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[Cancer Research 65, 4051-4058, May 15, 2005]
© 2005 American Association for Cancer Research

Molecular Biology, Pathobiology, and Genetics

Gene Expression Profiling and Genetic Markers in Glioblastoma Survival

Jeremy N. Rich1,2,5, Christopher Hans5,6, Beatrix Jones5,6,8, Edwin S. Iversen5,6, Roger E. McLendon3,5, B.K. Ahmed Rasheed3,5, Adrian Dobra4,6,7, Holly K. Dressman4,5,7, Darell D. Bigner3,5, Joseph R. Nevins4,5,7 and Mike West5,6,7

Departments of 1 Medicine, 2 Neurobiology, 3 Pathology, and 4 Molecular Genetics and Microbiology, 5 W.M. Keck Center for Neuro-Oncogenomics, Institutes of 6 Statistics and Decision Sciences, and 7 Genome Sciences and Policy, Duke University Medical Center, Durham, North Carolina; and 8 Institute of Information and Mathematical Sciences, Massey University, New Zealand

Requests for reprints: Jeremy N. Rich, Duke University Medical Center, Box 2900, Durham, NC 27710. Phone: 919-681-1693; Fax: 919-684-6514; E-mail: rich0001{at}mc.duke.edu.

Despite the strikingly grave prognosis for older patients with glioblastomas, significant variability in patient outcome is experienced. To explore the potential for developing improved prognostic capabilities based on the elucidation of potential biological relationships, we did analyses of genes commonly mutated, amplified, or deleted in glioblastomas and DNA microarray gene expression data from tumors of glioblastoma patients of age >50 for whom survival is known. No prognostic significance was associated with genetic changes in epidermal growth factor receptor (amplified in 17 of 41 patients), TP53 (mutated in 11 of 41 patients), p16INK4A (deleted in 15 of 33 patients), or phosphatase and tensin homologue (mutated in 15 of 41 patients). Statistical analysis of the gene expression data in connection with survival involved exploration of regression models on small subsets of genes, based on computational search over multiple regression models with cross-validation to assess predictive validity. The analysis generated a set of regression models that, when weighted and combined according to posterior probabilities implied by the statistical analysis, identify patterns in expression of a small subset of genes that are associated with survival and have value in assessing survival risks. The dominant genes across such multiple regression models involve three key genes—SPARC (Osteonectin), Doublecortex, and Semaphorin3B—which play key roles in cellular migration processes. Additional analysis, based on statistical graphical association models constructed using similar computational analysis methods, reveals other genes which support the view that multiple mediators of tumor invasion may be important prognostic factor in glioblastomas in older patients.




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Copyright © 2005 by the American Association for Cancer Research.