@article{Tobias Herold_Vindi Jurinovic_Aarif M. N. Batcha_Stefanos A. Bamopoulos_Maja Rothenberg-Thurley_Bianka Ksienzyk_Luise Hartmann_Philipp A. Greif_Julia Phillippou-Massier_Stefan Krebs_Helmut Blum_Susanne Amler_Stephanie Schneider_Nikola Konstandin_Maria Cristina Sauerland_Dennis Görlich_Wolfgang E. Berdel_Bernhard J. Wörmann_Johanna Tischer_Marion Subklewe_Stefan K. Bohlander_Jan Braess_Wolfgang Hiddemann_Klaus H. Metzeler_Ulrich Mansmann_Karsten Spiekermann_2018, place={Pavia, Italy}, title={A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia}, volume={103}, url={https://haematologica.org/article/view/8383}, DOI={10.3324/haematol.2017.178442}, abstractNote={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, <em>P</em>=8.63·10<sup>−9</sup>, AUC=0.76) and as a dichotomous classifier (OR=8.03, <em>P</em>=4.29·10<sup>−9</sup>); accuracy was 77%. In multivariable models, only <em>TP53</em> mutation, age and PS29MRC (continuous: OR=1.75, <em>P</em>=0.0011; dichotomous: OR=4.44, <em>P</em>=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 (<em>P</em>=4.01·10<sup>−10</sup&gt;). PS29MRC will make it possible to design trials which stratify induction treatment according to the probability of response, and refines the ELN 2017 classification.}, number={3}, journal={Haematologica}, author={Tobias Herold and Vindi Jurinovic and Aarif M. N. Batcha and Stefanos A. Bamopoulos and Maja Rothenberg-Thurley and Bianka Ksienzyk and Luise Hartmann and Philipp A. Greif and Julia Phillippou-Massier and Stefan Krebs and Helmut Blum and Susanne Amler and Stephanie Schneider and Nikola Konstandin and Maria Cristina Sauerland and Dennis Görlich and Wolfgang E. Berdel and Bernhard J. Wörmann and Johanna Tischer and Marion Subklewe and Stefan K. Bohlander and Jan Braess and Wolfgang Hiddemann and Klaus H. Metzeler and Ulrich Mansmann and Karsten Spiekermann}, year={2018}, month={Feb.}, pages={456-465} }