A parsimonious 3-gene signature predicts clinical outcomes in an acute myeloid leukemia multicohort study.

ABSTRACT: Acute myeloid leukemia (AML) is a genetically heterogeneous hematological malignancy with variable responses to chemotherapy. Although recurring cytogenetic abnormalities and gene mutations are important predictors of outcome, 50% to 70% of AMLs harbor normal or risk-indeterminate karyotypes. Therefore, identifying more effective biomarkers predictive of treatment success and failure is essential for informing tailored therapeutic decisions. We applied an artificial neural network (ANN)-based machine learning approach to a publicly available data set for a discovery cohort of 593 adults with nonpromyelocytic AML. ANN analysis identified a parsimonious 3-gene expression signature comprising CALCRL, CD109, and LSP1, which was predictive of event-free survival (EFS) and o

SUBMITTER: Wagner S 

PROVIDER: S-EPMC6482359 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature