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Blood, 12 November 2009, Vol. 114, No. 20, pp. 4486-4493. Prepublished online as a Blood First Edition Paper on September 15, 2009; DOI 10.1182/blood-2009-04-218123. This Article Full Text Full Text (PDF) Supplemental Methods and Tables Additional Supplemental Tables and Supplemental Figure All Versions of this Article: blood-2009-04-218123v1 114/20/4486    most recent Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Rights and Permissions Citing Articles Citing Articles via CrossRef Google Scholar Articles by Li, Z. Articles by Han, J.-D. J. PubMed PubMed Citation Articles by Li, Z. Articles by Han, J.-D. J. Related Collections Lymphoid Neoplasia Social Bookmarking                   What's this?  Previous Article  |  Table of Contents  |  Next Article  LYMPHOID NEOPLASIA Gene expression–based classification and regulatory networks of pediatric acute lymphoblastic leukemia Zhigang Li1,*, Wei Zhang2,3,*, Minyuan Wu1, Shanshan Zhu2,3, Chao Gao1, Lin Sun1, Ruidong Zhang1, Nan Qiao2,3, Huiling Xue2, Yamei Hu1, Shilai Bao4, Huyong Zheng1, and Jing-Dong J. Han2 1 Beijing Children's Hospital of Capital Medical University, Beijing; 2 Chinese Academy of Sciences Key Laboratory of Molecular and Developmental Biology, Center for Molecular Systems Biology, Beijing; 3 Graduate School, Chinese Academy of Sciences, Beijing; and 4 Chinese Academy of Sciences Key Laboratory of Molecular and Developmental Biology, Center for Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China 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. CiteULike    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this?

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