Machine learning derived genomics driven prognostication for acute myeloid leukemia with RUNX1-RUNX1T1

Panel based next generation sequencing was performed on a discovery cohort of AML with RUNX1-RUNX1T1. Supervised machine learning identified NRAS mutation and absence of mutations in ASXL2, RAD21, KIT and FLT3 genes as well as a low mutation to be associated with favorable outcome. Based on this data patients were classified into favorable and poor genetic risk classes. Patients classified as poor genetic risk had a significantly lower overall survival (OS) and relapse free survival (RFS). We could validate these findings independently on a validation cohort (n = 61). Patients in the poor genetic risk group were more likely to harbor measurable residual disease. Poor genetic risk emerged as an independent risk factor predictive of inferior outcome. Using an unbiased computational approach based we provide evidence for gene panel-based testing in AML with RUNX1-RUNX1T1 and a framework for integration of genomic markers toward clinical decision making in this heterogeneous disease entity.