Quantitative and functional characterization of therapeutic resistance in cancer

总体--项目摘要 尽管我们对癌症发病机制的理解取得了巨大的进步，但个体的治疗 接受常规化疗或靶向药物治疗的患者仍然具有高度的经验性。目前的努力是 药物疗效的预测通常集中在途径激活或药物的遗传和转录标记上。 结合，例如在空间上阻碍小分子结合或激活平行或 正交信号通路。这些标记物存在于所有癌症中的一小部分，因此大多数 患者在接受治疗时，很少或根本不知道他们是否会对某个个体有反应 心理治疗。这导致许多患者接受无效和/或不必要的毒性治疗。有一个 迫切需要改变这种模式。描述治疗敏感性的理想方法是允许 用于：实时决策、识别具有治疗耐药性的稀有亚群、分析 非常小的样本(例如MRD)，并为下游分析保持单个细胞的活性以确定其特征 表型、遗传型、转录和其他敏感性决定因素。我们U54的总体目标是 应用程序是使用预测治疗反应的新策略来解决这一需求，其中 配对的表型和基因组特性是在单细胞水平上测量的。表型特性 将包括物理参数(例如质量、质量累积率)和分子标记(例如蛋白质 分泌物，表面免疫表型)，迅速受到有效治疗的影响，并先于更长时间- 术语表型(例如，丧失生存能力)。因为这些属性是针对每个单个细胞、克隆细胞 将在每个肿瘤样本中建立基于治疗反应的体系结构，方法是 来自大量细胞的分子和物理参数数据。在深度治疗反应的环境中， 治疗前和MRD样本将进行比较，以确定治疗对克隆结构的影响。这个 对表现出特殊功能特性的细胞(如表型无反应细胞)进行分离和分析 这些特性的基因组决定因素。然后这些数据将被合并到数学模型中 设计和优化治疗方法，克服个体肿瘤内的异质性 对治疗失败负责。通过推行这一方法，我们的中心将建立一个框架， 在来自临床相关标本的新颖单细胞测量之间实现迭代周期 以及产生可测试预测的计算方法。

Overall – Project Summary Despite tremendous advances in our understanding of cancer pathogenesis, the treatment of individual patients with either conventional chemotherapy or targeted agents remains highly empiric. Current efforts to predict drug efficacy are generally focused on genetic and transcriptional markers of pathway activation or drug binding, such as resistance mutations that sterically hinder small molecule binding or activate parallel or orthogonal signaling pathways. These markers exist in a very small fraction of all cancers, such that most patients are treated with little or no understanding of whether they will respond to an individual therapy. This results in many patients receiving ineffective and/or unnecessarily toxic therapies. There is a desperate need to change this paradigm. The ideal for characterizing therapeutic sensitivity would allow for: real-time decision making, identification of rare subpopulations with therapeutic resistance, analysis of very small samples (e.g. MRD), and maintains viability individual cells for downstream assays to characterize phenotypic, genotypic, transcriptional and other determinants of sensitivity. The overall goal of our U54 application is to address this need using new strategies for predicting therapeutic response in which paired phenotypic and genomic properties are measured at the single-cell level. Phenotypic properties will include both physical parameters (e.g. mass, mass accumulation rate) and molecular markers (e.g. protein secretion, surface immunophenotype) that are rapidly affected by effective therapeutics and precede longer- term phenotypes (e.g. loss of viability). Because these properties are measured for each single cell, clonal architectures based on therapeutic response will be established across each tumor sample by incorporating molecular and physical parameter data from large numbers of cells. In settings of deep treatment response, pre-treatment and MRD samples will be compared to define the effects of therapy on clonal architecture. The cells that exhibit particular functional properties (e.g. phenotypic non-responders) will be isolated and analyzed for genomic determinants of these properties. These data will then be incorporated into mathematical models to design and optimize therapeutic approaches that overcome the heterogeneity within individual tumors responsible for treatment failure. By pursuing this approach, our center will establish a framework that enables an iterative cycle between novel single-cell measurements from clinically-relevant specimens and computational approaches that result in testable predictions.