Show simple item record

dc.contributor.authorWitkiewicz, Agnieszka K
dc.contributor.authorBalaji, Uthra
dc.contributor.authorEslinger, Cody
dc.contributor.authorMcMillan, Elizabeth
dc.contributor.authorConway, William
dc.contributor.authorPosner, Bruce
dc.contributor.authorMills, Gordon B
dc.contributor.authorO'Reilly, Eileen M
dc.contributor.authorKnudsen, Erik S
dc.date.accessioned2016-11-03T02:51:35Z
dc.date.available2016-11-03T02:51:35Z
dc.date.issued2016-08-16
dc.identifier.citationIntegrated Patient-Derived Models Delineate Individualized Therapeutic Vulnerabilities of Pancreatic Cancer. 2016, 16 (7):2017-31 Cell Repen
dc.identifier.issn2211-1247
dc.identifier.pmid27498862
dc.identifier.doi10.1016/j.celrep.2016.07.023
dc.identifier.urihttp://hdl.handle.net/10150/621251
dc.description.abstractPancreatic 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.
dc.description.sponsorshipCancer Center Support grant of the Simmons Cancer Center [P30 CA142543]; NIH [CA142543-05S2]en
dc.language.isoenen
dc.publisherCELL PRESSen
dc.relation.urlhttp://www.cell.com/cell-reports/fulltext/S2211-1247(16)30924-Xen
dc.rights© 2016 The Author(s). Creative Commons Attribution – NonCommercial – NoDerivs (CC BY-NC-ND 4.0).en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleIntegrated Patient-Derived Models Delineate Individualized Therapeutic Vulnerabilities of Pancreatic Cancer.en
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Dept Patholen
dc.contributor.departmentUniv Arizona, Ctr Cancen
dc.contributor.departmentUniv Arizona, Dept Meden
dc.identifier.journalCell reportsen
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
refterms.dateFOA2018-09-11T15:31:21Z
html.description.abstractPancreatic 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.


Files in this item

Thumbnail
Name:
PIIS221112471630924X.pdf
Size:
7.324Mb
Format:
PDF
Description:
Final Published Version

This item appears in the following Collection(s)

Show simple item record

© 2016 The Author(s). Creative Commons Attribution – NonCommercial – NoDerivs (CC BY-NC-ND 4.0).
Except where otherwise noted, this item's license is described as © 2016 The Author(s). Creative Commons Attribution – NonCommercial – NoDerivs (CC BY-NC-ND 4.0).