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Using a rhabdomyosarcoma patient-derived xenograft to examine precision medicine approaches and model acquired
David J Monsma1, David M Cherba, Patrick J Richardson
1Van Andel Research Institute, Center for Translational Medicine, Grand Rapids, Michigan.
Background:
Precision (Personalized) medicine has the potential to revolutionize patient health care especially for many cancers where the fundamental disease etiology remains either elusive or has 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 (PDX) model to test bioinformatically 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 therapy (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-transplanted ICE-T treated tumorgrafts demonstrated a decreased 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 PDX models are suitable 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.
Insights
Patient-derived xenograft (PDX) models effectively test precision medicine strategies for alveolar rhabdomyosarcoma. Gene expression profiling identified effective therapies, including BGJ398 and cytarabine, demonstrating PDX model utility in predicting patient treatment responses.
Area of Science:
- Oncology
- Genomics
- Pharmacology
Background:
- Precision medicine offers transformative potential for cancer care, particularly for diseases with unknown causes or limited treatment options.
- Alveolar rhabdomyosarcoma presents challenges in understanding disease etiology and developing effective therapies.
Purpose of the Study:
- To evaluate the utility of patient-derived xenograft (PDX) models in testing precision medicine strategies for alveolar rhabdomyosarcoma.
- To identify effective therapies by combining gene expression profiling, drug prediction algorithms, and PDX models.
Main Methods:
- Developed a PDX model from a patient biopsy.
- Utilized gene expression analysis and drug prediction algorithms to identify potential therapies.
- Tested predicted drugs and standard-of-care (ICE-T) in vivo within the PDX model.
- Conducted secondary analysis on tumors resistant to ICE-T to identify further therapeutic options.
Main Results:
- BGJ398 (FGFR2 inhibitor) and ICE-T showed significant anti-tumor activity in the PDX model.
- Tumors that regrew after ICE-T treatment exhibited reduced sensitivity, mirroring clinical drug resistance.
- Gene expression profiling of resistant tumors identified cytarabine (SLC29A1) as a promising therapy, which demonstrated high in vivo efficacy alongside BGJ398.
Conclusions:
- PDX models are validated as suitable platforms for testing gene expression-guided therapeutic strategies.
- PDX models can effectively recapitulate clinical drug resistance, aiding in the study of refractory disease.
- This approach holds promise for guiding prospective patient care in precision oncology.
