title: Co-acting gene networks predict TRAIL responsiveness of tumour cells with high accuracy.
abstract: BACKGROUND
  Identification of differentially expressed genes from transcriptomic studies is one of the most common mechanisms to identify tumor biomarkers. This approach however is not well suited to identify interaction between genes whose protein products potentially influence each other, which limits its power to identify molecular wiring of tumour cells dictating response to a drug. Due to the fact that signal transduction pathways are not linear and highly interlinked, the biological response they drive may be better described by the relative amount of their components and their functional relationships than by their individual, absolute expression.
  
  
  RESULTS
  Gene expression microarray data for 109 tumor cell lines with known sensitivity to the death ligand cytokine tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) was used to identify genes with potential functional relationships determining responsiveness to TRAIL-induced apoptosis. The machine learning technique Random Forest in the statistical environment "R" with backward elimination was used to identify the key predictors of TRAIL sensitivity and differentially expressed genes were identified using the software GeneSpring. Gene co-regulation and statistical interaction was assessed with q-order partial correlation analysis and non-rejection rate. Biological (functional) interactions amongst the co-acting genes were studied with Ingenuity network analysis. Prediction accuracy was assessed by calculating the area under the receiver operator curve using an independent dataset. We show that the gene panel identified could predict TRAIL-sensitivity with a very high degree of sensitivity and specificity (AUC=0·84). The genes in the panel are co-regulated and at least 40% of them functionally interact in signal transduction pathways that regulate cell death and cell survival, cellular differentiation and morphogenesis. Importantly, only 12% of the TRAIL-predictor genes were differentially expressed highlighting the importance of functional interactions in predicting the biological response.
  
  
  CONCLUSIONS
  The advantage of co-acting gene clusters is that this analysis does not depend on differential expression and is able to incorporate direct- and indirect gene interactions as well as tissue- and cell-specific characteristics. This approach (1) identified a descriptor of TRAIL sensitivity which performs significantly better as a predictor of TRAIL sensitivity than any previously reported gene signatures, (2) identified potential novel regulators of TRAIL-responsiveness and (3) provided a systematic view highlighting fundamental differences between the molecular wiring of sensitive and resistant cell types.
authors.forename: Paul
authors.initials: P
authors.lastname: O'Reilly
authors.forename: Csaba
authors.initials: C
authors.lastname: Ortutay
authors.forename: Grainne
authors.initials: G
authors.lastname: Gernon
authors.forename: Enda
authors.initials: E
authors.lastname: O'Connell
authors.forename: Cathal
authors.initials: C
authors.lastname: Seoighe
authors.forename: Susan
authors.initials: S
authors.lastname: Boyce
authors.forename: Luis
authors.initials: L
authors.lastname: Serrano
authors.affiliation: Apoptosis Research Centre, National University of Ireland Galway, University Rd, Galway, Ireland. eva.szegezdi@nuigalway.ie.
authors.forename: Eva
authors.initials: E
authors.lastname: Szegezdi
mesh_terms: D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000235:genetics; D045744:Cell Line, Tumor; D019295:Computational Biology; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks / Q000187:drug effects*; D006801:Humans; D005810:Multigene Family / Q000187:drug effects / Q000235:genetics; D053221:TNF-Related Apoptosis-Inducing Ligand / Q000494:pharmacology*
chemical_list: D014408:Biomarkers, Tumor; D053221:TNF-Related Apoptosis-Inducing Ligand