Detecting Differentially Co-expressed Genes for Drug Target Analysis

Many novel therapeutics originally aimed at a specific protein have in fact complex target profiles and interact promiscuously with many other proteins and pathways. Discovering new molecular targets and related pharmacodynamic effectors for existing drugs can help us understand mechanisms behind drug resistance, discover potential side effects, and point to target for new drugs. Often, the study of novel targets and receptors starts with building up diverse panel of drug sensitive and resistant cell lines, which is then profiled using high-throughput method such as gene expression microarrays or proteomic arrays. Analysis of profiling data requires statistical methods that move beyond univariate tests of differential expression between sensitive and resistant cell lines. Here, we propose a new approach for analysing differential co-expression, which allows for detecting changes of co-expression pattern in gene pairs, bringing spotlight on the differences in complex dynamic relationships and regulation mechanisms between genes in sensitive and resistant phenotypes. In contrast to existing methods, the proposed approach can deal with confounding factors such as tissue heterogeneity of the cell line panels that leads to presence of clusters and outliers, and together with relatively small number of samples can result in many false discoveries. We applied our method to study differences of gene co-expression patterns between cell lines sensitive and resistant to dasatinib, a novel targeted anticancer drug, and we discovered a closely-linked network of differentially co-expressed genes related to molecular effects of the drug