HOMEPRODUCTSCOMPANYCONTACTFAQResearchDictionaryPharmaSign Up FREE or Login

ZAP-70 expression identifies a chronic lymphocytic leukemia subtype with unmutated immunoglobulin genes, inferior clinical outcome, and distinct gene expression profile.

Abstract
The presence or absence of somatic mutations in the expressed immunoglobulin heavy chain variable regions (IgVH) of chronic lymphocytic leukemia (CLL) cells provides prognostic information. Patients whose leukemic cells express unmutated IgVH regions (Ig-unmutated CLL) often have progressive disease, whereas patients whose leukemic cells express mutated IgVH regions (Ig-mutated CLL) more often have an indolent disease. Given the difficulty in performing IgVH sequencing in a routine diagnostic laboratory, this prognostic distinction is currently unavailable to most patients. Pilot gene expression profiling studies in patients with CLL identified genes that were differentially expressed between the Ig-unmutated and Ig-mutated CLL subtypes. Here, we have profiled an expanded cohort of 107 patients and show that ZAP-70 is the gene that best distinguishes the CLL subtypes. Ig-unmutated CLL expressed ZAP-70 5.54-fold more highly than Ig-mutated CLL (P < 10(-21)). ZAP-70 expression correctly predicted IgVH mutation status in 93% of patients. ZAP-70 expression and IgVH mutation status were comparable in their ability to predict time to treatment requirement following diagnosis. In 7 patients, ZAP-70 expression and IgVH mutation status were discordant: 4 Ig-mutated CLLs had high ZAP-70 expression and 3 Ig-unmutated CLLs had low ZAP-70 expression. Among these ZAP-70 "outliers," those with Ig-mutated CLL had clinical features that are uncharacteristic of this CLL subtype: 2 required early treatment and 2 used a mutated VH3-21 gene, an IgVH gene that has been associated with progressive disease. We developed reverse transcriptase-polymerase chain reaction and immunohistochemical assays for ZAP-70 expression that can be applied clinically and would yield important prognostic information for patients with CLL.
AuthorsAdrian Wiestner, Andreas Rosenwald, Todd S Barry, George Wright, R Eric Davis, Sarah E Henrickson, Hong Zhao, Rachel E Ibbotson, Jenny A Orchard, Zadie Davis, Maryalice Stetler-Stevenson, Mark Raffeld, Diane C Arthur, Gerald E Marti, Wyndham H Wilson, Terry J Hamblin, David G Oscier, Louis M Staudt
JournalBlood (Blood) Vol. 101 Issue 12 Pg. 4944-51 (Jun 15 2003) ISSN: 0006-4971 [Print] United States
PMID12595313 (Publication Type: Journal Article)
Chemical References
  • Antigens, CD
  • Immunoglobulin Heavy Chains
  • Immunoglobulin Variable Region
  • Membrane Glycoproteins
  • RNA, Messenger
  • Protein-Tyrosine Kinases
  • ZAP-70 Protein-Tyrosine Kinase
  • ZAP70 protein, human
  • ADP-ribosyl Cyclase
  • CD38 protein, human
  • ADP-ribosyl Cyclase 1
Topics
  • ADP-ribosyl Cyclase (analysis)
  • ADP-ribosyl Cyclase 1
  • Adult
  • Aged
  • Aged, 80 and over
  • Antigens, CD (analysis)
  • Blotting, Western
  • Bone Marrow (chemistry)
  • Chromosomes, Human, Pair 11
  • Chromosomes, Human, Pair 12
  • Chromosomes, Human, Pair 13
  • Chromosomes, Human, Pair 17
  • Cytogenetic Analysis
  • Female
  • Gene Deletion
  • Gene Expression
  • Gene Expression Profiling
  • Humans
  • Immunoglobulin Heavy Chains (genetics)
  • Immunoglobulin Variable Region (genetics)
  • Immunohistochemistry
  • Leukemia, Lymphocytic, Chronic, B-Cell (genetics, immunology)
  • Male
  • Membrane Glycoproteins
  • Middle Aged
  • Mutation
  • Oligonucleotide Array Sequence Analysis
  • Prognosis
  • Protein-Tyrosine Kinases (analysis, genetics)
  • RNA, Messenger (analysis)
  • Reverse Transcriptase Polymerase Chain Reaction
  • T-Lymphocytes (chemistry)
  • Trisomy
  • ZAP-70 Protein-Tyrosine Kinase

Join CureHunter, for free Research Interface BASIC access!

Take advantage of free CureHunter research engine access to explore the best drug and treatment options for any disease. Find out why thousands of doctors, pharma researchers and patient activists around the world use CureHunter every day.
Realize the full power of the drug-disease research graph!


Choose Username:
Email:
Password:
Verify Password:
Enter Code Shown: