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clinical settings and this will help to improve risk classifica- tion of patients.
In summary, failure of induction treatment is one of the remaining challenges in the treatment of AML. From a clin- ical perspective, risk stratification before the start of treat- ment would be desirable. Patients with a high risk of RD could avoid the side effects of intensive induction and might be assigned to novel, experimental treatment strate- gies. We were able to validate a predictor that reached out- standing specificity and accuracy in an independent data set. PS29MRC could be instrumental in helping design clin- ical trials that overcome the current paradigm of intensive induction as standard treatment in all eligible patients.
Acknowledgments
This work is in memory of Thomas Büchner, who died while helping the authors with this project.
The authors thank all participants of the AMLCG trials and recruiting centers.
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 includ- ed. 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.
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Funding
This work was supported by the Wilhelm-Sander-Stiftung (grant 2013.086.1) to TH, UM and KS; by funding from Deutsche Forschungsgemeinschaft (DFG Collaborative Research Centre SFB 1243) to PAG, HB, MS, WH, KHM and KS; by the “Verein zur Förderung von Wissenschaft und Forschung an der Medizinischen Fakultät der LMU München“ to TH. SKB is supported by Leukaemia & Blood Cancer New Zealand and the family of Marijanna Kumerich.
Contribution: TH, UM and KS conceived and designed the experiments. TH, MR-T, BK, LH and KHM performed exper- iments. TH, VJ, AMNB, SAB, MR-T and KHM analyzed data. VJ, AMNB, SAB and UM provided bioinformatics sup- port. JP-M, SK and HB managed the Genome Analyzer IIx platform and the RNA sequencing. MR-T, BK, LH, PAG, SS, NK, JT, MS, JB, SKB and KS characterized patient samples; MCS, DG, JB, SA, WEB, BJW and WH coordinated the AMLCG clinical trials. UM and KS supervised the project. TH, VJ, SAB, KHM and SKB wrote the manuscript.
haematologica | 2018; 103(3)