The DNA methylation landscape of advanced prostate cancer

Zhao, Shuang G.; Chen, William S.; Li, Haolong; Foye, Adam; Zhang, Meng; Sjöström, Martin; Aggarwal, Rahul; Playdle, Denise; Liao, Arnold; Alumkal, Joshi J.; Das, Rajdeep; Chou, Jonathan; Hua, Junjie; Barnard, Travis J.; Bailey, Adina; Chow, Eric D.; Perry, Marc D.; Dang, Ha X.; Yang, Rendong; Moussavi-Baygi, Ruhollah; Zhang, Li; Alshalalfa, Mohammed; Chang, Susan; Houlahan, Kathleen E.; Shiah, Yu Jia; Beer, Tomasz M.; Thomas, George V.; N., Kim; Gleave, Martin; Zoubeidi, Amina; Reiter, Robert E.; Rettig, Matthew B.; Witte, Owen N.; Kim, M. Yvonne; Fong, Lawrence; Spratt, Daniel E.; Morgan, Todd M.; Bose, Rohit; Huang, Franklin W.; Li, Hui; Chesner, Lisa N.; Shenoy, Tanushree; Goodarzi, Hani; Asangani, Irfan A.; Sandhu, Shahneen; Lang, Joshua M.; Mahajan, Nupam P.; Lara, Primo N.; Evans, Christopher P.; Febbo, Phillip G.; Batzoglou, Serafim; Knudsen, Karen E.; He, Housheng Hansen; Huang, Jiaoti; Zwart, Wilbert; Costello, J; Luo, Jianhua; Tomlins, Scott A.; Wyatt, Alexander W.; Dehm, Scott M.; Ashworth, Alan; Gilbert, Luke A.; Boutros, Paul C.; Farh, Kyle Kai‐How; Chinnaiyan, Arul M.; Maher, Christopher A.; Small, Eric J.; Quigley, David A.; Feng, Felix Y.

doi:10.1038/s41588-020-0648-8

Cited by 233 publications

(235 citation statements)

References 106 publications

(120 reference statements)

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“…There has been a vast multitude of genetic characteristics identified as potentially relevant biomarkers in phylogenetic analyses [ 146 , 147 ] and our maturing understanding of epigenetics [ 148 ]. There is also significant discussion surrounding how additional biomarkers of a PCa’s molecular function could assist in determining how aggressive to be in the setting of early metastatic disease.…”

Section: Prostate Cancer Radiogenomicsmentioning

confidence: 99%

Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review

Chaddad

Kucharczyk

Cheddad

et al. 2021

Cancers

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The management of prostate cancer (PCa) is dependent on biomarkers of biological aggression. This includes an invasive biopsy to facilitate a histopathological assessment of the tumor’s grade. This review explores the technical processes of applying magnetic resonance imaging based radiomic models to the evaluation of PCa. By exploring how a deep radiomics approach further optimizes the prediction of a PCa’s grade group, it will be clear how this integration of artificial intelligence mitigates existing major technological challenges faced by a traditional radiomic model: image acquisition, small data sets, image processing, labeling/segmentation, informative features, predicting molecular features and incorporating predictive models. Other potential impacts of artificial intelligence on the personalized treatment of PCa will also be discussed. The role of deep radiomics analysis-a deep texture analysis, which extracts features from convolutional neural networks layers, will be highlighted. Existing clinical work and upcoming clinical trials will be reviewed, directing investigators to pertinent future directions in the field. For future progress to result in clinical translation, the field will likely require multi-institutional collaboration in producing prospectively populated and expertly labeled imaging libraries.

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Section: Prostate Cancer Radiogenomicsmentioning

confidence: 99%

Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review

Chaddad

Kucharczyk

Cheddad

et al. 2021

Cancers

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“…In particular, an unsupervised hierarchical cluster analysis of the 5,000 most variable hypermethylated CpG sites revealed four epigenetically distinct subtypes of PCa. In another multicenter study, a new epigenetic CpG methylator phenotype in advanced PCa was reported, and this subtype is characterized by hypermethylation both within and outside CpG sites, shores, and shelves ( Zhao et al, 2020 ). These important studies have improved our understanding of the DNA methylation landscape in PCa, providing a reference for future research.…”

Section: Introductionmentioning

confidence: 99%

Establishment of Novel DNA Methylation-Based Prostate Cancer Subtypes and a Risk-Predicting Eight-Gene Signature

Zhang

Shiori

Mu³

et al. 2021

Front. Cell Dev. Biol.

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Prostate cancer (PCa) is the most common malignant tumor affecting males worldwide. The substantial heterogeneity in PCa presents a major challenge with respect to molecular analyses, patient stratification, and treatment. Least absolute shrinkage and selection operator was used to select eight risk-CpG sites. Using an unsupervised clustering analysis, called consensus clustering, we found that patients with PCa could be divided into two subtypes (Methylation_H and Methylation_L) based on the DNA methylation status at these CpG sites. Differences in the epigenome, genome, transcriptome, disease status, immune cell composition, and function between the identified subtypes were explored using The Cancer Genome Atlas database. This analysis clearly revealed the risk characteristics of the Methylation_H subtype. Using a weighted correlation network analysis to select risk-related genes and least absolute shrinkage and selection operator, we constructed a prediction signature for prognosis based on the subtype classification. We further validated its effectiveness using four public datasets. The two novel PCa subtypes and risk predictive signature developed in this study may be effective indicators of prognosis.

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“…In addition to implicating cancer genes, genome sequencing studies have revealed structural variation in non-coding regions, including enhancer elements driving oncogene expression (Takeda et al, 2018;Viswanathan et al, 2018). Epigenetic characterization studies have further extended understanding of the noncoding genome by revealing the role of DNA methylation patterns (Bedford and van Helden, 1987;Börno et al, 2012;Friedlander et al, 2012;Jimenez et al, 2000;Lee et al, 1997;Mahapatra et al, 2012;Varambally et al, 2002;Xu et al, 2012;Zhao et al, 2020), specific transcription factor (TF) binding sites and histone modifications, including the characterization of the active enhancer landscape in PC tissues (Kron et al, 2017;Pomerantz et al, 2015Pomerantz et al, , 2020Stelloo et al, 2018;Urbanucci et al, 2012Urbanucci et al, , 2017Yu et al, 2010). Still, how the chromatin landscape evolves during PC progression and drives aberrant transcriptome (Cancer Genome Atlas Research Network, 2015) and proteome (Latonen et al, 2018;Sinha et al, 2019), is unclear.…”

Section: Introductionmentioning

confidence: 99%

Chromatin accessibility analysis uncovers regulatory element landscape in prostate cancer progression

Uusi-Mäkelä

Afyounian

Tabaro

et al. 2020

Preprint

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Aberrant oncogene functions and structural variation alter the chromatin structure in cancer cells. While gene regulation by chromatin states has been studied extensively, chromatin accessibility and its relevance in aberrant gene expression during prostate cancer progression is not well understood. Here, we report a genome-wide chromatin accessibility analysis of clinical tissue samples of benign prostatic hyperplasia (BPH), untreated primary prostate cancer (PC) and castration-resistant prostate cancer (CRPC) and integrative analysis with transcriptome, methylome, and proteome profiles of the same samples to uncover disease-relevant regulatory elements and their association to altered gene expression during prostate cancer progression. While promoter accessibility is consistent during disease initiation and progression, at distal sites chromatin accessibility is variable enabling transcription factors (TFs) binding patterns that are differently activated in different patients and disease stages. We identify consistent progression-related chromatin alterations during the progression to CRPC. By studying the TF binding patterns, we demonstrate the activation and suppression of androgen receptor-driven regulatory programs during PC progression and identify complementary TF regulatory modules characterized by e.g. MYC and glucocorticoid receptor. By correlation analysis we assign at least one putative regulatory region for 62% of genes and 85% of proteins differentially expressed during prostate cancer progression. Taken together, our analysis of the chromatin landscape in PC identifies putative regulatory elements for the majority of cancer-associated genes and characterizes their impact on the cancer phenotype.

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The DNA methylation landscape of advanced prostate cancer

Cited by 233 publications

References 106 publications

Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review

Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review

Establishment of Novel DNA Methylation-Based Prostate Cancer Subtypes and a Risk-Predicting Eight-Gene Signature

Chromatin accessibility analysis uncovers regulatory element landscape in prostate cancer progression

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