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A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia
Primary therapy resistance is a major problem in acute myeloid leukemia treatment. We set out to develop a powerful and robust predictor for therapy resistance for intensively treated adult patients. We used two large gene expression data sets (n=856) to develop a predictor of therapy resistance, wh...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Ferrata Storti Foundation
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5830382/ https://www.ncbi.nlm.nih.gov/pubmed/29242298 http://dx.doi.org/10.3324/haematol.2017.178442 |
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author | Herold, Tobias Jurinovic, Vindi Batcha, Aarif M. N. Bamopoulos, Stefanos A. Rothenberg-Thurley, Maja Ksienzyk, Bianka Hartmann, Luise Greif, Philipp A. Phillippou-Massier, Julia Krebs, Stefan Blum, Helmut Amler, Susanne Schneider, Stephanie Konstandin, Nikola Sauerland, Maria Cristina Görlich, Dennis Berdel, Wolfgang E. Wörmann, Bernhard J. Tischer, Johanna Subklewe, Marion Bohlander, Stefan K. Braess, Jan Hiddemann, Wolfgang Metzeler, Klaus H. Mansmann, Ulrich Spiekermann, Karsten |
author_facet | Herold, Tobias Jurinovic, Vindi Batcha, Aarif M. N. Bamopoulos, Stefanos A. Rothenberg-Thurley, Maja Ksienzyk, Bianka Hartmann, Luise Greif, Philipp A. Phillippou-Massier, Julia Krebs, Stefan Blum, Helmut Amler, Susanne Schneider, Stephanie Konstandin, Nikola Sauerland, Maria Cristina Görlich, Dennis Berdel, Wolfgang E. Wörmann, Bernhard J. Tischer, Johanna Subklewe, Marion Bohlander, Stefan K. Braess, Jan Hiddemann, Wolfgang Metzeler, Klaus H. Mansmann, Ulrich Spiekermann, Karsten |
author_sort | Herold, Tobias |
collection | PubMed |
description | Primary therapy resistance is a major problem in acute myeloid leukemia treatment. We set out to develop a powerful and robust predictor for therapy resistance for intensively treated adult patients. We used two large gene expression data sets (n=856) to develop a predictor of therapy resistance, which was validated in an independent cohort analyzed by RNA sequencing (n=250). In addition to gene expression markers, standard clinical and laboratory variables as well as the mutation status of 68 genes were considered during construction of the model. The final predictor (PS29MRC) consisted of 29 gene expression markers and a cytogenetic risk classification. A continuous predictor is calculated as a weighted linear sum of the individual variables. In addition, a cut off was defined to divide patients into a high-risk and a low-risk group for resistant disease. PS29MRC was highly significant in the validation set, both as a continuous score (OR=2.39, P=8.63·10(−9), AUC=0.76) and as a dichotomous classifier (OR=8.03, P=4.29·10(−9)); accuracy was 77%. In multivariable models, only TP53 mutation, age and PS29MRC (continuous: OR=1.75, P=0.0011; dichotomous: OR=4.44, P=0.00021) were left as significant variables. PS29MRC dominated all models when compared with currently used predictors, and also predicted overall survival independently of established markers. When integrated into the European LeukemiaNet (ELN) 2017 genetic risk stratification, four groups (median survival of 8, 18, 41 months, and not reached) could be defined (P=4.01·10(−10)). PS29MRC will make it possible to design trials which stratify induction treatment according to the probability of response, and refines the ELN 2017 classification. |
format | Online Article Text |
id | pubmed-5830382 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Ferrata Storti Foundation |
record_format | MEDLINE/PubMed |
spelling | pubmed-58303822018-03-16 A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia Herold, Tobias Jurinovic, Vindi Batcha, Aarif M. N. Bamopoulos, Stefanos A. Rothenberg-Thurley, Maja Ksienzyk, Bianka Hartmann, Luise Greif, Philipp A. Phillippou-Massier, Julia Krebs, Stefan Blum, Helmut Amler, Susanne Schneider, Stephanie Konstandin, Nikola Sauerland, Maria Cristina Görlich, Dennis Berdel, Wolfgang E. Wörmann, Bernhard J. Tischer, Johanna Subklewe, Marion Bohlander, Stefan K. Braess, Jan Hiddemann, Wolfgang Metzeler, Klaus H. Mansmann, Ulrich Spiekermann, Karsten Haematologica Article Primary therapy resistance is a major problem in acute myeloid leukemia treatment. We set out to develop a powerful and robust predictor for therapy resistance for intensively treated adult patients. We used two large gene expression data sets (n=856) to develop a predictor of therapy resistance, which was validated in an independent cohort analyzed by RNA sequencing (n=250). In addition to gene expression markers, standard clinical and laboratory variables as well as the mutation status of 68 genes were considered during construction of the model. The final predictor (PS29MRC) consisted of 29 gene expression markers and a cytogenetic risk classification. A continuous predictor is calculated as a weighted linear sum of the individual variables. In addition, a cut off was defined to divide patients into a high-risk and a low-risk group for resistant disease. PS29MRC was highly significant in the validation set, both as a continuous score (OR=2.39, P=8.63·10(−9), AUC=0.76) and as a dichotomous classifier (OR=8.03, P=4.29·10(−9)); accuracy was 77%. In multivariable models, only TP53 mutation, age and PS29MRC (continuous: OR=1.75, P=0.0011; dichotomous: OR=4.44, P=0.00021) were left as significant variables. PS29MRC dominated all models when compared with currently used predictors, and also predicted overall survival independently of established markers. When integrated into the European LeukemiaNet (ELN) 2017 genetic risk stratification, four groups (median survival of 8, 18, 41 months, and not reached) could be defined (P=4.01·10(−10)). PS29MRC will make it possible to design trials which stratify induction treatment according to the probability of response, and refines the ELN 2017 classification. Ferrata Storti Foundation 2018-03 /pmc/articles/PMC5830382/ /pubmed/29242298 http://dx.doi.org/10.3324/haematol.2017.178442 Text en Copyright© 2018 Ferrata Storti Foundation Material published in Haematologica is covered by copyright. All rights are reserved to the Ferrata Storti Foundation. Use of published material is allowed under the following terms and conditions: https://creativecommons.org/licenses/by-nc/4.0/legalcode. Copies of published material are allowed for personal or internal use. Sharing published material for non-commercial purposes is subject to the following conditions: https://creativecommons.org/licenses/by-nc/4.0/legalcode, sect. 3. Reproducing and sharing published material for commercial purposes is not allowed without permission in writing from the publisher. |
spellingShingle | Article Herold, Tobias Jurinovic, Vindi Batcha, Aarif M. N. Bamopoulos, Stefanos A. Rothenberg-Thurley, Maja Ksienzyk, Bianka Hartmann, Luise Greif, Philipp A. Phillippou-Massier, Julia Krebs, Stefan Blum, Helmut Amler, Susanne Schneider, Stephanie Konstandin, Nikola Sauerland, Maria Cristina Görlich, Dennis Berdel, Wolfgang E. Wörmann, Bernhard J. Tischer, Johanna Subklewe, Marion Bohlander, Stefan K. Braess, Jan Hiddemann, Wolfgang Metzeler, Klaus H. Mansmann, Ulrich Spiekermann, Karsten A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia |
title | A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia |
title_full | A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia |
title_fullStr | A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia |
title_full_unstemmed | A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia |
title_short | A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia |
title_sort | 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5830382/ https://www.ncbi.nlm.nih.gov/pubmed/29242298 http://dx.doi.org/10.3324/haematol.2017.178442 |
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