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A Rational Systematic Approach to Find Combinations of Pharmacologic and Immune Therapies that Target Identifiable Oncogenic States

寻找针对可识别致癌状态的药物和免疫疗法组合的合理系统方法

基本信息

批准号：
10226232
负责人：
Ezra Cohen
金额：
$ 94.21万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-12 至 2023-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10226232
关键词：
Anatomy Antibodies Bayesian Method Cancer Vaccines Cancer cell line Catalogs Cell Survival Cells Cellular immunotherapy Clinical Clinical Trials Clustered Regularly Interspaced Short Palindromic Repeats Communities Complex Computer Models Data Data Set Development Elements Epitopes Foundations Gene Expression Generations Genetic Genetically Engineered Mouse Genomics Human Immune Immune Targeting Immune checkpoint inhibitor Immunologics Immunotherapy Knock-out Lead Maintenance Malignant Neoplasms Mediating Molecular Target Mutation Natural Killer Cells Oncogenes Oncogenic Pathway interactions Pattern Pharmacology Resistance Resources Signal Pathway Solid Neoplasm Source System T-Lymphocyte Testing Therapeutic Intervention anticancer research base biological heterogeneity cancer cell cancer classification cancer genome chimeric antigen receptor clinical development clinical heterogeneity immune checkpoint innovation mouse model mutational status neoantigens new therapeutic target novel patient derived xenograft model precision medicine predictive modeling response synergism targeted treatment therapeutically effective treatment strategy tumor tumor heterogeneity tumor microenvironment tumor-immune system interactions

项目摘要

A Rational Systematic Approach to Identify Combinations of Pharmacologic and Immune Therapies that Target Identifiable Oncogenic States. Abstract Efforts to sequence large number of human cancers have provided a rich catalog of the most common genetic alterations that driven cancer formation and maintenance. This increasingly accurate mutational landscape has led to the identification of novel targets for therapeutic interventions. However, there is widespread biological and clinical heterogeneity in tumors, even when they share the same driver oncogene mutation. In addition, a high degree of dynamic plasticity and adaptability makes cancers display complex patterns of acquired resistance that manifest clinically. In a similar way, the wide variability of clinical responses to immunotherapy and the onset of immune escape, are becoming a formidable obstacle to fully realize the potential of many new and potentially effective immunotherapies. In this project we will evaluate a rational systematic approach to characterize oncogenic states and their most salient genomic and immune hallmarks in order to infer optimal combinations of pharmacologic and immunological perturbagens that disrupt cancer cell and tumor microenvironment interaction and viability. Our approach is based on our preliminary data, which suggests that in each identifiable oncogenic state there is a close interplay between activation of oncogenic elements, cellular pathways and the immune microenvironment. The project will test this approach with three Specific Aims: Aim 1. Characterize 5-10 pan-cancer oncogenic states with well-defined genomic and immune hallmarks including their specific molecular targets and sensitivity to perturbagens. Aim 2. Computationally infer optimal combinations of pharmacological and immunological perturbagens. Aim 3. Experimentally validate single and combinations of perturbagens identified in Aim 2. This innovative approach will provide a rich source of CTD2 datasets and resources including a catalog of oncogenic states, their most salient genomic and immune hallmarks, associated targets and validated combinations of pharmacologic and immune therapies that are effective at targeting tumors. These results will lead directly to the development of clinical trials, novel treatment strategies and provide the foundation for a new generation of more comprehensive, functional-based, precision medicine approaches.

一种合理的系统方法来识别药物和免疫的组合针对可识别的致癌状态的治疗。摘要对大量人类癌症进行测序的努力提供了一个丰富的目录，常见的基因改变导致癌症的形成和维持。这个日益准确的突变景观导致了新的治疗目标的确定，干预措施。然而，在肿瘤中存在广泛的生物学和临床异质性，甚至当他们有相同的致癌基因突变时此外，高度动态可塑性和适应性使癌症表现出复杂的获得性抵抗模式，临床表现。以类似的方式，对免疫疗法的临床反应的广泛变异性和免疫逃逸的发生，正在成为一个巨大的障碍，充分实现许多新的和潜在有效的免疫疗法的潜力。在这个项目中，我们将评价一种合理的系统方法来表征致癌状态及其最显著的基因组和免疫标志，以推断药理学和免疫学的最佳组合。破坏癌细胞和肿瘤微环境相互作用免疫干扰剂和生存能力。我们的方法是基于我们的初步数据，这表明，在每一个可识别致癌状态致癌元件的激活之间存在密切的相互作用，细胞通路和免疫微环境。该项目将测试这种方法，三个具体目标：目标1。用明确定义的基因组和免疫标志，包括其特异性分子靶点和对不安目标二。通过计算推断药理学和免疫学的最佳组合。免疫干扰剂目标3。通过实验验证单个和组合目标2中确定的扰动。这种创新的方法将提供丰富的CTD来源2 数据集和资源，包括致癌状态的目录，它们最突出的基因组和免疫标志、相关靶点和药理学和免疫学的有效组合，免疫疗法对靶向肿瘤有效。这些结果将直接导致临床试验的发展，新的治疗策略，并提供新的基础，产生更全面的、基于功能的精准医学方法。