000304584 001__ 304584
000304584 005__ 20250916115009.0
000304584 0247_ $$2doi$$a10.1186/s12935-025-03969-7
000304584 0247_ $$2pmid$$apmid:40946104
000304584 037__ $$aDKFZ-2025-01907
000304584 041__ $$aEnglish
000304584 082__ $$a610
000304584 1001_ $$aWansch, Katharina$$b0
000304584 245__ $$aMulti-drug pharmacotyping improves therapy prediction in pancreatic cancer organoids.
000304584 260__ $$aLondon$$bBioMed Central$$c2025
000304584 3367_ $$2DRIVER$$aarticle
000304584 3367_ $$2DataCite$$aOutput Types/Journal article
000304584 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1757942328_16521
000304584 3367_ $$2BibTeX$$aARTICLE
000304584 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000304584 3367_ $$00$$2EndNote$$aJournal Article
000304584 520__ $$aPatient-Derived Organoids (PDOs) represent a promising technology for therapy prediction in pancreatic cancer, with the potential of enhancing treatment outcomes and allowing more effective, personalized treatment choices. However, classification approaches into sensitive and resistant models remain very variable and are based on single-agent testing only, neglecting interactive effects of multi-drug combinations. Here, we established 13 PDOs and performed both single-agent and multi-drug testing. By comparing different clustering approaches of drug-response metrics and establishing a new classification approach based on pharmacokinetic modelling, we were able to evaluate which score best predicts the clinical response of patients. Our newly developed score considered the Area Under The Curve (AUC) of cell viability curves and reached a prediction accuracy of 85%. Our data supports previous findings for PDOs to constitute an effective platform for translational drug testing. Furthermore, our results suggest that the AUC is a more accurate drug-response metric than the half maximal inhibitory concentration (IC50), and that multi-drug testing yields a higher accuracy than single-agent testing. The methodology and outcomes presented in this study are of critical relevance for future PDO-based translational trials as they allow a new physiology-based approach towards multi-drug testing and classification of organoid response, which improves PDO prediction accuracy.
000304584 536__ $$0G:(DE-HGF)POF4-899$$a899 - ohne Topic (POF4-899)$$cPOF4-899$$fPOF IV$$x0
000304584 588__ $$aDataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
000304584 650_7 $$2Other$$aMulti-Drug response metrics
000304584 650_7 $$2Other$$aPancreatic adenocarcinoma
000304584 650_7 $$2Other$$aPatient-derived organoids
000304584 650_7 $$2Other$$aPharmacokinetic modelling
000304584 7001_ $$aPelzer, Uwe$$b1
000304584 7001_ $$aSchneider, François$$b2
000304584 7001_ $$aDölvers, Florian$$b3
000304584 7001_ $$aKühn, Anna$$b4
000304584 7001_ $$0P:(DE-He78)396821e874b632341e4bcabd27bcad3f$$aDragomir, Mihnea-Paul$$b5$$udkfz
000304584 7001_ $$aIhlow, Jana$$b6
000304584 7001_ $$aHilfenhaus, Georg$$b7
000304584 7001_ $$aVecchione, Loredana$$b8
000304584 7001_ $$aFelsenstein, Matthäus$$b9
000304584 7001_ $$aMa, Dou$$b10
000304584 7001_ $$aLerchbaumer, Markus$$b11
000304584 7001_ $$aJürgensen, Christian$$b12
000304584 7001_ $$aBahra, Marcus$$b13
000304584 7001_ $$aGranada, Adrian E$$b14
000304584 7001_ $$aDuwe, Gregor$$b15
000304584 7001_ $$aStintzing, Sebastian$$b16
000304584 7001_ $$aKeilholz, Ulrich$$b17
000304584 7001_ $$aNeumann, Christopher C M$$b18
000304584 773__ $$0PERI:(DE-600)2091573-1$$a10.1186/s12935-025-03969-7$$gVol. 25, no. 1, p. 321$$n1$$p321$$tCancer cell international$$v25$$x1475-2867$$y2025
000304584 909CO $$ooai:inrepo02.dkfz.de:304584$$pVDB
000304584 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)396821e874b632341e4bcabd27bcad3f$$aDeutsches Krebsforschungszentrum$$b5$$kDKFZ
000304584 9131_ $$0G:(DE-HGF)POF4-899$$1G:(DE-HGF)POF4-890$$2G:(DE-HGF)POF4-800$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bProgrammungebundene Forschung$$lohne Programm$$vohne Topic$$x0
000304584 9141_ $$y2025
000304584 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bCANCER CELL INT : 2022$$d2024-12-13
000304584 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2024-12-13
000304584 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2024-12-13
000304584 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2024-04-10T15:36:04Z
000304584 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2024-04-10T15:36:04Z
000304584 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Anonymous peer review$$d2024-04-10T15:36:04Z
000304584 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2024-12-13
000304584 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2024-12-13
000304584 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2024-12-13
000304584 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2024-12-13
000304584 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2024-12-13
000304584 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2024-12-13
000304584 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2024-12-13
000304584 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2024-12-13
000304584 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bCANCER CELL INT : 2022$$d2024-12-13
000304584 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2024-12-13
000304584 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2024-12-13
000304584 9201_ $$0I:(DE-He78)BE01-20160331$$kBE01$$lDKTK Koordinierungsstelle Berlin$$x0
000304584 980__ $$ajournal
000304584 980__ $$aVDB
000304584 980__ $$aI:(DE-He78)BE01-20160331
000304584 980__ $$aUNRESTRICTED