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Achievement of complete remission signifies a crucial milestone in the therapy of acute myeloid leukemia (AML) while refractory disease is associated with dismal outcomes. Hence, accurately identifying patients at risk is essential to tailor treatment concepts individually to disease biology. We used nine machine learning (ML) models to predict complete remission and 2-year overall survival in a large multicenter cohort of 1,383 AML patients who received intensive induction therapy. Clinical, laboratory, cytogenetic and molecular genetic data were incorporated and our results were validated on an external multicenter cohort. Our ML models autonomously selected predictive features including established markers of favorable or adverse risk as well as identifying markers of so-far controversial relevance. De novo AML, extramedullary AML, double-mutated CEBPA, mutations of CEBPA-bZIP, NPM1, FLT3-ITD, ASXL1, RUNX1, SF3B1, IKZF1, TP53, and U2AF1, t(8;21), inv(16)/t(16;16), del(5)/del(5q), del(17)/del(17p), normal or complex karyotypes, age and hemoglobin concentration at initial diagnosis were statistically significant markers predictive of complete remission, while t(8;21), del(5)/del(5q), inv(16)/t(16;16), del(17)/del(17p), double-mutated CEBPA, CEBPA-bZIP, NPM1, FLT3-ITD, DNMT3A, SF3B1, U2AF1, and TP53 mutations, age, white blood cell count, peripheral blast count, serum lactate dehydrogenase level and hemoglobin concentration at initial diagnosis as well as extramedullary manifestations were predictive for 2-year overall survival. For prediction of complete remission and 2-year overall survival areas under the receiver operating characteristic curves ranged between 0.77-0.86 and between 0.63-0.74, respectively in our test set, and between 0.71-0.80 and 0.65-0.75 in the external validation cohort. We demonstrated the feasibility of ML for risk stratification in AML as a model disease for hematologic neoplasms, using a scalable and reusable ML framework. Our study illustrates the clinical applicability of ML as a decision support system in hematology.

A cute myeloid leukemia is a disease of the elderly (median age at diagnosis, 65-70 years). The prognosis of older acute myeloid leukemia patients is generally poor. While genetic markers have become important tools for risk stratification and treatment selection in young and middle-aged patients, their applicability in very old patients is less clear. We sought to validate existing genetic risk classification systems and identify additional factors associated with outcomes in intensively treated patients aged ≥75 years. In 151 patients who received induction chemotherapy in the AMLCG-1999 trial, we investigated recurrently mutated genes using a targeted sequencing assay covering 64 genes. The median number of mutated genes per patient was four. The most commonly mutated genes were TET2 (42%), DNMT3A (35%), NPM1 (32%), SRSF2 (25%) and ASXL1 (21%). The complete remission rate was 44% and the 3-year survival was 21% for the entire cohort. While adverse-risk cytogenetics (MRC classification) were associated with shorter overall survival ( P =0.001), NPM1 and FLT3 -ITD mutations (present in 18%) did not have a significant impact on overall survival. Notably, none of the 13 IDH1 -mutated patients (9%) reached complete remission. Consequently, the overall survival of this subgroup was significantly shorter than that of IDH1 -wildtype patients ( P <0.001). In summary, even among very old, intensively treated, acute myeloid leukemia patients, adverse-risk cytogenetics predict inferior survival. The spectrum and relevance of driver gene mutations in elderly patients differs from that in younger patients. Our data implicate IDH1 mutations as a novel marker for chemorefractory disease and inferior prognosis. (AMLCG-1999 trial: clinicaltrials.gov identifier, NCT00266136)., (Copyright© 2018 Ferrata Storti Foundation.)

We investigated whether octogenarian patients with acute myeloid leukemia enrolled onto Cooperative Group clinical trials and treated with intensive induction therapy could be cured, and whether karyotype and selected molecular markers had any prognostic significance in these patients. Among 138 patients with cytogenetic information, normal karyotype was the most common (47.1%) followed by complex karyotype (14.5%) and sole +8 (9.4%). Among these patients, the relapse-free survival rate at 1 year was 37% and 13% at 3 years, and the respective overall survival rates were 24% and 8%. Whereas the 90 patients who survived beyond 30 days had the same relapse-free survival rates, their 1-year and 3-year overall survival rates were 36% and 11%, respectively. Of the 66 patients surviving beyond 30 days who could be classified into European LeukemiaNet genetic groups, those in the intermediate-I group had better overall survival than patients in the adverse group (P=0.01). Among patients with cytogenetically normal acute myeloid leukemia who were tested for the European LeukemiaNet-associated molecular alterations, FLT3-internal tandem duplication and NPM1 mutations, it was found that FLT3-internal tandem duplication (detected in 29% of patients) did not associate with overall survival (P=0.31), whereas NPM1 mutations (30%) were associated with a significantly longer overall survival (P=0.002). We conclude that intensive induction is effective and indicated in selected octogenarians with acute myeloid leukemia, that their overall survival varies among the European LeukemiaNet genetic groups and that NPM1 mutations may be of prognostic significance among octogenarian patients with cytogenetically normal acute myeloid leukemia.