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Prediction of primary resistant AML
substantial number of patients do not benefit from induc- tion treatment. PS29MRC would offer an accurate tool to design and implement such trials.
We were able to demonstrate that the information on treatment response is included in the AML bulk itself at ini- tial diagnosis. However, even though our study included a large amount of data in order to construct a better predictor, we still were only just able to reach a fair AUC in an inde- pendent validation set which is higher, but still in the range, of recent publications.3,6,7 Similar to the work of Walter et al.,6 we seem to have reached an obstacle that could not be overcome even by the addition of more information. Since all patients were considered eligible for intensive treatment, there seem to be additional, currently unknown variables that influence the response to induction treatment. It is tempting to speculate which other variables in addition to the disease itself affect response. Maybe clonal heterogene- ity, individual drug metabolism or co-medications and interactions are more important than currently assumed. For example, CYP2E1 expression levels, which are associat- ed with response to treatment in our study, influence cytarabine metabolism,30 and CYP2E1 expression levels
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might be influenced by smoking.31 Furthermore, maybe the inclusion of more patients could help to increase the predic- tive ability. A recent publication analyzing 1540 AML patients and 231 predictor variables suggested that large knowledge banks of matched genomic-clinical data can support clinical decision making.32 Considering, for exam- ple, the work by Walter et al.6 and our study, we would strongly recommend including gene expression markers into these approaches because of their predictive potential. Gene expression data sets published by TCGA,23 HOVON,13,14 AMLSG33 and AMLCG,11,12 as well as the LEUCEGENE Project,34 already summarize more than 2000 AML patients that could be used to improve our prognostic and predictive abilities to personalize AML treatment.
The implementation of a gene expression-based classifi- er in routine clincial practice is difficult because gene expression analysis is currently not included in the recom- mended molecular work up of newly diagnosed AML.35 However, advances in next generation sequencing result in more cost effective and robust methods to measure gene expression, such as NanoString or RNAseq.36,37 It is highly probable that these techniques will be available in future
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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 classi- fication.
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