Transcriptional Profiling of Hematologic Malignancies with a Low-Density DNA Microarray

Abstract

High-density microarrays are powerful tools for expression analysis of thousands of genes simultaneously; however, experience with low-density microarrays in gene expression studies has been limited. We developed an optimized procedure for gene expression analysis based on a microarray containing 538 oligonucleotides and used this procedure to analyze neoplastic cell lines and whole-blood samples from healthy individuals and patients with different hematologic neoplasias. Hierarchical clustering and the Welch t-test with adjusted P values were used for data analysis. This procedure detects 0.2 fmol of mRNA and generates a linear response of 2 orders of magnitude, with CV values of <20% for hybridization and label replicates. We found statistically significant differences between Jurkat and U937 cell lines, between blood samples from 15 healthy donors and 59 chronic lymphocytic leukemia (CLL) samples, and between 6 acute myeloid leukemia patients and 4 myelodysplastic syndrome patients. A classification system constructed from the expression data predicted healthy or CLL status from a whole-blood sample with a 97% success rate. Transcriptional profiling of whole-blood samples was carried out without any cellular or sample manipulation before RNA extraction. This gene expression analysis procedure uncovered statistically significant differences associated with different hematologic neoplasias and made possible the construction of a classification system that predicts the healthy or CLL status from a whole-blood sample.