Quantitative and functional characterization of therapeutic resistance in cancer

癌症治疗耐药性的定量和功能表征

• 批准号: 9925049
• 负责人: DOUGLAS A LAUFFENBURGER
• 金额: $ 222.75万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-07 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9925049
• 关键词: Acute leukemia; Address; Affect; Animals; Architecture; Award; Binding; Biological Assay; Cell Line; Cells; Cellular Assay; Clinical Research; Colon Carcinoma; DNA sequencing; Data; Decision Making; Docking; Epithelial; Epithelium; Exhibits; Failure; Genetic; Genetic Transcription; Genomics; Genotype; Goals; Hematopoietic; Heterogeneity; Immunophenotyping; In Situ; In Vitro; Individual; Malignant Neoplasms; Malignant neoplasm of pancreas; Measurement; Measures; Mediating; Minority; Molecular; Neoplasm Metastasis; Non-Malignant; Oncology; Organoids; Pathogenesis; Pathway interactions; Patients; Pharmaceutical Preparations; Phenotype; Population; Preclinical Testing; Prediction of Response to Therapy; Primary Neoplasm; Property; Protein Secretion; Research; Residual Neoplasm; Resistance; Sampling; Signal Pathway; Specimen; Surface; Systems Biology; Testing; Therapeutic; Time; Treatment Efficacy; Treatment Failure; Treatment-related toxicity; Xenograft procedure; base; cancer cell; career; cell killing; chemotherapy; clinically actionable; clinically relevant; cost; design; drug efficacy; drug sensitivity; drug testing; improved; in vivo; individual patient; inhibitor/antagonist; leukemia; mathematical model; molecular marker; neoplastic cell; new technology; novel; outreach program; pancreatic neoplasm; paracrine; population based; precision medicine; predicting response; preservation; resistance mutation; response; small molecule; targeted agent; therapy resistant; treatment response; tumor; tumor heterogeneity; tumor microenvironment

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.

总体-项目摘要 尽管我们对癌症发病机制的理解有了巨大的进步，但个体化治疗仍然是一个难题。 接受常规化疗或靶向药物治疗的患者仍然高度依赖经验。目前努力 预测药物疗效一般集中在遗传和转录标记的途径激活或药物 结合，例如在空间上阻碍小分子结合或激活平行或平行结合的抗性突变， 正交信号通路这些标记物存在于所有癌症中的一小部分中，因此大多数 患者在接受治疗时，很少或根本不了解他们是否会对一个人产生反应， 疗法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。理想的治疗敏感性的表征将允许 用于：实时决策制定、鉴定具有治疗耐药性的罕见亚群、分析 非常小的样品（例如MRD），并保持单个细胞的活力，用于下游试验，以表征 表型、基因型、转录和其他敏感性决定因素。我们U 54的总体目标 本申请的目的是使用预测治疗反应的新策略来解决这一需求，其中 在单细胞水平上测量成对的表型和基因组特性。表型性质 将包括物理参数（例如质量、质量累积速率）和分子标记（例如蛋白质 分泌，表面免疫表型），迅速受到有效治疗的影响，并在更长的时间之前- 术语表型（例如活力丧失）。由于这些特性是针对每个单细胞测量的，因此克隆 将在每个肿瘤样品中建立基于治疗反应的结构， 来自大量细胞的分子和物理参数数据。在深度治疗反应的情况下， 将比较治疗前和MRD样品，以确定治疗对克隆结构的影响。的 将分离并分析表现出特定功能特性的细胞（例如，表型无应答者 这些特性的基因组决定因素。然后这些数据将被纳入数学模型 设计和优化治疗方法，克服个体肿瘤内的异质性， 导致治疗失败。通过这种方法，我们的中心将建立一个框架， 使得能够在来自临床相关样本的新颖单细胞测量之间进行迭代循环 和计算方法，导致可测试的预测。