RT Journal Article
SR Electronic
T1 Gene Expression Profiles of CD133-positive Fractions Predict the Survival of Individuals with Acute Myeloid Leukemia
JF Cancer Genomics - Proteomics
JO Cancer Genomics Proteomics
FD International Institute of Anticancer Research
SP 169
OP 181
VO 3
IS 3-4
A1 YAMASHITA, YOSHIHIRO
A1 OHASHI, JUN
A1 HIRAI, YUJI
A1 CHOI, YOUNG LIM
A1 KANEDA, RURI
A1 FUJIWARA, SHIN-ICHIRO
A1 ARAI, YUKIHIRO
A1 AKUTSU, MIYUKI
A1 TSUTSUMI, CHIZUKO
A1 MIYAZAKI, YASUSHI
A1 USUKI, KENSUKE
A1 TERAMURA, MASANAO
A1 MITANI, KINUKO
A1 KANO, YASUHIKO
A1 O'NEILL, MICHAEL C.
A1 URABE, AKIO
A1 TOMONAGA, MASAO
A1 OZAWA, KEIYA
A1 MANO, HIROYUKI
YR 2006
UL http://cgp.iiarjournals.org/content/3/3-4/169.abstract
AB Background: The current classification of acute myeloid leukemia (AML) is based predominantly on the cytogenetic abnormalities and morphology of the malignant blasts and is not always helpful for optimization of the treatment strategy. Gene expression profiles of AML blasts were obtained and a gene expression-based means of predicting the outcome of AML patients was developed. Materials and Methods: CD133-positive hematopoietic stem cell-like fractions were purified from the bone marrow of 99 individuals with AML-related disorders and the expression profiles of ~33,000 human transcripts in these cells were characterized with the use of DNA microarray analysis. Results: The comparison of the expression data between individuals with short- or long-term survival by application of Cox's proportional hazard model led to the identification of four genes, whose expression patterns discriminated between the two groups. The gene expression-based stratification (GES) system, based on a combination of the karyotype approach and the risk index calculated from the expression levels of the four outcome predictor genes, was developed to separate the patients into subgroups with distinct prognoses. Conclusion: DNA microarray analysis of purified fractions provides novel stratification schemes for AML based on the expression profiles of a handful of genes.