Gene clusters to predict response to medication Protein interactions constitute a key element in our understanding of disease-associated genes. A European consortium aimed to use the prognostic significance of this information for predicting patients' responses to medication. Health © Thinkstock Most drugs currently on the market work effectively on some patients only, reflecting the complex multifactorial nature of diseases like cancer and allergy. Environmental, genetic and epigenetic determinants influence disease development, progression and patients’ response to a particular therapy. To prevent unnecessary cost and suffering, it would be desirable to tailor medication based on an individual patient’s profile. To this end, diagnostic markers are required that can be routinely measured in the clinic. Based on this, the EU-funded 'Multi-layer network modules to identify markers for personalized medication in complex diseases' (MULTIMOD) project employed a systems medical strategy to identify markers for personalised medication. Researchers used seasonal allergic rhinitis (SAR) as a model disease because it represents a common, well defined disease. The external cause (pollen) is known and can be analysed at both the experimental and clinical levels. Researchers performed high-throughput microarray analyses (mRNA, exon, methylation and microRNA) of allergen-challenged T lymphocytes from SAR patients or healthy controls to identify disease-associated genes. Mapping of these genes on a network of human protein interactions revealed that they were highly interconnected and functionally related. This was based on the principle that genes whose protein products interact tend to be co-expressed and will therefore co-localise in the protein–protein interaction network. These gene clusters were termed susceptibility modules (SuM), and were subsequently analysed by bioinformatics to identify the implicated pathways. Gene expression and genome-wide association study (GWAS) data from almost 5 000 subjects were combined to validate the method. Researchers found that genetic polymorphisms were more frequent in the SuM genes, including the gene FGF2, which has not been previously implicated in allergies. Analyses of other genomic layers (microRNAs, transcription factors and methylation) highlighted the importance of genome-wide epigenetics in the stratification of immune diseases. Combined, these tools facilitated the identification of prognostic protein markers for predicting patient response to medications such as glucocorticoids. For the functional annotation and interpretation of the modules, the consortium invented new tools and databases. These linked modules and genes with biological information, genetic and phenotypic variations, as well as tissue specificity. The MULTIMOD tool for the identification of modules is available on the project website. Its commercial implementation for interpreting gene expression patterns or diagnostic protein markers could revolutionise the way medicines are prescribed.
