TITLE Project 3: Predicting therapeutic sensitivity in cancer. ABSTRACT Somatic genetic alterations in cancer have been linked with response to targeted therapeutics and resistance to therapy. Methods that model and predict therapeutic sensitivity of cancer can be extremely useful in the development of more effective treatments. Our goal is to model, predict, and target therapeutic sensitivity and resistance of cancer. Our proposed approach is to utilize 3-dimensional (3-D) models of glioblastoma (gliomaspheres, GS) that recapitulate the nature of genetics lesions in primary tumors to experimentally validate computational Machine Learning predictions based on genomic information. Our approach in Aim 1 is to build a computational framework that incorporates genomic data, drug properties and responses, and known drug targets and network models of pathways and protein complexes to predict the therapeutic response of 80 genomically annotated GSs. Then, in Aim 2 we will experimentally validate our computational predictions of therapeutic sensitivity across the library of GSs, test combinatorial predictions, and examine the molecular mechanisms by which candidate genes alter drug responses. Single or multi-lesion sensitivity will be evaluated by RNA interference or cDNA overexpression. Finally, we will investigate the role of cell heterogeneity in the mechanism of drug resistance at the single cell level using topological methods, developed in Project 2 and in the Mathematical Core. The ultimate goal of our studies will be to uncover mechanistic insights into genotype- dependent sensitivity to drugs or synthetic lethal relationships. Ultimately, our studies will deliver key information for the development of multiple gene- and pathway-based biomarkers for personalized cancer therapies.
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