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Prediction of primary resistant AML
We also classified the heterogeneously treated AML patients included in the TCGA analysis23 with PS29MRCdic (available gene expression samples n=183). The predictor was highly predictive for OS in the inde- pendent data set (Online Supplementary Figure S6).
Performance of the classifier in genetic subgroups
Mutation profiles defining genetic subgroups in AML have been recently defined.17,24 We used these subgroups to further characterize the predictive potential of PS29MRCdic. A detailed picture of the analysis is shown in Figure 5A and Online Supplementary Figure S7A. The classifier reached very high accuracy in the genetic sub- groups defined by Metzeler et al.17 as core binding factor alterations (CBF), KMT2A rearranged AML, AML with biallelic CEBPA mutations, and AML with NPM1 muta- tions. Moderate predictive accuracy was achieved in the high-risk subgroups defined by TP53 and RUNX1 alter- ations. Here, PS29MRCdic was not able to significantly predict OS (Figure 5B and D). In the large subgroup of patients without classifiable genetic alterations, PS29MRCdic was able to significantly predict survival (P=0.027) (Figure 5E).
The results of the classification of genetic subgroups according to Papaemmanuil et al.24 show comparable results with a moderate predictive accuracy of PS29MRC in high-risk subgroups defined by TP53 mutations, chro- mosomal aneuploidy or both, AML with mutated chro- matin, RNA-splicing genes or both, and AML with driver mutations but no class-defining lesions. In all other sub- groups, even though some had small sample sizes, PS29MRC reached very high predictive accuracy. In con- trast to the results seen with the classification according to Metzeler et al.,17 significantly, OS could only be predicted in the subgroup of patients with mutated chromatin, RNA-splicing genes, or both (Online Supplementary Figure S7B).
Performance of the classifier in comparison to currently used models
In pairwise comparisons to published, predictive classi- fiers like the model by Walter et al.3 (integrating informa- tion on age, performance status, white blood cell count, platelet count, bone marrow blasts, sex, type of AML, cytogenetics and NPM1 and FLT3-ITD status), or the modified molecular version of this score,6 PS29MRCdic
A
B
Figure 3. Receiver operating characteristic curve (ROC) of predictive score PS29MRC as a continuous variable (PS29MRCcont) and barplots showing the predictive performance of the PS29MRC as a dichotomous vari- able (PS29MRCdic) in the validation set. (A) ROC curve showing the performance of PS29MRCcont and other predictive scores in the validation set at varying thresh- olds. Area under receiver-operating characteristic curve (AUC): PS29MRCcont: 0.76; Walter-Score: 0.71; Retrained response LSC17: 0.61. (B) Bar plots showing the performance of PS29MRCdic in subgroups defined by the European LeukemiaNet (ELN) 2017 genetic risk classification (ELN2017). CR: complete remission.
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