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.