title: GTI: a novel algorithm for identifying outlier gene expression profiles from integrated microarray datasets.
abstract: BACKGROUND
  Meta-analysis of gene expression microarray datasets presents significant challenges for statistical analysis. We developed and validated a new bioinformatic method for the identification of genes upregulated in subsets of samples of a given tumour type ('outlier genes'), a hallmark of potential oncogenes.
  
  
  METHODS
  A new statistical method (the gene tissue index, GTI) was developed by modifying and adapting algorithms originally developed for statistical problems in economics. We compared the potential of the GTI to detect outlier genes in meta-datasets with four previously defined statistical methods, COPA, the OS statistic, the t-test and ORT, using simulated data. We demonstrated that the GTI performed equally well to existing methods in a single study simulation. Next, we evaluated the performance of the GTI in the analysis of combined Affymetrix gene expression data from several published studies covering 392 normal samples of tissue from the central nervous system, 74 astrocytomas, and 353 glioblastomas. According to the results, the GTI was better able than most of the previous methods to identify known oncogenic outlier genes. In addition, the GTI identified 29 novel outlier genes in glioblastomas, including TYMS and CDKN2A. The over-expression of these genes was validated in vivo by immunohistochemical staining data from clinical glioblastoma samples. Immunohistochemical data were available for 65% (19 of 29) of these genes, and 17 of these 19 genes (90%) showed a typical outlier staining pattern. Furthermore, raltitrexed, a specific inhibitor of TYMS used in the therapy of tumour types other than glioblastoma, also effectively blocked cell proliferation in glioblastoma cell lines, thus highlighting this outlier gene candidate as a potential therapeutic target.
  
  
  CONCLUSIONS
  Taken together, these results support the GTI as a novel approach to identify potential oncogene outliers and drug targets. The algorithm is implemented in an R package (Text S1).
authors.affiliation: FIMM, Institute of Molecular Medicine Finland, University of Helsinki, Helsinki, Finland. john.mpindi@helsinki.fi
authors.forename: John Patrick
authors.initials: JP
authors.lastname: Mpindi
authors.forename: Henri
authors.initials: H
authors.lastname: Sara
authors.forename: Saija
authors.initials: S
authors.lastname: Haapa-Paananen
authors.forename: Sami
authors.initials: S
authors.lastname: Kilpinen
authors.forename: Tommi
authors.initials: T
authors.lastname: Pisto
authors.forename: Elmar
authors.initials: E
authors.lastname: Bucher
authors.forename: Kalle
authors.initials: K
authors.lastname: Ojala
authors.forename: Kristiina
authors.initials: K
authors.lastname: Iljin
authors.forename: Paula
authors.initials: P
authors.lastname: Vainio
authors.forename: Mari
authors.initials: M
authors.lastname: Björkman
authors.forename: Santosh
authors.initials: S
authors.lastname: Gupta
authors.forename: Pekka
authors.initials: P
authors.lastname: Kohonen
authors.forename: Matthias
authors.initials: M
authors.lastname: Nees
authors.forename: Olli
authors.initials: O
authors.lastname: Kallioniemi
mesh_terms: D000465:Algorithms; D001254:Astrocytoma / Q000235:genetics; D001932:Brain Neoplasms / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods / Q000706:statistics & numerical data*; D056726:Genetic Association Studies / Q000379:methods; D005909:Glioblastoma / Q000235:genetics; D006801:Humans; D046228:Microarray Analysis / Q000379:methods / Q000706:statistics & numerical data*; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods; D012984:Software; D014407:Tumor Cells, Cultured