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
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Research Article Genetics Oncology

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

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

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Figure 9

Transcriptional regulation and clinical correlates of the chromatin interaction targets of the PCa risk SNP rs684232.

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Transcriptional regulation and clinical correlates of the chromatin inte...
(A) qRT-PCR validation of AR knockdown and the expression of VPS53, GEMIN4, and FAM57A genes upon treatment of LNCaP cells with AR siRNA. **P < 0.01, and ***P < 0.001, by 2-tailed Student’s t test. Error bars indicate the SD of 3 technical replicates. (B) Box plot shows AR ChIP-Seq signal intensity stratified by genotype in the Porto cohort (Mann-Whitney U test for the recessive model). The y axis shows the number of AR ChIP-Seq read counts mapped to the SNP rs684232 region, which were normalized by the TMM method. Box plot represents the median and the 0.25 and 0.75 quantiles, with whiskers at 1.5 times the IQR. (C) The regulatory impact of rs684232 was enhanced in the presence of the TMPRSS2-ERG fusion. Box plots show the mRNA abundance (FPKM) of each gene stratified by genotype and further split by ERG status in the CPC-GENE cohort. βpositive and βnegative, and the associated P values quantify the eQTL within ERG-positive and -negative patients, respectively (linear model). (D) Box plots show the mRNA abundance of FAM57A, GEMIN4, and VPS53 genes in PCa specimens from TCGA cohort. Tumors were classified into various ISUP grade groups. Relationship between mRNA abundance and ISUP Grade Group was quantified using Spearman’s correlation and represented as Spearman’s rho and corresponding P values. The P values for Spearman’s correlation were computed using the algorithm AS 89 (47). (E) BCR-free survival curves for PCa patient groups defined by transcript abundance for FAM57A, GEMIN4, and VPS53 genes in the CPC-GENE cohort. P values in E were determined by log-rank test.