Project 3: Predicting therapeutic sensitivity in cancer

标题 项目3：预测癌症的治疗敏感性。 摘要 癌症中的体细胞遗传改变与靶向治疗和耐药性的反应有关 接受治疗对癌症的治疗敏感性进行建模和预测的方法在癌症治疗中是非常有用的。 开发更有效的治疗方法。我们的目标是建模，预测和靶向治疗敏感性， 癌症的抵抗力。我们提出的方法是利用胶质母细胞瘤的三维（3-D）模型 （胶质瘤球，GS），其概括了原发性肿瘤中遗传学病变的性质， 验证基于基因组信息的计算机器学习预测。我们在目标1中的方法是 建立一个计算框架，结合基因组数据，药物特性和反应， 药物靶点和网络模型的途径和蛋白质复合物，以预测治疗反应的80 基因组注释的GS。然后，在目标2中，我们将通过实验验证我们的计算预测， 治疗敏感性在整个图书馆的GS，测试组合预测，并检查分子 候选基因改变药物反应的机制。将评价单病灶或多病灶敏感性 通过RNA干扰或cDNA过表达。最后，我们将研究细胞异质性在细胞分化中的作用。 在单细胞水平上使用拓扑方法的耐药性机制，在项目2和 数学核心我们研究的最终目标将是揭示基因型的机理- 对药物的依赖性敏感或合成致死关系。最终，我们的研究将提供关键的 用于开发个性化癌症的多种基于基因和途径的生物标志物的信息 治疗

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.