Integrated Genetic Profiling Can Identify Predictors of Outcome and Improve Risk Stratification in AML Patients

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A mutational analysis of 18 genes in 398 patients with acute myeloid leukemia (AML) found at least one somatic alteration in 97.3% of the patients and identified genetic predictors of outcome that improved risk stratification among patients with AML, independent of age, white-cell count, induction dose, and postremission therapy. The mutational analysis was performed with diagnostic samples obtained from patients in the Eastern Cooperative Oncology Group (ECOG) E1900 trial who had AML and were randomly assigned to receive induction therapy with high-dose or standard-dose daunorubicin. The prognostic findings were validated in an independent set of 104 patients.

“Previous studies have suggested that mutational analysis of CEBPA, NPM1, and FLT3-ITD can be used to stratify risk among patients with intermediate-risk. Using data from a large cohort of patients treated in a single clinical trial, we found that more extensive mutational analysis can better discriminate patients with AML into various prognostic groups,” the authors wrote.

“Taken together, these data show that mutational analysis of a larger set of genetic alterations than that currently used in the clinic setting could be used to retrospectively classify patients with AML into more precise subgroups with favorable-risk, intermediate-risk, or unfavorable-risk profiles, with marked differences in the overall outcome. This approach could be used to identify an additional subgroup of patients who would have a mutationally defined favorable outcome with induction and consolidation therapy alone and a subgroup of patients with mutationally defined unfavorable risk who would potentially be candidates for allogeneic stem-cell transplantation or participation in a clinical trial, given the prediction of a poor outcome with standard AML therapy,” the authors stated.

“The challenge,” they concluded, “is to provide genetic information in a timely and affordable way and show that this information could prospectively influence treatment decisions.” ■ 

Patel JP, et al: N Engl J Med 366:1079-1089, 2012.




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