Gene expression–based classification and regulatory networks of pediatric acute lymphoblastic leukemia

Pediatric acute lymphoblastic leukemia (ALL) contains cytogenetically distinct subtypes that respond differently to cytotoxic drugs. Subtype classification can be also achieved through gene expression profiling. However, how to apply such classifiers to a single patient and correctly diagnose the di...

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Bibliographic Details
Published in:Blood 2009-11, Vol.114 (20), p.4486-4493
Main Authors: Li, Zhigang, Zhang, Wei, Wu, Minyuan, Zhu, Shanshan, Gao, Chao, Sun, Lin, Zhang, Ruidong, Qiao, Nan, Xue, Huiling, Hu, Yamei, Bao, Shilai, Zheng, Huyong, Han, Jing-Dong J.
Format: Article
Language:English
Subjects:
Biological and medical sciences
Child
Data Mining
Databases, Genetic
Gene Expression
Gene Expression Profiling - methods
Hematologic and hematopoietic diseases
Humans
Leukemias. Malignant lymphomas. Malignant reticulosis. Myelofibrosis
Medical sciences
Oligonucleotide Array Sequence Analysis
Precursor Cell Lymphoblastic Leukemia-Lymphoma - classification
Precursor Cell Lymphoblastic Leukemia-Lymphoma - genetics
Reproducibility of Results
Reverse Transcriptase Polymerase Chain Reaction
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Summary:Pediatric acute lymphoblastic leukemia (ALL) contains cytogenetically distinct subtypes that respond differently to cytotoxic drugs. Subtype classification can be also achieved through gene expression profiling. However, how to apply such classifiers to a single patient and correctly diagnose the disease subtype in an independent patient group has not been addressed. Furthermore, the underlying regulatory mechanisms responsible for the subtype-specific gene expression patterns are still largely unknown. Here, by combining 3 published microarray datasets on 535 mostly white children's samples and generating a new dataset on 100 Chinese children's ALL samples, we were able to (1) identify a 62-gene classifier with 97.6% accuracy from the white children's samples and validated it on the completely independent set of 100 Chinese samples, and (2) uncover potential regulatory networks of ALL subtypes. The classifier we identified was, thus far, the only one that could be applied directly to a single sample and that sustained validation in a large independent patient group. Our results also suggest that the etiology of ALL is largely the same among different ethnic groups, and that the transcription factor hubs in the predicted regulatory network might play important roles in regulating gene expression and development of ALL.
ISSN:0006-4971
1528-0020
DOI:10.1182/blood-2009-04-218123