Automated brain tumor biopsy prediction using single-labeling cDNA microarrays-based gene expression profiling

• Published in: Diagnostic molecular pathology Vol. 18; no. 4; p. 206
• Main Authors: Castells, Xavier, García-Gómez, Juan Miguel, Navarro, Alfredo, Acebes, Juan José, Godino, Oscar, Boluda, Susana, Barceló, Anna, Robles, Montserrat, Ariño, Joaquín, Arús, Carles
• Format: Journal Article
• Language: English
• Published: United States 01-12-2009
• Subjects: Automation, Laboratory; Biopsy; Brain Neoplasms - diagnosis; Brain Neoplasms - genetics; Brain Neoplasms - surgery; DNA, Neoplasm - analysis; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Glioblastoma - diagnosis; Glioblastoma - genetics; Glioblastoma - surgery; Humans; Meningeal Neoplasms - diagnosis; Meningeal Neoplasms - genetics; Meningeal Neoplasms - surgery; Meningioma - diagnosis; Meningioma - genetics; Meningioma - surgery; Oligonucleotide Array Sequence Analysis; Predictive Value of Tests; Reproducibility of Results; Reverse Transcriptase Polymerase Chain Reaction; RNA, Messenger - metabolism; RNA, Neoplasm - analysis
• ISSN: 1533-4066
• DOI: 10.1097/PDM.0b013e31818f071b

Gene signatures obtained from microarray experiments may be of use to improve the prediction of brain tumor diagnosis. Nevertheless, automated and objective prediction with accuracy comparable to or better than the gold standard should be convincingly demonstrated for possible clinician uptake of the new methodology. Herewith, we demonstrate that primary brain tumor types can be discriminated using microarray data in an automated and objective way.
Postsurgical biopsies from 35 patients [17 glioblastoma multiforme (Gbm) and 18 meningothelial meningioma (Mm)] were stored in liquid nitrogen, total RNA was extracted, and cDNA was labeled with Cy3 fluorochrome and hybridized onto a cDNA-based microarray containing 11,500 cDNA clones representing 9300 loci. Scanned data were preprocessed, normalized, and used for predictor development. The predictive functions were fitted to a subset of samples and their performance evaluated with an independent subset. Expression results were validated by means of real time-polymerase chain reaction.
Some gene expression-based predictors achieved 100% accuracy both in training resampling validation and independent testing. One of them, composed of GFAP, PTPRZ1, GPM6B and PRELP, produced a 100% prediction accuracy for both training and independent test datasets. Furthermore, the gene signatures obtained, increased cell detoxification, motility and intracellular transport in Gbm, and increased cell adhesion and cytochrome-family genes in Mm, agree well with the expected biologic and pathologic characteristics of the studied tumors.
The ability of gene signatures to automate prediction of brain tumors through a fully objective approach has been demonstrated. A comparison of gene expression profiles between Gbm and Mm may provide additional clues about patterns associated with each tumor type.