Computational inference of gene expression signatures differentiating between breast cancers responsive or resistant to letrozole.
Authors: A Georgieva, DB Evans, J Miller, T Sing, J Dixon, WR Miller
Journal: Cancer Research (2009)

Abstract Abstract #3024 Introduction:Genes which have been shown either to be differentially expressed or differentially changed between responders and non-responders to the neoadjuvant treatment with the aromatase inhibitor letrozole have been described. However, their network structure and interconnectivity have not been formally analysed.Bayesian network (BN) inference algorithms have demonstrated promise in the analysis of genomics data to recover fragments of biological pathways due to their ability to capture many types of relationships between variables and to handle noisy data.An inference score (4), normalized to be between -1 and 1, is a measure of the strength of the interaction (either positive or negative) between different nodes in the recovered network.&amp;#x2028; Materials and methods:This computational study uses BN methodology to elucidate any possible network structure and refine a gene expression signature distinguishing responders from non-responders groups to letrozole treatment. It has been applied to 205 covariables (69 baseline expression, 45 day 14 expression and 91 changes in expression with treatment), identified via Random Forests as being differentially expressed between 37 tumours responding to neoadjuvant letrozole and 15 non-responders. The number of false positive relationships was limited by considering 4 different sub-sets of the original data 1) all 205 variables, but allowing for limited connectivity between them; 2) only the baseline variables 3) only the change variables with treatment and 4) only day 14 post-treatment variables. It was conjectured that the variables linking significantly to response status would appear in the overlap between lists 1) and 2), lists 1) and 3) and lists 1) and 4).&amp;#x2028; Results:The analysis of the overall dataset highlighted the importance and the interconnectivity of ribosomal proteins. More specifically, the BN algorithm found two connected networks of 20 genes each with 13 and 14 ribosomal proteins and an average inference score of 0.65. In addition, BN links patient type (responder vs. non-responder) directly to genes that have been implicated in the natural history of breast cancer such as BCL-2 and KIAA0101 with inference scores of 0.4 and 0.45, respectively. The inferred network also contained a link with inference score of 0.4 between patient type and phosphatidylinositol glycan, whose oncogenic role has been suggested by others. The preliminary network analysis of the baseline covariables did not yield any meaningful pattern that differentiates the two patient types, except for the involvement of the cyclin E2 pathway with a “composite” inference score of 0.33.&amp;#x2028; Conclusions: With initial encouraging results, further refinement of the BN analysis, as well as enrichment of the data should facilitate the discovery of a predictive baseline gene expression pattern. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 3024.