Article: Clinical and molecular models of Glioblastoma multiforme survival Journal: International Journal of Data Mining and Bioinformatics (IJDMB) 2013 Vol.7 No.3 pp.245 - 265 Abstract: Glioblastoma multiforme (GBM), a highly aggressive form of brain cancer, results in a median survival of 12-15 months. For decades, researchers have explored the effects of clinical and molecular factors on this disease and have identified several candidate prognostic markers. In this study, we evaluated the use of multivariate classification models for differentiating between subsets of patients who survive a relatively long or short time. Data for this study came from The Cancer Genome Atlas (TCGA), a public repository containing clinical, treatment, histological and biomolecular variables for hundreds of patients. We applied variable-selection and classification algorithms in a cross-validated design and observed that predictive performance of the resulting models varied substantially across the algorithms and categories of data. The best-performing models were based on age, treatments and global DNA methylation. In this paper, we summarise our findings, discuss lessons learned in analysing TCGA data and offer recommendations for performing such analyses. Inderscience Publishers - linking academia, business and industry through research

Title: Clinical and molecular models of Glioblastoma multiforme survival

Authors: Stephen R. Piccolo; Lewis J. Frey

Addresses: Department of Pharmacology and Toxicology, University of Utah, 201 Presidents Circle, Salt Lake City, 84112 UT, USA ' Department of Biomedical Informatics and Huntsman Cancer Institute, University of Utah, 26 South 2000 East Room 5775 HSEB, Salt Lake City, UT 84112, USA

Abstract: Glioblastoma multiforme (GBM), a highly aggressive form of brain cancer, results in a median survival of 12-15 months. For decades, researchers have explored the effects of clinical and molecular factors on this disease and have identified several candidate prognostic markers. In this study, we evaluated the use of multivariate classification models for differentiating between subsets of patients who survive a relatively long or short time. Data for this study came from The Cancer Genome Atlas (TCGA), a public repository containing clinical, treatment, histological and biomolecular variables for hundreds of patients. We applied variable-selection and classification algorithms in a cross-validated design and observed that predictive performance of the resulting models varied substantially across the algorithms and categories of data. The best-performing models were based on age, treatments and global DNA methylation. In this paper, we summarise our findings, discuss lessons learned in analysing TCGA data and offer recommendations for performing such analyses.

Keywords: multivariate classification; GBM; Glioblastoma multiforme survival; patient survival; prognosis; brain cancer; microarrays; DNA methylation; somatic mutation; machine learning; data mining; bioinformatics; patient age; cancer treatments; molecular models; clinical models.

DOI: 10.1504/IJDMB.2013.053310

International Journal of Data Mining and Bioinformatics, 2013 Vol.7 No.3, pp.245 - 265

Received: 01 Sep 2011
Accepted: 12 Feb 2012
Published online: 12 Jun 2013 *

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Title: Clinical and molecular models of Glioblastoma multiforme survival

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