Using a rhabdomyosarcoma patient-derived xenograft to examine precision medicine approaches and model acquired resistance.

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.

Genomic characterization of explant tumorgraft models derived from fresh patient tumor tissue.

BACKGROUND: There is resurgence within drug and biomarker development communities for the use of primary tumorgraft models as improved predictors of patient tumor response to novel therapeutic strategies. Despite perceived advantages over cell line derived xenograft models, there is limited data comparing the genotype and phenotype of tumorgrafts to the donor patient tumor, limiting the determination of molecular relevance of the tumorgraft model. This report directly compares the genomic characteristics of patient tumors and the derived tumorgraft models, including gene expression, and oncogenic mutation status. METHODS: Fresh tumor tissues from 182 cancer patients were implanted subcutaneously into immune-compromised mice for the development of primary patient tumorgraft models. Histological assessment was performed on both patient tumors and the resulting tumorgraft models. Somatic mutations in key oncogenes and gene expression levels of resulting tumorgrafts were compared to the matched patient tumors using the OncoCarta (Sequenom, San Diego, CA) and human gene microarray (Affymetrix, Santa Clara, CA) platforms respectively. The genomic stability of the established tumorgrafts was assessed across serial in vivo generations in a representative subset of models. The genomes of patient tumors that formed tumorgrafts were compared to those that did not to identify the possible molecular basis to successful engraftment or rejection. RESULTS: Fresh tumor tissues from 182 cancer patients were implanted into immune-compromised mice with forty-nine tumorgraft models that have been successfully established, exhibiting strong histological and genomic fidelity to the originating patient tumors. Comparison of the transcriptomes and oncogenic mutations between the tumorgrafts and the matched patient tumors were found to be stable across four tumorgraft generations. Not only did the various tumors retain the differentiation pattern, but supporting stromal elements were preserved. Those genes down-regulated specifically in tumorgrafts were enriched in biological pathways involved in host immune response, consistent with the immune deficiency status of the host. Patient tumors that successfully formed tumorgrafts were enriched for cell signaling, cell cycle, and cytoskeleton pathways and exhibited evidence of reduced immunogenicity. CONCLUSIONS: The preservation of the patient's tumor genomic profile and tumor microenvironment supports the view that primary patient tumorgrafts provide a relevant model to support the translation of new therapeutic strategies and personalized medicine approaches in oncology.

Establishment of genetically diverse patient-derived xenografts of colorectal cancer.

Preclinical compounds tested in animal models often show limited efficacy when transitioned into human clinical trials. As a result, many patients are stratified into treatment regimens that have little impact on their disease. In order to create preclinical models that can more accurately predict tumor responses, we established patient-derived xenograft (PDX) models of colorectal cancer (CRC). Surgically resected tumor specimens from colorectal cancer patients were implanted subcutaneously into athymic nude mice. Following successful establishment, fourteen models underwent further evaluation to determine whether these models exhibit heterogeneity, both at the cellular and genetic level. Histological review revealed properties not found in CRC cell lines, most notably in overall architecture (predominantly columnar epithelium with evidence of gland formation) and the presence of mucin-producing cells. Custom CRC gene panels identified somatic driver mutations in each model, and therapeutic efficacy studies in tumor-bearing mice were designed to determine how models with known mutations respond to PI3K, mTOR, or MAPK inhibitors. Interestingly, MAPK pathway inhibition drove tumor responses across most models tested. Noteworthy, the MAPK inhibitor PD0325901 alone did not significantly mediate tumor response in the context of a KRAS(G12D) model, and improved tumor responses resulted when combined with mTOR inhibition. As a result, these genetically diverse models represent a valuable resource for preclinical efficacy and drug discovery studies.