title: Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia.
abstract: BACKGROUND
  In patients with acute myeloid leukemia (AML), the presence or absence of recurrent cytogenetic aberrations is used to identify the appropriate therapy. However, the current classification system does not fully reflect the molecular heterogeneity of the disease, and treatment stratification is difficult, especially for patients with intermediate-risk AML with a normal karyotype.
  
  
  METHODS
  We used complementary-DNA microarrays to determine the levels of gene expression in peripheral-blood samples or bone marrow samples from 116 adults with AML (including 45 with a normal karyotype). We used unsupervised hierarchical clustering analysis to identify molecular subgroups with distinct gene-expression signatures. Using a training set of samples from 59 patients, we applied a novel supervised learning algorithm to devise a gene-expression-based clinical-outcome predictor, which we then tested using an independent validation group comprising the 57 remaining patients.
  
  
  RESULTS
  Unsupervised analysis identified new molecular subtypes of AML, including two prognostically relevant subgroups in AML with a normal karyotype. Using the supervised learning algorithm, we constructed an optimal 133-gene clinical-outcome predictor, which accurately predicted overall survival among patients in the independent validation group (P=0.006), including the subgroup of patients with AML with a normal karyotype (P=0.046). In multivariate analysis, the gene-expression predictor was a strong independent prognostic factor (odds ratio, 8.8; 95 percent confidence interval, 2.6 to 29.3; P<0.001).
  
  
  CONCLUSIONS
  The use of gene-expression profiling improves the molecular classification of adult AML.
authors.affiliation: Department of Pathology, Stanford University, Stanford, Calif, USA.
authors.forename: Lars
authors.initials: L
authors.lastname: Bullinger
authors.forename: Konstanze
authors.initials: K
authors.lastname: Döhner
authors.forename: Eric
authors.initials: E
authors.lastname: Bair
authors.forename: Stefan
authors.initials: S
authors.lastname: Fröhling
authors.forename: Richard F
authors.initials: RF
authors.lastname: Schlenk
authors.forename: Robert
authors.initials: R
authors.lastname: Tibshirani
authors.forename: Hartmut
authors.initials: H
authors.lastname: Döhner
authors.forename: Jonathan R
authors.initials: JR
authors.lastname: Pollack
mesh_terms: D000208:Acute Disease; D000328:Adult; D000465:Algorithms; D002869:Chromosome Aberrations; D016000:Cluster Analysis; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods; D006801:Humans; D007621:Karyotyping; D007951:Leukemia, Myeloid / Q000145:classification / Q000235:genetics* / Q000401:mortality; D015999:Multivariate Analysis; D009154:Mutation; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D011379:Prognosis; D018570:Risk Assessment / Q000379:methods; D016019:Survival Analysis