Go to JCI Insight
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Publication ethics
  • Publication alerts by email
  • Advertising
  • Job board
  • Contact
  • Clinical Research and Public Health
  • Current issue
  • Past issues
  • By specialty
    • COVID-19
    • Cardiology
    • Gastroenterology
    • Immunology
    • Metabolism
    • Nephrology
    • Neuroscience
    • Oncology
    • Pulmonology
    • Vascular biology
    • All ...
  • Videos
    • ASCI Milestone Awards
    • Video Abstracts
    • Conversations with Giants in Medicine
  • Reviews
    • View all reviews ...
    • The cGAS-STING pathway: DNA sensing in health and disease (Jun 2026)
    • Neurodegeneration (Mar 2026)
    • Clinical innovation and scientific progress in GLP-1 medicine (Nov 2025)
    • Pancreatic Cancer (Jul 2025)
    • Complement Biology and Therapeutics (May 2025)
    • Evolving insights into MASLD and MASH pathogenesis and treatment (Apr 2025)
    • Microbiome in Health and Disease (Feb 2025)
    • View all review series ...
  • Viewpoint
  • Collections
    • In-Press Preview
    • Clinical Research and Public Health
    • Research Letters
    • Letters to the Editor
    • Editorials
    • Commentaries
    • Editor's notes
    • Reviews
    • Viewpoints
    • 100th anniversary
    • Top read articles

  • Current issue
  • Past issues
  • Specialties
  • Reviews
  • Review series
  • ASCI Milestone Awards
  • Video Abstracts
  • Conversations with Giants in Medicine
  • In-Press Preview
  • Clinical Research and Public Health
  • Research Letters
  • Letters to the Editor
  • Editorials
  • Commentaries
  • Editor's notes
  • Reviews
  • Viewpoints
  • 100th anniversary
  • Top read articles
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Publication ethics
  • Publication alerts by email
  • Advertising
  • Job board
  • Contact
The landscape of RNA polymerase II–associated chromatin interactions in prostate cancer
Susmita G. Ramanand, Yong Chen, Jiapei Yuan, Kelly Daescu, Maryou B.K. Lambros, Kathleen E. Houlahan, Suzanne Carreira, Wei Yuan, GuemHee Baek, Adam Sharp, Alec Paschalis, Mohammed Kanchwala, Yunpeng Gao, Adam Aslam, Nida Safdar, Xiaowei Zhan, Ganesh V. Raj, Chao Xing, Paul C. Boutros, Johann de Bono, Michael Q. Zhang, Ram S. Mani
Susmita G. Ramanand, Yong Chen, Jiapei Yuan, Kelly Daescu, Maryou B.K. Lambros, Kathleen E. Houlahan, Suzanne Carreira, Wei Yuan, GuemHee Baek, Adam Sharp, Alec Paschalis, Mohammed Kanchwala, Yunpeng Gao, Adam Aslam, Nida Safdar, Xiaowei Zhan, Ganesh V. Raj, Chao Xing, Paul C. Boutros, Johann de Bono, Michael Q. Zhang, Ram S. Mani
View: Text | PDF
Research Article Genetics Oncology

The landscape of RNA polymerase II–associated chromatin interactions in prostate cancer

  • Text
  • PDF
Abstract

Transcriptional dysregulation is a hallmark of prostate cancer (PCa). We mapped the RNA polymerase II–associated (RNA Pol II–associated) chromatin interactions in normal prostate cells and PCa cells. We discovered thousands of enhancer-promoter, enhancer-enhancer, as well as promoter-promoter chromatin interactions. These transcriptional hubs operate within the framework set by structural proteins — CTCF and cohesins — and are regulated by the cooperative action of master transcription factors, such as the androgen receptor (AR) and FOXA1. By combining analyses from metastatic castration-resistant PCa (mCRPC) specimens, we show that AR locus amplification contributes to the transcriptional upregulation of the AR gene by increasing the total number of chromatin interaction modules comprising the AR gene and its distal enhancer. We deconvoluted the transcription control modules of several PCa genes, notably the biomarker KLK3, lineage-restricted genes (KRT8, KRT18, HOXB13, FOXA1, ZBTB16), the drug target EZH2, and the oncogene MYC. By integrating clinical PCa data, we defined a germline-somatic interplay between the PCa risk allele rs684232 and the somatically acquired TMPRSS2-ERG gene fusion in the transcriptional regulation of multiple target genes — VPS53, FAM57A, and GEMIN4. Our studies implicate changes in genome organization as a critical determinant of aberrant transcriptional regulation in PCa.

Authors

Susmita G. Ramanand, Yong Chen, Jiapei Yuan, Kelly Daescu, Maryou B.K. Lambros, Kathleen E. Houlahan, Suzanne Carreira, Wei Yuan, GuemHee Baek, Adam Sharp, Alec Paschalis, Mohammed Kanchwala, Yunpeng Gao, Adam Aslam, Nida Safdar, Xiaowei Zhan, Ganesh V. Raj, Chao Xing, Paul C. Boutros, Johann de Bono, Michael Q. Zhang, Ram S. Mani

×

Figure 1

Analysis of RNA Pol II–associated chromatin interactions.

Options: View larger image (or click on image) Download as PowerPoint
Analysis of RNA Pol II–associated chromatin interactions.
(A) Pipeline f...
(A) Pipeline for ChIA-PET data processing and identification of chromatin interactions. (B) Enhancers per gene and genes per enhancer for each cell line. For enhancers per gene, all expressed genes (fragments per kb per million mapped reads [FPKM] >1) were included; for genes per enhancer, all enhancers located in RNA Pol II peak anchor regions were included. Box plot represents the median and the 25% and 75% quantiles, with lines at 1.5 times the IQR. The significance for each pair comparison was tested using the Kolmogorov-Smirnov test (†P < 2.2 × 10–16).The analysis was normalized by adjusting for sequencing depth. (C) Correlation between RNA expression and chromatin interaction across the 4 cell lines. The expression level was measured by FPKM transformed by log2, and chromatin interactions were measured by the number of promoter PETs. The correlation efficient was calculated by Spearman’s correlation, and P values are shown. (D) Significant gain and loss of RNA Pol II–associated chromatin interactions in the 4 cell lines. The 3 solid lines show the distributions of the 3 PCa cell lines compared with benign cells (RWPE-1), whereas the 3 broken lines show the distributions among the 3 PCa cell lines. The significant gain or loss interactions were obtained using the DNB model. The total number of interactions that were significantly altered between cell lines are listed accordingly. (E) Scatter plot shows the correlation between changes in gene expression and changes in promoter-associated chromatin interactions. Graphs show Spearman’s correlation coefficients for the top 10%, 20%, 40%, 60%, and 80% differentially expressed genes (right panel). FC, fold change. (F) Total number of PETs at promoter regions in the 4 cell lines. Genes are ranked by increasing number of PETs. Eight representative genes are labeled 1 through 8 in the plots.

Copyright © 2026 American Society for Clinical Investigation
ISSN: 0021-9738 (print), 1558-8238 (online)

Sign up for email alerts