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dc.contributor.advisor
dc.contributor.authorBarish, Syndi
dc.contributor.authorOchs, Michael F.
dc.contributor.authorSontag, Eduardo D.
dc.contributor.authorGevertz, Jana L.
dc.date.accessioned2017-09-14T18:15:45Z
dc.date.available2017-09-14T18:15:45Z
dc.date.issued2017
dc.identifier.citationSyndi Barish, Michael F. Ochs, Eduardo D. Sontag, and Jana L. Gevertz. Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy, PNAS 2017 114 (31), E6277-E6286.en_US
dc.identifier.urihttp://dx.doi.org/10.1073/pnas.1703355114
dc.description.abstractCancer is a highly heterogeneous disease, exhibiting spatial and temporal variations that pose challenges for designing robust therapies. Here, we propose the VEPART (Virtual Expansion of Populations for Analyzing Robustness of Therapies) technique as a platform that integrates experimental data, mathematical modeling, and statistical analyses for identifying robust optimal treatment protocols. VEPART begins with time course experimental data for a sample population, and a mathematical model fit to aggregate data from that sample population. Using nonparametric statistics, the sample population is amplified and used to create a large number of virtual populations. At the final step of VEPART, robustness is assessed by identifying and analyzing the optimal therapy (perhaps restricted to a set of clinically realizable protocols) across each virtual population. As proof of concept, we have applied the VEPART method to study the robustness of treatment response in a mouse model of melanoma subject to treatment with immunostimulatory oncolytic viruses and dendritic cell vaccines. Our analysis (i) showed that every scheduling variant of the experimentally used treatment protocol is fragile (nonrobust) and (ii) discovered an alternative region of dosing space (lower oncolytic virus dose, higher dendritic cell dose) for which a robust optimal protocol exists.en_US
dc.description.sponsorshipNational Institute of Health (U.S.)en_US
dc.description.sponsorshipUnited States. Air Force. Office of Scientific Researchen_US
dc.language.isoen_USen_US
dc.publisherNational Academy of Sciencesen_US
dc.subjectRobust therapiesen_US
dc.subjectCancer treatmenten_US
dc.subjectMathematical modelingen_US
dc.subjectVirotherapyen_US
dc.subjectImmunotherapyen_US
dc.titleEvaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapyen_US
dc.typeArticleen_US
dc.typeTexten_US
dc.typePostprinten_US
prism.publicationNamePNASen_US
prism.volume114
prism.issueIdentifier31
prism.publicationDate2017
prism.startingPageE6277
prism.endingPageE6286
dc.identifier.handlehttps://dr.tcnj.edu/handle/2900/1396


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