title: Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes.
abstract: Gene expression signatures of toxicity and clinical response benefit both safety assessment and clinical practice; however, difficulties in connecting signature genes with the predicted end points have limited their application. The Microarray Quality Control Consortium II (MAQCII) project generated 262 signatures for ten clinical and three toxicological end points from six gene expression data sets, an unprecedented collection of diverse signatures that has permitted a wide-ranging analysis on the nature of such predictive models. A comprehensive analysis of the genes of these signatures and their nonredundant unions using ontology enrichment, biological network building and interactome connectivity analyses demonstrated the link between gene signatures and the biological basis of their predictive power. Different signatures for a given end point were more similar at the level of biological properties and transcriptional control than at the gene level. Signatures tended to be enriched in function and pathway in an end point and model-specific manner, and showed a topological bias for incoming interactions. Importantly, the level of biological similarity between different signatures for a given end point correlated positively with the accuracy of the signature predictions. These findings will aid the understanding, and application of predictive genomic signatures, and support their broader application in predictive medicine.
authors.affiliation: GeneGo Inc., St Joseph, MI, USA.
authors.forename: W
authors.initials: W
authors.lastname: Shi
authors.forename: M
authors.initials: M
authors.lastname: Bessarabova
authors.forename: D
authors.initials: D
authors.lastname: Dosymbekov
authors.forename: Z
authors.initials: Z
authors.lastname: Dezso
authors.forename: T
authors.initials: T
authors.lastname: Nikolskaya
authors.forename: M
authors.initials: M
authors.lastname: Dudoladova
authors.forename: T
authors.initials: T
authors.lastname: Serebryiskaya
authors.forename: A
authors.initials: A
authors.lastname: Bugrim
authors.forename: A
authors.initials: A
authors.lastname: Guryanov
authors.forename: R J
authors.initials: RJ
authors.lastname: Brennan
authors.forename: R
authors.initials: R
authors.lastname: Shah
authors.forename: J
authors.initials: J
authors.lastname: Dopazo
authors.forename: M
authors.initials: M
authors.lastname: Chen
authors.forename: Y
authors.initials: Y
authors.lastname: Deng
authors.forename: T
authors.initials: T
authors.lastname: Shi
authors.forename: G
authors.initials: G
authors.lastname: Jurman
authors.forename: C
authors.initials: C
authors.lastname: Furlanello
authors.forename: R S
authors.initials: RS
authors.lastname: Thomas
authors.forename: J C
authors.initials: JC
authors.lastname: Corton
authors.forename: W
authors.initials: W
authors.lastname: Tong
authors.forename: L
authors.initials: L
authors.lastname: Shi
authors.forename: Y
authors.initials: Y
authors.lastname: Nikolsky
mesh_terms: D000465:Algorithms; D030541:Databases, Genetic; D023381:Endpoint Determination / Q000706:statistics & numerical data; D020869:Gene Expression Profiling; D023281:Genomics / Q000706:statistics & numerical data*; D006801:Humans; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis; D010641:Phenotype; D011237:Predictive Value of Tests; D011506:Proteins / Q000145:classification / Q000235:genetics; D011786:Quality Control
chemical_list: D011506:Proteins