A novel DNA methylation signature is associated with androgen receptor activity and patient prognosis in bone metastatic prostate cancer

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.

Deciphering the genetic and epigenetic architecture of prostate cancer

Prostate cancer, one of the most frequently diagnosed cancers in men, leads to significant mortality worldwide. Its study is important due to the complexity and diversity in its progression, highlighting the urgent need for improved therapeutic strategies. This chapter probes into the genetic and epigenetic factors influencing prostate cancer progression, underscoring the importance of understanding the disease's molecular fundamentals for the development of targeted therapies. It specifically reviews the role of key genetic mutations in genes such as Androgen Receptor, TP53, SPOP, FOXA1 and PTEN which are crucial for the disease onset and a progression. Furthermore, it examines the impact of epigenetic modifications, including DNA methylation and histone modification, which contribute to the cancer's progression by affecting gene expression and cellular behavior. Further, in this chapter we delve into the underlying signaling mechanism, the advancements in targeting genetic and epigenetic alterations in prostate cancer. These findings have revealed promising targets for therapeutic advancements, aiming to understand and identify promising avenues for future therapies. This chapter improves our current understanding of prostate cancer genetic and epigenetic landscape, emphasizing the necessity of advancing our knowledge to refine and expand treatment options for prostate cancer patients.

Deep learning and machine learning approaches to classify stomach distant metastatic tumors using DNA methylation profiles

Distant metastasis of cancer is a significant contributor to cancer-related complications, and early identification of unidentified stomach adenocarcinoma is crucial for a positive prognosis. Changes inDNA methylation are being increasingly recognized as a crucial factor in predicting cancer progression. Within this research, we developed machine learning and deep learning models for distinguishing distant metastasis in samples of stomach adenocarcinoma based on DNA methylation profile. Employing deep neural networks (DNN), support vector machines (SVM), random forest (RF), Naive Bayes (NB) and decision tree (DT), and models for forecasting distant metastasis in stomach adenocarcinoma. The results show that the performance of DNN is better than that of other models, AUC and AUPR achieving 99.9 % and 99.5 % respectively. Additionally, a weighted random sampling technique was utilized to address the issue of imbalanced datasets, enabling the identification of crucial methylation markers associated with functionally significant genes in stomach distant metastasis tumors with greater performance.

Liquid Biomarkers in Prostate Cancer Diagnosis: Current Status and Emerging Prospects

Prostate cancer (PCa) is a major health concern that necessitates appropriate diagnostic approaches for timely intervention. This review critically evaluates the role of liquid biopsy techniques, focusing on blood- and urine-based biomarkers, in overcoming the limitations of conventional diagnostic methods. The 4Kscore test and Prostate Health Index have demonstrated efficacy in distinguishing PCa from benign conditions. Urinary biomarker tests such as PCa antigen 3, MyProstateScore, SelectMDx, and ExoDx Prostate IntelliScore test have revolutionized risk stratification and minimized unnecessary biopsies. Emerging biomarkers, including non-coding RNAs, circulating tumor DNA, and prostate-specific antigen (PSA) glycosylation, offer valuable insights into PCa biology, enabling personalized treatment strategies. Advancements in non-invasive liquid biomarkers for PCa diagnosis may facilitate the stratification of patients and avoid unnecessary biopsies, particularly when PSA is in the gray area of 4 to 10 ng/mL.

Epigenetic control of the vicious cycle

Epigenetic alterations, including DNA methylation and post translational modifications to histones, drive tumorigenesis and metastatic progression. In the context of bone metastasis, epigenetic modifications in tumor cells can modulate dissemination of cancer cells to the bone, tumor progression in the bone marrow, and may be associated with patient survival rates. Bone disseminated tumor cells may enter a dormant state or stimulate osteolysis through the "vicious cycle" of bone metastasis where bone disseminated tumor cells disrupt the bone microenvironment, which fuels tumor progression. Epigenetic alterations may either exacerbate or abrogate the vicious cycle by regulating tumor suppressors and oncogenes, which alter proliferation of bone-metastatic cancer cells. This review focuses on the specific epigenetic alterations that regulate bone metastasis, including DNA methylation, histone methylation, and histone acetylation. Here, we summarize key findings from researchers identifying epigenetic changes that drive tumor progression in the bone, along with pre-clinical and clinical studies investigating the utility of targeting aberrant epigenetic alterations to treat bone metastatic cancer.

Regulation of SPDEF expression by DNA methylation in advanced prostate cancer

Introduction

Prostate cancer (PCa) presents a significant health challenge in men, with a substantial number of deaths attributed to metastatic castration resistant PCa (mCRPC). Moreover, African American men experience disproportionately high mortality rates due to PCa. This study delves into the pivotal role of SPDEF, a prostate specific Ets transcription factor, and its regulation by DNA methylation in the context of PCa progression.

Methods

We performed Epigenetic reprogramming using daily treatment with non-toxic dose of 5Aza-2-deoxycytidine (5Aza-dC) for two weeks to assess its impact on PDEF expression in prostate cancer cells. Next, we conducted functional studies on reprogrammed cells, including cell migration (wound-healing assay), invasion (Boyden-Chamber test), and proliferation (MTT assay) to comprehensively evaluate the consequences of altered PDEF expression. We used bisulfite sequencing (BSP) to examine DNA methylation at SPDEF promoter. Simultaneously, we utilized siRNA-mediated targeting of key DNMTs (DNMT1, DNMT3A, and DNMT3B) to elucidate their specific role in regulating PDEF. We measured mRNA and protein expressions using qRT-PCR and immune-blotting techniques, respectively.

Results

In this report, we observed that: a) there is a gradual decrease in SPDEF expression with a concomitant increase in methylated CpG sites within the SPDEF gene during prostate cancer progression from lower to higher Gleason grade; b) Expression of DNMT's (DNMT1, 3a and 3b) is increased during prostate cancer progression, and there is an inverse correlation between SPDEF and DNMT expression; c) SPDEF levels are decreased in RC77/T, a line of PCa cells from African American origin similar to PC3 and DU145 cells (CRPC cells), as compared to LNCaP cells , a line of androgen dependent cells,; d) the 5' CpG island of SPDEF gene are hypermethylated in SPDEF-negative CRPC ( PC3, DU145 and RC77/T) cell lines but the same regions are hypomethylated in SPDEF-positive castrate sensitive (LNCaP) cell line ; (e) expression of SPDEF in PCa cells lacking SPDEF decreases cell migration and invasion, but has no significant effect on cell proliferation, and; (f) treatment with the demethylating agent, 5-aza-2'-deoxycytidine, or silencing of the DNMT's by siRNA, partially restores SPDEF expression in SPDEF-negative PCa cell lines, and decreases cell migration and invasion.

Discussion

These results indicate hypermethylation is a prevalent mechanism for decreasing SPDEF expression during prostate cancer progression. The data demonstrate that loss of SPDEF expression in prostate cancer cells, a critical step in cellular plasticity, results from a potentially reversible process of aberrant DNA methylation. These studies suggest DMNT activity as a potential therapeutic vulnerability that can be exploited for limiting cellular plasticity, tumor progression, and therapy resistance in prostate cancer.

erarchical tumor artificial intelligence classifier traces tissue of origin and tumor type in primary and metastasized tumors using DNA methylation

Human cancers are heterogenous by their cell composition and origination site. Cancer metastasis generates the conundrum of the unknown origin of migrated tumor cells. Tracing tissue of origin and tumor type in primary and metastasized cancer is vital for clinical significance. DNA methylation alterations play a crucial role in carcinogenesis and mark cell fate differentiation, thus can be used to trace tumor tissue of origin. In this study, we employed a novel tumor-type-specific hierarchical model using genome-scale DNA methylation data to develop a multilayer perceptron model, HiTAIC, to trace tissue of origin and tumor type in 27 cancers from 23 tissue sites in data from 7735 tumors with high resolution, accuracy, and specificity. In tracing primary cancer origin, HiTAIC accuracy was 99% in the test set and 93% in the external validation data set. Metastatic cancers were identified with a 96% accuracy in the external data set. HiTAIC is a user-friendly web-based application through https://sites.dartmouth.edu/salaslabhitaic/. In conclusion, we developed HiTAIC, a DNA methylation-based algorithm, to trace tumor tissue of origin in primary and metastasized cancers. The high accuracy and resolution of tumor tracing using HiTAIC holds promise for clinical assistance in identifying cancer of unknown origin.

meth-SemiCancer: a cancer subtype classification framework via semi-supervised learning utilizing DNA methylation profiles

BACKGROUND

Identification of the cancer subtype plays a crucial role to provide an accurate diagnosis and proper treatment to improve the clinical outcomes of patients. Recent studies have shown that DNA methylation is one of the key factors for tumorigenesis and tumor growth, where the DNA methylation signatures have the potential to be utilized as cancer subtype-specific markers. However, due to the high dimensionality and the low number of DNA methylome cancer samples with the subtype information, still, to date, a cancer subtype classification method utilizing DNA methylome datasets has not been proposed.

RESULTS

In this paper, we present meth-SemiCancer, a semi-supervised cancer subtype classification framework based on DNA methylation profiles. The proposed model was first pre-trained based on the methylation datasets with the cancer subtype labels. After that, meth-SemiCancer generated the pseudo-subtypes for the cancer datasets without subtype information based on the model's prediction. Finally, fine-tuning was performed utilizing both the labeled and unlabeled datasets.

CONCLUSIONS

From the performance comparison with the standard machine learning-based classifiers, meth-SemiCancer achieved the highest average F1-score and Matthews correlation coefficient, outperforming other methods. Fine-tuning the model with the unlabeled patient samples by providing the proper pseudo-subtypes, encouraged meth-SemiCancer to generalize better than the supervised neural network-based subtype classification method. meth-SemiCancer is publicly available at https://github.com/cbi-bioinfo/meth-SemiCancer .

Epigenetic mechanism of therapeutic resistance and potential of epigenetic therapeutics in chemorefractory prostate cancer

Prostate cancer is the second leading cause of cancer death among men in the United States. Depending upon the histopathological subtypes of prostate cancers, various therapeutic options, such as androgen deprivation therapy (ADT), androgen receptor signaling inhibitors (ARSI), immunotherapy, and chemotherapy, are available to treat prostate cancer. While these therapeutics are effective in the initial stages during treatments, the tumors subsequently develop resistance to these therapies. Despite all the progress made so far, therapeutic resistance remains a major challenge in the treatment of prostate cancer. Although various mechanisms have been reported for the resistance development in prostate cancer, altered expression of genes either directly or indirectly involved in drug response pathways is a common event. In addition to the genetic basis of gene regulation such as mutations and gene amplifications, epigenetic alterations involved in the aberrant expression of genes have frequently been shown to be associated not only with cancer initiation and progression but also with therapeutic resistance development. There are several review articles compiling reports on genetic mechanisms involved in therapeutic resistance in prostate cancer. However, epigenetic mechanisms for the therapeutic resistance development in prostate cancer have not yet been summarized in a review article. Therefore, the objective of this article is to compile various reports and provide a comprehensive review of the epigenetic aberrations, and aberrant expression of genes by epigenetic mechanisms involved in CRPCs and therapeutic resistance development in prostate cancer. Additionally, the potential of epigenetic-based therapeutics in the treatment of chemorefractory prostate cancer as evidenced by clinical trials has also been discussed.

Androgen Receptor Signaling Inhibition in Advanced Castration Resistance Prostate Cancer: What Is Expected for the Near Future?

The androgen signaling pathway is the cornerstone in the treatment of high risk or advanced prostate cancer patients. However, in recent years, different mechanisms of resistance have been defined in this field, limiting the efficacy of the currently approved antiandrogen drugs. Different therapeutic approaches are under research to assess the role of combination therapies against escape signaling pathways or the development of novel antiandrogen drugs to try to solve the primary or acquired resistance against androgen dependent or independent pathways. The present review aims to summarize the current state of androgen inhibition in the therapeutic algorithm of patients with advanced prostate cancer and the mechanisms of resistance to those available drugs. In addition, this review conducted a comprehensive overview of the main present and future research approaches in the field of androgen receptor inhibition to overcome these resistances and the potential new drugs under research coming into this setting.

Epithelial and Stromal Characteristics of Primary Tumors Predict the Bone Metastatic Subtype of Prostate Cancer and Patient Survival after Androgen-Deprivation Therapy

Simple Summary

Metastatic prostate cancer is a lethal disease and metastasis-specific treatments need to be developed. Mechanisms driving metastases and primary tumor growth could be different, but this is largely unexplored. We previously discovered that bone metastases can be separated into transcriptomic-based subtypes, showing different responses to standard androgen-deprivation therapy for metastatic prostate cancer. One subtype, named MetB, is particularly aggressive and has the worst prognosis. Here, we describe similarities and differences between primary tumors and their metastases, and specifically examine if the development of specific subtype of bone metastases can be predicted by analyzing the primary tumor. Results show that many aspects of prostate cancer bone metastases morphology are related to those in the primary tumor, while others are not. Importantly, men with primary tumors with high cell proliferation and low cellular PSA expression tend to develop metastases enriched for the MetB subtype, have poor prognosis, and need complementary treatment to standard hormone treatment.

Abstract

Prostate cancer (PC) bone metastases can be divided into transcriptomic subtypes, by us termed MetA-C. The MetB subtype, constituting about 20% of the cases, is characterized by high cell cycle activity, low androgen receptor (AR) activity, and a limited response to standard androgen deprivation therapy (ADT). Complementary treatments should preferably be introduced early on if the risk of developing metastases of the MetB subtype is predicted to behigh. In this study, we therefore examined if the bone metastatic subtype and patient outcome after ADT could be predicted by immunohistochemical analysis of epithelial and stromal cell markers in primary tumor biopsies obtained at diagnosis (n = 98). In this advanced patient group, primary tumor International Society of Urological Pathology (ISUP) grade was not associated with outcome or metastasis subtype. In contrast, high tumor cell Ki67 labeling (proliferation) in combination with low tumor cell immunoreactivity for PSA, and a low fraction of AR positive stroma cells in the primary tumors were prognostic for poor survival after ADT. Accordingly, the same tissue markers were associated with developing metastases enriched for the aggressive MetB subtype. The development of the contrasting MetA subtype, showing the best response to ADT, could be predicted by the opposite staining pattern. We conclude that outcome after ADT and metastasis subtype can, at least to some extent, be predicted by analysis of primary tumor characteristics, such as tumor cell proliferation and PSA expression, and AR expression in stromal cells.

Prostate cancer in omics era

Recent advances in omics technology have prompted extraordinary attempts to define the molecular changes underlying the onset and progression of a variety of complex human diseases, including cancer. Since the advent of sequencing technology, cancer biology has become increasingly reliant on the generation and integration of data generated at these levels. The availability of multi-omic data has transformed medicine and biology by enabling integrated systems-level approaches. Multivariate signatures are expected to play a role in cancer detection, screening, patient classification, assessment of treatment response, and biomarker identification. This review reports current findings and highlights a number of studies that are both novel and groundbreaking in their application of multi Omics to prostate cancer.

Establishment of a Novel Combined Nomogram for Predicting the Risk of Progression Related to Castration Resistance in Patients With Prostate Cancer

Background: The emergence of castration resistance is fatal for patients with prostate cancer (PCa); however, there is still a lack of effective means to detect the early progression. In this study, a novel combined nomogram was established to predict the risk of progression related to castration resistance.

Methods: The castration-resistant prostate cancer (CRPC)-related differentially expressed genes (DEGs) were identified by R packages “limma” and “WGCNA” in GSE35988-GPL6480 and GSE70768-GPL10558, respectively. Relationships between DEGs and progression-free interval (PFI) were analyzed using the Kaplan–Meier method in TCGA PCa patients. A multigene signature was built by lasso-penalized Cox regression analysis, and assessed by the receiver operator characteristic (ROC) curve and Kaplan–Meier curve. Finally, the univariate and multivariate Cox regression analyses were used to establish a combined nomogram. The prognostic value of the nomogram was validated by concordance index (C-index), calibration plots, ROC curve, and decision curve analysis (DCA).

Results: 15 CRPC-related DEGs were identified finally, of which 13 genes were significantly associated with PFI and used as the candidate genes for modeling. A two-gene (KIFC2 and BCAS1) signature was built to predict the risk of progression. The ROC curve indicated that 5-year area under curve (AUC) in the training, testing, and whole TCGA dataset was 0.722, 0.739, and 0.731, respectively. Patients with high-risk scores were significantly associated with poorer PFI (p < 0.0001). A novel combined nomogram was successfully established for individualized prediction integrating with T stage, Gleason score, and risk score. While the 1-year, 3-year, and 5-year AUC were 0.76, 0.761, and 0.762, respectively, the good prognostic value of the nomogram was also validated by the C-index (0.734), calibration plots, and DCA.

Conclusion: The combined nomogram can be used to predict the individualized risk of progression related to castration resistance for PCa patients and has been preliminarily verified to have good predictive ability.

Feature Selection of OMIC Data by Ensemble Swarm Intelligence Based Approaches

OMIC datasets have high dimensions, and the connection among OMIC features is very complicated. It is difficult to establish linkages among these features and certain biological traits of significance. The proposed ensemble swarm intelligence-based approaches can identify key biomarkers and reduce feature dimension efficiently. It is an end-to-end method that only relies on the rules of the algorithm itself, without presets such as the number of filtering features. Additionally, this method achieves good classification accuracy without excessive consumption of computing resources.

Comprehensive Landscape of HOXA2, HOXA9, and HOXA10 as Potential Biomarkers for Predicting Progression and Prognosis in Prostate Cancer

Prostate cancer (PCa) is recognized as a common malignancy in male patients. The homeobox A cluster (HOXA) family members have been confirmed to be implicated in the development of several types of tumors. However, the expression pattern and prognostic values of HOXA genes in PCa have not been investigated. In this study, we analyzed TCGA datasets and identified six HOXA family members which showed a dysregulated expression in PCa specimens compared with nontumor specimens. We also explored the potential mechanisms involved in the dysregulation of HOXA family members in PCa, and the results of Pearson's correlation revealed that most HOXA members were negatively related to the methylation degree. Moreover, we explored the prognostic values of HOXA family members and identified six survival-related HOXA members. Importantly, HOXA2, HOXA9, and HOXA10 were identified as critical PCa-related genes which were abnormally expressed in PCa and associated with clinical outcomes of PCa patients. Then, we explored the association between the above three genes and immune cell infiltration. We observed that the levels of HOXA2, HOXA9, and HOXA10 were associated with the levels of immune infiltration of several kinds of immune cells. Overall, our findings identified the potential values of the HOXA family for outcome prediction in PCa, which might facilitate personalized counselling and treatment in PCa.

Clinical and biological relevance of the transcriptomic-based prostate cancer metastasis subtypes MetA-C

To improve treatment of metastatic prostate cancer, the biology of metastases needs to be understood. We recently described three subtypes of prostate cancer bone metastases (MetA-C), based on differential gene expression. The aim of this study was to verify the clinical relevance of these subtypes and to explore their biology and relations to genetic drivers. Freshly-frozen metastasis samples were obtained as hormone-naive (n = 17), short-term castrated (n = 21), or castration-resistant (n = 65) from a total of 67 patients. Previously published sequencing data from 573 metastasis samples were also analyzed. Through transcriptome profiling and sample classification based on a set of predefined MetA-C-differentiating genes, we found that most metastases were heterogeneous for the MetA-C subtypes. Overall, MetA was the most common subtype, while MetB was significantly enriched in castration-resistant samples and in liver metastases, and consistently associated with poor prognosis. By gene set enrichment analysis, the phenotype of MetA was described by high androgen response, protein secretion and adipogenesis, MetB by high cell cycle activity and DNA repair, and MetC by epithelial-to-mesenchymal transition and inflammation. The MetB subtype demonstrated single nucleotide variants of RB transcriptional corepressor 1 (RB1) and loss of 21 genes at chromosome 13, including RB1, but provided independent prognostic value to those genetic aberrations. In conclusion, a distinct set of gene transcripts can be used to classify prostate cancer metastases into the subtypes MetA-C. The MetA-C subtypes show diverse biology, organ tropism, and prognosis. The MetA-C classification may be used independently, or in combination with genetic markers, primarily to identify MetB patients in need of complementary therapy to conventional androgen receptor-targeting treatments.

DNA methylation in genes associated with the evolution of ageing and disease: A critical review

Ageing is characterised by a physical decline in biological functioning which results in a progressive risk of mortality with time. As a biological phenomenon, it is underpinned by the dysregulation of a myriad of complex processes. Recently, however, ever-increasing evidence has associated epigenetic mechanisms, such as DNA methylation (DNAm) with age-onset pathologies, including cancer, cardiovascular disease, and Alzheimer's disease. These diseases compromise healthspan. Consequently, there is a medical imperative to understand the link between epigenetic ageing, and healthspan. Evolutionary theory provides a unique way to gain new insights into epigenetic ageing and health. This review will: (1) provide a brief overview of the main evolutionary theories of ageing; (2) discuss recent genetic evidence which has revealed alleles that have pleiotropic effects on fitness at different ages in humans; (3) consider the effects of DNAm on pleiotropic alleles, which are associated with age related disease; (4) discuss how age related DNAm changes resonate with the mutation accumulation, disposable soma and programmed theories of ageing; (5) discuss how DNAm changes associated with caloric restriction intersect with the evolution of ageing; and (6) conclude by discussing how evolutionary theory can be used to inform investigations which quantify age-related DNAm changes which are linked to age onset pathology.