Novel potential ALL low-risk markers revealed by gene expression profiling with new high-throughput SSH-CCS-PCR.

TitleNovel potential ALL low-risk markers revealed by gene expression profiling with new high-throughput SSH-CCS-PCR.
Publication TypeJournal Article
Year of Publication2003
AuthorsQiu, J, Gunaratne, P, Peterson, LE, Khurana, D, Walsham, N, Loulseged, H, Karni, RJ, Roussel, E, Gibbs, RA, Margolin, JF, Gingras, M-C
Date Published2003 Sep
KeywordsB-Lymphocytes, Biomarkers, Tumor, Case-Control Studies, DNA Primers, DNA, Complementary, DNA, Neoplasm, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Humans, Neoplasm Proteins, Nucleic Acid Hybridization, Oligonucleotide Array Sequence Analysis, Polymerase Chain Reaction, Precursor B-Cell Lymphoblastic Leukemia-Lymphoma, Precursor Cell Lymphoblastic Leukemia-Lymphoma, Risk Factors, RNA, Messenger, RNA, Neoplasm, Subtraction Technique

The current systems of risk grouping in pediatric acute lymphoblastic leukemia (ALL) fail to predict therapeutic success in 10-35% of patients. To identify better predictive markers of clinical behavior in ALL, we have developed an integrated approach for gene expression profiling that couples suppression subtractive hybridization, concatenated cDNA sequencing, and reverse transcriptase real-time quantitative PCR. Using this approach, a total of 600 differentially expressed genes were identified between t(4;11) ALL and pre-B ALL with no determinant chromosomal translocation. The expression of 67 genes was analyzed in different cytogenetic ALL subgroups and B lymphocytes isolated from healthy donors. Three genes, BACH1, TP53BPL, and H2B/S, were consistently expressed as a significant cluster associated with the low-risk ALL subgroups. A total of 42 genes were differentially expressed in ALL vs normal B lymphocytes, with no specific association with any particular ALL subgroups. The remaining 22 genes were part of a specific expression profile associated with the hyperdiploid, t(12;21), or t(4;11) subgroups. Using an unsupervised hierarchical cluster analysis, the discriminating power of these specific expression profiles allowed the clustering of patients according to their subgroups. These genes could help to understand the difference in treatment response and become therapeutical targets to improve ALL clinical outcomes.

Alternate JournalLeukemia
PubMed ID12970791
Grant List5 U01 CA80200 / CA / NCI NIH HHS / United States