title: Prognostically useful gene-expression profiles in acute myeloid leukemia.
abstract: BACKGROUND
  In patients with acute myeloid leukemia (AML) a combination of methods must be used to classify the disease, make therapeutic decisions, and determine the prognosis. However, this combined approach provides correct therapeutic and prognostic information in only 50 percent of cases.
  
  
  METHODS
  We determined the gene-expression profiles in samples of peripheral blood or bone marrow from 285 patients with AML using Affymetrix U133A GeneChips containing approximately 13,000 unique genes or expression-signature tags. Data analyses were carried out with Omniviz, significance analysis of microarrays, and prediction analysis of microarrays software. Statistical analyses were performed to determine the prognostic significance of cases of AML with specific molecular signatures.
  
  
  RESULTS
  Unsupervised cluster analyses identified 16 groups of patients with AML on the basis of molecular signatures. We identified the genes that defined these clusters and determined the minimal numbers of genes needed to identify prognostically important clusters with a high degree of accuracy. The clustering was driven by the presence of chromosomal lesions (e.g., t(8;21), t(15;17), and inv(16)), particular genetic mutations (CEBPA), and abnormal oncogene expression (EVI1). We identified several novel clusters, some consisting of specimens with normal karyotypes. A unique cluster with a distinctive gene-expression signature included cases of AML with a poor treatment outcome.
  
  
  CONCLUSIONS
  Gene-expression profiling allows a comprehensive classification of AML that includes previously identified genetically defined subgroups and a novel cluster with an adverse prognosis.
authors.affiliation: Department of Hematology, Erasmus University Medical Center, Rotterdam, The Netherlands. p.valk@erasmusmc.nl
authors.forename: Peter J M
authors.initials: PJ
authors.lastname: Valk
authors.forename: Roel G W
authors.initials: RG
authors.lastname: Verhaak
authors.forename: M Antoinette
authors.initials: MA
authors.lastname: Beijen
authors.forename: Claudia A J
authors.initials: CA
authors.lastname: Erpelinck
authors.forename: Sahar
authors.initials: S
authors.lastname: Barjesteh van Waalwijk van Doorn-Khosrovani
authors.forename: Judith M
authors.initials: JM
authors.lastname: Boer
authors.forename: H Berna
authors.initials: HB
authors.lastname: Beverloo
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Moorhouse
authors.forename: Peter J
authors.initials: PJ
authors.lastname: van der Spek
authors.forename: Bob
authors.initials: B
authors.lastname: Löwenberg
authors.forename: Ruud
authors.initials: R
authors.lastname: Delwel
mesh_terms: D000208:Acute Disease; D000328:Adult; D000465:Algorithms; D002869:Chromosome Aberrations; D016000:Cluster Analysis; D020732:Cytogenetic Analysis; D005260:Female; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods; D006801:Humans; D007621:Karyotyping; D007951:Leukemia, Myeloid / Q000145:classification / Q000235:genetics* / Q000401:mortality; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D009154:Mutation; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D011379:Prognosis; D012984:Software; D016019:Survival Analysis