A novel risk classification method that involves a 24-gene signature may predict survival outcomes in acute myeloid leukemia, according to study results.
The researchers aimed to describe a prognostic gene expression signature with the capacity to independently predict survival in patients with AML. Such a signature may improve current risk classification methods.
Four cohorts that included 499 AML patients with a variety of cytogenetic and molecular abnormalities served as the training set. Two independent validation sets included a combined 825 patients.
Different protocols were used to treat patients from different cohorts. Also, various microarray platforms described the gene expression profiles of patients.
Cox regression analysis of the four training sets yielded a prognostic signature composed of 24 genes. Multivariate model results indicated that a higher sum value of the signature independently predicted shorter OS and EFS in the training and validation patient sets (P<.01).
Researchers observed significantly distinct OS and EFS (P<.001) in three new risk groups classified by the integrated risk classification model.
“The integrated risk classification incorporating this gene signature provides a better framework for risk stratification and outcome prediction than the [European LeukemiaNet] classification,” the researchers concluded.