Abstract

The androgen receptor (AR) plays a critical role in the development of the normal prostate as well as prostate cancer. Using an integrative transcriptomic analysis of prostate cancer cell lines and tissues, we identified ARLNC1 (AR-regulated long noncoding RNA 1) as an important long noncoding RNA that is strongly associated with AR signaling in prostate cancer progression. Not only was ARLNC1 induced by the AR protein, but ARLNC1 stabilized the AR transcript via RNA–RNA interaction. ARLNC1 knockdown suppressed AR expression, global AR signaling and prostate cancer growth in vitro and in vivo. Taken together, these data support a role for ARLNC1 in maintaining a positive feedback loop that potentiates AR signaling during prostate cancer progression and identify ARLNC1 as a novel therapeutic target.

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References

  1. 1.

    Mercer, T. R., Dinger, M. E. & Mattick, J. S. Long non-coding RNAs: insights into functions. Nat. Rev. Genet. 10, 155–159 (2009).

  2. 2.

    Wang, K. C. & Chang, H. Y. Molecular mechanisms of long noncoding RNAs. Mol. Cell 43, 904–914 (2011).

  3. 3.

    Rinn, J. L. & Chang, H. Y. Genome regulation by long noncoding RNAs. Annu. Rev. Biochem. 81, 145–166 (2012).

  4. 4.

    Rinn, J. L. et al. Functional demarcation of active and silent chromatin domains in human HOX loci by noncoding RNAs. Cell 129, 1311–1323 (2007).

  5. 5.

    Lee, N., Moss, W. N., Yario, T. A. & Steitz, J. A. EBV noncoding RNA binds nascent RNA to drive host PAX5 to viral DNA. Cell 160, 607–618 (2015).

  6. 6.

    Wutz, A., Rasmussen, T. P. & Jaenisch, R. Chromosomal silencing and localization are mediated by different domains of Xist RNA. Nat. Genet. 30, 167–174 (2002).

  7. 7.

    Prensner, J. R. et al. The long noncoding RNA SChLAP1 promotes aggressive prostate cancer and antagonizes the SWI/SNF complex. Nat. Genet. 45, 1392–1398 (2013).

  8. 8.

    Gupta, R. A. et al. Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature 464, 1071–1076 (2010).

  9. 9.

    Faghihi, M. A. et al. Expression of a noncoding RNA is elevated in Alzheimeras disease and drives rapid feed-forward regulation of β-secretase. Nat. Med 14, 723–730 (2008).

  10. 10.

    Iyer, M. K. et al. The landscape of long noncoding RNAs in the human transcriptome. Nat. Genet. 47, 199–208 (2015).

  11. 11.

    Malik, R. et al. The lncRNA PCAT29 inhibits oncogenic phenotypes in prostate cancer. Mol. Cancer Res. 12, 1081–1087 (2014).

  12. 12.

    Shukla, S. et al. Identification and validation of PCAT14 as prognostic biomarker in prostate cancer. Neoplasia 18, 489–499 (2016).

  13. 13.

    Lu-Yao, G. L. et al. Fifteen-year survival outcomes following primary androgen-deprivation therapy for localized prostate cancer. JAMA Intern. Med. 174, 1460–1467 (2014).

  14. 14.

    Huggins, C. & Hodges, C. V. Studies on prostatic cancer. I. The effect of castration, of estrogen and of androgen injection on serum phosphatases in metastatic carcinoma of the prostate. 1941. J. Urol. 167, 948–951 (2002).

  15. 15.

    The Veterans Administration Co-operative Urological Research Group. Treatment and survival of patients with cancer of the prostate. Surg. Gynecol. Obstet. 124, 1011–1017 (1967).

  16. 16.

    Chen, Y., Sawyers, C. L. & Scher, H. I. Targeting the androgen receptor pathway in prostate cancer. Curr. Opin. Pharmacol. 8, 440–448 (2008).

  17. 17.

    Wong, Y. N., Ferraldeschi, R., Attard, G. & de Bono, J. Evolution of androgen receptor targeted therapy for advanced prostate cancer. Nat. Rev. Clin. Oncol. 11, 365–376 (2014).

  18. 18.

    Mukherji, D., Pezaro, C. J. & De-Bono, J. S. MDV3100 for the treatment of prostate cancer. Expert Opin. Investig. Drugs 21, 227–233 (2012).

  19. 19.

    Scher, H. I. et al. Increased survival with enzalutamide in prostate cancer after chemotherapy. N. Engl. J. Med. 367, 1187–1197 (2012).

  20. 20.

    Tran, C. et al. Development of a second-generation antiandrogen for treatment of advanced prostate cancer. Science 324, 787–790 (2009).

  21. 21.

    Scher, H. I. et al. Antitumour activity of MDV3100 in castration-resistant prostate cancer: a phase 1-2 study. Lancet 375, 1437–1446 (2010).

  22. 22.

    Stein, M. N., Goodin, S. & Dipaola, R. S. Abiraterone in prostate cancer: a new angle to an old problem. Clin. Cancer Res. 18, 1848–1854 (2012).

  23. 23.

    Reid, A. H. et al. Significant and sustained antitumor activity in post-docetaxel, castration-resistant prostate cancer with the CYP17 inhibitor abiraterone acetate. J. Clin. Oncol. 28, 1489–1495 (2010).

  24. 24.

    de Bono, J. S. et al. Abiraterone and increased survival in metastatic prostate cancer. N. Engl. J. Med. 364, 1995–2005 (2011).

  25. 25.

    Watson, P. A., Arora, V. K. & Sawyers, C. L. Emerging mechanisms of resistance to androgen receptor inhibitors in prostate cancer. Nat. Rev. Cancer 15, 701–711 (2015).

  26. 26.

    Antonarakis, E. S. et al. AR-V7 and resistance to enzalutamide and abiraterone in prostate cancer. N. Engl. J. Med. 371, 1028–1038 (2014).

  27. 27.

    Attard, G., Richards, J. & de Bono, J. S. New strategies in metastatic prostate cancer: targeting the androgen receptor signaling pathway. Clin. Cancer Res. 17, 1649–1657 (2011).

  28. 28.

    Hearn, J. W. et al. HSD3B1 and resistance to androgen-deprivation therapy in prostate cancer: a retrospective, multicohort study. Lancet Oncol. 17, 1435–1444 (2016).

  29. 29.

    Chan, S. C., Li, Y. & Dehm, S. M. Androgen receptor splice variants activate androgen receptor target genes and support aberrant prostate cancer cell growth independent of canonical androgen receptor nuclear localization signal. J. Biol. Chem. 287, 19736–19749 (2012).

  30. 30.

    Robinson, D. et al. Integrative clinical genomics of advanced prostate cancer. Cell 161, 1215–1228 (2015).

  31. 31.

    Visakorpi, T. et al. In vivo amplification of the androgen receptor gene and progression of human prostate cancer. Nat. Genet. 9, 401–406 (1995).

  32. 32.

    Asangani, I. A. et al. Therapeutic targeting of BET bromodomain proteins in castration-resistant prostate cancer. Nature 510, 278–282 (2014).

  33. 33.

    Roche, P. J., Hoare, S. A. & Parker, M. G. A consensus DNA-binding site for the androgen receptor. Mol. Endocrinol. 6, 2229–2235 (1992).

  34. 34.

    Pomerantz, M. M. et al. The androgen receptor cistrome is extensively reprogrammed in human prostate tumorigenesis. Nat. Genet. 47, 1346–1351 (2015).

  35. 35.

    Cancer Genome Atlas Research Network. The molecular taxonomy of primary prostate cancer. Cell 163, 1011–1025 (2015).

  36. 36.

    Takayama, K. et al. Androgen-responsive long noncoding RNA CTBP1-AS promotes prostate cancer. EMBO J. 32, 1665–1680 (2013).

  37. 37.

    GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

  38. 38.

    Mele, M. et al. The human transcriptome across tissues and individuals. Science 348, 660–665 (2015).

  39. 39.

    Rhodes, D. R. et al. Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. Neoplasia 9, 166–180 (2007).

  40. 40.

    Engreitz, J. M. et al. RNA-RNA interactions enable specific targeting of noncoding RNAs to nascent pre-mRNAs and chromatin sites. Cell 159, 188–199 (2014).

  41. 41.

    Kretz, M. et al. Control of somatic tissue differentiation by the long non-coding RNA TINCR. Nature 493, 231–235 (2013).

  42. 42.

    Gong, C. & Maquat, L. E. lncRNAs transactivate STAU1-mediated mRNA decay by duplexing with 3′ UTRs via Alu elements. Nature 470, 284–288 (2011).

  43. 43.

    Gawronski, A. R. et al. MechRNA: prediction of lncRNA mechanisms from RNA-RNA and RNA-protein interactions. Bioinformatics (2018) https://doi.org/10.1093/bioinformatics/bty208

  44. 44.

    Mann, M., Wright, P. R. & Backofen, R. IntaRNA 2.0: enhanced and customizable prediction of RNA–RNA interactions. Nucleic Acids Res. 45, W435–W439 (2017).

  45. 45.

    Lennox, K. A. & Behlke, M. A. Cellular localization of long non-coding RNAs affects silencing by RNAi more than by antisense oligonucleotides. Nucleic Acids Res. 44, 863–877 (2016).

  46. 46.

    Meng, L. et al. Towards a therapy for Angelman syndrome by targeting a long non-coding RNA. Nature 518, 409–412 (2015).

  47. 47.

    Wheeler, T. M. et al. Targeting nuclear RNA for in vivo correction of myotonic dystrophy. Nature 488, 111–115 (2012).

  48. 48.

    Hua, Y. et al. Antisense correction of SMN2 splicing in the CNS rescues necrosis in a type III SMA mouse model. Genes Dev. 24, 1634–1644 (2010).

  49. 49.

    Evers, M. M., Toonen, L. J. & van Roon-Mom, W. M. Antisense oligonucleotides in therapy for neurodegenerative disorders. Adv. Drug Deliv. Rev. 87, 90–103 (2015).

  50. 50.

    Yeap, B. B. et al. Novel binding of HuR and poly(C)-binding protein to a conserved UC-rich motif within the 3'-untranslated region of the androgen receptor messenger RNA. J. Biol. Chem. 277, 27183–27192 (2002).

  51. 51.

    Lebedeva, S. et al. Transcriptome-wide analysis of regulatory interactions of the RNA-binding protein HuR. Mol. Cell 43, 340–352 (2011).

  52. 52.

    Prensner, J. R. et al. Transcriptome sequencing across a prostate cancer cohort identifies PCAT-1, an unannotated lincRNA implicated in disease progression. Nat. Biotechnol. 29, 742–749 (2011).

  53. 53.

    Cieslik, M. et al. The use of exome capture RNA-seq for highly degraded RNA with application to clinical cancer sequencing. Genome Res. 25, 1372–1381 (2015).

  54. 54.

    Anders, S., Pyl, P. T. & Huber, W. HTSeq-a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

  55. 55.

    Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

  56. 56.

    Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).

  57. 57.

    Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

  58. 58.

    Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

  59. 59.

    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

  60. 60.

    Cline, M. S. et al. Integration of biological networks and gene expression data using Cytoscape. Nat. Protoc. 2, 2366–2382 (2007).

  61. 61.

    Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

  62. 62.

    Hansen, P. et al. Saturation analysis of ChIP-seq data for reproducible identification of binding peaks. Genome Res. 25, 1391–1400 (2015).

  63. 63.

    Kent, W. J., Zweig, A. S., Barber, G., Hinrichs, A. S. & Karolchik, D. BigWig and BigBed: enabling browsing of large distributed datasets. Bioinformatics 26, 2204–2207 (2010).

  64. 64.

    Bailey, T. L. et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009).

  65. 65.

    Mehra, R. et al. A novel RNA in situ hybridization assay for the long noncoding RNA SChLAP1 predicts poor clinical outcome after radical prostatectomy in clinically localized prostate cancer. Neoplasia 16, 1121–1127 (2014).

  66. 66.

    Newton, M. A., Quintana, F. A., Den Boon, J. A., Sengupta, S. & Ahlquist, P. Random-set methods identify distinct aspects of the enrichment signal in gene-set analysis. Ann. Appl. Stat. 1, 85–106 (2007).

  67. 67.

    Raj, A., van den Bogaard, P., Rifkin, S. A., van Oudenaarden, A. & Tyagi, S. Imaging individual mRNA molecules using multiple singly labeled probes. Nat. Methods 5, 877–879 (2008).

  68. 68.

    Niknafs, Y. S. et al. The lncRNA landscape of breast cancer reveals a role for DSCAM-AS1 in breast cancer progression. Nat. Commun. 7, 12791 (2016).

  69. 69.

    Rossiello, F. et al. DNA damage response inhibition at dysfunctional telomeres by modulation of telomeric DNA damage response RNAs. Nat. Commun. 8, 13980 (2017).

  70. 70.

    Paulsen, M. T. et al. Coordinated regulation of synthesis and stability of RNA during the acute TNF-induced proinflammatory response. Proc. Natl Acad. Sci. USA 110, 2240–2245 (2013).

  71. 71.

    Paulsen, M. T. et al. Use of Bru-Seq and BruChase-Seq for genome-wide assessment of the synthesis and stability of RNA. Methods 67, 45–54 (2014).

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Acknowledgements

We thank A. Poliakov, A. Parolia, V. Kothari and J. Siddiqui for helpful discussions, the University of Michigan Sequencing Core for Sanger sequencing, H. Johansson (LGC-Biosearch) for initial assistance with smFISH probe design, and S. Ellison, S. Gao and K. Giles for critically reading the manuscript and submitting documents. This work was supported in part by NCI Prostate SPORE (P50CA186786 to A.M.C.) and EDRN (U01 CA214170 to A.M.C.) grants. A.M.C. is also supported by the Prostate Cancer Foundation and by the Howard Hughes Medical Institute. A.M.C. is an American Cancer Society Research Professor and a Taubman Scholar of the University of Michigan. R. Malik was supported by a Department of Defense Postdoctoral Award (W81XWH-13-1-0284). Y.Z. is supported by a Department of Defense Early Investigator Research Award (W81XWH-17-1-0134). R. Malik, M.C., Y.S.N., J.C.-Y.T. and Y.Q. were supported by the Prostate Cancer Foundation Young Investigator Award. R. Mehra was supported by a Department of Defense Idea Development Award (W81XWH-16-1-0314). Y.S.N. is supported by a University of Michigan Cellular and Molecular Biology National Research Service Award Institutional Predoctoral Training Grant. S.P. was supported by an AACR-Bayer Prostate Cancer Research Fellowship (16-40-44-PITC). L.X. is supported by a US Department of Defense Postdoctoral Fellowship (W81XWH-16-1-0195). M.B. was supported by NIH DP5 grant OD012160. G.C.S. was supported by the Department of Defense awards W81XWH-14-1-0508 and W81XWH-14-1-0509. M.U. was funded by the German Research Foundation (DFG grant BA2168/11-1 SPP 1738).

Author information

Author notes

    • Sudhanshu K. Shukla

    Present address: Department of Biosciences and Bioengineering, Indian Institute of Technology Dharwad, Dharwad, India

    • Felix Y. Feng

    Present address: Departments of Radiation Oncology, Urology, and Medicine, Helen Diller Family Comprehensive Cancer Center, University of California at San Francisco, San Francisco, CA, USA

    • Rohit Malik

    Present address: Bristol-Myers Squibb, Princeton, NJ, USA

  1. These authors contributed equally: Yajia Zhang, Sethuramasundaram Pitchiaya, Marcin Cieślik.

  2. These authors jointly supervised this work: Rohit Malik, Arul M. Chinnaiyan.

Affiliations

  1. Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA

    • Yajia Zhang
    • , Sethuramasundaram Pitchiaya
    • , Marcin Cieślik
    • , Yashar S. Niknafs
    • , Jean C.-Y. Tien
    • , Yasuyuki Hosono
    • , Matthew K. Iyer
    • , Sahr Yazdani
    • , Shruthi Subramaniam
    • , Sudhanshu K. Shukla
    • , Xia Jiang
    • , Lisha Wang
    • , Yuanyuan Qiao
    • , Lanbo Xiao
    • , Saravana M. Dhanasekaran
    • , Kristin M. Juckette
    • , Lakshmi P. Kunju
    • , Xuhong Cao
    • , Felix Y. Feng
    • , Rohit Malik
    •  & Arul M. Chinnaiyan
  2. Department of Pathology, University of Michigan, Ann Arbor, MI, USA

    • Yajia Zhang
    • , Sethuramasundaram Pitchiaya
    • , Marcin Cieślik
    • , Jean C.-Y. Tien
    • , Yuanyuan Qiao
    • , Saravana M. Dhanasekaran
    • , Lakshmi P. Kunju
    • , Rohit Mehra
    •  & Arul M. Chinnaiyan
  3. Molecular and Cellular Pathology Program, University of Michigan, Ann Arbor, MI, USA

    • Yajia Zhang
  4. Department of Computational Medicine and Bioinformatics, Ann Arbor, MI, USA

    • Yajia Zhang
    • , Matthew K. Iyer
    •  & Arul M. Chinnaiyan
  5. Department of Cellular and Molecular Biology, University of Michigan, Ann Arbor, MI, USA

    • Yashar S. Niknafs
    •  & Arul M. Chinnaiyan
  6. Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA

    • Tzu-Ying Liu
    •  & Hui Jiang
  7. Department of Computer Science and Centre for Biological Signaling Studies (BIOSS), University of Freiburg, Freiburg, Germany

    • Michael Uhl
    •  & Rolf Backofen
  8. School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada

    • Alexander R. Gawronski
  9. Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA

    • Yuanyuan Qiao
    • , Lakshmi P. Kunju
    • , Michelle T. Paulsen
    • , Mats Ljungman
    • , Hui Jiang
    • , Rohit Mehra
    • , Felix Y. Feng
    •  & Arul M. Chinnaiyan
  10. Howard Hughes Medical Institute, University of Michigan, Ann Arbor, MI, USA

    • Xuhong Cao
    •  & Arul M. Chinnaiyan
  11. New Jersey Medical School, Rutgers University, Newark, NJ, USA

    • Utsav Patel
    •  & Mona Batish
  12. Department of Medical Laboratory Sciences, University of Delaware, Newark, DE, USA

    • Mona Batish
  13. Department of Biological, Geological and Environmental Sciences, Center for Gene Regulation in Health and Disease, Cleveland State Univesity, Cleveland, OH, USA

    • Girish C. Shukla
  14. Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA

    • Michelle T. Paulsen
    • , Mats Ljungman
    •  & Felix Y. Feng
  15. Department of Urology, University of Michigan, Ann Arbor, MI, USA

    • Rohit Mehra
    • , John T. Wei
    •  & Arul M. Chinnaiyan
  16. School of Informatics and Computing, Indiana University, Bloomington, IN, USA

    • Cenk S. Sahinalp
  17. Vancouver Prostate Centre, Vancouver, British Columbia, Canada

    • Cenk S. Sahinalp
  18. Ionis Pharmaceuticals, Carlsbad, CA, USA

    • Susan M. Freier
    • , Andrew T. Watt
    •  & Shuling Guo
  19. Breast Oncology Program, University of Michigan, Ann Arbor, MI, USA

    • Felix Y. Feng

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Contributions

R. Malik, Y.Z., M.C., S.P. and A.M.C. conceived the study and designed the research. Y.Z. and R. Malik performed most of the cellular and molecular biology experiments with the assistance of Y.H., S.Y., S.S., S.K.S., L.X., X.J., S.M.D., X.C., J.T.W. and F.Y.F. M.C. performed most of the bioinformatics analyses with the help of Y.S.N. and M.K.I. S.P., U.P. and M.B. performed all smFISH work, and S.P. performed the mechanistic work-up. J.C.-Y.T. and K.M.J. carried out the in vivo mouse xenograft studies, and Y.Q. performed the 3D sphere model work. L.P.K. performed the histopathological analyses. L.W. and R. Mehra carried out RNA ISH on tissue microarrays, and T.-Y.L. and H.J. performed the statistical analysis for this technique. M.U., A.R.G., R.B. and C.S.S. performed the in silico binding predictions. S.M.F., A.T.W. and S.G. provided ASOs. G.C.S. provided the AR expression construct. M.T.P. and M.L. performed BrU and BrUChase sample preparation. Y.Z., M.C., R. Malik, S.P. and A.M.C. wrote the manuscript. All authors discussed the results and commented on the manuscript.

Competing interests

The University of Michigan has filed a patent on lncRNAs as biomarkers of cancer, and A.M.C., R. Malik, Y.Z., M.C. and S.P. are named as co-inventors. A.M.C. is a co-founder of LynxDx, which is developing lncRNA biomarkers. S.M.F., A.T.W. and S.G. are employees of Ionis Pharmaceuticals, which developed the ASOs against ARLNC1 that were used in this study.

Corresponding author

Correspondence to Arul M. Chinnaiyan.

Integrated supplementary information

  1. Supplementary Figure 1 Landscape of AR-regulated transcriptome in prostate cancer.

    (a) A schematic illustration of the procedure used to discover AR-regulated genes (ARGs) in LNCaP and VCaP prostate cancer cell lines. (b) Venn diagram indicating the overlap between AR-regulated genes in LNCaP and VCaP cells. (c) qPCR analysis of ARLNC1 expression and AR signaling gene (KLK3, TMPRSS2) expression in LNCaP cells (top panel) and VCaP cells (bottom panel), following DHT treatment of 6 hours or 24 hours. Mean ± s.e.m. are shown, n = 3. Significance was determined by two-tailed Student’s t-test. (d) Bar plot depicting the distribution of gene biotypes (protein, lncRNA, and other) of all overlapped ARGs identified in both LNCaP and VCaP cells. (e) Aggregate ChIP-Seq enrichment profile depicting AR ChIP-Seq signaling density on ARG promoters in LNCaP and VCaP cells. Representative of two biological replicates are shown. (f) Aggregate ChIP-Seq enrichment profile illustrating AR ChIP-Seq signaling density on ARG promoters in LNCaP cells following DHT stimulation, AR antagonist (enzalutamide) treatment, or BRD4 inhibitor (JQ1) treatment. Representative of two biologically replicated assays are shown. (g) Transcriptional response to DHT and enzalutamide treatment in VCaP cells, plotting AR regulated protein-coding genes (top panel), or AR regulated lncRNAs (bottom panel). (h) Motif discovery analysis of the top 250 AR ChIP-Seq peaks on AR promoters identifies a binding motif similar to the canonical AR response element. P value was calculated in MEME suite. n = 2 independent AR ChIP-Seq assays. (i) Aggregated ChIP-Seq enrichment profiles depicting ChIP-Seq signal density on direct ARG promoters for H3K27ac, H3K4me1, H3K4me3, H3K36me3, Pol II, and BRD4 in LNCaP cells. (j) Pie chart showing prostate cell line or tissue distribution of direct ARGs with AR binding at transcription start sites (TSS) in ChIP-Seq. (k,l) Cumulative distribution plots of distances between transcription start sites (TSS) of genes to nearest AR peak. (k) AR binding near ARGs in benign prostate, prostate cancer tissues (PCa), and prostate cell lines. Approximately 50% (75%) of ARGs have an AR peak within 1 kb (10 kb) from the TSS. (l) Comparison of distances between AR binding sites for ARGs and genes not regulated by AR. Globally, ARGs are positioned closer to AR binding sites.

  2. Supplementary Figure 2 ARLNC1 is prioritized as a lineage-specific, cancer-associated lncRNA in prostate cancer.

    (a) The top ten AR-regulated, localized prostate cancer-associated genes identified in Figure 2a, after applying an expression filter of at least four fold change (log2FC = 2) upon DHT stimulation and at least 1 FPKM average expression in prostate cancer tissues. Genes are listed in the order of log2-gene expression level difference between benign and localized prostate cancer tissues. (b) The top ten AR-regulated, metastatic prostate cancer-associated genes identified in Figure2b, after applying an expression filter of at least four fold change (log2FC = 2) upon DHT stimulation and at least 1 FPKM average expression in prostate cancer tissues. Genes are listed in the order of log2-gene expression level difference between benign and metastatic prostate cancer tissues. (c) Schematic illustration of the procedure used to nominate prostate lineage-specific, cancer-associated lncRNAs in prostate cancer. (d) The top 12 prostate tissue-specific, prostate cancer-associated lncRNAs identified in Figure 2c, after applying an expression filter of at least 10 FPKM in the prostate samples in the top 5% based on gene expression level. Genes are listed in the order of SSEA percentile (total n = 7,256 samples). (e) Relative expression (FPKM) of ARLNC1 across a panel of normal tissues in GTEx normal tissue RNA-seq cohort (n = 9,435 samples) (www.gtexportal.org). Box-plot definition: center, median; box limits, 1st and 3rd quartile; whiskers follow the 1.5 rule. (f) Tissue and cancer-specific expression of ARLNC1 according to MiTranscriptome. The SSEA test (total n = 7,256 samples) was used to determine whether each gene was significantly associated with a set of samples (e.g. prostate cancer) or cancer progression in a given lineage (e.g. prostate normal to prostate cancer). The genes were ranked according to their strength of association. Shown is the percentile rank for ARLNC1 (enrichment, positive; depletion, negative) across the tested sample sets/comparisons. (g) Oncomine concepts analysis of genes positively (top panel) or negatively (bottom panel) correlated with ARLNC1. The network was visualized using the Force-Directed Layout algorithm in the Cytoscape tool, with node sizes representing the number of genes in each molecular concept and node names indicating author of the primary study.

  3. Supplementary Figure 3 Characterization of ARLNC1 and its expression.

    a) Relative expression of ARLNC1 (FPKM) across 14 prostate cancer cell lines. (b) qPCR analysis of ARLNC1 expression in nine prostate cancer cell lines. Expression levels of several known prostate cancer-associated lncRNAs are also shown. Mean ± s.e.m. are graphed, n = 3. (c) Left: Representative image of ARLNC1 gene structure in AR-positive prostate cancer cells, generated from RACE analysis. Annotations of ARLNC1 in Ensembl and the Encyclopedia of DNA Elements (ENCODE) are also shown. Inset: Expression of ARLNC1 transcripts in MDA-PCa-2b cells, validated by Northern blot. ARLNC1-negative DU145 cells serve as negative control. Primer sequences for Northern blotting probe are listed in Supplementary Table 3. Right: 5’ RACE and 3’ RACE results in MDA-PCa-2b cells and LNCaP cells. Experiments were repeated 3 times with similar results. (d) smFISH images depicting localization of ARLNC1 transcripts in a panel of prostate cancer cell lines. Representative pseudocolored images of MDA-PCa-2b, LNCaP, VCaP, 22Rv1, PC3, RWPE, and DU145 cells probed for ARLNC1 (gray) or GAPDH (gray, control). Nucleus is stained with DAPI (blue). Scale bar, 5 µm; n = 3 independent experiments for each cell line. (e) Scatter plot representing the average number of ARLNC1 transcripts per cell in a panel of prostate cancer cell lines, including MDA-PCa-2b, LNCaP, VCaP, 22Rv1, PC3, RWPE, and DU145. Black line and whiskers depict the mean and s.e.m., respectively (n = 50 cells for each cell line aggregated from 3 independent experiments). (f) Representative gray-scale images of MDA-PCa-2b cells stained for DAPI (nucleus, blue) and ARLNC1, AR or GAPDH transcripts (smFISH, gray). Scale bar, 10 µm. Quantification of the number of molecules per cell and the nucleo-cytoplasmic distribution of each transcript is also represented (n = 60 cells for each sample aggregated from 3 independent experiments). Center line represents mean and error bars represent s.e.m. (g) Percentage of nuclear/cytoplasmic RNA levels of ARLNC1, ACTB, and U1, measured by qRT–PCR after subcellular fractionation of MDA-PCa-2b and LNCaP cells. U1 serves as a positive control for nuclear gene expression, while ACTB RNA serves as a positive control for cytoplasmic gene expression. The graphs show mean ± s.e.m., n = 3 independent experiments for each cell line.

  4. Supplementary Figure 4 ARLNC1 expression is regulated by AR and FOXA1.

    (a) ChIP-seq peaks of H3K4me1, MED1, BRD4, FOXA1, and NKX3-1 from LNCaP cells at the ARLNC1 promoter region. AR ChIP-seq tracks from normal prostate and prostate cancer are also shown. Motif analysis results are summarized at the bottom, suggesting possible binding of AR, FOXA1, NKX3-1, IRF1 and POU1F1 at the ARLNC1 promoter region. (b) Top panel: qPCR analysis of ARLNC1 expression in LNCaP cells, following treatment with siRNAs targeting AR, FOXA1, NKX3-1, BRD4, EZH2, LSD1, IRF1, and POU1F1. Mean ± s.e.m. are shown, n = 3. *Adjusted P = 0.0436, **Adjusted P = 0.0264, ****Adjusted P = 0.0001, compared to control siRNA (si-NT) by ANOVA analysis with Dunnett correction. Bottom panel: On-target effect of siRNAs was evaluated by qPCR analysis in the bottom panel. Mean ± s.e.m. are shown, n = 3. **P = 0.006, ***P < 0.001, compared to control siRNA (si-NT) by two-tailed Student’s t-test. (c) ChIP-PCR analysis in MDA-PCa-2b cells showing relative enrichment (ChIP/input) of AR, FOXA1, NKX3-1 or IgG over ARLNC1 promoter region or control region. Error bars represent mean ± s.e.m. (n = 3). ***Adjusted P = 0.0001 compared to negative control, by ANOVA analysis with Dunnett correction for multiple comparisons. (d) Relative expression (TPM) of AR (Left) and FOXA1 (Right) across a panel of normal tissues in GTEx normal tissue RNA-seq cohort (n = 8,745 samples). Box-plot definition: center, median; box limits, 1st and 3rd quartile; whiskers follow the 1.5 rule.

  5. Supplementary Figure 5 A positive feedback loop between ARLNC1 and AR signaling.

    (a) Reproducibility of expression profiling following 10 nM DHT treatment in MDA-PCa-2b cells. The most significant AR targets were used to derive a gene signature of the AR response. (b) Overlap between genes differentially expressed upon AR knockdown and ARLNC1 knockdown in MDA-PCa-2B cells. (c) siRNA knockdown of ARLNC1 in LNCaP cells impaired AR signaling by AR reporter gene assay. siRNA against AR served as a positive control for AR signaling inhibition. Mean ± s.e.m. are shown, n = 3. *P = 0.0233; ns, not significant. P values determined by two-tailed Student’s t-test. Veh, vehicle control. (d) qRT–PCR analysis of KLK2, KLK3, and STEAP2 in MDA-PCa-2b cells transfected with siRNAs against ARLNC1, AR, EZH2, or non-specific control. siRNA against AR serves as a positive control for inhibited AR signaling, while siRNA against EZH2 serves as a negative control. Mean ± s.e.m. are shown, n = 3. ***P < 0.001; ns, not significant, determined by ANOVA with Dunnett correction. (e) qPCR analysis of ARLNC1 and AR signaling genes in LNCaP cells (left panel) and MDA-PCa-2b cells (right panel) transfected with ARLNC1 expressing vector or control vector. Mean ± s.d. are shown, n = 3. ns, not significant, compared to vector group by two-tailed Student’s t-test.

  6. Supplementary Figure 6 Post-transcriptional regulation of AR by ARLNC1.

    a) In silico prediction of ARLNC1 RNA-binding partners, with y-axis representing log2-absolute RNA binding energy between ARLNC1 and various RNA species, and x-axis depicting log2-average expression level of these RNAs in prostate cancer. (b) Stoichiometry of ARLNC1:AR co-localization. Dotted line represents a 1:1 stoichiometry, whereas bold dotted line represents 2:1 stoichiometry. Each dot represents the number of molecules when AR and ARLNC1 co-localizes in any given cell (n = 1,000 co-localized spots from 9 independent samples). (c) Representative pseudo-colored images of U2-OS cells ectopically expressing ARLNC1 alone (green, left and right panels), or both ARLNC1 and AR (red, middle panel), and stained for the appropriate transcripts and DAPI (blue). Scale bar, 10 µm. 5.5 x 5.5 µm2 inset depicts zoomed-in view of orange box in the image. (d,e) Representative pseudo-colored images of MDA-PCa-2b cells or U2-OS cells stained for DAPI (nucleus, blue) and ARLNC1 (green) and AR transcripts (red) following treatment of blocking ASOs targeting the ARLNC1:AR 3’UTR interaction. U2-OS cells were transfected with ARLNC1 and AR expression vector prior to blocking ASO treatment. Scale bar, 10 µm. Quantification of colocalization in U2-OS cells are depicted in e as a box plot, whereas quantifications of co-localization in MDA-PCa-2b cells are in Figure 6f. Center line and whiskers depict the median and range respectively and box extends from 25th to 75th percentiles (n = 60 cells for each sample aggregated from 3 independent experiments). P value computed by two-tailed Student’s t-test. (f) qPCR analysis of ARLNC1, AR and AR signaling gene (KLK2, KLK3, NKX3-1, TMPRSS2, FKBP5) expression in LNCaP cells transfected with control ASO or blocking oligos targeting the interaction sites between ARLNC1 and AR 3’UTR. Mean ± s.e.m. are shown, n = 3. Two-tailed Student’s t-test was used to determine significance. (g) Half-life of GAPDH, AR, ARLNC1, and MYC RNA transcripts in LNCaP cells. Cells were incubated with actinomycin D for the indicated times, and target RNA quantities were evaluated by qPCR. RNA half-life was calculated by linear regression analysis. n = 3. At different time points, mean ± s.d. are shown. (h,i) Quantification of ARLNC1 levels, as measured by smFISH, after treatment of MDA-PCa-2b cells with siRNA against AR (si-AR), siRNA against ARLNC1 (si-ARLNC1-3), ASO against ARLNC1 (ASO-ARLNC1-1) or blocking ASO against AR-ARLNC1 colocalizing segment (ASO-Blocking). Data were normalized to si-NT (h) or ASO-Control (i). Mean ± s.e.m. are shown, n = 3 independent experiments and 60 cells analyzed for each sample. P values were computed by two-tailed Student’s t-test. (j) Nucleo-cytoplasmic distribution of ARLNC1 after appropriate treatment of MDA-PCa-2b cells with siRNA against AR (si-AR), siRNA against ARLNC1 (si-ARLNC1-3), ASO against ARLNC1 (ASO-ARLNC1-1) or blocking ASO against AR-ARLNC1 colocalizing segment (ASO-Blocking). Mean ± s.e.m. are shown, n = 3 independent experiments and 60 cells analyzed for each sample. P values were computed by comparing to si-NT or ASO-Control treated cells, by two-tailed Student’s t-test. (k,l) Quantification of AR transcript levels, as measured by smFISH, after treatment of MDA-PCa-2b cells with siRNA against AR (si-AR), siRNA against ARLNC1 (si-ARLNC1-3), ASO against ARLNC1 (ASO-ARLNC1-1) or blocking ASO against AR-ARLNC1 colocalizing segment (ASO-Blocking). Data were normalized to si-NT (k) or ASO-Control (l). Mean ± s.e.m. are shown, n = 3 independent experiments and 60 cells analyzed for each sample. P values were computed by two-tailed Student’s t-test. (m,n) BrU-seq alignment track (m) and BrUChase-seq alignment track (n) at AR gene locus. Data acquired from MDA-PCa-2b cells, following treatment with control siRNA (si-NT) or si-ARLNC1.

  7. Supplementary Figure 7 Evaluation of the phenotypic effect of ARLNC1 in vitro.

    (a) Knockdown efficacy of three independent siRNAs targeting ARLNC1 in MDA-PCa-2b cells. Mean ± s.e.m. are shown, n = 3. ***P = 0.0001 determined by ANOVA with Dunnett correction. (b) ARLNC1 siRNA transfection has no effect on cell proliferation in AR-negative prostate cancer cells, PC3. This serves as an additional negative control for Figure 8a. Mean ± s.e.m. are shown, n = 6. P value not significant (ns, P = 0.7207) compared to si-NT treated cells, by one way ANOVA analysis. (c) Increased apoptosis observed in MDA-PCa-2b and LNCaP cells 48 hours after transfected with ARLNC1 siRNAs. ARLNC1-negative PNT2 cells serve as negative control. Fold change of apoptosis was calculated relative to si-NT treated samples. n = 3 independent cell cultures. Mean ± s.e.m. are shown. P values determined by two-tailed Student’s t-test. (d) Positions of ARLNC1 ASO-targeting sites (1 to 6) is indicated on the schematic representation of the ARLNC1 transcript. (e) MDA-PCa-2b cells were transfected with six independent ASOs targeting ARLNC1. Knockdown efficacy was evaluated by qPCR analysis. Mean ± s.e.m. are shown, n = 3. ***P = 0.0001 determined by ANOVA with Dunnett correction. (f) Correlation analysis of siRNA-mediated knockdown and ASO-mediated knockdown of ARLNC1 among replicated microarray experiments in MDA-PCa-2b cells (n = 2 biological replicates per ASO treatment group and n = 3 biological replicates per siRNA treatment group). (g) Free-uptake efficacy of ARLNC1 ASOs was examined in MDA-PCa-2B cells 72 hours post ASO addition to the culture medium (10 μM). ARLNC1 expression was evaluated by qPCR analysis. Mean ± s.e.m. are shown, n = 3. ***P = 0.0001, **P = 0.0032; determined by ANOVA with Dunnett correction. (h) Free-uptake treatment of ASOs targeting ARLNC1 resulted in retarded growth of MDA-PCa-2b cells in vitro. ARLNC1-negative prostate cell line PNT2 served as a negative control. Mean ± s.e.m. are shown, n = 6. P values were determined by two-tailed Student’s t-test (ns: not significant). (i,j) ARLNC1 ASOs inhibit MDA-PCa-2b cell proliferation in 3D sphere models. Cells were harvested at the end of the experiment and ARLNC1 expression was evaluated by qPCR analysis. Mean ± s.d. are shown, n = 6. ***P < 0.0001 compared to control ASO treated cells, by two-tailed Student’s t-test.

  8. Supplementary Figure 8 Knockdown of ARLNC1 by ASOs inhibits tumor growth in vivo.

    (a) Representative image of in situ hybridization for ARLNC1 in MDA-PCa-2b cell line-derived xenograft. H&E staining is shown for tumor tissue and murine kidney. Experiments were repeated 5 times with similar results. (b) qRT–PCR analysis of ARLNC1, NKX3-1 and AR in MDA-PCa-2b xenografts treated with control ASO (n = 15) or ASO targeting ARLNC1 (n = 13). Data were normalized to a housekeeping gene (GAPDH), and the average expression level in control ASO group was set to 1. Mean ± s.e.m. are shown. *P = 0.0483, ***P = 0.0004; compared to control group by two-tailed Student’s t-test. (c) Left: Immunoblots of AR and GAPDH in MDA-PCa-2b xenografts treated with control ASO (n = 15) or ASO targeting ARLNC1 (n = 13). Right: Relative intensity of the bands was quantified using ImageJ. Mean ± s.e.m. are shown. **P < 0.005, ns: not significant; compared to control ASO treated group by two-tailed Student’s t-test. Uncropped images are shown in Supplementary Figure 9. (d) Left: Immunohistochemistry staining for Ki67 in MDA-PCa-2b xenograft treated with control ASO or ASO against ARLNC1. Right: Summary of Ki67 tumor staining for control (n = 15) or ARLNC1 ASO-treated tumors (n = 13) shows significant difference in Ki67 staining intensity. Mean ± s.e.m. are shown. P value determined by two-tailed Student’s t-test. (e) Percent change in mice body weight over the time of ASO treatment in MDA-PCa-2b xenografts treated with control ASO (n = 15) or ASO targeting ARLNC1 (n = 13). Mean ± s.e.m. are shown. Significance was determined by two-tailed Student’s t-test. (f) ARLNC1 expression levels are not associated with Gleason score. AURKA expression levels are significantly associated with Gleason score. Based on n = 500 samples from TCGA. Significance determined by two-sided t-test. Box-plot definition: center, median; box limits, 1st and 3rd quartile; whiskers follow the 1.5 rule. (g) Curated pathway signature analysis between ARLNC1 high (top-quartile) and ARLNC1 low (bottom-quartile) mCRPC samples (n = 100). Z-score represents the effect size and direction for the relative signature enrichment. (h) Signatures associated with prostate cancer and luminal differentiation were selected from the MSigDB and contrasted between ARLNC1 high (top-quartile) and ARLNC1 low (bottom-quartile) mCRPC samples (n = 100). Z-score represents the effect size and direction for the relative signature enrichment. For each signature, “Up” signifies that a signature is expected to be up-regulated in the tested condition, whereas “Dn” signifies that a signature is expected to be down-regulated. (i) Cancer hallmark signature analysis between ARLNC1 high expression (top-quartile) and ARLNC1 low expression (bottom-quartile) mCRPC samples (n = 100 samples). Z-score represents the effect-size and direction for the relative signature enrichment, determined by two-sided Random-Set test. (j) Tumor content estimated from whole-exome sequencing is compared between ARLNC1 high (top-quartile) and ARLNC1 low (bottom-quartile) expression in mCRPC samples (n = 100). Box-plot definition: center, median; box limits, 1st and 3rd quartile; whiskers follow the 1.5 rule.

  9. Supplementary Figure 9

    Uncropped scans of western blotting images.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–9

  2. Reporting Summary

  3. Supplementary Table 1

    Androgen-regulated gene (ARG) list

  4. Supplementary Table 2

    AR-regulated gene signature identified from MDA-PCa-2b cells

  5. Supplementary Table 3

    Sequences of primers, siRNAs and shRNAs used in this study

  6. Supplementary Table 4

    ARLNC1 sequence for overexpression

  7. Supplementary Table 5

    Single-molecule (smFISH) probe sequences

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DOI

https://doi.org/10.1038/s41588-018-0120-1