Gene expression profiling of adult acute myeloid leukemia identifies novel biologic clusters for risk classification and outcome prediction. Academic Article uri icon

start page

  • 685

end page

  • 696

abstract

  • To determine whether gene expression profiling could improve risk classification and outcome prediction in older acute myeloid leukemia (AML) patients, expression profiles were obtained in pretreatment leukemic samples from 170 patients whose median age was 65 years. Unsupervised clustering methods were used to classify patients into 6 cluster groups (designated A to F) that varied significantly in rates of resistant disease (RD; P < .001), complete response (CR; P = .023), and disease-free survival (DFS; P = .023). Cluster A (n = 24), dominated by NPM1 mutations (78%), normal karyotypes (75%), and genes associated with signaling and apoptosis, had the best DFS (27%) and overall survival (OS; 25% at 5 years). Patients in clusters B (n = 22) and C (n = 31) had the worst OS (5% and 6%, respectively); cluster B was distinguished by the highest rate of RD (77%) and multidrug resistant gene expression (ABCG2, MDR1). Cluster D was characterized by a "proliferative" gene signature with the highest proportion of detectable cytogenetic abnormalities (76%; including 83% of all favorable and 34% of unfavorable karyotypes). Cluster F (n = 33) was dominated by monocytic leukemias (97% of cases), also showing increased NPM1 mutations (61%). These gene expression signatures provide insights into novel groups of AML not predicted by traditional studies that impact prognosis and potential therapy.

date/time value

  • 2006

Digital Object Identifier (DOI)

  • 10.1182/blood-2004-12-4633

PubMed Identifier

  • 16597596

volume

  • 108

number

  • 2

keywords

  • Acute Disease
  • Adult
  • Aged
  • Aged, 80 and over
  • Apoptosis
  • Cluster Analysis
  • Disease-Free Survival
  • Drug Resistance, Multiple
  • Female
  • Gene Expression Profiling
  • Humans
  • Leukemia, Myeloid
  • Male
  • Middle Aged
  • Nuclear Proteins
  • Prognosis
  • Remission Induction
  • Risk Assessment
  • Signal Transduction