Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Patient-derived tumour models for personalized therapeutics in urological cancers

Abstract

Preclinical knowledge of dysregulated pathways and potential biomarkers for urological cancers has undergone limited translation into the clinic. Moreover, the low approval rate of new anticancer drugs and the heterogeneous drug responses in patients indicate that current preclinical models do not always reflect the complexity of malignant disease. Patient-derived tumour models used in preclinical uro-oncology research include 3D culture systems, organotypic tissue slices and patient-derived xenograft models. Technological innovations have enabled major improvements in the capacity of these tumour models to reproduce the clinical complexity of urological cancers. Each type of patient-derived model has inherent advantages and limitations that can be exploited, either alone or in combination, to gather specific knowledge on clinical challenges and address unmet clinical needs. Nevertheless, few opportunities exist for patients with urological cancers to benefit from personalized therapeutic approaches. Clinical validation of experimental data is needed to facilitate the translation and implementation of preclinical knowledge into treatment decision making.

Key points

  • Personalized therapeutic approaches currently have limited use in uro-oncology clinics.

  • Discrepancies between preclinical data and clinical outcomes, high drug attrition rates and heterogeneous drug responses indicate the need for additional clinically relevant patient-derived tumour models including 3D cultures, organotypic tissue slices and patient-derived xenograft models.

  • Each patient-derived model has advantages and limitations and can be used alone or in combination to gather knowledge on clinical challenges in uro-oncology.

  • Co-clinical trials and cross-validation of preclinical results with patient outcomes are expected to advance the implementation of patient-derived models in treatment decision-making.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: 3D culture systems.
Fig. 2: Organotypic tissue slice models.
Fig. 3: PDX models.
Fig. 4: Contribution and translational value of preclinical patient-derived models.

References

  1. 1.

    Dy, G. W., Gore, J. L., Forouzanfar, M. H., Naghavi, M. & Fitzmaurice, C. Global burden of urologic cancers, 1990–2013. Eur. Urol. 71, 437–446 (2017).

    PubMed  Google Scholar 

  2. 2.

    Bray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68, 394–424 (2018).

    PubMed  Google Scholar 

  3. 3.

    Spratlin, J. L., Serkova, N. J. & Eckhardt, S. G. Clinical applications of metabolomics in oncology: a review. Clin. Cancer Res. 15, 431–440 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Zhang, A., Yan, G., Han, Y. & Wang, X. Metabolomics approaches and applications in prostate cancer research. Appl. Biochem. Biotechnol. 174, 6–12 (2014).

    CAS  PubMed  Google Scholar 

  5. 5.

    Yadav, S. S., Li, J., Lavery, H. J., Yadav, K. K. & Tewari, A. K. Next-generation sequencing technology in prostate cancer diagnosis, prognosis, and personalized treatment. Urol. Oncol. 33, 267.e1–e13 (2015).

    CAS  Google Scholar 

  6. 6.

    Mottet, N. et al. EAU-ESTRO-SIOG guidelines on prostate cancer. Part 1: screening, diagnosis, and local treatment with curative intent. Eur. Urol. 71, 618–629 (2017).

    PubMed  Google Scholar 

  7. 7.

    Lowrance, W. et al. Adavanced prostate cancer: AUA/ASTRO/SUO guideline. AUA https://www.auanet.org/guidelines/advanced-prostate-cancer (2020).

  8. 8.

    Yossepowitch, O. Digital rectal examination remains an important screening tool for prostate cancer. Eur. Urol. 54, 483–484 (2008).

    PubMed  Google Scholar 

  9. 9.

    Hessels, D. et al. DD3(PCA3)-based molecular urine analysis for the diagnosis of prostate cancer. Eur. Urol. 44, 8–15 (2003).

    CAS  PubMed  Google Scholar 

  10. 10.

    Deras, I. L. et al. PCA3: a molecular urine assay for predicting prostate biopsy outcome. J. Urol. 179, 1587–1592 (2008).

    PubMed  Google Scholar 

  11. 11.

    Nakanishi, H. et al. PCA3 molecular urine assay correlates with prostate cancer tumor volume: implication in selecting candidates for active surveillance. J. Urol. 179, 1804–1809; discussion 1809–1810 (2008).

    PubMed  Google Scholar 

  12. 12.

    Hessels, D. et al. Predictive value of PCA3 in urinary sediments in determining clinico-pathological characteristics of prostate cancer. Prostate 70, 10–16 (2010).

    CAS  PubMed  Google Scholar 

  13. 13.

    Van Neste, L. et al. Detection of high-grade prostate cancer using a urinary molecular biomarker-based risk score. Eur. Urol. 70, 740–748 (2016).

    PubMed  Google Scholar 

  14. 14.

    McKiernan, J. et al. A novel urine exosome gene expression assay to predict high-grade prostate cancer at initial biopsy. JAMA Oncol. 2, 882–889 (2016).

    PubMed  Google Scholar 

  15. 15.

    Oeyen, E. et al. Bladder cancer diagnosis and follow-up: the current status and possible role of extracellular vesicles. Int. J. Mol. Sci. 20, 821 (2019).

    CAS  PubMed Central  Google Scholar 

  16. 16.

    Ljungberg, B. et al. EAU guidelines on renal cell carcinoma: 2014 update. Eur. Urol. 67, 913–924 (2015).

    PubMed  Google Scholar 

  17. 17.

    Pastore, A. L. et al. Serum and urine biomarkers for human renal cell carcinoma. Dis. Markers 2015, 251403 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Scelo, G. et al. KIM-1 as a blood-based marker for early detection of kidney cancer: a prospective nested case-control study. Clin. Cancer Res. 24, 5594–5601 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Sim, S. H. et al. Prognostic utility of pre-operative circulating osteopontin, carbonic anhydrase IX and CRP in renal cell carcinoma. Br. J. Cancer 107, 1131–1137 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Mak, I. W., Evaniew, N. & Ghert, M. Lost in translation: animal models and clinical trials in cancer treatment. Am. J. Transl. Res. 6, 114–118 (2014).

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Box, G.E.P. & Draper, N. R. Empirical Model-Building and Response Surfaces (Wiley, 1987).

  22. 22.

    Gheibi, P. et al. Microchamber cultures of bladder cancer: a platform for characterizing drug responsiveness and resistance in PDX and primary cancer cells. Sci. Rep. 7, 12277 (2017).

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Fan, Q. et al. A novel 3-D bio-microfluidic system mimicking in vivo heterogeneous tumour microstructures reveals complex tumour-stroma interactions. Lab Chip 17, 2852–2860 (2017).

    CAS  PubMed  Google Scholar 

  24. 24.

    Baudoin, R., Griscom, L., Monge, M., Legallais, C. & Leclerc, E. Development of a renal microchip for in vitro distal tubule models. Biotechnol. Prog. 23, 1245–1253 (2007).

    CAS  PubMed  Google Scholar 

  25. 25.

    Kettunen, K. et al. Personalized drug sensitivity screening for bladder cancer using conditionally reprogrammed patient-derived cells. Eur. Urol. 76, 430–434 (2019).

    PubMed  Google Scholar 

  26. 26.

    Timofeeva, O. A. et al. Conditionally reprogrammed normal and primary tumor prostate epithelial cells: a novel patient-derived cell model for studies of human prostate cancer. Oncotarget 8, 22741–22758 (2017).

    PubMed  Google Scholar 

  27. 27.

    Saeed, K. et al. Comprehensive drug testing of patient-derived conditionally reprogrammed cells from castration-resistant prostate cancer. Eur. Urol. 71, 319–327 (2017).

    PubMed  Google Scholar 

  28. 28.

    Redekop, W. K. & Mladsi, D. The faces of personalized medicine: a framework for understanding its meaning and scope. Value Health 16, S4–S9 (2013).

    PubMed  Google Scholar 

  29. 29.

    Tomlins, S. A. et al. Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science 310, 644–648 (2005).

    CAS  PubMed  Google Scholar 

  30. 30.

    Sibley, K., Cuthbert-Heavens, D. & Knowles, M. A. Loss of heterozygosity at 4p16.3 and mutation of FGFR3 in transitional cell carcinoma. Oncogene 20, 686–691 (2001).

    CAS  PubMed  Google Scholar 

  31. 31.

    Ledford, H. Translational research: 4 ways to fix the clinical trial. Nature 477, 526–528 (2011).

    CAS  PubMed  Google Scholar 

  32. 32.

    Attarwala, H. TGN1412: from discovery to disaster. J. Young Pharm. 2, 332–336 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Ogi, C. & Aruga, A. Immunological monitoring of anticancer vaccines in clinical trials. Oncoimmunology 2, e26012 (2013).

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Hutchinson, L. & Kirk, R. High drug attrition rates — where are we going wrong? Nat. Rev. Clin. Oncol. 8, 189–190 (2011).

    PubMed  Google Scholar 

  35. 35.

    Arrowsmith, J. Trial watch: phase III and submission failures: 2007–2010. Nat. Rev. Drug Discov. 10, 87 (2011).

    CAS  PubMed  Google Scholar 

  36. 36.

    CenterWatch. FDA approved drugs. CenterWatch https://www.centerwatch.com/directories/1067 (2020).

  37. 37.

    Petrylak, D. P. Practical guide to the use of chemotherapy in castration resistant prostate cancer. Can. J. Urol. 21, 77–83 (2014).

    PubMed  Google Scholar 

  38. 38.

    Weiswald, L. B., Bellet, D. & Dangles-Marie, V. Spherical cancer models in tumor biology. Neoplasia 17, 1–15 (2015).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Karthaus, W. R. et al. Identification of multipotent luminal progenitor cells in human prostate organoid cultures. Cell 159, 163–175 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Chua, C. W. et al. Single luminal epithelial progenitors can generate prostate organoids in culture. Nat. Cell Biol. 16, 951–954 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Grassi, L. et al. Organoids as a new model for improving regenerative medicine and cancer personalized therapy in renal diseases. Cell Death Dis. 10, 201 (2019).

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Lee, S. H. et al. Tumor evolution and drug response in patient-derived organoid models of bladder cancer. Cell 173, 515–528.e17 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Drost, J. et al. Organoid culture systems for prostate epithelial and cancer tissue. Nat. Protoc. 11, 347–358 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Mullenders, J. et al. Mouse and human urothelial cancer organoids: a tool for bladder cancer research. Proc. Natl Acad. Sci. USA 116, 4567–4574 (2019).

    CAS  PubMed  Google Scholar 

  45. 45.

    Puca, L. et al. Patient derived organoids to model rare prostate cancer phenotypes. Nat. Commun. 9, 2404 (2018).

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Gao, D. et al. Organoid cultures derived from patients with advanced prostate cancer. Cell 159, 176–187 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Ma, L. et al. Organoid culture of human prostate cancer cell lines LNCaP and C4-2B. Am. J. Clin. Exp. Urol. 5, 25–33 (2017).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Beshiri, M. L. et al. A PDX/organoid biobank of advanced prostate cancers captures genomic and phenotypic heterogeneity for disease modeling and therapeutic screening. Clin. Cancer Res. 24, 4332–4345 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Gao, D. & Chen, Y. Organoid development in cancer genome discovery. Curr. Opin. Genet. Dev. 30, 42–48 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Shu, Y. & Chua, C. W. An organoid assay for long-term maintenance and propagation of mouse prostate luminal epithelial progenitors and cancer cells. Methods Mol. Biol. 1940, 231–254 (2019).

    CAS  PubMed  Google Scholar 

  51. 51.

    Wang, S., Gao, D. & Chen, Y. The potential of organoids in urological cancer research. Nat. Rev. Urol. 14, 401–414 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Gleave, A. M., Ci, X., Lin, D. & Wang, Y. A synopsis of prostate organoid methodologies, applications, and limitations. Prostate 80, 518–526 (2020).

    PubMed  Google Scholar 

  53. 53.

    Hribar, K. C. et al. A simple three-dimensional hydrogel platform enables ex vivo cell culture of patient and PDX tumors for assaying their response to clinically relevant therapies. Mol. Cancer Ther. 18, 718–725 (2019).

    CAS  PubMed  Google Scholar 

  54. 54.

    Fong, E. L. et al. Hydrogel-based 3D model of patient-derived prostate xenograft tumors suitable for drug screening. Mol. Pharm. 11, 2040–2050 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Fong, E. L. et al. A 3D in vitro model of patient-derived prostate cancer xenograft for controlled interrogation of in vivo tumor-stromal interactions. Biomaterials 77, 164–172 (2016).

    CAS  PubMed  Google Scholar 

  56. 56.

    Sachs, N. & Clevers, H. Organoid cultures for the analysis of cancer phenotypes. Curr. Opin. Genet. Dev. 24, 68–73 (2014).

    CAS  PubMed  Google Scholar 

  57. 57.

    Bleijs, M., van de Wetering, M., Clevers, H. & Drost, J. Xenograft and organoid model systems in cancer research. EMBO J. 38, e101654 (2019).

    PubMed  PubMed Central  Google Scholar 

  58. 58.

    van de Wetering, M. et al. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 161, 933–945 (2015).

    PubMed  PubMed Central  Google Scholar 

  59. 59.

    Conteduca, V. et al. Clinical features of neuroendocrine prostate cancer. Eur. J. Cancer 121, 7–18 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Pauli, C. et al. Personalized in vitro and in vivo cancer models to guide precision medicine. Cancer Discov. 7, 462–477 (2017).

    PubMed  PubMed Central  Google Scholar 

  61. 61.

    Ben-David, U., Beroukhim, R. & Golub, T. R. Genomic evolution of cancer models: perils and opportunities. Nat. Rev. Cancer 19, 97–109 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Galletti, G., Leach, B. I., Lam, L. & Tagawa, S. T. Mechanisms of resistance to systemic therapy in metastatic castration-resistant prostate cancer. Cancer Treat. Rev. 57, 16–27 (2017).

    CAS  PubMed  Google Scholar 

  63. 63.

    Li, Y. et al. Diverse AR gene rearrangements mediate resistance to androgen receptor inhibitors in metastatic prostate cancer. Clin. Cancer Res. 26, 1965–1976 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Eder, T. et al. Cancer-associated fibroblasts modify the response of prostate cancer cells to androgen and anti-androgens in three-dimensional spheroid culture. Int. J. Mol. Sci. 17, 1458 (2016).

    PubMed Central  Google Scholar 

  65. 65.

    Chambers, K. F., Mosaad, E. M., Russell, P. J., Clements, J. A. & Doran, M. R. 3D Cultures of prostate cancer cells cultured in a novel high-throughput culture platform are more resistant to chemotherapeutics compared to cells cultured in monolayer. PLoS One 9, e111029 (2014).

    PubMed  PubMed Central  Google Scholar 

  66. 66.

    Mosaad, E., Chambers, K., Futrega, K., Clements, J. & Doran, M. R. Using high throughput microtissue culture to study the difference in prostate cancer cell behavior and drug response in 2D and 3D co-cultures. BMC Cancer 18, 592 (2018).

    PubMed  PubMed Central  Google Scholar 

  67. 67.

    Frankel, A., Man, S., Elliott, P., Adams, J. & Kerbel, R. S. Lack of multicellular drug resistance observed in human ovarian and prostate carcinoma treated with the proteasome inhibitor PS-341. Clin. Cancer Res. 6, 3719–3728 (2000).

    CAS  PubMed  Google Scholar 

  68. 68.

    Harma, V. et al. A comprehensive panel of three-dimensional models for studies of prostate cancer growth, invasion and drug responses. PLoS One 5, e10431 (2010).

    PubMed  PubMed Central  Google Scholar 

  69. 69.

    Richards, Z. et al. Prostate stroma increases the viability and maintains the branching phenotype of human prostate organoids. iScience 12, 304–317 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Ramamoorthy, P. et al. Metastatic tumor-in-a-dish, a novel multicellular organoid to study lung colonization and predict therapeutic response. Cancer Res. 79, 1681–1695 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Clohessy, J. G. & Pandolfi, P. P. Mouse hospital and co-clinical trial project — from bench to bedside. Nat. Rev. Clin. Oncol. 12, 491–498 (2015).

    PubMed  Google Scholar 

  72. 72.

    Nardella, C., Lunardi, A., Patnaik, A., Cantley, L. C. & Pandolfi, P. P. The APL paradigm and the “co-clinical trial” project. Cancer Discov. 1, 108–116 (2011).

    PubMed  Google Scholar 

  73. 73.

    Centenera, M. M., Raj, G. V., Knudsen, K. E., Tilley, W. D. & Butler, L. M. Ex vivo culture of human prostate tissue and drug development. Nat. Rev. Urol. 10, 483–487 (2013).

    CAS  PubMed  Google Scholar 

  74. 74.

    van de Merbel, A. F. et al. An ex vivo tissue culture model for the assessment of individualized drug responses in prostate and bladder cancer. Front. Oncol. 8, 400 (2018).

    PubMed  PubMed Central  Google Scholar 

  75. 75.

    Annels, N. E. et al. Oncolytic immunotherapy for bladder cancer using coxsackie A21 virus. Mol. Ther. Oncolytics 9, 1–12 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Zhang, W. et al. Ex vivo treatment of prostate tumor tissue recapitulates in vivo therapy response. Prostate 79, 390–402 (2018).

    PubMed  PubMed Central  Google Scholar 

  77. 77.

    Davies, E. J. et al. Capturing complex tumour biology in vitro: histological and molecular characterisation of precision cut slices. Sci. Rep. 5, 17187 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Maund, S. L., Nolley, R. & Peehl, D. M. Optimization and comprehensive characterization of a faithful tissue culture model of the benign and malignant human prostate. Lab. Invest. 94, 208–221 (2014).

    CAS  PubMed  Google Scholar 

  79. 79.

    Shafi, A. A. et al. Patient-derived models reveal impact of the tumor microenvironment on therapeutic response. Eur. Urol. Oncol. 1, 325–337 (2018).

    PubMed  PubMed Central  Google Scholar 

  80. 80.

    Varani, J., Dame, M. K., Wojno, K., Schuger, L. & Johnson, K. J. Characteristics of nonmalignant and malignant human prostate in organ culture. Lab. Invest. 79, 723–731 (1999).

    CAS  PubMed  Google Scholar 

  81. 81.

    Parrish, A. R. et al. Culturing precision-cut human prostate slices as an in vitro model of prostate pathobiology. Cell Biol. Toxicol. 18, 205–219 (2002).

    CAS  PubMed  Google Scholar 

  82. 82.

    Fleck, C. et al. Ex vivo stimulation of renal tubular p-aminohippurate transport by dexamethasone and triiodothyronine in human renal cell carcinoma. Urol. Res. 28, 383–390 (2000).

    CAS  PubMed  Google Scholar 

  83. 83.

    Vickers, A. E. et al. Kidney slices of human and rat to characterize cisplatin-induced injury on cellular pathways and morphology. Toxicol. Pathol. 32, 577–590 (2004).

    CAS  PubMed  Google Scholar 

  84. 84.

    van der Horst, G. et al. Cationic amphiphilic drugs as potential anti-cancer therapy for bladder cancer. Mol. Oncol. https://doi.org/10.1002/1878-0261.12793 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  85. 85.

    Centenera, M. M. et al. A patient-derived explant (PDE) model of hormone-dependent cancer. Mol. Oncol. 12, 1608–1622 (2018).

    PubMed  PubMed Central  Google Scholar 

  86. 86.

    Jiang, X., Seo, Y. D., Sullivan, K. M. & Pillarisetty, V. G. Establishment of slice cultures as a tool to study the cancer immune microenvironment. Methods Mol. Biol. 1884, 283–295 (2019).

    CAS  PubMed  Google Scholar 

  87. 87.

    Lim, C. Y. et al. Organotypic slice cultures of pancreatic ductal adenocarcinoma preserve the tumor microenvironment and provide a platform for drug response. Pancreatology 18, 913–927 (2018).

    CAS  PubMed  Google Scholar 

  88. 88.

    Ozdemir, B. C. et al. The molecular signature of the stroma response in prostate cancer-induced osteoblastic bone metastasis highlights expansion of hematopoietic and prostate epithelial stem cell niches. PLoS One 9, e114530 (2014).

    PubMed  PubMed Central  Google Scholar 

  89. 89.

    van der Horst, G., Bos, L. & van der Pluijm, G. Epithelial plasticity, cancer stem cells, and the tumor-supportive stroma in bladder carcinoma. Mol. Cancer Res. 10, 995–1009 (2012).

    PubMed  Google Scholar 

  90. 90.

    Voorwerk, L. et al. Immune induction strategies in metastatic triple-negative breast cancer to enhance the sensitivity to PD-1 blockade: the TONIC trial. Nat. Med. 25, 920–928 (2019).

    CAS  PubMed  Google Scholar 

  91. 91.

    Majumder, B. et al. Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity. Nat. Commun. 6, 6169 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. 92.

    Malaney, P., Nicosia, S. V. & Davé, V. One mouse, one patient paradigm: new avatars of personalized cancer therapy. Cancer Lett. 344, 1–12 (2014).

    CAS  PubMed  Google Scholar 

  93. 93.

    Davies, A. H., Wang, Y. & Zoubeidi, A. Patient-derived xenografts: a platform for accelerating translational research in prostate cancer. Mol. Cell. Endocrinol. 462, 17–24 (2018).

    CAS  PubMed  Google Scholar 

  94. 94.

    Gao, H. et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat. Med. 21, 1318–1325 (2015).

    CAS  PubMed  Google Scholar 

  95. 95.

    Okada, S., Vaeteewoottacharn, K. & Kariya, R. Application of highly immunocompromised mice for the establishment of patient-derived xenograft (PDX) models. Cells 8, 889 (2019).

    CAS  PubMed Central  Google Scholar 

  96. 96.

    Lawrence, M. G. et al. Establishment of primary patient-derived xenografts of palliative TURP specimens to study castrate-resistant prostate cancer. Prostate 75, 1475–1483 (2015).

    CAS  PubMed  Google Scholar 

  97. 97.

    Russell, P. J. et al. Establishing prostate cancer patient derived xenografts: lessons learned from older studies. Prostate 75, 628–636 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. 98.

    Wang, Y. et al. Development and characterization of efficient xenograft models for benign and malignant human prostate tissue. Prostate 64, 149–159 (2005).

    CAS  PubMed  Google Scholar 

  99. 99.

    Brennen, W. N. & Isaacs, J. T. The what, when, and why of human prostate cancer xenografts. Prostate 78, 646–654 (2018).

    PubMed  Google Scholar 

  100. 100.

    van Weerden, W. M. et al. Development of seven new human prostate tumor xenograft models and their histopathological characterization. Am. J. Pathol. 149, 1055–1062 (1996).

    PubMed  PubMed Central  Google Scholar 

  101. 101.

    van Weerden, W. M. & Romijn, J. C. Use of nude mouse xenograft models in prostate cancer research. Prostate 43, 263–271 (2000).

    PubMed  Google Scholar 

  102. 102.

    Namekawa, T., Ikeda, K., Horie-Inoue, K. & Inoue, S. Application of prostate cancer models for preclinical study: advantages and limitations of cell lines, patient-derived xenografts, and three-dimensional culture of patient-derived cells. Cells 8, 74 (2019).

    CAS  PubMed Central  Google Scholar 

  103. 103.

    Navone, N. M. et al. Movember GAP1 PDX project: an international collection of serially transplantable prostate cancer patient-derived xenograft (PDX) models. Prostate 78, 1262–1282 (2018).

    CAS  PubMed  Google Scholar 

  104. 104.

    Patel, A. et al. Patient-derived xenograft models to optimize kidney cancer therapies. Transl. Androl. Urol. 8, S156–S165 (2019).

    PubMed  PubMed Central  Google Scholar 

  105. 105.

    Lang, H. et al. Establishment of a large panel of patient-derived preclinical models of human renal cell carcinoma. Oncotarget 7, 59336–59359 (2016).

    PubMed  PubMed Central  Google Scholar 

  106. 106.

    Bernardo, C., Costa, C., Sousa, N., Amado, F. & Santos, L. Patient-derived bladder cancer xenografts: a systematic review. Transl. Res. 166, 324–331 (2015).

    PubMed  Google Scholar 

  107. 107.

    Castillo-Avila, W. et al. Sunitinib inhibits tumor growth and synergizes with cisplatin in orthotopic models of cisplatin-sensitive and cisplatin-resistant human testicular germ cell tumors. Clin. Cancer Res. 15, 3384–3395 (2009).

    CAS  PubMed  Google Scholar 

  108. 108.

    Lam, H. M. et al. Durable response of enzalutamide-resistant prostate cancer to supraphysiological testosterone is associated with a multifaceted growth suppression and impaired DNA damage response transcriptomic program in patient-derived xenografts. Eur. Urol. 77, 144–155 (2019).

    PubMed  Google Scholar 

  109. 109.

    Zeng, S. X. et al. The phosphatidylinositol 3-kinase pathway as a potential therapeutic target in bladder cancer. Clin. Cancer Res. 23, 6580–6591 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  110. 110.

    Pan, C. X. et al. Development and characterization of bladder cancer patient-derived xenografts for molecularly guided targeted therapy. PLoS One 10, e0134346 (2015).

    PubMed  PubMed Central  Google Scholar 

  111. 111.

    Lange, T. et al. Development and characterization of a spontaneously metastatic patient-derived xenograft model of human prostate cancer. Sci. Rep. 8, 17535 (2018).

    PubMed  PubMed Central  Google Scholar 

  112. 112.

    Thong, A. E. et al. Tissue slice grafts of human renal cell carcinoma: an authentic preclinical model with high engraftment rate and metastatic potential. Urol. Oncol. 32, 43.e23–30 (2014).

    Google Scholar 

  113. 113.

    Valta, M. P. et al. Development of a realistic in vivo bone metastasis model of human renal cell carcinoma. Clin. Exp. Metastasis 31, 573–584 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. 114.

    Schneeberger, V. E., Allaj, V., Gardner, E. E., Poirier, J. T. & Rudin, C. M. Quantitation of murine stroma and selective purification of the human tumor component of patient-derived xenografts for genomic analysis. PLoS One 11, e0160587 (2016).

    PubMed  PubMed Central  Google Scholar 

  115. 115.

    Mestas, J. & Hughes, C. C. Of mice and not men: differences between mouse and human immunology. J. Immunol. 172, 2731–2738 (2004).

    CAS  PubMed  Google Scholar 

  116. 116.

    Rickinson, A. & Kieff, E. in Fields Virology 5th edn (ed. Knipe, D. M. & Howley, P.M.) 2655–2700 (Lippincott Williams & Wilkins, 2001).

  117. 117.

    Wetterauer, C. et al. Early development of human lymphomas in a prostate cancer xenograft program using triple knock-out immunocompromised mice. Prostate 75, 585–592 (2015).

    PubMed  Google Scholar 

  118. 118.

    Taurozzi, A. J. et al. Spontaneous development of Epstein-Barr Virus associated human lymphomas in a prostate cancer xenograft program. PLoS One 12, e0188228 (2017).

    PubMed  PubMed Central  Google Scholar 

  119. 119.

    Williams, A. P. et al. Corruption of neuroblastoma patient derived xenografts with human T cell lymphoma. J. Pediatr. Surg. 54, 2117–2119 (2018).

    PubMed  PubMed Central  Google Scholar 

  120. 120.

    Bondarenko, G. et al. Patient-derived tumor xenografts are susceptible to formation of human lymphocytic tumors. Neoplasia 17, 735–741 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  121. 121.

    Choi, Y. Y. et al. Establishment and characterisation of patient-derived xenografts as paraclinical models for gastric cancer. Sci. Rep. 6, 22172 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  122. 122.

    Fujii, E. et al. Characterization of EBV-related lymphoproliferative lesions arising in donor lymphocytes of transplanted human tumor tissues in the NOG mouse. Exp. Anim. 63, 289–296 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  123. 123.

    Kalavska, K. et al. Lymphoma transformation of tumor infiltrating lymphocytes observed in testicular patient-derived xenograft models. Oncol. Rep. 40, 3593–3602 (2018).

    CAS  PubMed  Google Scholar 

  124. 124.

    Yao, L. C. et al. Creation of PDX-bearing humanized mice to study immuno-oncology. Methods Mol. Biol. 1953, 241–252 (2019).

    CAS  PubMed  Google Scholar 

  125. 125.

    Capasso, A. et al. Characterization of immune responses to anti-PD-1 mono and combination immunotherapy in hematopoietic humanized mice implanted with tumor xenografts. J. Immunother. Cancer 7, 37 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  126. 126.

    Lin, S. et al. Establishment of peripheral blood mononuclear cell-derived humanized lung cancer mouse models for studying efficacy of PD-L1/PD-1 targeted immunotherapy. mAbs 10, 1301–1311 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  127. 127.

    Wang, M. et al. Humanized mice in studying efficacy and mechanisms of PD-1-targeted cancer immunotherapy. FASEB J. 32, 1537–1549 (2018).

    CAS  PubMed  Google Scholar 

  128. 128.

    Williams, J. A. Using PDX for preclinical cancer drug discovery: the evolving field. J. Clin. Med. 7, 41 (2018).

    PubMed Central  Google Scholar 

  129. 129.

    Hidalgo, M. et al. A pilot clinical study of treatment guided by personalized tumorgrafts in patients with advanced cancer. Mol. Cancer Ther. 10, 1311–1316 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  130. 130.

    Koga, Y. & Ochiai, A. Systematic review of patient-derived xenograft models for preclinical studies of anti-cancer drugs in solid tumors. Cells 8, 418 (2019).

    CAS  PubMed Central  Google Scholar 

  131. 131.

    Sia, D., Moeini, A., Labgaa, I. & Villanueva, A. The future of patient-derived tumor xenografts in cancer treatment. Pharmacogenomics 16, 1671–1683 (2015).

    CAS  PubMed  Google Scholar 

  132. 132.

    Clohessy, J. G. & Pandolfi, P. P. The mouse hospital and its integration in ultra-precision approaches to cancer care. Front. Oncol. 8, 340 (2018).

    PubMed  PubMed Central  Google Scholar 

  133. 133.

    Vlachogiannis, G. et al. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science 359, 920–926 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  134. 134.

    Beltran, H. et al. A phase II trial of the aurora kinase A inhibitor alisertib for patients with castration-resistant and neuroendocrine prostate cancer: efficacy and biomarkers. Clin. Cancer Res. 25, 43–51 (2019).

    CAS  PubMed  Google Scholar 

  135. 135.

    Dong, Y. et al. Tumor xenografts of human clear cell renal cell carcinoma but not corresponding cell lines recapitulate clinical response to sunitinib: feasibility of using biopsy samples. Eur. Urol. Focus 3, 590–598 (2017).

    PubMed  Google Scholar 

Download references

Acknowledgements

The authors’ research work is supported by a personalized medicine grant from the Dutch Cancer Society (KWF) and Alpe D’Huzes (UL2014-7058) (to A.F.v.d.M., G.v.d.H. and G.v.d.P.).

Author information

Affiliations

Authors

Contributions

A.F.v.d.M researched data for the manuscript, A.F.v.d.M, G.v.d.H and G.v.d.P. wrote the manuscript, and all authors made substantial contributions to discussions of content and reviewed and edited the manuscript before submission.

Corresponding author

Correspondence to Gabri van der Pluijm.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information

Nature Reviews Urology thanks C.-X. Pan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Jackson Laboratory: https://www.jax.org

National Cancer Institute Patient-Derived Models Repository: https://pdmr.cancer.gov

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

van de Merbel, A.F., van der Horst, G. & van der Pluijm, G. Patient-derived tumour models for personalized therapeutics in urological cancers. Nat Rev Urol 18, 33–45 (2021). https://doi.org/10.1038/s41585-020-00389-2

Download citation

Further reading

Search

Quick links