Functional Genomic Discovery of Pathway Targeted and Immune Modulatory Therapeutic Combinations in Hematologic Malignancies

血液系统恶性肿瘤中通路靶向和免疫调节治疗组合的功能基因组发现

• 批准号: 9362929
• 负责人: BRIAN J DRUKER
• 金额: $ 100.48万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9362929
• 关键词: Acute; Acute Myelocytic Leukemia; Aftercare; Algorithms; Area; Automobile Driving; Biological; Biological Assay; CRISPR screen; Cell physiology; Cells; Characteristics; Chemicals; Chronic Lymphocytic Leukemia; Clinical; Clinical Trials; Clustered Regularly Interspaced Short Palindromic Repeats; Combined Modality Therapy; Computational algorithm; Computer Analysis; Cytometry; Data; Data Set; Disease; Drug Combinations; Drug resistance; Foundations; Future; Gene Expression; Genes; Genetic; Genomics; Genotype; Goals; Hematologic Neoplasms; Immune; Immune Targeting; Immunologics; Immunophenotyping; Knowledge; Laboratories; Leadership; Libraries; Malignant Neoplasms; Molecular; Myelogenous; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacogenomics; Phenotype; Physiological; Process; Quantum Dots; RNA; Research; Residual state; Resistance; Resistance development; Resources; Sampling; System; Techniques; Testing; Therapeutic; Translating; Update; Validation; base; cancer genomics; chemotherapy; cohort; computer framework; computerized tools; data resource; design; drug development; drug sensitivity; drug testing; exome; experimental study; functional genomics; gene function; genetic profiling; genome-wide; genomic profiles; high throughput screening; immune checkpoint; improved; inhibitor/antagonist; interest; leukemia; miniaturize; novel; novel drug combination; novel therapeutics; pre-clinical; predictive marker; programs; prospective; research clinical testing; response; screening; small molecule; small molecule inhibitor; targeted agent; targeted biomarker; targeted treatment; tool; transcriptome sequencing; treatment strategy; tumor

PROJECT SUMMARY Targeted therapies have been a recent focus of drug development for acute myeloid leukemia (AML) and chronic lymphocytic leukemia (CLL), but the majority of patients eventually develop resistance even to these new drugs. There is thus an urgent need to better understand the pathways underlying drug resistance to identify novel drugs or combinations of drugs that can effectively inhibit these pathways. Through our leadership of the Beat AML program as well as other programs in our laboratories oriented towards CLL, we are amassing a large cohort of patient samples with corresponding genomic, functional, clinical and immune annotation. We are developing novel computational tools to extract useful conclusions from these large datasets. The overall goals of this proposal are to leverage our existing cohorts, high-throughput screening tools, and datasets for prediction and pre-clinical testing of novel drug combinations that will eventually be translated into clinical trials. The specific aims of this project are to: (1) use genome-wide CRISPR screening and mass cytometry to create a discovery resource of genomic and immune profiles of 500 primary samples from leukemia patients; (2) develop an integrated computational framework (called PRECEPTS) to infer the cellular processes driving resistance to perturbagens and predict combination targets that can overcome resistance; (3) identify synergistic drug combinations by combining ex vivo testing of single drugs with CRISPR/Cas synthetic lethality screening with genes prioritized by computational prediction, and identify resistance pathways by using RNAseq to profile any residual resistant cells; and (4) use the data from (3) to identify and test drug combinations. This proposed project will contribute to all 3 areas of research interest for the CTD2 by improving our understanding of the molecular processes underlying drug sensitivity and resistance in leukemias, developing algorithms to predict markers and targets in these processes, and identifying drugs and/or combinations that will maximize drug sensitivity and minimize resistance. The proposed studies have direct translational relevance in selecting novel treatment strategies for clinical trials, and will benefit the CTD2 by generating large-scale data sets and providing novel computational tools that can be applied to future studies and expanded beyond leukemias.

项目概要 靶向治疗已成为急性髓系白血病 (AML) 药物开发的近期焦点 慢性淋巴细胞白血病（CLL），但大多数患者最终甚至对这些药物产生耐药性 新药。因此，迫切需要更好地了解耐药性的潜在途径。 识别可以有效抑制这些途径的新药或药物组合。通过我们的 在 Beat AML 项目以及我们实验室中针对 CLL 的其他项目的领导下，我们 正在收集大量具有相应基因组、功能、临床和免疫的患者样本 注解。我们正在开发新颖的计算工具，以从这些大型数据中提取有用的结论 数据集。该提案的总体目标是利用我们现有的队列、高通量筛选 用于预测和临床前测试新药物组合的工具和数据集，最终将 转化为临床试验。该项目的具体目标是：（1）利用全基因组CRISPR筛选 和质谱流式分析仪创建 500 个初级样本的基因组和免疫谱的发现资源 来自白血病患者； (2) 开发一个集成的计算框架（称为 PRECEPTS）来推断 细胞过程驱动对扰动物的抵抗力并预测可以克服的组合目标 反抗; (3) 通过将单一药物的离体测试与 通过计算预测对基因进行优先排序的 CRISPR/Cas 合成致死率筛选，并识别 通过使用 RNAseq 分析任何残留的耐药细胞来确定耐药途径； (4) 使用 (3) 中的数据 识别和测试药物组合。该拟议项目将为所有 3 个研究兴趣领域做出贡献 CTD2 通过提高我们对药物敏感性分子过程的理解和 白血病的耐药性，开发算法来预测这些过程中的标记物和目标，以及 识别可最大化药物敏感性并最小化耐药性的药物和/或组合。这 拟议的研究对于选择临床试验的新治疗策略具有直接的转化相关性， 并将通过生成大规模数据集并提供新颖的计算工具来使 CTD2 受益 应用于未来的研究并扩展到白血病以外的领域。