Independent confirmation of a prognostic gene-expression signature in adult acute myeloid leukemia with a normal karyotype: a Cancer and Leukemia Group B study

Abstract

Patients with acute myeloid leukemia (AML) and normal karyotype are classified in an intermediate-risk group, albeit this subset is heterogeneous for clinical outcome. A recent complementary DNA microarray study identified a gene-expression signature that--when used to cluster normal karyotype patients--separated them into 2 prognostically relevant subgroups. We sought the first independent validation of the prognostic value of this signature. Using oligonucleotide microarrays to measure gene expression in samples from uniformly treated adults with karyotypically normal AML, we performed cluster analysis based on the previously identified signature. We also developed a well-defined classification rule using the signature to predict outcome for individual patients. Cluster analysis confirmed the prognostic utility of the signature: patient clusters differed in overall (P = .001) and disease-free (P = .001) survival. The signature-based classifier identified groups with differences in overall (P = .02) and disease-free (P = .05) survival. A strong association of the outcome classifier with the prognostically adverse FLT3 internal tandem duplication (FLT3 ITD) potentially explained the prognostic significance of the signature. However, in the subgroup of patients without FLT3 ITD there was a moderate difference in survival for the classifier-derived groups. Our analysis confirms the applicability of the gene-expression profiling strategy for outcome prediction in cytogenetically normal AML.

Figures

Figure 1.

Prognostic significance of signature-based clusters in cytogenetically normal AML. (A) The Bullinger validation signature dichotomized uniformly treated CALGB patients with cytogenetically normal AML into 2 distinct clusters. Patients included in cluster I had a worse overall (B) and disease-free (C) survival compared with those in cluster II.

Figure 2.

Compound covariate prediction (CCP) algorithm predicts outcome for individual patients. Overall (A) and disease-free (B) survival according to CCP class membership. Accuracy rates of the signature-based CCP classifier for overall (C) and disease-free (D) survival.

Figure 3.

Interaction between FLT3 ITD status and CCP class membership. (A) Overall (i) and disease-free (ii) survival for patients with FLT3 ITD according to poor and good CCP class membership. (B) Overall (i) and disease-free (ii) survival for patients with wild-type FLT3 according to poor and good CCP class membership.