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Blood, 8 October 2009, Vol. 114, No. 15, pp. 3292-3298. Prepublished online as a Blood First Edition Paper on August 4, 2009; DOI 10.1182/blood-2009-03-212969. This Article Full Text Full Text (PDF) Supplemental Methods, Table, and Figure All Versions of this Article: blood-2009-03-212969v1 114/15/3292    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 Oehler, V. G. Articles by Radich, J. P. PubMed PubMed Citation Articles by Oehler, V. G. Articles by Radich, J. P. Related Collections Myeloid Neoplasia Clinical Trials and Observations Social Bookmarking                   What's this?  Previous Article  |  Table of Contents  |  Next Article  MYELOID NEOPLASIA The derivation of diagnostic markers of chronic myeloid leukemia progression from microarray data Vivian G. Oehler1,*, Ka Yee Yeung2,*, Yongjae E. Choi1, Roger E. Bumgarner2, Adrian E. Raftery3, and Jerald P. Radich1 1 Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA; and Departments of 2 Microbiology and 3 Statistics, University of Washington, Seattle Currently, limited molecular markers exist that can determine where in the spectrum of chronic myeloid leukemia (CML) progression an individual patient falls at diagnosis. Gene expression profiles can predict disease and prognosis, but most widely used microarray analytical methods yield lengthy gene candidate lists that are difficult to apply clinically. Consequently, we applied a probabilistic method called Bayesian model averaging (BMA) to a large CML microarray dataset. BMA, a supervised method, considers multiple genes simultaneously and identifies small gene sets. BMA identified 6 genes (NOB1, DDX47, IGSF2, LTB4R, SCARB1, and SLC25A3) that discriminated chronic phase (CP) from blast crisis (BC) CML. In CML, phase labels divide disease progression into discrete states. BMA, however, produces posterior probabilities between 0 and 1 and predicts patients in "intermediate" stages. In validation studies of 88 patients, the 6-gene signature discriminated early CP from late CP, accelerated phase, and BC. This distinction between early and late CP is not possible with current classifications, which are based on known duration of disease. BMA is a powerful tool for developing diagnostic tests from microarray data. Because therapeutic outcomes are so closely tied to disease phase, these probabilities can be used to determine a risk-based treatment strategy at diagnosis. CiteULike    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this?

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