Stem Cell Marker CD133 Affects Clinical Outcome in Glioma Patients

Felix Zeppernick(Heidelberg University), Rezvan Ahmadi(Heidelberg University), Benito Campos(Heidelberg University), Christine Dictus(Heidelberg University), Burkhard Helmke(Heidelberg University), Natália Becker(German Cancer Research Center), Peter Lichter(German Cancer Research Center), Andreas Unterberg(Heidelberg University), Bernhard Radlwimmer(German Cancer Research Center), Christel Herold‐Mende(Heidelberg University)
Clinical Cancer Research
January 1, 2008
Cited by 590

Abstract

PURPOSE: The CD133 antigen has been identified as a putative stem cell marker in normal and malignant brain tissues. In gliomas, it is used to enrich a subpopulation of highly tumorigenic cancer cells. According to the cancer stem cell hypothesis, CD133-positive cells determine long-term tumor growth and, therefore, are suspected to influence clinical outcome. To date, a correlation between CD133 expression in primary tumor tissues and patients' prognosis has not been reported. EXPERIMENTAL DESIGN: To address this question, we analyzed the expression of the CD133 stem cell antigen in a series of 95 gliomas of various grade and histology by immunohistochemistry on cryostat sections. Staining data were correlated with patient outcome. RESULTS: By multivariate survival analysis, we found that both the proportion of CD133-positive cells and their topological organization in clusters were significant (P < 0.001) prognostic factors for adverse progression-free survival and overall survival independent of tumor grade, extent of resection, or patient age. Furthermore, proportion of CD133-positive cells was an independent risk factor for tumor regrowth and time to malignant progression in WHO grade 2 and 3 tumors. CONCLUSIONS: These findings constitute the first conclusive evidence that CD133 stem cell antigen expression correlates with patient survival in gliomas, lending support to the current cancer stem cell hypothesis.


Related Papers

No related papers found

Powered by citation graph analysis