Page 15 - Haematologica3

Can we forecast induction failure in acute myeloid leukemia?
Felicitas Thol
Department of Hematology, Hemostasis, Oncology, and Stem Cell Transplantation, Hannover Medical School, Hannover, Germany
E-mail: thol.felicitas@mh-hannover.de doi:10.3324/haematol.2018.187575
Standard induction therapy for fit patients with acute myeloid leukemia (AML) consists of a combination ther- apy with anthracycline and cytarabine. This classical reg- imen, typically called “7+3”, has not changed for several decades.1 While many patients achieve a complete remission (CR) with standard induction therapy, approximately 10-40% of patients fail to respond to induction treatment.2,3 These patients are classified as having primary refractory disease (RD) or treatment failure, defined as a failure to achieve CR or incomplete hematologic recovery (Cri) after two courses of induction treatment.4 Unfortunately, treatment of patients with RD is extremely challenging, as even with salvage thera- py followed by allogeneic stem cell transplantation, patient outcomes remain poor.3
It is still difficult for hematologists to reliably predict RD in newly diagnosed AML patients prior to initiation of ther- apy. At time of diagnosis, we typically risk stratify our patients based on their cytogenetic and molecular profile. A very helpful classification was introduced by the European Leukemia Net (ELN) in 2010,5 (revised in 20174) and this cur- rently includes three prognostic groups integrating cytoge- netics as well as the mutational status of FLT3-ITD (includ- ing mutational load), NPM1, ASXL1, TP53, RUNX1, CEBPA (biallelic mutants). However, this risk stratification is geared towards the estimation of overall survival (OS) and event- free survival (EFS), and not primarily towards forecasting RD.4 Although there is a strong correlation between treat- ment failure and OS, they still present different outcome measures.4,6
Several groups have attempted to develop specific scores to predict induction failure in AML. A reliable score primari- ly focusing on the likelihood of treatment failure rather than OS could improve patient care and treatment in many ways. If we could reliably predict that a patient would not respond to “7+3” treatment prior to induction therapy, we would be compelled to search for alternatives at the time of diagnosis, potentially sparing the patient from the toxicity of treat- ments that prove to be ineffective. As several new agents are being studied front line (e.g. FLT3 and IDH1/2 inhibitors with intensive chemotherapy, BCL2-inhibitors in combina- tion with low-dose cytarabine or azacitidine, etc.) alterna- tives for “7+3” might soon become a reality. In addition, a reliable RD score could allow us to identify those patients who require an urgent donor search at the time of diagno- sis.7-10 Furthermore, an RD score could become an important consideration when designing clinical trials that specifically target this high-risk patient group.
In this issue of Haematologica, Herold et al. introduce a 29- gene and cytogenetic score that can help to predict resistance to induction chemotherapy in adult AML patients.11 Importantly, this score was developed on the basis of various categories of prognostic markers, considering clinical charac- teristics, laboratory variables, cytogenetics, mutational sta- tus of 68 genes that are frequently mutated in AML, and the
expression profile of 29 genes known to be prognostic for AML. Their score estimates the likelihood of primary RD based on large independent clinical training sets. The first cohort (training set 1) included 407 patients of the AML Cooperative Group (AMLCG trials between 1999-2005), the second cohort (training set 2) consisted of 462 AML patients treated in the Haemato-Oncology Foundation for Adults in the Netherlands (HOVON) trials and the validation cohort was based on 210 AMLCG-2008 trial patients with the addi- tion of 40 patients with RD from the AMLCG 1999 trial. The implementation of a large validation cohort is critical for assessing the reliability of any score, especially for clinical practice. The score was calculated as a weighted linear sum of the individual predictors. Interestingly, the final predictor by Herold et al. (predictive score 29 MRC or PS29MRC) included expression levels of 29 genes and the UK Medical Research Council (MRC) cytogenetic risk classification, while other parameters such as gene mutations were tested but were excluded from the final score.12 Importantly, this predictive classifier proved to be significant for RD, both as a continuous variable as well as a dichotomous variable that divides patients into high and low risk. In the multivariate analysis, only PS29MRC, age and TP53 mutations remained independently significant for RD prediction. While the pre- dictor was primarily designed to be associated with RD on day 16 after induction chemotherapy, the score also proved to be strongly associated with survival. When examining dif- ferent groups of the current ELN 2017 classification, the pre- dictive power of the score was shown in the intermediate and the unfavorable ELN groups, while it could not be shown in the favorable genetic group (likely related to low RD rate in patients with favorable cytogenetics). The valida- tion cohort nicely reproduced the data of the training cohort. All these aspects are suggestive of a very reliable predictive score.
The area under receiver-operating characteristic curve (AUC) can be used as a measure for the predictive ability of a score, with an AUC of 0.7-0.8 classified as fair and less than we would desire for primary treatment decisions.13,14 The classifier by Herold et al. reached an AUC of 0.76 in the validation set. In contrast, Walter et al. developed a model for resistance prediction in AML based on the analysis of 4601 patients treated within European and US AML trials.13 They found that age, performance status, white blood cell count, secondary disease, cytogenetic risk and NPM1/FLT3-ITD mutational status were strongly associated independently with primary resistance. Unlike Herold et al., they did not include a complex mutational and gene expression profile in their analysis (Table 1). However, with their model, they achieved a similar AUC (0.78) to that of Herold et al.
Krug et al. also developed a model based on a cohort of 1406 patients aged over 60 years diagnosed with AML but otherwise medically fit, and who underwent treatment with two intense induction chemotherapy cycles within the
haematologica | 2018; 103(3)
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