Tumor xenografts of human clear cell renal cell carcinoma but not corresponding cell lines recapitulate clinical response to sunitinib: Feasibility of using biopsy samples Journal Article


Authors: Dong, Y.; Manley, B. J.; Becerra, M. F.; Redzematovic, A.; Casuscelli, J.; Tennenbaum, D. M.; Reznik, E.; Han, S.; Benfante, N.; Chen, Y. B.; Arcila, M. E.; Aras, O.; Voss, M. H.; Feldman, D. R.; Motzer, R. J.; Fabbri, N.; Healey, J. H.; Boland, P. J.; Chawla, M.; Durack, J. C.; Lee, C. H.; Coleman, J. A.; Russo, P.; Hakimi, A. A.; Cheng, E. H.; Hsieh, J. J.
Article Title: Tumor xenografts of human clear cell renal cell carcinoma but not corresponding cell lines recapitulate clinical response to sunitinib: Feasibility of using biopsy samples
Abstract: Background: Parallel development of preclinical models that recapitulate treatment response observed in patients is central to the advancement of personalized medicine. Objective: To evaluate the use of biopsy specimens to develop patient-derived xenografts and the use of corresponding cell lines from renal cell carcinoma (RCC) tumors for the assessment of histopathology, genomics, and treatment response. Design, setting, and participants: A total of 74 tumor specimens from 66 patients with RCC were implanted into immunocompromised NOD-SCID IL2Rg−/− mice. Four cell lines generated from patients’ specimens with clear cell pathology were used for comparative studies. Outcome measurements and statistical analysis: Preclinical models were established and assessed. Engraftment rates were analyzed using chi-square testing. Analysis of variance (two-way analysis of variance) was conducted to assess tumor growth. Results and limitations: Overall, 33 RCC mouse xenograft models were generated with an overall engraftment rate of 45% (33 of 74). Tumor biopsies engrafted comparably with surgically resected tumors (58% vs 41%; p = 0.3). Xenograft tumors and their original tumors showed high fidelity in regard to histology, mutation status, copy number change, and targeted therapy response. Engraftment rates from metastatic tumors were higher but not more significant than primary tumors (54% vs 34%; p = 0.091). Our engraftment rate using metastases or biopsies was comparable with recent reports using resected primary tumors. In stark contrast to corresponding cell lines, all tested xenografts recapitulated patients’ clinical response to sunitinib. Conclusions: Patient-derived xenograft models can be effectively established from tumor biopsies. Preclinical xenograft models but not matched cell lines reflected clinical responses to sunitinib. Patient summary: Matched patient-derived clear cell renal cell carcinoma xenografts and cell lines from responsive and refractory patients treated with sunitinib were established and evaluated for pharmacologic response to anti–vascular endothelial growth factor treatment. Both models accurately reflected the genetic characteristics of original tumors, but only xenografts recapitulated drug responses observed in patients. These models could serve as a powerful platform for precision medicine. Patient-derived xenograft models can be effectively established from tumor biopsies. Preclinical xenograft models but not matched cell lines reflected clinical responses to sunitinib. © 2016 European Association of Urology
Keywords: sunitinib; biopsy; renal cell carcinoma; cancer cell lines; preclinical studies; precision medicine; patient-derived xenografts
Journal Title: European Urology Focus
Volume: 3
Issue: 6
ISSN: 2405-4569
Publisher: European Association of Urology  
Date Published: 2017-12-01
Start Page: 590
End Page: 598
Language: English
DOI: 10.1016/j.euf.2016.08.005
PROVIDER: scopus
PMCID: PMC5608640
PUBMED: 28753786
DOI/URL:
Notes: Article -- Export Date: 1 June 2018 -- Source: Scopus
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MSK Authors
  1. Jonathan Coleman
    184 Coleman
  2. Paul Russo
    449 Russo
  3. Robert Motzer
    762 Motzer
  4. Patrick J Boland
    132 Boland
  5. Darren Richard Feldman
    172 Feldman
  6. Martin Henner Voss
    127 Voss
  7. Mohit Chawla
    21 Chawla
  8. Yingbei Chen
    221 Chen
  9. Yiyu Dong
    16 Dong
  10. James J Hsieh
    114 Hsieh
  11. Emily H Cheng
    55 Cheng
  12. Maria Eugenia Arcila
    343 Arcila
  13. John H Healey
    377 Healey
  14. Jeremy Charles Durack
    84 Durack
  15. Abraham Ari Hakimi
    129 Hakimi
  16. Nicola Fabbri
    13 Fabbri
  17. Omer Aras
    29 Aras
  18. Eduard Reznik
    32 Reznik
  19. Chung-Han   Lee
    38 Lee
  20. Brandon John Manley
    21 Manley
  21. Maria Fernanda   Becerra
    15 Becerra
  22. Song   Han
    5 Han