A Revised Prognostic Model for Patients with Acute Myeloid Leukemia and First Relapse.
Van Der Maas NG., Breems DA., Klerk CPW., Pabst T., Gradowska P., Thomas A., Biemond BJ., Kuball J., van Elssen CHMJ., Visser OJ., Vekemans M-CM., Graux C., Maertens JA., Knapper S., Dennis M., Freeman SD., Thomas I., Beverloo BH., Huls GA., Craddock C., Valk PJM., Vyas P., Russell NH., Ossenkoppele GJ., Löwenberg B., Cornelissen JJ., Versluis J.
Most patients with acute myeloid leukemia (AML) may obtain remission upon induction chemotherapy, but relapse is frequent and associated with poor survival. Previous prognostic models for outcomes after relapse lacked analysis of comprehensive molecular data. A validated prognostic model integrating clinical, cytogenetic, and molecular variables may support treatment decisions. We studied 943 AML patients who relapsed after first-line intensive induction treatment in a development cohort (HOVON-SAKK). A random survival forest algorithm was used to evaluate the association of clinical parameters, cytogenetic abnormalities, and molecular variables at diagnosis with overall survival (OS). Relapsing patients (n=377) who were enrolled in the NCRI-AML18 trial were used for model validation. In the development cohort, the median age at relapse was 58 years, and patients were classified as 2022 ELN favorable (22%), intermediate (31%), and adverse (48%) risk. One-third underwent allogeneic transplantation in first complete remission. Variable selection yielded nine variables significantly associated with 1-year OS, including relapse-free interval, age, white blood cell count, mutated TP53, FLT3-ITD, core-binding factor abnormalities, t(v;11q23)/KMT2A-rearranged and complex/monosomal karyotype, which were assigned points according to their estimated hazard ratios. Three prognostic groups were defined with distinct 1-year OS in both development (favorable: 51±3%, intermediate: 29±3% and poor: 14±2%, respectively) and validation cohorts (51±4%, 26±5% and 14±3%, respectively). Independent validation confirmed the improved accuracy in predicting outcomes for AML patients in first relapse. The revised AML relapse model improved on previous classification systems for prognostication of outcomes after first AML relapse. It provides stratification which might support tailoring second line treatment.