Published online before print August 9, 2007

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Copyright © 2007 American Society for Investigative Pathology
Journal of Molecular Diagnostics, doi:10.2353/jmoldx.2007.070001

Accepted for publication June 26, 2007.

Article

Identification and Validation of Biomarkers of IgV_H Mutation Status in Chronic Lymphocytic Leukemia Using Microfluidics Quantitative Real-Time Polymerase Chain Reaction Technology

Lynne V. Abruzzo^*@, Lynn L. Barron^*, Keith Anderson, Rachel J. Newman, William G. Wierda, Susan O'Brien, Alessandra Ferrajoli, Madan Luthra, Sameer Talwalkar^*, Rajyalakshmi Luthra^*, Dan Jones^*, Michael J. Keating, and Kevin R. Coombes

From the Departments of Hematopathology,* Bioinformatics and Computational Biology, Leukemia, and Pathology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas

^@ To whom correspondence should be addressed. E-mail: labruzzo{at}mdanderson.org.

	Abstract

To develop a model incorporating relevant prognostic biomarkers for untreated chronic lymphocytic leukemia patients, we re-analyzed the raw data from four published gene expression profiling studies. We selected 88 candidate biomarkers linked to immunoglobulin heavy-chain variable region gene (IgV_H) mutation status and produced a reliable and reproducible microfluidics quantitative real-time polymerase chain reaction array. We applied this array to a training set of 29 purified samples from previously untreated patients. In an unsupervised analysis, the samples clustered into two groups. Using a cutoff point of 2% homology to the germline IgV_H sequence, one group contained all 14 IgV_H-unmutated samples; the other contained all 15 mutated samples. We confirmed the differential expression of 37 of the candidate biomarkers using two-sample t-tests. Next, we constructed 16 different models to predict IgV_H mutation status and evaluated their performance on an independent test set of 20 new samples. Nine models correctly classified 11 of 11 IgV_H-mutated cases and eight of nine IgV_H-unmutated cases, with some models using three to seven genes. Thus, we can classify cases with 95% accuracy based on the expression of as few as three genes.