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

ABSTRACT: 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

SUBMITTER: Shaikh AF 

PROVIDER: S-EPMC7116445 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature