Publication:
Resistance prediction in AML: analysis of 4601 patients from MRC/NCRI, HOVON/SAKK, SWOG and MD Anderson Cancer Center

cris.virtualsource.author-orcid1b65be99-ede2-4b0e-8e6d-1c720e453513
datacite.rightsrestricted
dc.contributor.authorWalter, R B
dc.contributor.authorOthus, M
dc.contributor.authorBurnett, A K
dc.contributor.authorLöwenberg, B
dc.contributor.authorKantarjian, H M
dc.contributor.authorOssenkoppele, G J
dc.contributor.authorHills, R K
dc.contributor.authorRavandi, F
dc.contributor.authorPabst, Thomas Niklaus
dc.contributor.authorEvans, A
dc.contributor.authorPierce, S R
dc.contributor.authorVekemans, M-C
dc.contributor.authorAppelbaum, F R
dc.contributor.authorEstey, E H
dc.date.accessioned2024-10-23T17:29:19Z
dc.date.available2024-10-23T17:29:19Z
dc.date.issued2014-09-05
dc.description.abstractTherapeutic resistance remains the principal problem in acute myeloid leukemia (AML). We used area under receiver-operating characteristic curves (AUCs) to quantify our ability to predict therapeutic resistance in individual patients, where AUC=1.0 denotes perfect prediction and AUC=0.5 denotes a coin flip, using data from 4601 patients with newly diagnosed AML given induction therapy with 3+7 or more intense standard regimens in UK Medical Research Council/National Cancer Research Institute, Dutch–Belgian Cooperative Trial Group for Hematology/Oncology/Swiss Group for Clinical Cancer Research, US cooperative group SWOG and MD Anderson Cancer Center studies. Age, performance status, white blood cell count, secondary disease, cytogenetic risk and FLT3-ITD/NPM1 mutation status were each independently associated with failure to achieve complete remission despite no early death (‘primary refractoriness’). However, the AUC of a bootstrap-corrected multivariable model predicting this outcome was only 0.78, indicating only fair predictive ability. Removal of FLT3-ITD and NPM1 information only slightly decreased the AUC (0.76). Prediction of resistance, defined as primary refractoriness or short relapse-free survival, was even more difficult. Our limited ability to forecast resistance based on routinely available pretreatment covariates provides a rationale for continued randomization between standard and new therapies and supports further examination of genetic and posttreatment data to optimize resistance prediction in AML.
dc.description.numberOfPages9
dc.description.sponsorshipDepartement Klinische Forschung, Forschungsgruppe Med. Onkologie / Hämatologie (Erw.)
dc.identifier.doi10.7892/boris.62863
dc.identifier.pmid25113226
dc.identifier.publisherDOI10.1038/leu.2014.242
dc.identifier.urihttps://boris-portal.unibe.ch/handle/20.500.12422/129069
dc.language.isoen
dc.publisherNature Publishing Group
dc.relation.ispartofLeukemia
dc.relation.issn0887-6924
dc.relation.organizationDCD5A442BE58E17DE0405C82790C4DE2
dc.relation.organizationDCD5A442C448E17DE0405C82790C4DE2
dc.subject.ddc600 - Technology::610 - Medicine & health
dc.titleResistance prediction in AML: analysis of 4601 patients from MRC/NCRI, HOVON/SAKK, SWOG and MD Anderson Cancer Center
dc.typearticle
dspace.entity.typePublication
dspace.file.typetext
oaire.citation.endPage320
oaire.citation.issue2
oaire.citation.startPage312
oaire.citation.volume29
oairecerif.author.affiliationDepartement Klinische Forschung, Forschungsgruppe Med. Onkologie / Hämatologie (Erw.)
oairecerif.author.affiliation2Universitätsklinik für Medizinische Onkologie
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unibe.description.ispublishedpub
unibe.eprints.legacyId62863
unibe.journal.abbrevTitleLEUKEMIA
unibe.refereedtrue
unibe.subtype.articlejournal

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