Page 100 - Haematologica3

T. Herold et al.
ables with P≤0.05 in training set 1 and the validation set for the prediction of RD. The results of PS29MRCdic are shown in Table 2. Only PS29MRCdic, age and TP53 muta- tions remained significant in the model. Comparable results were seen with PS29MRC as a continuous variable (Online Supplementary Table S1).
When we included all non-responders in the analysis (Figure 1A), PS29MRCcont (OR 2.34, 95%CI: 1.80, 3.13; P=1.48·10-9, AUC=0.75) and PS29MRCdic (OR 8.04, 95%CI: 4.20, 16.06; P=9.27·10-10) remained highly signifi- cant. In the multivariable model, only PS29MRCdic, age and TP53 mutations remained significant (PS29MRCdic: OR 5.08, 95%CI: 2.39; 11.19; P=3.41·10-5; age: OR 1.06, 95%CI: 1.03, 1.09; P=0.00013; TP53: OR 6.13, 95%CI: 1.56, 31.90; P=0.016). Comparable results were seen when only patients treated in the AMLCG 2008 trial were con- sidered (Online Supplementary Table S2).
To exclude the possibility that patients that achieve a CR after two courses of induction treatment are misclassi- fied as resistant, we also tested if PS29MRC was able to correctly forecast RD at day 60. In univariate, as well as in multivariable analysis, PS29MRC showed comparable results (Online Supplementary Table S3).
Characterization of the dichotomized classifier
PS29MRCdic was designed in the training set to identify resistant AML patients with high specificity. By applying the pre-defined cut off, we were able to reach a specificity of 148 of 164 (90%) and sensitivity of 33 of 71 (46%) in the independent validation set. The performance of the classifier in the training sets and the validation set is shown in Online Supplementary Figure S3. The accuracy of PS29MRCdic was 77% in the validation set.
When we included all patients with death in aplasia or
of indeterminate cause in the analysis (Figure 1A), the sen- sitivity of PS29MRCdic in predicting non-response to induction treatment was 40 of 86 (47%) and the accuracy 75%.
In the cytogenetic subgroups favorable (n=14; resistant: n=0; CR/CRi: n=13), intermediate (n=188; resistant: n=42; CR/CRi: n=136) and adverse (n=48; resistant: n=29; CR/CRi: n=15), the classifier showed an accuracy of 100%, 78% and 66%, respectively (Online Supplementary Figure S4). When we applied the ELN2017 genetic risk stratification, the accuracy of PS29MRCdic in the sub- groups favorable, intermediate and unfavorable was 89%, 74% and 70%, respectively (Figure 3B).
Since not achieving a CR/CRi is highly correlated with OS, we analyzed the performance of PS29MRCdic to serve as a prognostic tool. The classifier was a highly sig- nificant predictor of survival in univariate (HR 2.81, 95%CI: 1.98, 3.99; P=7.73·10-9) and multivariable (HR 2.15, 95%CI: 1.39, 3.31; P=0.00052) models, including all variables significant (P≤0.05) in training set 1 and the vali- dation set (Online Supplementary Table S4). When we inte- grated PS29MRCdic in the ELN2017 genetic risk stratifica- tion, four risk groups with a median OS of 8 months (95%CI: 5-10) in the PS29MRCdic high-risk group, 16 (95%CI: 8-41) months in the ELN2017 unfavorable group, and 'not reached' in the intermediate and favorable risk groups could be defined (Figure 4A-C). Approximately 50% of all ELN2017 unfavorable and approximately 12% of all ELN2017 intermediate-risk patients were classified as high risk according to PS29MRCdic. The probability of survival in the four risk groups at 24 months was 12%, 38%, 57% and 76%, respectively. Comparable results were seen when OS was not censored for SCT (Online Supplementary Figure S5).
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.
460
haematologica | 2018; 103(3)