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T. Herold et al.
reached superior predictive power (Table 3). When we restricted the analysis to patients aged 60 years or over (n=118), only PS29MRCdic was left as significant variable (HR=2.04, 95%CI: 1.35, 3.21; P=0.0012).
Recently, a highly significant prognostic tool based on “stemness” gene expression markers (LSC17) was pub- lished.25 A modified version of this classifier (retrained response LSC17) was developed to predict resistant dis- ease. In multivariable testing, PS29MRCdic outperformed these predictors that are solely based on gene expression variables (Table 3). The extremely high OR is the result of a very low variance of LSC17 (range -0.40 to 0.41).
Comparable results were achieved for PS29MRC as continuous variable (Online Supplementary Table S5 and Online Supplementary Figure S3A).
Discussion
We developed a powerful predictor for primary therapy resistance in AML. PS29MRC was validated in a fully independent patient cohort using a different technique to measure gene expression. The predictor also strongly associated with survival, which emphasizes the impor- tance of the initial response to therapy. In our analysis, the predictive power for survival was limited to the interme- diate and unfavorable ELN2017 genetic risk groups, possi- bly due to the low rate of resistant patients in the favor- able genetic risk subgroup. PS29MRC identified a high- risk patient group of approximately 20% of all intensively treated AML patients with a median survival of only eight months, and a survival probability of only 12% at 24 months. Therefore, regarding the toxicity and side effects of induction treatment, it appears questionable as to whether this treatment can be still considered “standard” for this patient subgroup. PS29MRC showed limited effectiveness in high-risk groups defined by, for example, TP53 alterations, but was stronger in patients without cur-
rently established predictive markers and the intermediate cytogenetic risk group.
The development process of PS29MRC included exten- sive evaluation of mutational data from recurrently mutat- ed genes in AML. Interestingly, as shown previously by Walter et al.,6 we were not able to improve the predictive ability of our model by including information on the muta- tional status of these AML associated genes in the classifi- er.6
The gene expression markers included in our signature can only be surrogates of cellular pathways predicting resistant disease or directly responsible for the refractory phenotype. MIR155HG, the host gene of miR-155, is one of the most important markers in our signature. High expression of the microRNA miR-155 has already been shown to be associated with an aggressive phenotype in AML with normal karyotype.26 mir-155 is regulated by NF- κB and could be repressed by the inhibitor MLN4924 (Pevonedistat) which has already entered phase II clinical trials (clinicaltrials.gov identifier: 02610777).27
Allogeneic SCT is currently the only curative option for patients with RD.2 However, to our knowledge there is currently no accepted standard treatment that can be offered to patients with a very high probability of RD before undergoing SCT.28 Approaches including low-dose chemotherapy or hypomethylating agents are possible options.29 Of note, the rate of SCT was lower in AMLCG patients that were resistant to induction treatment than for patients with an indication for SCT in the post-remission phase due to unfavorable cytogenetic markers (data not shown). One explanation could be the usually poor physical condition of resistant AML patients due to prolonged cytopenia after induction treatment and refractory disease. However, so far no randomized trial has demonstrated that avoiding intensive induction treatment and selecting other approaches would result in a higher SCT rate. The extremely poor prognosis of patients with RD shows an urgent need for alternative treatment approaches since a
Table 3. Univariate and multivariable analysis of the prediction of resistant disease of PS29MRCdic and alternative models in the validation set.
Variable
PS29MRCdic
AML-score by Walter et al.6
Variable
PS29MRCdic
Molecular Version of the AML-score by Walter et al.6
Variable
PS29MRCdic LSC17
Variable
PS29MRCdic
Retrained response LSC17
OR [95%-CI]
8.77 [4.27; 18.84] 1.21 [1.05; 1.39]
P
8.15·10-9 0.0089
OR [95%-CI]
9.98 [4.92; 21.25] 1.28 [1.12; 1.48]
P
5.95·10-10 0.00068
Multivariable analysis, n=225§
Univariate analysis*
Multivariable analysis, n=225§
Univariate analysis*
OR [95%-CI]
8.82 [4.28; 18.98] 1.21 [0.96; 1.53]
P
8.54·10-9 0.11
OR [95%-CI]
9.98 [4.92; 21.25] 1.37 [1.11; 1.69]
Univariate analysis
8.03 [4.07; 16.46] 51.36 [7.80; 388.15]
Univariate analysis
8.03 [4.07; 16.46] 1.97 [1.16; 3.39]
P
5.95·10-10 0.0032
P
4.29·10-9 7.29·10-5
P
4.29·10-9 0.013
Multivariable analysis, n=235
OR [95%-CI]
6.10 [2.99; 12.87] 11.51 [1.44; 99.78]
P
1.10·10-6 0.023
OR [95%-CI]
Multivariable analysis, n=235
462
OR [95%-CI]
7.44 [3.65; 15.78]
1.22 [0.66; 2.24]
P
6.81·10-8 0.51
OR [95%-CI]
§N=10 patients had to be excluded due to missing variables to calculate the AML-score by Walter et al.6 * To allow a fair comparison, univariate analyses were performed on the subset of patients with available information on all compared variables. OR: Odds Ratio; CI: Confidence Interval.
haematologica | 2018; 103(3)