A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia

Abstract

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.

Figure 1.

Definitions of response and study design. (A) Figure showing the details of the response definition. (B) Flow chart showing the study design and distribution of patients. *Patients analyzed by targeted sequencing for 68 genes recurrently mutated in acute myeloid leukemia. RD: resistant disease; AML: acute myeloid leukemia; PB: peripheral blood; PS29MRC: Predictive Score 29 MRC; HOVON: Haemato-Oncology Foundation for Aults in the Netherlands.

Figure 2.

Signature and weights. Variables included in the predictive score PS29MRC. The final score is calculated as the weighted sum of these values (MRC high risk/low risk as 1 or −1, respectively). The final classifier consisted of 29 gene expression markers and the favorable and unfavorable cytogenetic MRC groups. Variables in red are associated with resistant disease; variables in blue are predictive for a response to induction treatment.

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 variable (PS29MRCdic) in the validation set. (A) ROC curve showing the performance of PS29MRCcont and other predictive scores in the validation set at varying thresholds. 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.

Figure 4.

Refinement of the European LeukemiaNet (ELN) 2017 genetic risk classification (ELN2017) by predictive score PS29MRC. (A) Pie charts showing the distribution of patients according to ELN2017 and refined risk criteria. (B) Kaplan-Meier estimates of acute myeloid leukemia (AML) patients in the validation set according to ELN2017 and the refined ELN2017 classification. (C) Scheme of reclassification of the three ELN2017 risk groups into four groups by integrating PS29MRC as a dichotomous variable (PS29MRCdic) (high risk) with the ELN2017 risk classification.

Figure 5.

Predictive ability of predictive score PS29MRC in genetic subgroups of acute myeloid leukemia (AML). (A) Bar plots showing the predictive ability of PS29MRC as a dichotomous variable (PS29MRCdic) in various genetic subgroups. The y-axis shows the absolute number of patients included. Patients in blue were predicted to respond to treatment (PS29MRCdic, low risk). Patients in red were predicted as resistant disease (PS29MRCdic, high risk). The accuracy is given in percentage. (B-E) Overall survival of AML patients in selected genetic subgroups. Kaplan-Meier estimates of AML patients classified according to PS29MRCdic as low risk and high risk.