Page 16 - Haematologica3

Editorials
Table 1. Schematic overview of recent studies developing a model for response prediction to induction chemotherapy in intensively treated acute myeloid leukemia (AML) patients.
Publication (website for score)
Herold et al.11
Krug et al.15 (http://www.aml-score.org/)
Prediction for
RD
Patient population AUC
1079 adult patients 0.76 (including 210 patients (VC) in VC)
1406 patients (TC) 0.68 + 801 patients (VC) (VC) (only ≥ 60 years)
4601 adult patients 0.78
Gerstung et al.16 (http://cancer.sanger.ac.uk/aml-multistage/)
Variables considered
Clinical characteristics, cytogenetics, laboratory variables, mutational status of 68 frequently mutated genes
in AML, expression profile of 29 genes
Body temperature, WBC, BM blasts, PB blasts, PB neutrophils, age, disease
type, hemoglobin, platelet count, serum protein, ALT, bilirubin, BMI, extramedullary disease, fibrinogen, LDH, cytogenetics
Age, PS,sex, WBC, platelet count, BM blast percentage, disease type, cytogenetic risk, FLT3-ITD and NPM1 mutation status
Clinical data, cytogenetics, mutational data of 111 frequently mutated genes
Variables in the final model
Cytogenetics risk according to MRC, expression data of 29 genes
Body temperature, age, disease type, hemoglobin, platelet count, fibrinogen, LDH and cytogenetics
Age, PS, WBC, disease type, cytogenetic risk, FLT3-ITD/NPM1 mutation status
Age, sex, PS, WBC, platelet count, PB blasts, BM blasts, splenomegaly, disease type, hemoglobin, cytogenetics, mutational status of 58 genes
CR+ ED
Walter et al.14
RD
Not primarily RD
1540 AML N.A.
AUC: area under receiver-operating characteristic curve; CR: complete remission; RD: residual disease; ED: early death;VC: validation cohort;TC: training cohort;WBC: white blood cell count; BM: bone marrow; PB: peripheral blood; disease type: de novo leukemia versus leukemia secondary to cytotoxic treatment or an antecedent hematologic disease; ALT: alanine aminotransferase; BMI: Body Mass Index; LDH: serum concentration of lactate dehydrogenase; MRC: UK Medical Research Council; PS: Performance Status; N.A.: not applicable.
376
AML-CG.15 The validation cohort consisted of an inde- pendent cohort of 801 patients aged over 60 years. Their score was based on body temperature, age, secondary disease, hemoglobin, platelet count, fibrinogen, serum concentration of lactate dehydrogenase and cytogenetics. Instead of RD, the achievement of CR and early death were the primary outcome parameters of this score (Table 1). Using CR prediction, the model of Krug et al. had an AUC of 0.68 in the validation set.15
Gerstung et al. have also developed a prognostic algo- rithm based on a knowledge bank of 1540 AML patients whose cytogenetic, molecular profile, and clinical data were analyzed in detail.16,17 Here, a number of outcome parameters can be obtained (including death without remission, death without and after relapse, alive after relapse, alive in first CR and alive without CR), and RD can be indirectly calculated (Table 1).
Thus, prediction of RD remains complex, and these scoring systems have yet to find their way into routine clinical practice. The questions of when and how we employ them for everyday clinical evaluation and treat- ment decisions remain. Here, feasibility and predictability must be considered. It will not be feasible to use a score requiring far more laboratory evaluation (e.g. microarray data, etc.) than is routinely performed. For example, gene expression analysis is not routinely performed in clinical practice and the time required might become relevant for patients with a high leukemic burden in need of urgent therapy. Furthermore, unlike sequencing, gene expression analysis is not covered by the healthcare systems of many countries. However, with the advances being made in technologies, such evaluation could quickly become more feasible. Just as important as feasibility is the level of pre- dictability. We can only justify primarily basing our treat-
ment decisions on scoring systems with a sufficiently high predictability. That none of the proposed scoring systems reach an AUC close to 0.9, even when including all parameters currently known to be prognostic, under- scores the challenges of reliably predicting patient out- come at the time of diagnosis. This is highlighted by Herold et al., who used all prognostic parameters current- ly considered relevant, studied these parameters exten- sively in the context of RD prediction, and thus, rightfully described an “obstacle” to achieving a higher AUC that is difficult to overcome.
Herold et al. describe an innovative approach of how to tackle the pressing question of RD prediction. Independently of its clinical use, it can potentially help us to better understand the biology of primary refractory disease. It is still unknown why some patients with a molecularly more favorable risk profile still fail induction chemotherapy. The gene expression data that predict pri- mary refractory disease might also lead the way to iden- tifying novel targets for AML therapy. Even if the predic- tive classifier of Herold et al. may not find its way into clinical practice just yet, it carries the potential of becom- ing a tool for designing clinical trials and developing novel treatment strategies.
References
1. Dohner H, Weisdorf DJ, Bloomfield CD. Acute Myeloid Leukemia. N Engl J Med. 2015;373(12):1136-1152.
2. Burnett A. Treatment of acute myeloid leukemia: are we making progress? Hematology Am Soc Hematol Educ Program. 2012;2012:1- 6.
3. Thol F, Schlenk RF, Heuser M, Ganser A. How I treat refractory and early relapsed acute myeloid leukemia. Blood. 2015;126(3):319-327. 4. Dohner H, Estey E, Grimwade D, et al. Diagnosis and management
haematologica | 2018; 103(3)