Computational discovery of genetic targets and interactions : applications to lung cancer
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We present new modes of computational drug discovery in each of the three key themes of target identification, mechanism, and therapy regimen design. In identifying candidate targets for therapeutic intervention, we develop novel applications of unsupervised clustering of whole genome RNAi screening in prioritizing biological systems whose inhibition differentially sensitizes diseased cells apart from a normal population. When applied to lung cancer, our approach identified protein complexes for which various tumor subtypes are especially dependent. Consequently, each complex represents a candidate drug target specifically intended for a particular patient sub-population. The cellular functions impacted by the protein complexes include splicing, translation, and protein folding. We obtained experimental validation for the predicted sensitivity of a lung adenocarcinoma cell line to Wnt inhibition. For our second theme, we focus on genetic interactions as a mechanism underlying sensitivity to target inhibition. Experimental characterization of such interactions has relied on brute-force assessment of gene pairs. To alleviate the experimental burden, our hypothesis is that functionally related genes tend to share common genetic interaction partners. We thereby examine a method that recognizes functional network clusters to generate high-confidence predictions of different types of genetic interactions across yeast, fly and human. Our predictions are leave-one-out cross-validated on known interactions. Moreover, by using yeast as a model, we simulatr the degree to which further human genetic interactions need to be screened in order to understand their distribution in biological systems. Finally, we employ yeast as a model organism to assess the feasibility of designing synergistic or antagonistic drug pairs based on genetic interactions between their targets. The hypothesis is that if the target genes of one chemical compound are close to those of a second compound in a genetic interaction network, then synergistic or antagonistic growth effects will occur. Proximity between sets in a gene network are quantified through graph metrics, and predictions of synergy and antagonism are validated by literature-curated gold standards. Ultimately, integrating knowledge of druggable targets, how gene perturbations interact with the genetic background of an individual, and design of personalized therapeutic regimens will provide a general framework to treat a variety of diseases.