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Characterization of a castrate-resistant prostate cancer xenograft derived from a patient of West African ancestry

Abstract

Background

Prostate cancer is a clinically and molecularly heterogeneous disease, with highest incidence and mortality among men of African ancestry. To date, prostate cancer patient-derived xenograft (PCPDX) models to study this disease have been difficult to establish because of limited specimen availability and poor uptake rates in immunodeficient mice. Ancestrally diverse PCPDXs are even more rare, and only six PCPDXs from self-identified African American patients from one institution were recently made available.

Methods

In the present study, we established a PCPDX from prostate cancer tissue from a patient of estimated 90% West African ancestry with metastatic castration resistant disease, and characterized this model’s pathology, karyotype, hotspot mutations, copy number, gene fusions, gene expression, growth rate in normal and castrated mice, therapeutic response, and experimental metastasis.

Results

This PCPDX has a mutation in TP53 and loss of PTEN and RB1. We have documented a 100% take rate in mice after thawing the PCPDX tumor from frozen stock. The PCPDX is castrate- and docetaxel-resistant and cisplatin-sensitive, and has gene expression patterns associated with such drug responses. After tail vein injection, the PCPDX tumor cells can colonize the lungs of mice.

Conclusion

This PCPDX, along with others that are established and characterized, will be useful pre-clinically for studying the heterogeneity of prostate cancer biology and testing new therapeutics in models expected to be reflective of the clinical setting.

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Fig. 1: Patient clinical history.
Fig. 2: Patient and PDX immunohistochemical profiles.
Fig. 3: PCPDX DNA and RNA analysis.
Fig. 4: Response of the PCPDX and individual PCPDX tumors to clinically relevant doses of standard-of-care prostate cancer therapies.
Fig. 5: PCPDX gene expression post-treatment with enzalutamide, docetaxel, or cisplatin.
Fig. 6: H&E of mouse lung tissue with metastatic tumor foci.

Data availability

All RNA-sequencing data are available from GEO (GSE146402).

References

  1. 1.

    Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. Cancer J Clin. 2019;69:7–34.

    Article  Google Scholar 

  2. 2.

    Cancer Genome Atlas Research Network. The molecular taxonomy of primary prostate cancer. Cell. 2015;163:1011–25.

    Article  Google Scholar 

  3. 3.

    Irshad S, Bansal M, Castillo-Martin M, Zheng T, Aytes A, Wenske S, et al. A molecular signature predictive of indolent prostate cancer [published correction appears in Sci Transl Med. 2013 Sep 18;5(203):203er9]. Sci Transl Med. 2013;5:202ra122.

    Article  Google Scholar 

  4. 4.

    Robbins AS, Whittemore AS, Thom DH. Differences in socioeconomic status and survival among white and black men with prostate cancer. Am J Epidemiol. 2000;151:409–16.

    CAS  Article  Google Scholar 

  5. 5.

    Pietro GD, Chornokur G, Kumar NB, Davis C, Park JY. Racial differences in the diagnosis and treatment of prostate cancer. Int Neurourol J. 2016;20:S112–S119. https://doi.org/10.5213/inj.1632722.361

    Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Polite BN, Adams-Campbell LL, Brawley OW, Bickell N, Carethers JM, Flowers CR, et al. Charting the future of cancer health disparities research: a position statement from the American Association for Cancer Research, the American Cancer Society, the American Society of Clinical Oncology, and the National Cancer Institute. CA Cancer J Clin. 2017;67:353–61.

    Article  Google Scholar 

  7. 7.

    Navone NM, van Weerden WM, Vessella RL, Williams ED, Wang Y, Isaacs JT, et al. Movember gap1 pdx project: an international collection of serially transplantable prostate cancer patient-derived xenograft (pdx) models. Prostate. 2018;78:1262–82.

    CAS  Article  Google Scholar 

  8. 8.

    Palanisamy N, Yang J, Shepherd PDA, Li-Ning-Tapia EM, Labanca E, Manyam GC, et al. The MD Anderson prostate cancer patient-derived xenograft series (MDA PCa PDX) captures the molecular landscape of prostate cancer and facilitates marker-driven therapy development. Clin Cancer Res. 2020. https://doi.org/10.1158/1078-0432.ccr-20-0479.

  9. 9.

    Bluemn EG, Coleman IM, Lucas JM, Coleman RT, Hernandez-Lopez S, Tharakan R, et al. Androgen receptor pathway-independent prostate cancer is sustained through FGF signaling. Cancer Cell. 2017;32:474–89. e6.

    CAS  Article  Google Scholar 

  10. 10.

    Gao H, Korn JM, Ferretti S, Monahan JE, Wang Y, Singh M, et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat Med. 2015;21:1318–25. https://doi.org/10.1038/nm.3954.

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Russell PJ, Russell P, Rudduck C, Tse BW, Williams ED, Raghavan D. Establishing prostate cancer patient derived xenografts: lessons learned from older studies. Prostate. 2015;75:628–36. https://doi.org/10.1002/pros.22946.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Zhao H, Nolley R, Chen Z, Peehl DM. Tissue slice grafts: an in vivo model of human prostate androgen signaling. Am J Pathol. 2010;177:229–39. https://doi.org/10.2353/ajpath.2010.090821.

    Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Zhao H, Thong A, Nolley R, Reese SW, Santos J, Ingles A, et al. Patient-derived tissue slice grafts accurately depict response of high-risk primary prostate cancer to androgen deprivation therapy. J Transl Med. 2013;11:199. https://doi.org/10.1186/1479-5876-11-199.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Wang Y, Revelo MP, Sudilovsky D, Cao M, Chen WG, Goetz L, et al. Development and characterization of efficient xenograft models for benign and malignant human prostate tissue. Prostate. 2005;64:149–59.

    CAS  Article  Google Scholar 

  15. 15.

    Priolo C, Agostini M, Vena N, Ligon AH, Fiorentino M, Shin E, et al. Establishment and genomic characterization of mouse xenografts of human primary prostate tumors. Am J Pathol. 2010;176:1901–13.

    CAS  Article  Google Scholar 

  16. 16.

    Lin D, Wyatt AW, Xue H, Wang Y, Dong X, Haegart A. et al. High fidelity patient-derived xenografts for accelerating prostate cancer discovery and drug development. Cancer Res. 2014;74:1272–83. https://doi.org/10.1158/0008-5472.CAN-13-2921-T.

    CAS  Article  PubMed  Google Scholar 

  17. 17.

    Goldstein AS, Drake JM, Burnes DL, Finley DS, Zhang H, Reiter RE, et al. Purification and direct transformation of epithelial progenitor cells from primary human prostate. Nat Protoc. 2011;6:656–67.

    CAS  Article  Google Scholar 

  18. 18.

    Uronis JM, Osada T, McCall S, Yang XY, Mantyh C, Morse MA, et al. Histological and molecular evaluation of patient-derived colorectal cancer explants. PLoS One. 2012;7:e38422.

    CAS  Article  Google Scholar 

  19. 19.

    Kim MK, Osada T, Barry WT, Yang XY, Freedman JA, Tsamis KA, et al. Characterization of an oxaliplatin sensitivity predictor in a preclinical murine model of colorectal cancer. Mol Cancer Ther. 2012;11:1500–9.

    CAS  Article  Google Scholar 

  20. 20.

    Nguyen HM, Vessella RL, Morrissey C, Brown LG, Coleman IM, Higano CS, et al. LuCaP prostate cancer patient-derived xenografts reflect the molecular heterogeneity of advanced disease an-d serve as models for evaluating cancer therapeutics. Prostate. 2017;77:654–71.

    CAS  Article  Google Scholar 

  21. 21.

    Lawrence MG, Obinata D, Sandhu S. Patient-derived models of abiraterone- and enzalutamide-resistant prostate cancer reveal sensitivity to ribosome-directed therapy. Eur Urol. 2018;74:562–72. https://doi.org/10.1016/j.eururo.2018.06.020.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Li Q, Deng Q, Chao HP, Liu X, Lu Y, Lin K, et al. Linking prostate cancer cell AR heterogeneity to distinct castration and enzalutamide responses. Nat Commun. 2018;9:3600.

    Article  Google Scholar 

  23. 23.

    Pienta KJ, Abate-Shen C, Agus DB, Attar RM, Chung LWK, Greenberg NM, et al. The current state of preclinical prostate cancer animal models. Prostate. 2008;68:629–39.

    Article  Google Scholar 

  24. 24.

    Wise JP Sr, Wise SS, Little JE. The cytotoxicity and genotoxicity of particulate and soluble hexavalent chromium in human lung cells. Mutat Res. 2002;517:221–9.

    CAS  Article  Google Scholar 

  25. 25.

    Al-Alem U, Rauscher G, Shah E, Batai K, Mahmoud A, Beisner E, et al. Association of genetic ancestry with breast cancer in ethnically diverse women from Chicago. PLoS ONE. 2014;9:e112916.

    Article  Google Scholar 

  26. 26.

    Gupta S, Li J, Kemeny G, Bitting RL, Beaver J, Somarelli JA, et al. Whole genomic copy number alterations in circulating tumor cells from men with abiraterone or enzalutamide-resistant metastatic castration-resistant prostate cancer. Clin Cancer Res. 2017;23:1346–57. https://doi.org/10.1158/1078-0432.CCR-16-1211.

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    Andrews S. FastQC a quality control tool for high throughput sequence data. 2014. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/.

  28. 28.

    Ewels P, Magnusson M, Lundin S, Kaller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32:3047–8.

    CAS  Article  Google Scholar 

  29. 29.

    Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.

    CAS  Article  Google Scholar 

  30. 30.

    Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski Z, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.

    CAS  Article  Google Scholar 

  31. 31.

    Anders S, Pyl PT, Huber W. HTSeq-a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–9.

    CAS  Article  Google Scholar 

  32. 32.

    Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11:R106.

    CAS  Article  Google Scholar 

  33. 33.

    Benjamini Y, Hochberg Y. Controlling the false discovery rate - a practical and powerful approach to multiple testing. J R Stat Soc Ser B-Methodol. 1995;57:289–300.

    Google Scholar 

  34. 34.

    Bass A, Storey J. qvalue: Q-value estimation for false discovery rate control. R (published bioinformatics package) package version 2.8.0, 2015. http://github.com/jdstorey/qvalue.

  35. 35.

    Haas B, Dobin A, Stransky N, Li B, Yang X, Tickle T, et al. STAR-fusion: fast and accurate fusion transcript detection from RNA-Seq. bioRxiv. 2017. https://doi.org/10.1101/120295.

  36. 36.

    Lagstad S. chimeraviz: visualization tools for gene fusions. R package version 1.10.0, 2019. https://www.bioconductor.org/packages/release/bioc/html/chimeraviz.html.

  37. 37.

    R Core Team. R: a language and environment for statistical computing. Vienna, Austria; R Core Team: 2019.

  38. 38.

    Gentlema R, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004;5:R80.

    Article  Google Scholar 

  39. 39.

    Xie Y. Dynamic documents with R and knitr. 2nd ed. Boca Raton: CRC Press/Taylor and Francis; 2015. xxvii, 266.

  40. 40.

    Chang JT, Nevins JR. GATHER: a systems approach to interpreting genomic signatures. Bioinformatics. 2006;22:2926–33. https://doi.org/10.1093/bioinformatics/btl483.

    CAS  Article  PubMed  Google Scholar 

  41. 41.

    Krämer A, Green J, Pollard J Jr, Tugendreich S. Causal analysis approaches in ingenuity pathway analysis. Bioinformatics. 2014;30:523–30.

    Article  Google Scholar 

  42. 42.

    Robinson D, Van Allen EM, Wu YM, Schultz N, Lonigro RJ, Mosquera JM, et al. Integrative clinical genomics of advanced prostate cancer. Cell. 2015;161:1215–28.

    CAS  Article  Google Scholar 

  43. 43.

    Zhu Z, Chung YM, Sergeeva O, Kepe V, Berk M, Li J, et al. Loss of dihydrotestosterone-inactivation activity promotes prostate cancer castration resistance detectable by functional imagingJ. Biol Chem. 2018;293:17829.

    CAS  Article  Google Scholar 

  44. 44.

    Leinonen KA, Saramäki OR, Furusato B, Kimura T, Takahashi H, Egawa S, et al. Loss of PTEN is associated with aggressive behavior in ERG-positive prostate cancer. Cancer Epidemiol Biomark Prev. 2013;22:2333–44.

    CAS  Article  Google Scholar 

  45. 45.

    Kregel S, Kiriluk KJ, Rosen AM, Cai Y, Reyes EE, Otto KB, et al. Sox2 is an androgen receptor-repressed gene that promotes castration-resistant prostate cancer. PLoS ONE. 2013;8:e53701.

    CAS  Article  Google Scholar 

  46. 46.

    Mu P, Zhang Z, Benelli M, Karthaus W, Hoover E, Chen C, et al. SOX2 promotes lineage plasticity and antiandrogen resistance in TP53- and RB1-deficient prostate cancer. Science. 2017;355:84–88. https://doi.org/10.1126/science.aah4307.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Lee M, Williams KA, Hu Y, Andreas J, Patel SJ, Zhang S, et al. GNL3 and SKA3 are novel prostate cancer metastasis susceptibility genes. Clin Exp Metastasis. 2015;32:769–82.

    CAS  Article  Google Scholar 

  48. 48.

    Magi-Galluzzi C, Tsusuki T, Elson P, Simmerman K, LaFargue C, Esgueva R, et al. TMPRSS2-ERG gene fusion prevalence and class are significantly different in prostate cancer of Caucasian, African-American and Japanese patients. Prostate. 2011;71:489–97.

    CAS  Article  Google Scholar 

  49. 49.

    Crea F, Quagliata L, Michael A, Liu H, Frumento P, Azad AA. et al. Integrated analysis of the prostate cancer small-nucleolar transcriptome reveals SNORA55 as a driver of prostate cancer progression. Mol Oncol. 2016;10:693–703. https://doi.org/10.1016/j.molonc.2015.12.010.

    CAS  Article  PubMed  Google Scholar 

  50. 50.

    Rocha CRR, Silva MM, Quinet A, Cabral-Neto JB, Menck CFM. DNA repair pathways and cisplatin resistance: an intimate relationship. Clinics. 2018;73:e478s. https://doi.org/10.6061/clinics/2018/e478s.

    Article  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Abida W, Cyrta J, Heller G, Prandi D, Armenia J, Coleman I. et al. Genomic correlates of clinical outcome in advanced prostate cancer. Proc Natl Acad Sci USA. 2019;116:11428–36. https://doi.org/10.1073/pnas.1902651116.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Xu L, Tang H, Wang K, Zheng Y, Feng J, Dong H, et al. Pharmacological inhibition of EZH2 combined with DNA-damaging agents interferes DNA damage response MM cells. Mol Med Rep. 2019;19:4249–55.

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Zhan J, Wang P, Li S, Song J, He H, Wang Y. et al. HOXB13 networking with ABCG1/EZH2/Slug mediates metastasis and confers resistance to cisplatin in lung adenocarcinoma patients. Theranostics. 2019;9:2084–99. https://doi.org/10.7150/thno.29463. 76.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Qin X, Guo H, Wang X, Zhu X, Yan M, Wang X, et al. Exosomal miR-196a derived from cancer-associated fibroblasts confers cisplatin resistance in head and neck cancer through targeting CDKN1B and ING5. Genome Biol. 2019;20:12. https://doi.org/10.1186/s13059-018-1604-0.

    Article  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Shen DW, Pouliot LM, Gillet JP, Ma W, Johnson AC, Hall MD, et al. The transcription factor GCF2 is an upstream repressor of the small GTPAse RhoA, regulating membrane protein trafficking, sensitivity to doxorubicin, and resistance to cisplatin. Mol Pharm. 2012;9:1822–33. https://doi.org/10.1021/mp300153z.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Street CA, Routhier AA, Spencer C, Perkins AL, Masterjohn K, Hackathorn A, et al. Pharmacological inhibition of Rho-kinase (ROCK) signaling enhances cisplatin resistance in neuroblastoma cells. Int J Oncol. 2010;37:1297–305. https://doi.org/10.3892/ijo_00000781.

    CAS  Article  PubMed  Google Scholar 

  57. 57.

    Yu C, Wu G, Li R, Gao L, Yang F, Zhao Y, et al. NDRG2 acts as a negative regulator downstream of androgen receptor and inhibits the growth of androgen-dependent and castration-resistant prostate cancer. Cancer Biol Ther. 2015;16: 287–96. https://doi.org/10.1080/15384047.2014.1002348.

  58. 58.

    Elvenes J, Thomassen EI, Johnsen SS, Kaino K, Sjøttem E, Johansen T. Pax6 represses androgen receptor-mediated transactivation by inhibiting recruitment of the coactivator SPBP. PloS One. 2011;6:e24659. https://doi.org/10.1371/journal.pone.0024659.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Shang Z, Niu Y, Cai Q, Chen J, Tian J, Yeh S. et al. Human kallikrein 2 (KLK2) promotes prostate cancer cell growth via function as a modulator to promote the ARA70-enhanced androgen receptor transactivation. Tumor Biol. 2014;35:1881–90. https://doi.org/10.1007/s13277-013-1253-6.

    CAS  Article  Google Scholar 

  60. 60.

    Patel R, Brzezinska EA, Repiscak P, Ahmad I, Mui E, Gao M, et al. Activation of β-catenin cooperates with loss of Pten to drive AR-independent castration-resistant prostate cancer. Cancer Res. 2020; 576–90. https://doi.org/10.1158/0008-5472.CAN-19-1684.

  61. 61.

    Nesbitt H, Browne G, O’Donovan KM, Byrne NM, Worthington J, McKeown SR. et al. Nitric oxide up-regulates RUNX2 in LNCaP prostate tumours: implications for tumour growth in vitro and in vivo. J Cell Physiol. 2016;23:473–82. https://doi.org/10.1002/jcp.25093.

    CAS  Article  Google Scholar 

  62. 62.

    Yang Y, Bai Y, He Y, Zhao Y, Chen J, Ma L. et al. PTEN loss promotes intratumoral androgen synthesis and tumor microenvironment remodeling via aberrant activation of RUNX2 in castration-resistant prostate cancer. Clin Cancer Res. 2018;24:834–46. https://doi.org/10.1158/1078-0432.CCR-17-2006.

    CAS  Article  PubMed  Google Scholar 

  63. 63.

    Ozaki T, Yu M, Yin D, Sun D, Zhu Y, Bu Y, et al. Impact of RUNX2 on drug-resistant human pancreatic cancer cells with p53 mutations. BMC Cancer. 2018;18:309. https://doi.org/10.1186/s12885-018-4217-9.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Yu L, Su YS, Zhao J, Wang H, Li W. Repression of NR4A1 by a chromatin modifier promotes docetaxel resistance in PC-3 human prostate cancer cells. FEBS Lett. 2013;587:2542–51.

    CAS  Article  Google Scholar 

  65. 65.

    Elkin M, Vlodavsky I. Tail vein assay of cancer metastasis. Curr Protoc Cell Biol. 2001. https://doi.org/10.1002/0471143030.cb1902s12.

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Acknowledgements

We acknowledge the BioRepository & Precision Pathology Center (BRPC), a shared resource of the Duke University School of Medicine and Duke Cancer Institute, for providing access to the human biospecimens used under Institutional Review Board oversight in this work, the assistance of the Duke University Health System Clinical Molecular Diagnostics Laboratory, Duke Sequencing and Genomic Technologies Shared Resource, and the Duke Cancer Institute Bioinformatics Shared Resource, and Bonnie LaCroix, laboratory manager. Support: P30 Cancer Center Support Grant (P30 CA014236), NIH Basic Research in Cancer Health Disparities R01 Award R01CA220314 to SRP PI, JAF and DSH Co-I, DJG and KO Collaborator, WCF Pathologist.

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BMP: Conceptualization, Investigation, Formal Analysis, Writing, Visualization; WCF: Investigation, Formal Analysis, Writing, Visualization; TA: Formal Analysis, Writing, Visualization; JAS: Methodology, Writing; KEW: Methodology, Writing; SG: Investigation, Formal Analysis, Writing, Visualization; SW: Investigation, Formal Analysis, Writing, Visualization; JPW: Methodology, Writing; XQ: Formal Analysis, Writing, Visualization; DZ: Formal Analysis, Writing, Visualization; LX: Methodology; YL: Methodology; XC: Methodology; BAI: Conceptualization, Resources, Writing; SJM: Resources, Writing; JH: Conceptualization, Writing; RAK: Investigation, Formal Analysis, Writing, Visualization; KO: Conceptualization, Validation, Writing; SG: Conceptualization, Validation, Writing; AJA: Conceptualization, Writing; DJG: Conceptualization, Writing; SRP: Conceptualization, Writing, Supervision, Funding Acquisition; DSH: Conceptualization, Writing, Supervision, Project Administration, Funding Acquisition; JAF: Conceptualization, Writing, Supervision, Project Administration, Funding Acquisition.

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Correspondence to Jennifer A. Freedman.

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Patierno, B.M., Foo, WC., Allen, T. et al. Characterization of a castrate-resistant prostate cancer xenograft derived from a patient of West African ancestry. Prostate Cancer Prostatic Dis (2021). https://doi.org/10.1038/s41391-021-00460-y

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