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• W2307888536 abstract "Abstract Purpose: We used human stem and progenitor cells to develop a genetically accurate novel model of MYC-driven Group 3 medulloblastoma. We also developed a new informatics method, Disease-model Signature versus Compound-Variety Enriched Response (“DiSCoVER”), to identify novel therapeutics that target this specific disease subtype. Experimental Design: Human neural stem and progenitor cells derived from the cerebellar anlage were transduced with oncogenic elements associated with aggressive medulloblastoma. An in silico analysis method for screening drug sensitivity databases (DiSCoVER) was used in multiple drug sensitivity datasets. We validated the top hits from this analysis in vitro and in vivo. Results: Human neural stem and progenitor cells transformed with c-MYC, dominant-negative p53, constitutively active AKT and hTERT formed tumors in mice that recapitulated Group 3 medulloblastoma in terms of pathology and expression profile. DiSCoVER analysis predicted that aggressive MYC-driven Group 3 medulloblastoma would be sensitive to cyclin-dependent kinase (CDK) inhibitors. The CDK 4/6 inhibitor palbociclib decreased proliferation, increased apoptosis, and significantly extended the survival of mice with orthotopic medulloblastoma xenografts. Conclusions: We present a new method to generate genetically accurate models of rare tumors, and a companion computational methodology to find therapeutic interventions that target them. We validated our human neural stem cell model of MYC-driven Group 3 medulloblastoma and showed that CDK 4/6 inhibitors are active against this subgroup. Our results suggest that palbociclib is a potential effective treatment for poor prognosis MYC-driven Group 3 medulloblastoma tumors in carefully selected patients. Clin Cancer Res; 22(15); 3903–14. ©2016 AACR." @default.
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• W2307888536 date "2016-07-31" @default.
• W2307888536 modified "2023-10-16" @default.
• W2307888536 title "DiSCoVERing Innovative Therapies for Rare Tumors: Combining Genetically Accurate Disease Models with <i>In Silico</i> Analysis to Identify Novel Therapeutic Targets" @default.
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• W2307888536 doi "https://doi.org/10.1158/1078-0432.ccr-15-3011" @default.
• W2307888536 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5055054" @default.
• W2307888536 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27012813" @default.
• W2307888536 hasPublicationYear "2016" @default.
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