Walter, R BR BWalterOthus, MMOthusBurnett, A KA KBurnettLöwenberg, BBLöwenbergKantarjian, H MH MKantarjianOssenkoppele, G JG JOssenkoppeleHills, R KR KHillsRavandi, FFRavandiPabst, Thomas NiklausThomas NiklausPabstEvans, AAEvansPierce, S RS RPierceVekemans, M-CM-CVekemansAppelbaum, F RF RAppelbaumEstey, E HE HEstey2024-10-232024-10-232014-09-05https://boris-portal.unibe.ch/handle/20.500.12422/129069Therapeutic 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.en600 - Technology::610 - Medicine & healtharticle10.7892/boris.628632511322610.1038/leu.2014.242