Integrated Patient-Derived Models Delineate Individualized Therapeutic Vulnerabilities of Pancreatic Cancer
| Autor: | Elizabeth A. McMillan, Uthra Balaji, Eileen M. O'Reilly, Erik S. Knudsen, Cody Eslinger, Agnieszka K. Witkiewicz, Gordon B. Mills, William C. Conway, Bruce A. Posner |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Combination therapy Pyridones Dasatinib Antineoplastic Agents Docetaxel Pyrimidinones Bioinformatics General Biochemistry Genetics and Molecular Biology Article 03 medical and health sciences Mice Pancreatic cancer Cell Line Tumor Antineoplastic Combined Chemotherapy Protocols Medicine Animals Humans Exome Everolimus Precision Medicine lcsh:QH301-705.5 Exome sequencing Models Statistical Models Genetic business.industry medicine.disease Precision medicine Prognosis Xenograft Model Antitumor Assays 3. Good health Clinical trial Pancreatic Neoplasms 030104 developmental biology lcsh:Biology (General) Drug Resistance Neoplasm Taxoids business Combination drug medicine.drug Carcinoma Pancreatic Ductal |
| Zdroj: | Cell Reports, Vol 16, Iss 7, Pp 2017-2031 (2016) |
| Popis: | SummaryPancreatic ductal adenocarcinoma (PDAC) harbors the worst prognosis of any common solid tumor, and multiple failed clinical trials indicate therapeutic recalcitrance. Here, we use exome sequencing of patient tumors and find multiple conserved genetic alterations. However, the majority of tumors exhibit no clearly defined therapeutic target. High-throughput drug screens using patient-derived cell lines found rare examples of sensitivity to monotherapy, with most models requiring combination therapy. Using PDX models, we confirmed the effectiveness and selectivity of the identified treatment responses. Out of more than 500 single and combination drug regimens tested, no single treatment was effective for the majority of PDAC tumors, and each case had unique sensitivity profiles that could not be predicted using genetic analyses. These data indicate a shortcoming of reliance on genetic analysis to predict efficacy of currently available agents against PDAC and suggest that sensitivity profiling of patient-derived models could inform personalized therapy design for PDAC. |
| Databáze: | OpenAIRE |
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