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. 2014 Feb 15;74(4):1272-83.
doi: 10.1158/0008-5472.CAN-13-2921-T. Epub 2013 Dec 19.

High fidelity patient-derived xenografts for accelerating prostate cancer discovery and drug development

Dong Lin  1 , Alexander W Wyatt, Hui Xue, Yuwei Wang, Xin Dong, Anne Haegert, Rebecca Wu, Sonal Brahmbhatt, Fan Mo, Lina Jong, Robert H Bell, Shawn Anderson, Antonio Hurtado-Coll, Ladan Fazli, Manju Sharma, Himisha Beltran, Mark Rubin, Michael Cox, Peter W Gout, James Morris, Larry Goldenberg, Stanislav V Volik, Martin E Gleave, Colin C Collins, Yuzhuo Wang
Affiliations

High fidelity patient-derived xenografts for accelerating prostate cancer discovery and drug development

Dong Lin et al. Cancer Res. .

Abstract

Standardized and reproducible preclinical models that recapitulate the dynamics of prostate cancer are urgently needed. We established a bank of transplantable patient-derived prostate cancer xenografts that capture the biologic and molecular heterogeneity currently confounding prognostication and therapy development. Xenografts preserved the histopathology, genome architecture, and global gene expression of donor tumors. Moreover, their aggressiveness matched patient observations, and their response to androgen withdrawal correlated with tumor subtype. The panel includes the first xenografts generated from needle biopsy tissue obtained at diagnosis. This advance was exploited to generate independent xenografts from different sites of a primary site, enabling functional dissection of tumor heterogeneity. Prolonged exposure of adenocarcinoma xenografts to androgen withdrawal led to castration-resistant prostate cancer, including the first-in-field model of complete transdifferentiation into lethal neuroendocrine prostate cancer. Further analysis of this model supports the hypothesis that neuroendocrine prostate cancer can evolve directly from adenocarcinoma via an adaptive response and yielded a set of genes potentially involved in neuroendocrine transdifferentiation. We predict that these next-generation models will be transformative for advancing mechanistic understanding of disease progression, response to therapy, and personalized oncology.

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