Resistance prediction in AML: analysis of 4601 patients from MRC/NCRI, HOVON/SAKK, SWOG and MD Anderson Cancer Center
Options
BORIS DOI
Date of Publication
September 5, 2014
Publication Type
Article
Division/Institute
Author
Walter, R B | |
Othus, M | |
Burnett, A K | |
Löwenberg, B | |
Kantarjian, H M | |
Ossenkoppele, G J | |
Hills, R K | |
Ravandi, F | |
Evans, A | |
Pierce, S R | |
Vekemans, M-C | |
Appelbaum, F R | |
Estey, E H |
Subject(s)
Series
Leukemia
ISSN or ISBN (if monograph)
0887-6924
Publisher
Nature Publishing Group
Language
English
Publisher DOI
PubMed ID
25113226
Description
Therapeutic 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.
File(s)
File | File Type | Format | Size | License | Publisher/Copright statement | Content | |
---|---|---|---|---|---|---|---|
leu2014242a.pdf | text | Adobe PDF | 801.77 KB | publisher | published |