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A clinically applicable gene expression–based score predicts resistance to induction treatment in acute myeloid leukemia

Prediction of resistant disease at initial diagnosis of acute myeloid leukemia (AML) can be achieved with high accuracy using cytogenetic data and 29 gene expression markers (Predictive Score 29 Medical Research Council; PS29MRC). Our aim was to establish PS29MRC as a clinically usable assay by usin...

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Autores principales: Moser, Christian, Jurinovic, Vindi, Sagebiel-Kohler, Sabine, Ksienzyk, Bianka, Batcha, Aarif M. N., Dufour, Annika, Schneider, Stephanie, Rothenberg-Thurley, Maja, Sauerland, Cristina M., Görlich, Dennis, Berdel, Wolfgang E., Krug, Utz, Mansmann, Ulrich, Hiddemann, Wolfgang, Braess, Jan, Spiekermann, Karsten, Greif, Philipp A., Vosberg, Sebastian, Metzeler, Klaus H., Kumbrink, Jörg, Herold, Tobias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society of Hematology 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8759116/
https://www.ncbi.nlm.nih.gov/pubmed/34535016
http://dx.doi.org/10.1182/bloodadvances.2021004814
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author Moser, Christian
Jurinovic, Vindi
Sagebiel-Kohler, Sabine
Ksienzyk, Bianka
Batcha, Aarif M. N.
Dufour, Annika
Schneider, Stephanie
Rothenberg-Thurley, Maja
Sauerland, Cristina M.
Görlich, Dennis
Berdel, Wolfgang E.
Krug, Utz
Mansmann, Ulrich
Hiddemann, Wolfgang
Braess, Jan
Spiekermann, Karsten
Greif, Philipp A.
Vosberg, Sebastian
Metzeler, Klaus H.
Kumbrink, Jörg
Herold, Tobias
author_facet Moser, Christian
Jurinovic, Vindi
Sagebiel-Kohler, Sabine
Ksienzyk, Bianka
Batcha, Aarif M. N.
Dufour, Annika
Schneider, Stephanie
Rothenberg-Thurley, Maja
Sauerland, Cristina M.
Görlich, Dennis
Berdel, Wolfgang E.
Krug, Utz
Mansmann, Ulrich
Hiddemann, Wolfgang
Braess, Jan
Spiekermann, Karsten
Greif, Philipp A.
Vosberg, Sebastian
Metzeler, Klaus H.
Kumbrink, Jörg
Herold, Tobias
author_sort Moser, Christian
collection PubMed
description Prediction of resistant disease at initial diagnosis of acute myeloid leukemia (AML) can be achieved with high accuracy using cytogenetic data and 29 gene expression markers (Predictive Score 29 Medical Research Council; PS29MRC). Our aim was to establish PS29MRC as a clinically usable assay by using the widely implemented NanoString platform and further validate the classifier in a more recently treated patient cohort. Analyses were performed on 351 patients with newly diagnosed AML intensively treated within the German AML Cooperative Group registry. As a continuous variable, PS29MRC performed best in predicting induction failure in comparison with previously published risk models. The classifier was strongly associated with overall survival. We were able to establish a previously defined cutoff that allows classifier dichotomization (PS29MRCdic). PS29MRCdic significantly identified induction failure with 59% sensitivity, 77% specificity, and 72% overall accuracy (odds ratio, 4.81; P = 4.15 × 10(−10)). PS29MRCdic was able to improve the European Leukemia Network 2017 (ELN-2017) risk classification within every category. The median overall survival with high PS29MRCdic was 1.8 years compared with 4.3 years for low-risk patients. In multivariate analysis including ELN-2017 and clinical and genetic markers, only age and PS29MRCdic were independent predictors of refractory disease. In patients aged ≥60 years, only PS29MRCdic remained as a significant variable. In summary, we confirmed PS29MRC as a valuable classifier to identify high-risk patients with AML. Risk classification can still be refined beyond ELN-2017, and predictive classifiers might facilitate clinical trials focusing on these high-risk patients with AML.
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spelling pubmed-87591162022-01-14 A clinically applicable gene expression–based score predicts resistance to induction treatment in acute myeloid leukemia Moser, Christian Jurinovic, Vindi Sagebiel-Kohler, Sabine Ksienzyk, Bianka Batcha, Aarif M. N. Dufour, Annika Schneider, Stephanie Rothenberg-Thurley, Maja Sauerland, Cristina M. Görlich, Dennis Berdel, Wolfgang E. Krug, Utz Mansmann, Ulrich Hiddemann, Wolfgang Braess, Jan Spiekermann, Karsten Greif, Philipp A. Vosberg, Sebastian Metzeler, Klaus H. Kumbrink, Jörg Herold, Tobias Blood Adv Myeloid Neoplasia Prediction of resistant disease at initial diagnosis of acute myeloid leukemia (AML) can be achieved with high accuracy using cytogenetic data and 29 gene expression markers (Predictive Score 29 Medical Research Council; PS29MRC). Our aim was to establish PS29MRC as a clinically usable assay by using the widely implemented NanoString platform and further validate the classifier in a more recently treated patient cohort. Analyses were performed on 351 patients with newly diagnosed AML intensively treated within the German AML Cooperative Group registry. As a continuous variable, PS29MRC performed best in predicting induction failure in comparison with previously published risk models. The classifier was strongly associated with overall survival. We were able to establish a previously defined cutoff that allows classifier dichotomization (PS29MRCdic). PS29MRCdic significantly identified induction failure with 59% sensitivity, 77% specificity, and 72% overall accuracy (odds ratio, 4.81; P = 4.15 × 10(−10)). PS29MRCdic was able to improve the European Leukemia Network 2017 (ELN-2017) risk classification within every category. The median overall survival with high PS29MRCdic was 1.8 years compared with 4.3 years for low-risk patients. In multivariate analysis including ELN-2017 and clinical and genetic markers, only age and PS29MRCdic were independent predictors of refractory disease. In patients aged ≥60 years, only PS29MRCdic remained as a significant variable. In summary, we confirmed PS29MRC as a valuable classifier to identify high-risk patients with AML. Risk classification can still be refined beyond ELN-2017, and predictive classifiers might facilitate clinical trials focusing on these high-risk patients with AML. American Society of Hematology 2021-11-22 /pmc/articles/PMC8759116/ /pubmed/34535016 http://dx.doi.org/10.1182/bloodadvances.2021004814 Text en © 2021 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved.
spellingShingle Myeloid Neoplasia
Moser, Christian
Jurinovic, Vindi
Sagebiel-Kohler, Sabine
Ksienzyk, Bianka
Batcha, Aarif M. N.
Dufour, Annika
Schneider, Stephanie
Rothenberg-Thurley, Maja
Sauerland, Cristina M.
Görlich, Dennis
Berdel, Wolfgang E.
Krug, Utz
Mansmann, Ulrich
Hiddemann, Wolfgang
Braess, Jan
Spiekermann, Karsten
Greif, Philipp A.
Vosberg, Sebastian
Metzeler, Klaus H.
Kumbrink, Jörg
Herold, Tobias
A clinically applicable gene expression–based score predicts resistance to induction treatment in acute myeloid leukemia
title A clinically applicable gene expression–based score predicts resistance to induction treatment in acute myeloid leukemia
title_full A clinically applicable gene expression–based score predicts resistance to induction treatment in acute myeloid leukemia
title_fullStr A clinically applicable gene expression–based score predicts resistance to induction treatment in acute myeloid leukemia
title_full_unstemmed A clinically applicable gene expression–based score predicts resistance to induction treatment in acute myeloid leukemia
title_short A clinically applicable gene expression–based score predicts resistance to induction treatment in acute myeloid leukemia
title_sort clinically applicable gene expression–based score predicts resistance to induction treatment in acute myeloid leukemia
topic Myeloid Neoplasia
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8759116/
https://www.ncbi.nlm.nih.gov/pubmed/34535016
http://dx.doi.org/10.1182/bloodadvances.2021004814
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