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Precision Medicine in Rhabdomyosarcoma: Using Patient Derived Xenografts as models of drug efficacy and acquired resistance

Monsma, DJ, Cherba, DM, Richardson, PJ, Vance, S, Rangarajan, S, Dylewski, D, Scott, SB, Beuschel, NL, Axtell, R, Mitchell, D, Lester, E, Junewick, JJ, Webb, CP and Monks, NR (2014) Precision Medicine in Rhabdomyosarcoma: Using Patient Derived Xenografts as models of drug efficacy and acquired resistance. Pediatric blood & cancer, 61 (9). pp. 1570-1577. ISSN 15455009


Background. Precision (Personalized) medicine has the potential to revolutionize patient health care and whilst there have been huge advances for a few cancers of known etiology, for many cancers, the fundamental causes of the disease process remain either elusive or have no available therapy. Here we outline a study in alveolar rhabdomyosarcoma, in which we use gene expression profiling and a series of drug prediction algorithms combined with a matched patient derived xenograft model (PDX) to test predicted therapies.
Procedure. A PDX model was developed from a patient biopsy and a number of drugs identified using gene expression analysis in combination with drug prediction algorithms. Drugs chosen from each of the predictive methodologies, along with the patient’s standard-of-care (ICE-T), were tested in vivo in the PDX tumor. A second study was initiated using the tumors that re-grew following the ICE-T treatment. Further expression analysis identified additional therapies with potential anti-tumor efficacy.
Results. A number of the predicted therapies were found to be active against the tumors in particular BGJ398 (FGFR2) and ICE-T. Re-transplantation of the ICE-T treated tumorgrafts demonstrated a decrease in response to ICE-T recapitulating the patient’s refractory disease. Gene expression profiling of the ICE-T treated tumorgrafts identified cytarabine (SLC29A1) as a potential therapy, which was shown, along with BGJ398, to be highly active in vivo.
Conclusions. This study illustrates that tumorgrafts are ideal surrogates for testing potential therapeutic strategies based on gene expression analysis, modeling clinical drug resistance and hold the potential to assist in guiding prospective patient care.

Item Type: Article
Date Deposited: 13 Apr 2017 00:45
Last Modified: 13 Apr 2017 00:45


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