An mRNA expression signature for prognostication in de novo acute myeloid leukemia patients with normal karyotype
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Ming-Kai Chuang1,*, Yu-Chiao Chiu2,5,*, Wen-Chien Chou1,3, Hsin-An Hou3, Mei-Hsuan Tseng3, Yi-Yi Kuo1,3, Yidong Chen5,6, Eric Y. Chuang2,4, Hwei-Fang Tien3
1Department of Laboratory Medicine, National Taiwan University Hospital, Taipei, Taiwan
2Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
3Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
4Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei, Taiwan
5Greehey Children’s Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America
6Department of Epidemiology and Biostatistics, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America
*These authors have contributed equally to this work
Wen-Chien Chou, e-mail: [email protected]
Eric Y. Chuang, e-mail: [email protected]
Hwei-Fang Tien, e-mail: [email protected]
Keywords: acute myeloid leukemia, normal cytogenetics, mRNA signature, prognosis
Received: July 19, 2015 Accepted: August 30, 2015 Published: October 23, 2015
Although clinical features, cytogenetics, and mutations are widely used to predict prognosis in patients with acute myeloid leukemia (AML), further refinement of risk stratification is necessary for optimal treatment, especially in cytogenetically normal (CN) patients. We sought to generate a simple gene expression signature as a predictor of clinical outcome through analyzing the mRNA arrays of 158 de novo CN AML patients. We compared the gene expression profiles of patients with poor response to induction chemotherapy with those who responded well. Forty-six genes expressed differentially between the two groups. Among them, expression of 11 genes was significantly associated with overall survival (OS) in univariate Cox regression analysis in 104 patients who received standard intensive chemotherapy. We integrated the z-transformed expression levels of these 11 genes to generate a risk scoring system. Higher risk scores were significantly associated with shorter OS (median 17.0 months vs. not reached, P < 0.001) in ours and another 3 validation cohorts. In addition, it was an independent unfavorable prognostic factor by multivariate analysis (HR 1.116, 95% CI 1.035~1.204, P = 0.004). In conclusion, we developed a simple mRNA expression signature for prognostication in CN-AML patients. This prognostic biomarker will help refine the treatment strategies for this group of patients.
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