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Moser, C.* ; Jurinovic, V.* ; Sagebiel-Kohler, S.* ; Ksienzyk, B.* ; Batcha, A.M.N.* ; Dufour, A.* ; Schneider, S.* ; Rothenberg-Thurley, M.* ; Sauerland, C.M.* ; Görlich, D. ; Berdel, W.E.* ; Krug, U.* ; Mansmann, U.R.* ; Hiddemann, W.* ; Braess, J.* ; Spiekermann, K.* ; Greif, P.A.* ; Vosberg, S.* ; Metzeler, K.H.* ; Kumbrink, J. ; Herold, T.

A clinically applicable gene expression based score predicts resistance to induction treatment in acute myeloid leukemia.

Blood Adv. 5, 4752-4761 (2021)

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Prediction of resistant disease at initial diagnosis of acute myeloid leukemia (AML) can be achieved with high accuracy by using cytogenetic data and 29 gene expression markers (PS29MRC). Our aim was to establish PS29MRC as a clinically usable assay by using the widely implemented NanoString platform and further validate the classifier in a more recently treated patient cohort. 351 patients with newly diagnosed AML intensively treated within the AMLCG registry were analyzed. As a continuous variable, PS29MRC performed best in predicting induction failure in comparison to previously published risk models (OR=2.37; p=1.20·10-9). The classifier was strongly associated with overall survival (HR=1.38; p=2.62·10-6). We were able to establish a previously defined cut-off that allows a classifier dichotomization (PS29MRCdic). PS29MRCdic significantly identified induction failure with 59% sensitivity, 77% specificity and 72% overall accuracy (OR=4.81; p=4.15·10-10). PS29MRCdic was able to improve the ELN-2017 risk classification within every category (favorable: OR=5.44; p=0.017; intermediate: OR=4.43; p=0.011; adverse: OR=2.52; p=0.034). Median patients' overall survival with high PS29MRCdic was 1.8 years compared to 4.3 years of low-risk patients. In multivariate analysis including ELN-2017, clinical and genetic markers, only age and PS29MRCdic were independent predictors of refractory disease. In patients aged 60 or older, only PS29MRCdic was left as significant variable. In summary, we confirmed PS29MRC as a valuable classifier that can be calculated and reproduced on a widely available platform to identify high-risk patients in AML. Risk classification can still be refined beyond ELN-2017 and predictive classifiers might facilitate clinical trials focusing on these high-risk AML patients.

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