingly, alterations in the prostate cancer epigenetic cityscape can be highly recurrent, a facet that can be exploited for development of biomarkers and potentially as therapeutic targets.

Increased Serine and One-Carbon Pathway Metabolism by PKCλ/ι Deficiency Promotes Neuroendocrine Prostate Cancer

Increasingly effective therapies targeting the androgen receptor have paradoxically promoted the incidence of neuroendocrine prostate cancer (NEPC), the most lethal subtype of castration-resistant prostate cancer (PCa), for which there is no effective therapy. Here we report that protein kinase C (PKC)λ/ι is downregulated in de novo and during therapy-induced NEPC, which results in the upregulation of serine biosynthesis through an mTORC1/ATF4-driven pathway. This metabolic reprogramming supports cell proliferation and increases intracellular S-adenosyl methionine (SAM) levels to feed epigenetic changes that favor the development of NEPC characteristics. Altogether, we have uncovered a metabolic vulnerability triggered by PKCλ/ι deficiency in NEPC, which offers potentially actionable targets to prevent therapy resistance in PCa.

Molecular Evolution of Early-Onset Prostate Cancer Identifies Molecular Risk Markers and Clinical Trajectories

Identifying the earliest somatic changes in prostate cancer can give important insights into tumor evolution and aids in stratifying high- from low-risk disease. We integrated whole genome, transcriptome and methylome analysis of early-onset prostate cancers (diagnosis ≤55 years). Characterization across 292 prostate cancer genomes revealed age-related genomic alterations and a clock-like enzymatic-driven mutational process contributing to the earliest mutations in prostate cancer patients. Our integrative analysis identified four molecular subgroups, including a particularly aggressive subgroup with recurrent duplications associated with increased expression of ESRP1, which we validate in 12,000 tissue microarray tumors. Finally, we combined the patterns of molecular co-occurrence and risk-based subgroup information to deconvolve the molecular and clinical trajectories of prostate cancer from single patient samples.

Onco-Multi-OMICS Approach: A New Frontier in Cancer Research

The acquisition of cancer hallmarks requires molecular alterations at multiple levels including genome, epigenome, transcriptome, proteome, and metabolome. In the past decade, numerous attempts have been made to untangle the molecular mechanisms of carcinogenesis involving single OMICS approaches such as scanning the genome for cancer-specific mutations and identifying altered epigenetic-landscapes within cancer cells or by exploring the differential expression of mRNA and protein through transcriptomics and proteomics techniques, respectively. While these single-level OMICS approaches have contributed towards the identification of cancer-specific mutations, epigenetic alterations, and molecular subtyping of tumors based on gene/protein-expression, they lack the resolving-power to establish the casual relationship between molecular signatures and the phenotypic manifestation of cancer hallmarks. In contrast, the multi-OMICS approaches involving the interrogation of the cancer cells/tissues in multiple dimensions have the potential to uncover the intricate molecular mechanism underlying different phenotypic manifestations of cancer hallmarks such as metastasis and angiogenesis. Moreover, multi-OMICS approaches can be used to dissect the cellular response to chemo- or immunotherapy as well as discover molecular candidates with diagnostic/prognostic value. In this review, we focused on the applications of different multi-OMICS approaches in the field of cancer research and discussed how these approaches are shaping the field of personalized oncomedicine. We have highlighted pioneering studies from “The Cancer Genome Atlas (TCGA)” consortium encompassing integrated OMICS analysis of over 11,000 tumors from 33 most prevalent forms of cancer. Accumulation of huge cancer-specific multi-OMICS data in repositories like TCGA provides a unique opportunity for the systems biology approach to tackle the complexity of cancer cells through the unification of experimental data and computational/mathematical models. In future, systems biology based approach is likely to predict the phenotypic changes of cancer cells upon chemo-/immunotherapy treatment. This review is sought to encourage investigators to bring these different approaches together for interrogating cancer at molecular, cellular, and systems levels.

Hypermethylation of gene body CpG islands predicts high dosage of functional oncogenes in liver cancer

Epigenetic modifications such as aberrant DNA methylation reshape the gene expression repertoire in cancer. Here, we used a clinically relevant hepatocellular carcinoma (HCC) mouse model (Alb-R26^Met) to explore the impact of DNA methylation on transcriptional switches associated with tumorigenesis. We identified a striking enrichment in genes simultaneously hypermethylated in CpG islands (CGIs) and overexpressed. These hypermethylated CGIs are located either in the 5′-UTR or in the gene body region. Remarkably, such CGI hypermethylation accompanied by gene upregulation also occurs in 56% of HCC patients, which belong to the “HCC proliferative-progenitor” subclass. Most of the genes upregulated and with hypermethylated CGIs in the Alb-R26^Met HCC model undergo the same change in a large proportion of HCC patients. Among reprogrammed genes, several are well-known oncogenes. For others not previously linked to cancer, we demonstrate here their action together as an “oncogene module”. Thus, hypermethylation of gene body CGIs is predictive of elevated oncogene levels in cancer, offering a novel stratification strategy and perspectives to normalise cancer gene dosages.

Changes in the DNA methylation status are commonly observed in cancer but their impact on cancer development is unclear. Here, combining DNA methylation and expression profiles from a murine model of hepatocellular carcinoma with those from human samples, the authors report an epigenetic reprogramming process ensuring increased dosage of an “oncogene module”.

A five-CpG DNA methylation score to predict metastatic-lethal outcomes in men treated with radical prostatectomy for localized prostate cancer

BACKGROUND

Prognostic biomarkers for localized prostate cancer (PCa) could improve personalized medicine. Our group previously identified a panel of differentially methylated CpGs in primary tumor tissue that predict disease aggressiveness, and here we further validate these biomarkers.

METHODS

Pyrosequencing was used to assess CpG methylation of eight biomarkers previously identified using the HumanMethylation450 array; CpGs with strongly correlated (r >0.70) results were considered technically validated. Logistic regression incorporating the validated CpGs and Gleason sum was used to define and lock a final model to stratify men with metastatic-lethal versus non-recurrent PCa in a training dataset. Coefficients from the final model were then used to construct a DNA methylation score, which was evaluated by logistic regression and Receiver Operating Characteristic (ROC) curve analyses in an independent testing dataset.

RESULTS

Five CpGs were technically validated and all were retained (P < 0.05) in the final model. The 5-CpG and Gleason sum coefficients were used to calculate a methylation score, which was higher in men with metastatic-lethal progression (P = 6.8 × 10-6 ) in the testing dataset. For each unit increase in the score there was a four-fold increase in risk of metastatic-lethal events (odds ratio, OR = 4.0, 95%CI = 1.8-14.3). At 95% specificity, sensitivity was 74% for the score compared to 53% for Gleason sum alone. The score demonstrated better prediction performance (AUC = 0.91; pAUC = 0.037) compared to Gleason sum alone (AUC = 0.87; pAUC = 0.025).

CONCLUSIONS

The DNA methylation score improved upon Gleason sum for predicting metastatic-lethal progression and holds promise for risk stratification of men with aggressive tumors. This prognostic score warrants further evaluation as a tool for improving patient outcomes.

Prostate cancer epigenetics and its clinical implications

Normal cells have a level of epigenetic programming that is superimposed on the genetic code to establish and maintain their cell identity and phenotypes. This epigenetic programming can be thought as the architecture, a sort of cityscape, that is built upon the underlying genetic landscape. The epigenetic programming is encoded by a complex set of chemical marks on DNA, on histone proteins in nucleosomes, and by numerous context-specific DNA, RNA, protein interactions that all regulate the structure, organization, and function of the genome in a given cell. It is becoming increasingly evident that abnormalities in both the genetic landscape and epigenetic cityscape can cooperate to drive carcinogenesis and disease progression. Large-scale cancer genome sequencing studies have revealed that mutations in genes encoding the enzymatic machinery for shaping the epigenetic cityscape are among the most common mutations observed in human cancers, including prostate cancer. Interestingly, although the constellation of genetic mutations in a given cancer can be quite heterogeneous from person to person, there are numerous epigenetic alterations that appear to be highly recurrent, and nearly universal in a given cancer type, including in prostate cancer. The highly recurrent nature of these alterations can be exploited for development of biomarkers for cancer detection and risk stratification and as targets for therapeutic intervention. Here, we explore the basic principles of epigenetic processes in normal cells and prostate cancer cells and discuss the potential clinical implications with regards to prostate cancer biomarker development and therapy.

WOMEN IN CANCER THEMATIC REVIEW: Diverse functions of DNA methylation: implications for prostate cancer and beyond

Prostate cancer is one of the most common malignancies in men worldwide. Current clinical screening ensures that most prostate cancers are diagnosed while still organ confined, but disease outcome is highly variable. Thus, a better understanding of the molecular features contributing to prostate cancer aggressiveness is being sought. For many cancers, aberrant genome-wide patterns of cytosine DNA methylation in CpG dinucleotides distinguish tumor from normal tissue and contribute to disease progression by altering the transcriptome. In prostate cancer, recent genomic studies identified cancer and high grade-specific differential DNA methylation in gene promoters, gene bodies, gene 3' ends and at distal regulatory elements. Using examples from developmental and disease systems, we will discuss how DNA methylation in each of these genomic contexts can contribute to transcriptome diversity by modulating transcription initiation, alternative transcription start site selection, alternative pre-mRNA splicing and alternative polyadenylation. Alternative transcripts from the same gene often exhibit altered protein-coding potential, translatability, stability and/or localization. All of these can have functional consequences in cells. In future work, it will be important to determine if DNA methylation abnormalities in prostate cancer modify the transcriptome through some or all of these mechanisms and if these DNA methylation-mediated transcriptome alterations impact prostate tumorigenesis and aggressiveness.

MethylAction: detecting differentially methylated regions that distinguish biological subtypes

DNA methylation differences capture substantial information about the molecular and gene-regulatory states among biological subtypes. Enrichment-based next generation sequencing methods such as MBD-isolated genome sequencing (MiGS) and MeDIP-seq are appealing for studying DNA methylation genome-wide in order to distinguish between biological subtypes. However, current analytic tools do not provide optimal features for analyzing three-group or larger study designs. MethylAction addresses this need by detecting all possible patterns of statistically significant hyper- and hypo- methylation in comparisons involving any number of groups. Crucially, significance is established at the level of differentially methylated regions (DMRs), and bootstrapping determines false discovery rates (FDRs) associated with each pattern. We demonstrate this functionality in a four-group comparison among benign prostate and three clinical subtypes of prostate cancer and show that the bootstrap FDRs are highly useful in selecting the most robust patterns of DMRs. Compared to existing tools that are limited to two-group comparisons, MethylAction detects more DMRs with strong differential methylation measurements confirmed by whole genome bisulfite sequencing and offers a better balance between precision and recall in cross-cohort comparisons. MethylAction is available as an R package at http://jeffbhasin.github.io/methylaction.