PMID- 15084693
OWN - NLM
STAT- MEDLINE
DCOM- 20040422
LR  - 20220318
IS  - 1533-4406 (Electronic)
IS  - 0028-4793 (Linking)
VI  - 350
IP  - 16
DP  - 2004 Apr 15
TI  - Use of gene-expression profiling to identify prognostic subclasses in adult acute 
      myeloid leukemia.
PG  - 1605-16
AB  - 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.
CI  - Copyright 2004 Massachusetts Medical Society
FAU - Bullinger, Lars
AU  - Bullinger L
AD  - Department of Pathology, Stanford University, Stanford, Calif, USA.
FAU - Dohner, Konstanze
AU  - Dohner K
FAU - Bair, Eric
AU  - Bair E
FAU - Frohling, Stefan
AU  - Frohling S
FAU - Schlenk, Richard F
AU  - Schlenk RF
FAU - Tibshirani, Robert
AU  - Tibshirani R
FAU - Dohner, Hartmut
AU  - Dohner H
FAU - Pollack, Jonathan R
AU  - Pollack JR
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - N Engl J Med
JT  - The New England journal of medicine
JID - 0255562
SB  - IM
CIN - N Engl J Med. 2004 Apr 15;350(16):1595-7. PMID: 15084689
CIN - N Engl J Med. 2004 Apr 15;350(16):1676-8. PMID: 15084701
MH  - Acute Disease
MH  - Adult
MH  - Algorithms
MH  - *Chromosome Aberrations
MH  - Cluster Analysis
MH  - Gene Expression
MH  - *Gene Expression Profiling/methods
MH  - Humans
MH  - Karyotyping
MH  - Leukemia, Myeloid/classification/*genetics/mortality
MH  - Multivariate Analysis
MH  - Mutation
MH  - Oligonucleotide Array Sequence Analysis/methods
MH  - Prognosis
MH  - Risk Assessment/methods
MH  - Survival Analysis
EDAT- 2004/04/16 05:00
MHDA- 2004/04/23 05:00
CRDT- 2004/04/16 05:00
PHST- 2004/04/16 05:00 [pubmed]
PHST- 2004/04/23 05:00 [medline]
PHST- 2004/04/16 05:00 [entrez]
AID - 350/16/1605 [pii]
AID - 10.1056/NEJMoa031046 [doi]
PST - ppublish
SO  - N Engl J Med. 2004 Apr 15;350(16):1605-16. doi: 10.1056/NEJMoa031046.