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

基本信息

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
项目类别：
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

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

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项目摘要

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.

项目摘要靶向疗法最近是急性髓样白血病（AML）和慢性淋巴细胞性白血病（CLL），但大多数患者最终甚至对这些患者产生抗药性新药。因此，迫切需要更好地了解耐药性的途径确定可以有效抑制这些途径的新型药物或药物组合。通过我们的 Beat AML计划以及以CLL为导向的实验室中的其他计划的领导正在积累大量具有基因组，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，以从这些大型的数据集。该提案的总体目标是利用我们现有的同伙，高通量筛查工具和数据集，用于预测和临床前测试新型药物组合，最终将是翻译成临床试验。该项目的具体目的是：（1）使用全基因组CRISPR筛选和质量细胞仪，以创建500个主要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（称为戒律）来推断蜂窝过程驱动对扰动的耐药性并预测可以克服的组合靶标反抗; （3）通过将单个药物的离体测试与 CRISPR/CAS综合致死性筛选，用计算预测优先级的基因，并确定通过使用RNASEQ介绍任何残留的抗性细胞来抗性途径；（4）使用（3）的数据识别和测试药物组合。这个拟议的项目将为所有3个研究领域的兴趣做出贡献 CTD2通过提高我们对药物敏感性的分子过程的理解和白血病的抗性，开发算法以预测这些过程中的标记和靶标，以及鉴定药物和/或组合，可以最大程度地提高药物敏感性并最大程度地减少耐药性。这拟议的研究在选择用于临床试验的新型治疗策略时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可以提供新颖的计算工具来使CTD2受益应用于未来的研究，并扩展到白血病之外。