Yiyu Dong1, Brandon J Manley2, Maria F Becerra1, Almedina Redzematovic3, Jozefina Casuscelli2, Daniel M Tennenbaum2, Ed Reznik4, Song Han1, Nicole Benfante2, Ying-Bei Chen5, Maria E Arcila4, Omer Aras6, Martin H Voss3, Darren R Feldman3, Robert J Motzer3, Nicola Fabbri7, John H Healey7, Patrick J Boland7, Mohit Chawla8, Jeremy C Durack9, Chung-Han Lee3, Jonathan A Coleman2, Paul Russo2, A Ari Hakimi2, Emily H Cheng1, James J Hsieh10. 1. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA. 2. Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA. 3. Genitourinary Oncology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA. 4. Computational Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA. 5. Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA. 6. Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA. 7. Orthopedics Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA. 8. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA. 9. Interventional Radiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA. 10. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA. Electronic address: hsiehj@mskcc.org.
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.
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 RCCmouse 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.
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