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Inter-platform comparability of microarrays in acute lymphoblastic leukemia
Stephanie A Mitchell1, Kevin M Brown, Michael M Henry
1Research Center for Genetic Medicine, Children's National Medical Center, Institute of Biomedical Sciences, George Washington University Medical Center, Washington, D.C. 20037, USA. smitchell@cnmcresearch.org
BMC Genomics
|September 25, 2004
Summary
This study successfully validated gene expression predictors for acute lymphoblastic leukemia (ALL) prognostic variables across different microarray platforms. This integration of diverse datasets enhances future ALL research and therapeutic development.
Area of Science:
- Genomics
- Oncology
- Bioinformatics
Background:
- Acute lymphoblastic leukemia (ALL) is a common pediatric cancer with improving survival rates.
- Genetic prognostic variables are known, but expression correlates lack validation across diverse datasets.
- Existing public expression data is fragmented across different array platforms, hindering comprehensive analysis.
Purpose of the Study:
- To validate previously reported gene expression lists as predictors of ALL subclasses.
- To assess the comparability and integration of gene expression data across different microarray platforms.
- To establish a unified data pool for robust ALL subclassification and prognostic analysis.
Main Methods:
- Accumulated publicly available Affymetrix and cDNA array data for pediatric ALL.
- Supplemented data with newly profiled diagnostic pediatric ALL samples.
- Performed cross-generation array validation to assess gene predictor accuracy.
Main Results:
- Successfully validated gene expression predictors for ALL prognostic variables across independent datasets and platforms.
- Demonstrated high sensitivity and specificity in cross-generation array validation.
- Achieved high accuracy in validating gene predictors using cDNA array data.
Conclusions:
- Interarray comparisons are crucial for integrating diverse microarray datasets.
- This approach breaks down barriers to data assimilation, creating a comprehensive ALL data pool.
- Enhanced data integration will facilitate more robust validation of prognostic markers and advance ALL research.