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Statistical classification of treatment responses in mouse clinical trials for stratified medicine in oncology drug discovery

dc.rights.licenseopenen_US
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorSAVEL, Helene
hal.structure.identifierIPSEN Innovation
dc.contributor.authorMEYER-LOSIC, Florence
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorPROUST LIMA, Cecile
ORCID: 0000-0002-9884-955X
IDREF: 114375747
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierInstitut Bergonié [Bordeaux]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorRICHERT, Laura
dc.date.accessioned2024-03-11T14:08:48Z
dc.date.available2024-03-11T14:08:48Z
dc.date.issued2024-01-09
dc.identifier.issn2045-2322en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/188684
dc.description.abstractEnTranslational oncology research strives to explore a new aspect: identifying subgroups that exhibit treatment response even during pre-clinical phases. In this study, we focus on PDX models and their implementation in mouse clinical trials (MCT). Our primary objective was to identify subgroups with different treatment responses using Latent Class Mixed Model (LCMM).We used a public dataset and focused on one treatment, encorafenib, and two indications, melanoma and colorectal cancer, for which efficacy depends on a specific mutation BRAF V600E. One LCMM per indication was implemented to classify treatment responses at the PDX level, analyzing the growth kinetics of treated tumors and matched controls within the PDX models. A simulation study was carried out to explore the performance of LCMM in this context. For both applications, LCMM identified classes for which the higher the proportion of mutated BRAF V600E PDX models the greater the treatment effect, which is aligned with encorafenib use recommendations. The simulation study showed that LCMM could identify classes with large differences in treatment effects. LCMM is a suitable tool for MCT to explore treatment response subgroups of PDX. Once these subgroups are defined, characterization of their phenotypes/genotypes could be performed to explore treatment response predictors.
dc.language.isoENen_US
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.title.enStatistical classification of treatment responses in mouse clinical trials for stratified medicine in oncology drug discovery
dc.typeArticle de revueen_US
dc.identifier.doi10.1038/s41598-023-51055-7en_US
dc.subject.halStatistiques [stat]en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
bordeaux.journalScientific Reportsen_US
bordeaux.page934en_US
bordeaux.volume14en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.issue1en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.institutionINRIAen_US
bordeaux.teamSISTM_BPHen_US
bordeaux.teamBIOSTAT_BPHen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.identifier.funderIDIpsenen_US
bordeaux.import.sourcehal
hal.identifierhal-04403059
hal.version1
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exportfalse
workflow.import.sourcehal
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Scientific%20Reports&rft.date=2024-01-09&rft.volume=14&rft.issue=1&rft.spage=934&rft.epage=934&rft.eissn=2045-2322&rft.issn=2045-2322&rft.au=SAVEL,%20Helene&MEYER-LOSIC,%20Florence&PROUST%20LIMA,%20Cecile&RICHERT,%20Laura&rft.genre=article


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