Identifying aggressive prostate cancer foci using a DNA methylation classifier

From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine

Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.

Molecular analysis of archival diagnostic prostate cancer biopsies identifies genomic similarities in cases with progression post-radiotherapy, and those with de novo metastatic disease

BACKGROUND

It is important to identify molecular features that improve prostate cancer (PCa) risk stratification before radical treatment with curative intent. Molecular analysis of historical diagnostic formalin-fixed paraffin-embedded (FFPE) prostate biopsies from cohorts with post-radiotherapy (RT) long-term clinical follow-up has been limited. Utilizing parallel sequencing modalities, we performed a proof-of-principle sequencing analysis of historical diagnostic FFPE prostate biopsies. We compared patients with (i) stable PCa (sPCa) postprimary or salvage RT, (ii) progressing PCa (pPCa) post-RT, and (iii) de novo metastatic PCa (mPCa).

METHODS

A cohort of 19 patients with diagnostic prostate biopsies (n = 6 sPCa, n = 5 pPCa, n = 8 mPCa) and mean 4 years 10 months follow-up (diagnosed 2009-2016) underwent nucleic acid extraction from demarcated malignancy. Samples underwent 3'RNA sequencing (3'RNAseq) (n = 19), nanoString analysis (n = 12), and Illumina 850k methylation (n = 8) sequencing. Bioinformatic analysis was performed to coherently identify differentially expressed genes and methylated genomic regions (MGRs).

RESULTS

Eighteen of 19 samples provided useable 3'RNAseq data. Principal component analysis (PCA) demonstrated similar expression profiles between pPCa and mPCa cases, versus sPCa. Coherently differentially methylated probes between these groups identified ~600 differentially MGRs. The top 50 genes with increased expression in pPCa patients were associated with reduced progression-free survival post-RT (p < 0.0001) in an external cohort.

CONCLUSIONS

3'RNAseq, nanoString and 850k-methylation analyses are each achievable from historical FFPE diagnostic pretreatment prostate biopsies, unlocking the potential to utilize large cohorts of historic clinical samples. Profiling similarities between individuals with pPCa and mPCa suggests biological similarities and historical radiological staging limitations, which warrant further investigation.

Prostate cancer: Molecular aspects, consequences, and opportunities of the multifocal nature

Prostate cancer is unique compared to other major cancers due to the presence of multiple primary malignant foci in the majority of patients at the time of diagnosis. Each malignant focus has distinct somatic mutations and gene expression patterns, which represents a challenge for the development of prognostic tests for localized prostate cancer. Additionally, the molecular heterogeneity of advanced prostate cancer has important implications for management, particularly for patients with metastatic and locally recurrent cancer. Studies have shown that prostate cancers with mutations in DNA damage response genes are more sensitive to drugs inhibiting the poly ADP-ribose polymerase (PARP) enzyme. However, testing for such mutations should consider both spatial and temporal heterogeneity. Here, we summarize studies where multiregional genomics and transcriptomics analyses have been performed for primary prostate cancer. We further discuss the vast interfocal heterogeneity and how prognostic biomarkers and a molecular definition of the index tumor should be developed. The concept of focal treatments in prostate cancer has been evolving as a demand from patients and clinicians and is one example where there is a need for defining an index tumor. Here, biomarkers must have proven value for individual malignant foci. The potential discovery and implementation of biomarkers that are agnostic to heterogeneity are also explored as an alternative to multisample testing. Thus, deciding upon whole-organ treatment, such as radical prostatectomy, should depend on information from biomarkers which are informative for the whole organ.

Epigenetic biomarkers for disease susceptibility and preventative medicine

The development of molecular biomarkers for disease makes it possible for preventative medicine approaches to be considered. Therefore, therapeutics, treatments, or clinical management can be used to delay or prevent disease development. The problem with genetic mutations as biomarkers is the low frequency with genome-wide association studies (GWASs), generally at best a 1% association of the patients with the disease. In contrast, epigenetic alterations have a high-frequency association of greater than 90%-95% of individuals with pathology in epigenome-wide association studies (EWASs). A wide variety of human diseases have been shown to have epigenetic biomarkers that are disease specific and that detect pathology susceptibility. This review is focused on the epigenetic biomarkers for disease susceptibility, and it distinct from the large literature on epigenetics of disease etiology or progression. The development of efficient epigenetic biomarkers for disease susceptibility will facilitate a paradigm shift from reactionary medicine to preventative medicine.

Site-specific detection of circulating tumor DNA methylation in biological samples utilizing phosphorothioated primer-based loop-mediated isothermal amplification

DNA methylation, a kind of epigenetic alteration, plays a vital role in tumorigenesis and offers a new class of targets for cancer treatment. DNA hypermethylation at the E-Box site (CACGTG, -288 bp) in the SLC22A2 promoter was related to multidrug resistance of renal cell carcinoma (RCC), which can provide the target for both treatment and monitoring. Herein, we developed a novel phosphorothioated primer based loop-mediated isothermal amplification (PS-LAMP) assay to detect circulating tumor DNA (ctDNA) methylation levels in E-Box sites in tumor tissue, urine, and plasma samples from patients with RCC. Bisulfite treatment converted methylated/unmethylated discrepancy to a single base discrepancy (C/U). PS-LAMP amplified the templates to a tremendous amount. One-step strand displacement (OSD) probe provided single base resolution in amplified products and finally realized the specific site methylation detection. Our proposed method provided a linear range from 0% to 100% for methylation levels and was available in samples at low concentrations (102 copies/μL). Visually observable colorimetric detection can be achieved by incorporating the OSD probe with gold nanoparticles (AuNP). Our assay performed better than traditional methods in biological samples with low ctDNA concentration. Further, we found a potential consistency of methylation levels between tumor tissue and plasma sample from the same patient (Spearman's ρ = 0.886, P = 0.019, n = 6). In general, this work provides a PS-LAMP assay combining OSD probes for site-specific methylation detection in various biological samples, offering a method for noninvasive detection.

Biomarkers for the Detection and Risk Stratification of Aggressive Prostate Cancer

Current strategies for the clinical management of prostate cancer are inadequate for a precise risk stratification between indolent and aggressive tumors. Recently developed tissue-based molecular biomarkers have refined the risk assessment of the disease. The characterization of tissue biopsy components and subsequent identification of relevant tissue-based molecular alterations have the potential to improve the clinical decision making and patient outcomes. However, tissue biopsies are invasive and spatially restricted due to tumor heterogeneity. Therefore, there is an urgent need for complementary diagnostic and prognostic options. Liquid biopsy approaches are minimally invasive with potential utility for the early detection, risk stratification, and monitoring of tumors. In this review, we focus on tissue and liquid biopsy biomarkers for early diagnosis and risk stratification of prostate cancer, including modifications on the genomic, epigenomic, transcriptomic, and proteomic levels. High-risk molecular alterations combined with orthogonal clinical parameters can improve the identification of aggressive tumors and increase patient survival.

GENOMICS OF PROSTATE CANCER: CLINICAL UTILITY AND CHALLENGES

The studying of prostate cancer genomics is important for understanding prostate cancer biology, it can provide clinically relevant stratification into subtypes, the development of new prognostic and predictive markers in the context of precision medicine, and the development of new targeted therapies. Recent studies have provided detailed insight into genomics, epigenomics and proteomics of prostate cancer, both primary and metastatic castration-resistant (mCRPC). Many mutations have been discovered, both those that occur early in the carcinogenesis and progression as well as those responsible for the resistance to therapy occurring later under the influence of treatment. A large number of characteristic mutated signaling pathways has been identified, e.g. the mutations in DNA repair pathway were found in 23% of mCRPC, which suggests potential response to PARP inhibitors. Multifocality and intralesional genomic heterogeneity of prostate cancer make the clinical application of genomics complicated. Although a great progress was made in understanding prostate cancer genomic, and clinical studies related to its routine application are ongoing, prostate cancer genomics still needs to find its standard wide routine application in patients with prostate cancer.

NCOA4: An Immunomodulation-Related Prognostic Biomarker in Colon Adenocarcinoma and Pan-Cancer

Treatment of cancer in humans requires a thorough understanding of the multiple pathways by which it develops. Recent studies suggest that nuclear receptor coactivator 4 (NCOA4) may be a predictive biomarker for renal cancer. In the present work, TCGA, GEPIA, and several bioinformatics approaches were used to analyze the NCOA4 expression patterns, prognostic relevance, and association between NCOA4 and clinicopathological features and immune cell infiltration. We investigated NCOA4 expression in malignancies. Low NCOA4 expression was associated with poor overall survival in individuals with malignancies such as cholangiocarcinoma, colon adenocarcinoma, and clear cell renal carcinoma. We also analyzed NCOA4 DNA methylation in normal and primary tumor tissues and investigated possible functional pathways underlying NCOA4-mediated oncogenesis. In conclusion, downregulation of NCOA4 is associated with poor prognosis, and NCOA4 may be a predictive biomarker for COAD.

CancerNet: a unified deep learning network for pan-cancer diagnostics

Background

Despite remarkable advances in cancer research, cancer remains one of the leading causes of death worldwide. Early detection of cancer and localization of the tissue of its origin are key to effective treatment. Here, we leverage technological advances in machine learning or artificial intelligence to design a novel framework for cancer diagnostics. Our proposed framework detects cancers and their tissues of origin using a unified model of cancers encompassing 33 cancers represented in The Cancer Genome Atlas (TCGA). Our model exploits the learned features of different cancers reflected in the respective dysregulated epigenomes, which arise early in carcinogenesis and differ remarkably between different cancer types or subtypes, thus holding a great promise in early cancer detection.

Results

Our comprehensive assessment of the proposed model on the 33 different tissues of origin demonstrates its ability to detect and classify cancers to a high accuracy (> 99% overall F-measure). Furthermore, our model distinguishes cancers from pre-cancerous lesions to metastatic tumors and discriminates between hypomethylation changes due to age related epigenetic drift and true cancer.

Conclusions

Beyond detection of primary cancers, our proposed computational model also robustly detects tissues of origin of secondary cancers, including metastatic cancers, second primary cancers, and cancers of unknown primaries. Our assessment revealed the ability of this model to characterize pre-cancer samples, a significant step forward in early cancer detection. Deployed broadly this model can deliver accurate diagnosis for a greatly expanded target patient population.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04783-y.

A prognostic hypoxia gene signature with low heterogeneity within the dominant tumour lesion in prostate cancer patients

BACKGROUND

Gene signatures measured in a biopsy have been proposed as hypoxia biomarkers in prostate cancer. We assessed a previously developed signature, and aimed to determine its relationship to hypoxia and its heterogeneity within the dominant (index) lesion of prostate cancer.

METHODS

The 32-gene signature was assessed from gene expression data of 141 biopsies from the index lesion of 94 patients treated with prostatectomy. A gene score calculated from the expression levels was applied in the analyses. Hypoxic fraction from pimonidazole immunostained whole-mount and biopsy sections was used as reference standard for hypoxia.

RESULTS

The gene score was correlated with pimonidazole-defined hypoxic fraction in whole-mount sections, and the two parameters showed almost equal association with clinical markers of tumour aggressiveness. Based on the gene score, incorrect classification according to hypoxic fraction in whole-mount sections was seen in one third of the patients. The incorrect classifications were apparently not due to intra-tumour heterogeneity, since the score had low heterogeneity compared to pimonidazole-defined hypoxic fraction in biopsies. The score showed prognostic significance in uni-and multivariate analysis in independent cohorts.

CONCLUSIONS

Our signature from the index lesion reflects tumour hypoxia and predicts prognosis in prostate cancer, independent of intra-tumour heterogeneity in pimonidazole-defined hypoxia.

Experimental challenges to modeling prostate cancer heterogeneity

Tumor heterogeneity plays a key role in prostate cancer prognosis, therapy selection, relapse, and acquisition of treatment resistance. Prostate cancer presents a heterogeneous diversity at inter- and intra-tumor and inter-patient levels which are influenced by multiple intrinsic and/or extrinsic factors. Recent studies have started to characterize the complexity of prostate tumors and these different tiers of heterogeneity. In this review, we discuss the most common factors that contribute to tumoral diversity. Moreover, we focus on the description of the in vitro and in vivo approaches, as well as high-throughput technologies, that help to model intra-tumoral diversity. Further understanding tumor heterogeneities and the challenges they present will guide enhanced patient risk stratification, aid the design of more precise therapies, and ultimately help beat this chameleon-like disease.

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.

Characterizing DNA methylation signatures and their potential functional roles in Merkel cell carcinoma

BACKGROUND

Merkel cell carcinoma (MCC) is a rare but aggressive skin cancer with limited treatment possibilities. Merkel cell tumors display with neuroendocrine features and Merkel cell polyomavirus (MCPyV) infection in the majority (80%) of patients. Although loss of histone H3 lysine 27 trimethylation (H3K27me3) has been shown during MCC tumorigenesis, epigenetic dysregulation has largely been overlooked.

METHODS

We conducted global DNA methylation profiling of clinically annotated MCC primary tumors, metastatic skin tumors, metastatic lymph node tumors, paired normal tissues, and two human MCC cell lines using the Illumina Infinium EPIC DNA methylation BeadArray platform.

RESULTS

Significant differential DNA methylation patterns across the genome are revealed between the four tissue types, as well as based on MCPyV status. Furthermore, 964 genes directly regulated by promoter or gene body DNA methylation were identified with high enrichment in neuro-related pathways. Finally, our findings suggest that loss of H3K27me3 occupancy in MCC is attributed to KDM6B and EZHIP overexpression as a consequence of promoter DNA hypomethylation.

CONCLUSIONS

We have demonstrated specific DNA methylation patterns for primary MCC tumors, metastatic MCCs, and adjacent-normal tissues. We have also identified DNA methylation markers that not only show potential diagnostic or prognostic utility in MCC management, but also correlate with MCC tumorigenesis, MCPyV expression, neuroendocrine features, and H3K27me3 status. The identification of DNA methylation alterations in MCC supports the need for further studies to understand the clinical implications of epigenetic dysregulation and potential therapeutic targets in MCC.

An overview of cancer health disparities: new approaches and insights and why they matter

Cancer health disparities remain stubbornly entrenched in the US health care system. The Affordable Care Act was legislation to target these disparities in health outcomes. Expanded access to health care, reduction in tobacco use, uptake of other preventive measures and cancer screening, and improved cancer therapies greatly reduced cancer mortality among women and men and underserved communities in this country. Yet, disparities in cancer outcomes remain. Underserved populations continue to experience an excessive cancer burden. This burden is largely explained by health care disparities, lifestyle factors, cultural barriers, and disparate exposures to carcinogens and pathogens, as exemplified by the COVID-19 epidemic. However, research also shows that comorbidities, social stress, ancestral and immunobiological factors, and the microbiome, may contribute to health disparities in cancer risk and survival. Recent studies revealed that comorbid conditions can induce an adverse tumor biology, leading to a more aggressive disease and decreased patient survival. In this review, we will discuss unanswered questions and new opportunities in cancer health disparity research related to comorbid chronic diseases, stress signaling, the immune response, and the microbiome, and what contribution these factors may have as causes of cancer health disparities.

Genomic and phenotypic heterogeneity in prostate cancer

From a clinical, morphological and molecular perspective, prostate cancer is a heterogeneous disease. Primary prostate cancers are often multifocal, having topographically and morphologically distinct tumour foci. Sequencing studies have revealed that individual tumour foci can arise as clonally distinct lesions with no shared driver gene alterations. This finding demonstrates that multiple genomically and phenotypically distinct primary prostate cancers can be present in an individual patient. Lethal metastatic prostate cancer seems to arise from a single clone in the primary tumour but can exhibit subclonal heterogeneity at the genomic, epigenetic and phenotypic levels. Collectively, this complex heterogeneous constellation of molecular alterations poses obstacles for the diagnosis and treatment of prostate cancer. However, advances in our understanding of intra-tumoural heterogeneity and the development of novel technologies will allow us to navigate these challenges, refine approaches for translational research and ultimately improve patient care.

Senescence Reprogramming by TIMP1 Deficiency Promotes Prostate Cancer Metastasis

Metastases account for most cancer-related deaths, yet the mechanisms underlying metastatic spread remain poorly understood. Recent evidence demonstrates that senescent cells, while initially restricting tumorigenesis, can induce tumor progression. Here, we identify the metalloproteinase inhibitor TIMP1 as a molecular switch that determines the effects of senescence in prostate cancer. Senescence driven either by PTEN deficiency or chemotherapy limits the progression of prostate cancer in mice. TIMP1 deletion allows senescence to promote metastasis, and elimination of senescent cells with a senolytic BCL-2 inhibitor impairs metastasis. Mechanistically, TIMP1 loss reprograms the senescence-associated secretory phenotype (SASP) of senescent tumor cells through activation of matrix metalloproteinases (MMPs). Loss of PTEN and TIMP1 in prostate cancer is frequent and correlates with resistance to docetaxel and worst clinical outcomes in patients treated in an adjuvant setting. Altogether, these findings provide insights into the dual roles of tumor-associated senescence and can potentially impact the treatment of prostate cancer.

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.

Clonal evaluation of prostate cancer molecular heterogeneity in biopsy samples by dual immunohistochemistry and dual RNA in situ hybridization

Prostate cancer is frequently multifocal. Although there may be morphological variation, the genetic underpinnings of each tumor are not clearly understood. To assess the inter and intra tumor molecular heterogeneity in prostate biopsy samples, we developed a combined immunohistochemistry and RNA in situ hybridization method for the simultaneous evaluation of ERG, SPINK1, ETV1, and ETV4. Screening of 601 biopsy cores from 120 consecutive patients revealed multiple alterations in a mutually exclusive manner in 37% of patients, suggesting multifocal tumors with considerable genetic differences. Furthermore, the incidence of molecular heterogeneity was higher in African Americans patients compared with Caucasian American patients. About 47% of the biopsy cores with discontinuous tumor foci showed clonal differences with distinct molecular aberrations. ERG positivity occurred in low-grade cancer, whereas ETV4 expression was observed mostly in high-grade cancer. Further studies revealed correlation between the incidence of molecular markers and clinical and pathologic findings, suggesting potential implications for diagnostic pathology practice, such as defining dominant tumor nodules and discriminating juxtaposed but molecularly different tumors of different grade patterns.

Prediction of tumor metastasis from sequencing data in the era of genome sequencing

Tumor metastasis is the key reason for the high mortality rate of tumor. Growing number of scholars have begun to pay attention to the research on tumor metastasis and have achieved satisfactory results in this field. The advent of the era of sequencing has enabled us to study cancer metastasis at the molecular level, which is essential for understanding the molecular mechanism of metastasis, identifying diagnostic markers and therapeutic targets and guiding clinical decision-making. We reviewed the metastasis-related studies using sequencing data, covering detection of metastasis origin sites, determination of metastasis potential and identification of distal metastasis sites. These findings include the discovery of relevant markers and the presentation of prediction tools. Finally, we discussed the challenge of studying metastasis considering the difficulty of obtaining metastatic cancer data, the complexity of tumor heterogeneity and the uncertainty of sample labels.

Computational Detection of Breast Cancer Invasiveness with DNA Methylation Biomarkers

Breast cancer is the most common female malignancy. It has high mortality, primarily due to metastasis and recurrence. Patients with invasive and noninvasive breast cancer require different treatments, so there is an urgent need for predictive tools to guide clinical decision making and avoid overtreatment of noninvasive breast cancer and undertreatment of invasive cases. Here, we divided the sample set based on the genome-wide methylation distance to make full use of metastatic cancer data. Specifically, we implemented two differential methylation analysis methods to identify specific CpG sites. After effective dimensionality reduction, we constructed a methylation-based classifier using the Random Forest algorithm to categorize the primary breast cancer. We took advantage of breast cancer (BRCA) HM450 DNA methylation data and accompanying clinical data from The Cancer Genome Atlas (TCGA) database to validate the performance of the classifier. Overall, this study demonstrates DNA methylation as a potential biomarker to predict breast tumor invasiveness and as a possible parameter that could be included in the studies aiming to predict breast cancer aggressiveness. However, more comparative studies are needed to assess its usability in the clinic. Towards this, we developed a website based on these algorithms to facilitate its use in studies and predictions of breast cancer invasiveness.

Cell-free circulating epimarks in cancer monitoring

Cancer numbers increasing, cases heterogeneity and the drug resistance emergence have pushed scientists to search for innovative solutions for patients and epimutations can be one. Methylated DNA, modified nucleosomes and noncoding RNAs are found in all cells, including tumor cells. They are intracellular actors but also have intercellular communication roles, being released in extracellular environment and in different body fluids. Here, we reviewed current literature on the use of these blood circulating epimarks in cancer monitoring. What stands out is that epimarkers must be considered as ‘real time’ images of the tumor, and can be isolated without invasive methods. In the future, the real challenge lies in the development of specific, sensitive, fast and clinically applicable detection and analysis methods of epimarkers.

Identification of aberrantly methylated‑differentially expressed genes and gene ontology in prostate cancer

Prostate cancer (PCa) is the most frequent urological malignancy in men worldwide. DNA methylation has an essential role in the etiology and pathogenesis of PCa. The purpose of the present study was to identify the aberrantly methylated-differentially expressed genes and to determine their potential roles in PCa. The important node genes identified were screened by integrated analysis. Gene expression microarrays and gene methylation microarrays were downloaded and aberrantly methylated-differentially expressed genes were obtained. Enrichment analysis and protein-protein interactions (PPI) were obtained, their interactive and visual networks were created, and the node genes in the PPI network were validated. A total of 105 hypomethylation-high expression genes and 561 hypermethylation-low expression genes along with their biological processes were identified. The top 10 node genes obtained from the PPI network were identified for each of the two gene groups. The methylation and gene expression status of node genes in TCGA database, GEPIA tool, and the HPA database were generally consistent with those of our results. In conclusion, the present study identified 20 aberrantly methylated-differentially expressed genes in PCa by combining bioinformatics analyses of gene expression and gene methylation microarrays, and concurrently, the survival of these genes was analyzed. Notably, methylation is a reversible biological process, which makes it of great biological significance for the diagnosis and treatment of prostate cancer using bioinformatics technology to determine abnormal methylation gene markers. The present study provided novel therapeutic targets for the treatment of PCa.

Phase I study of cancer lesion-targeted microwave coagulation therapy for localized prostate cancer: A pilot clinical study protocol

BACKGROUND

Whole-gland therapy for prostate cancer, which might cause more harm than no therapy (observation or active surveillance), might be a overtreatment. In order to avoid overtreatment as well as undertreatment of localize prostate cancer, novel strategy of organ-preserving therapies have been developed to achieve both cancer control and functional preservation. For the therapeutic techniques, microwave ablation would be an option for lesion-targeted focal therapy to eradicate biopsy-proven cancer lesion with its safety margin. Following our recent pilot clinical study of lesion-targeted focal cryotherapy, prospective clinical trial was designed to investigate the safety and therapeutic effects of lesion-targeted microwave therapy for localized prostate cancer.

METHODS

This is a single-center, phase I, clinical study to evaluate primarily the safety of lesion-targeted focal microwave treatment for prostate cancer. Patients with a magnetic resonance imaging (MRI)-visible, MR-ultrasound image-fusion targeted biopsy-proven clinically significant cancer will be enrolled. The target sample size is 5. Transrectal ultrasound-guided focal microwave ablation will be performed under general anesthesia. The primary endpoint is adverse events after microwave focal therapy. Secondary endpoint includes to assess both cancer control and quality of life (functional preservation).

DISCUSSION

This single-center, phase I, clinical study aims to evaluate the safety and efficacy of lesion-targeted focal microwave treatment for prostate cancer. The importance of this clinical trial is that it may establish new treatment for prostate cancer.

TRIAL REGISTRATION

This study was registered with Japan Registry of Clinical Trials (jRCTs052190026).

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.

Histone-Related Genes Are Hypermethylated in Lung Cancer and Hypermethylated HIST1H4F Could Serve as a Pan-Cancer Biomarker

Lung cancer is the leading cause of cancer-related deaths worldwide. Cytologic examination is the current "gold standard" for lung cancer diagnosis, however, this has low sensitivity. Here, we identified a typical methylation signature of histone genes in lung cancer by whole-genome DNA methylation analysis, which was validated by The Cancer Genome Atlas (TCGA) lung cancer cohort (n = 907) and was further confirmed in 265 bronchoalveolar lavage fluid samples with specificity and sensitivity of 96.7% and 87.0%, respectively. More importantly, HIST1H4F was universally hypermethylated in all 17 tumor types from TCGA datasets (n = 7,344), which was further validated in nine different types of cancer (n = 243). These results demonstrate that HIST1H4F can function as a universal-cancer-only methylation (UCOM) marker, which may aid in understanding general tumorigenesis and improve screening for early cancer diagnosis. SIGNIFICANCE: These findings identify a new biomarker for cancer detection and show that hypermethylation of histone-related genes seems to persist across cancers.

Clinicopathological Features of Triple-Negative Breast Cancer Epigenetic Subtypes

BACKGROUND/OBJECTIVE

Triple-negative breast cancer (TNBC) is a heterogeneous collection of breast tumors with numerous differences including morphological characteristics, genetic makeup, immune-cell infiltration, and response to systemic therapy. DNA methylation profiling is a robust tool to accurately identify disease-specific subtypes. We aimed to generate an epigenetic subclassification of TNBC tumors (epitypes) with utility for clinical decision-making.

METHODS

Genome-wide DNA methylation profiles from TNBC patients generated in the Cancer Genome Atlas project were used to build machine learning-based epigenetic classifiers. Clinical and demographic variables, as well as gene expression and gene mutation data from the same cohort, were integrated to further refine the TNBC epitypes.

RESULTS

This analysis indicated the existence of four TNBC epitypes, named as Epi-CL-A, Epi-CL-B, Epi-CL-C, and Epi-CL-D. Patients with Epi-CL-B tumors showed significantly shorter disease-free survival and overall survival [log rank; P = 0.01; hazard ratio (HR) 3.89, 95% confidence interval (CI) 1.3-11.63 and P = 0.003; HR 5.29, 95% CI 1.55-18.18, respectively]. Significant gene expression and mutation differences among the TNBC epitypes suggested alternative pathway activation that could be used as ancillary therapeutic targets. These epigenetic subtypes showed complementarity with the recently described TNBC transcriptomic subtypes.

CONCLUSIONS

TNBC epigenetic subtypes exhibit significant clinical and molecular differences. The links between genetic make-up, gene expression programs, and epigenetic subtypes open new avenues in the development of laboratory tests to more efficiently stratify TNBC patients, helping optimize tailored treatment approaches.

Androgen receptor enhancer usage and the chromatin regulatory landscape in human prostate cancers

The androgen receptor (AR) is commonly known as a key transcription factor in prostate cancer development, progression and therapy resistance. Genome-wide chromatin association studies revealed that transcriptional regulation by AR mainly depends on binding to distal regulatory enhancer elements that control gene expression through chromatin looping to gene promoters. Changes in the chromatin epigenetic landscape and DNA sequence can locally alter AR-DNA-binding capacity and consequently impact transcriptional output and disease outcome. The vast majority of reports describing AR chromatin interactions have been limited to cell lines, identifying numerous other factors and interacting transcription factors that impact AR chromatin interactions. Do these factors also impact AR cistromics - the genome-wide chromatin-binding landscape of AR - in vivo? Recent technological advances now enable researchers to identify AR chromatin-binding sites and their target genes in human specimens. In this review, we provide an overview of the different factors that influence AR chromatin binding in prostate cancer specimens, which is complemented with knowledge from cell line studies. Finally, we discuss novel perspectives on studying AR cistromics in clinical samples.

Epigenetic Classifiers for Precision Diagnosis of Brain Tumors

DNA methylation profiling has proven to be a powerful analytical tool, which can accurately identify the tissue of origin of a wide range of benign and malignant neoplasms. Using microarray-based profiling and supervised machine learning algorithms, we and other groups have recently unraveled DNA methylation signatures capable of aiding the histomolecular diagnosis of different tumor types. We have explored the methylomes of metastatic brain tumors from patients with lung cancer, breast cancer, and cutaneous melanoma and primary brain neoplasms to build epigenetic classifiers. Our brain metastasis methylation (BrainMETH) classifier has the ability to determine the type of brain tumor, the origin of the metastases, and the clinical-therapeutic subtype for patients with breast cancer brain metastases. To facilitate the translation of these epigenetic classifiers into clinical practice, we selected and validated the most informative genomic regions utilizing quantitative methylation-specific polymerase chain reaction (qMSP). We believe that the refinement, expansion, integration, and clinical validation of BrainMETH and other recently developed epigenetic classifiers will significantly contribute to the development of more comprehensive and accurate systems for the personalized management of patients with brain metastases.

Integrative analysis identifies potential DNA methylation biomarkers for pan-cancer diagnosis and prognosis

DNA methylation status is closely associated with diverse diseases, and is generally more stable than gene expression, thus abnormal DNA methylation could be important biomarkers for tumor diagnosis, treatment and prognosis. However, the signatures regarding DNA methylation changes for pan-cancer diagnosis and prognosis are less explored. Here we systematically analyzed the genome-wide DNA methylation patterns in diverse TCGA cancers with machine learning. We identified seven CpG sites that could effectively discriminate tumor samples from adjacent normal tissue samples for 12 main cancers of TCGA (1216 samples, AUC > 0.99). Those seven potential diagnostic biomarkers were further validated in the other 9 different TCGA cancers and 4 independent datasets (AUC > 0.92). Three out of the seven CpG sites were correlated with cell division, DNA replication and cell cycle. We also identified 12 CpG sites that can effectively distinguish 26 different cancers (7605 samples), and the result was repeatable in independent datasets as well as two disparate tumors with metastases (micro-average AUC > 0.89). Furthermore, a series of potential signatures that could significantly predict the prognosis of tumor patients for 7 different cancer were identified via survival analysis (p-value < 1e-4). Collectively, DNA methylation patterns vary greatly between tumor and adjacent normal tissues, as well as among different types of cancers. Our identified signatures may aid the decision of clinical diagnosis and prognosis for pan-cancer and the potential cancer-specific biomarkers could be used to predict the primary site of metastatic breast and prostate cancers.

Phenotype-independent DNA methylation changes in prostate cancer

Background

Human prostate cancers display numerous DNA methylation changes compared to normal tissue samples. However, definitive identification of features related to the cells’ malignant status has been compromised by the predominance of cells with luminal features in prostate cancers.

Methods

We generated genome-wide DNA methylation profiles of cell subpopulations with basal or luminal features isolated from matched prostate cancer and normal tissue samples.

Results

Many frequent DNA methylation changes previously attributed to prostate cancers are here identified as differences between luminal and basal cells in both normal and cancer samples. We also identified changes unique to each of the two cancer subpopulations. Those specific to cancer luminal cells were associated with regulation of metabolic processes, cell proliferation and epithelial development. Within the prostate cancer TCGA dataset, these changes were able to distinguish not only cancers from normal samples, but also organ-confined cancers from those with extraprostatic extensions. Using changes present in both basal and luminal cancer cells, we derived a new 17-CpG prostate cancer signature with high predictive power in the TCGA dataset.

Conclusions

This study demonstrates the importance of comparing phenotypically matched prostate cell populations from normal and cancer tissues to unmask biologically and clinically relevant DNA methylation changes.

Data integration by multi-tuning parameter elastic net regression

Background

To integrate molecular features from multiple high-throughput platforms in prediction, a regression model that penalizes features from all platforms equally is commonly used. However, data from different platforms are likely to differ in effect sizes, the proportion of predictive features, and correlations structures. Subtle but important features may be missed by shrinking all features equally.

Results

We propose an Elastic net (EN) model with separate tuning parameter penalties for each platform that is fit using standard software. In a comprehensive simulation study, we evaluated the performance of EN logistic regression with multiple tuning penalties. We found that when the number of informative features differs among the platforms, and when there is no notable correlation between the features from different platforms, the multi-tuning parameter EN yields more predictive models. Moreover, the multi-tuning parameter EN is robust, in the sense that there is no loss of predictivity relative to a single tuning parameter EN when features across all platforms have similar effects. We also investigated the performance of multi-tuning parameter EN using real cancer datasets.

Conclusion

The proposed multi-tuning parameter EN model, fit using standard penalized regression software, can achieve better prediction in sample classification when integrating multiple genomic platforms, compared to the traditional method where a single penalty parameter is used for all features in different platforms.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2401-1) contains supplementary material, which is available to authorized users.

Sentinel node evaluation in prostate cancer

Sentinel lymph node (SLN) based pelvic lymph node dissection (PLND) in prostate cancer (PCa) is appealing over the time, cost and morbidity classically attributed to conventional PLND during radical prostatectomy. The initial report of feasibility of the SLN concept in prostate cancer was nearly 20 years ago. However, PLND based on the SLN concept, either SLN biopsy of a single node or targeted SLN dissection of multiple nodes, is still considered investigational in PCa. To better appreciate the challenges, and potential solutions, associated with SLN-based PLND in PCa, this review will discuss the rationale behind PLND in PCa and evaluate current SLN efforts in the most commonly diagnosed malignancy in men in the US.

Diagnostic and prognostic biomarkers of common urological cancers based on aberrant DNA methylation

AIM

We intended to construct DNA methylation-based models for the diagnosis and prognosis of three common urological cancers including prostate adenocarcinoma, renal clear cell carcinoma and bladder urothelial carcinoma.

MATERIALS & METHODS

Total 450K methylation array data from the cancer genome atlas and gene expression omnibus datasets were downloaded. Moderated t-statistics and least absolute shrinkage and selection operator method were used to build diagnosis and prognosis models.

RESULTS

Our diagnostic panels including 128 CpG sites had high sensitivity and accuracy in distinguishing samples and could identify lymphatic metastases in prostate adenocarcinoma patients. The prognostic models with 19 CpG sites for renal clear cell carcinoma and 21 CpG sites for bladder urothelial carcinoma were able to distinguish high- and low-risk patients and improve the predictive ability of the tumor node metastasis staging system.

CONCLUSION

DNA methylation may afford reliable biomarkers in the diagnosis and prognosis of common urological cancers.

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.

Analysis of Tumor Biology to Advance Cancer Health Disparity Research

Cancer mortality rates in the United States continue to decline. Reductions in tobacco use, uptake of preventive measures, adoption of early detection methods, and better treatments have resulted in improved cancer outcomes for men and women. Despite this progress, some population groups continue to experience an excessive cancer burden when compared with other population groups. One of the most prominent cancer health disparities exists in prostate cancer. Prostate cancer mortality rates are highest among men of African ancestry when compared with other men, both in the United States and globally. This disparity and other cancer health disparities are largely explained by differences in access to health care, diet, lifestyle, cultural barriers, and disparate exposures to carcinogens and pathogens. Dietary and lifestyle factors, pathogens, and ancestry-related factors can modify tumor biology and induce a more aggressive disease. There are numerous examples of how environmental exposures, like tobacco, chronic stress, or dietary factors, induce an adverse tumor biology, leading to a more aggressive disease and decreased patient survival. Because of population differences in the exposure to these risk factors, they can be the cause of cancer disparities. In this review, we will summarize recent advances in our understanding of prostate and breast cancer disparities in the United States and discuss how the analysis of tumor biology can advance health disparity research.