Trajectory and Architecture of Tumor Intrinsic Drug Resistance in AML

AML 肿瘤内在耐药性的轨迹和结构

• 批准号: 10517760
• 负责人: Jeffrey Wallace Tyner
• 金额: $ 36.95万
• 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: 2017-09-30 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10517760
• 关键词: Acute Myelocytic Leukemia; Architecture; Atlases; Automobile Driving; BCL2 gene; Biological; Biology; Bone Marrow; Bypass; CD34 gene; CRISPR interference; CRISPR screen; Cell Line; Cells; Clinic; Clinical; Clinical Trials; Clonal Evolution; Clustered Regularly Interspaced Short Palindromic Repeats; Combined Modality Therapy; Complex; Data; Data Set; Disease; Disease remission; Drug Combinations; Drug Exposure; Drug resistance; Epigenetic Process; Event; Evolution; Exhibits; Expression Profiling; FLT3 inhibitor; Gene Expression; Gene Expression Profiling; Genes; Goals; Hematologic Neoplasms; Immune; Inferior; Intrinsic factor; Lead; Maps; Mediating; Modeling; Mutation; Myeloid Progenitor Cells; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Primary Neoplasm; Process; Protocols documentation; Recurrent disease; Refractory; Refractory Disease; Regimen; Relapse; Resistance; Rest; Salvage Therapy; Sampling; Signal Transduction; Stromal Cells; Supporting Cell; Techniques; Technology; Testing; Therapeutic; Therapeutic Intervention; Translating; Work; acquired drug resistance; acute myeloid leukemia cell; base; clinical translation; clinically relevant; cohort; computer framework; cytokine; data integration; data modeling; drug response prediction; drug sensitivity; epigenomics; functional genomics; genome-wide; genomic data; genomic platform; improved; innovation; insight; knock-down; knockout gene; models and simulation; neoplastic cell; next generation; novel; novel therapeutics; pressure; prevent; progenitor; resistance mechanism; response; single cell analysis; single cell sequencing; stem cells; therapy resistant; treatment response; tumor; Acute Myelocytic Leukemia; Architecture; Atlases; Automobile Driving; BCL2 gene; Biological; Biology; Bone Marrow; Bypass; CD34 gene; CRISPR interference; CRISPR screen; Cell Line; Cells; Clinic; Clinical; Clinical Trials; Clonal Evolution; Clustered Regularly Interspaced Short Palindromic Repeats; Combined Modality Therapy; Complex; Data; Data Set; Disease; Disease remission; Drug Combinations; Drug Exposure; Drug resistance; Epigenetic Process; Event; Evolution; Exhibits; Expression Profiling; FLT3 inhibitor; Gene Expression; Gene Expression Profiling; Genes; Goals; Hematologic Neoplasms; Immune; Inferior; Intrinsic factor; Lead; Maps; Mediating; Modeling; Mutation; Myeloid Progenitor Cells; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Primary Neoplasm; Process; Protocols documentation; Recurrent disease; Refractory; Refractory Disease; Regimen; Relapse; Resistance; Rest; Salvage Therapy; Sampling; Signal Transduction; Stromal Cells; Supporting Cell; Techniques; Technology; Testing; Therapeutic; Therapeutic Intervention; Translating; Work; acquired drug resistance; acute myeloid leukemia cell; base; clinical translation; clinically relevant; cohort; computer framework; cytokine; data integration; data modeling; drug response prediction; drug sensitivity; epigenomics; functional genomics; genome-wide; genomic data; genomic platform; improved; innovation; insight; knock-down; knockout gene; models and simulation; neoplastic cell; next generation; novel; novel therapeutics; pressure; prevent; progenitor; resistance mechanism; response; single cell analysis; single cell sequencing; stem cells; therapy resistant; treatment response; tumor

PROJECT SUMMARY/ABSTRACT: Project 1 Acute myeloid leukemia (AML) is a complex and genetically heterogenous disease and one of the most common hematologic malignancies. After 30-40 years without new therapies, several recent drugs have been approved for AML, including inhibitors of FLT3, IDH1/2, and BCL2. Despite improved initial response rates, none of these regimens lead to durable remissions. Acquired resistance to these agents develops due to diverse mechanisms that include tumor cell adaptation, often driven by microenvironmental signals. For the past decade, our collaborative team has employed numerous techniques, models, and analytical approaches to studying acquired drug resistance in AML – partly as a Center in the Drug Sensitivity and Resistance Network (DRSN) – the predecessor to ARTNet. We have developed the largest-to-date functional genomics platform of primary AML patient samples and implemented genome-wide CRISPR screening. Computational integration of these datasets has generated many predictions for mechanisms of drug resistance and nominated rationally selected drug combinations, some of which are in clinical trials. This analysis has led to a central hypothesis that tumor intrinsic biology can adapt in the face of therapeutic pressure, often with support from cell extrinsic signals, to undergo a multi-step process where early drug resistance is formed via cross-talk with immune and stromal cells that leads to an eventual late, cell autonomous resistant state with features of clonal evolution. For this project, our long-term goals are to optimize and translate the most effective drug combinations into the clinic for patients with AML. Our immediate goals are to understand tumor intrinsic mechanisms of acquired drug resistance. To accomplish these goals, three Aims are proposed: 1) Next-generation genome-wide interrogation of key acquired resistance scenarios – We created a panel of AML models of acquired drug resistance. These models have been generated with long-term drug exposure, sometimes with support from extrinsic cytokines. We will subject these drug resistant cells to genome-wide CRISPR screens with overlay of drugs or drug combinations. 2) Epigenomic evolution of acquired resistance – We will use protocols for expansion of myeloid progenitor cells from primary AML patient samples to study epigenetic adaptation. Using the same list of drugs and drug combinations as in Aim 1, we will profile shifts in epigenetic landscape using single-cell sequencing. 3) Atlas of intrinsic drug resistance in AML – We have expertise with broad data integration and modeling approaches. We will use these strategies to leverage our existing functional genomic dataset combined with the new data generated in Aims 1 and 2 to generate an Atlas of tumor intrinsic mechanisms of acquired drug resistance in AML. Cumulatively, we expect these innovative analyses to have a major impact on our understanding of acquired drug resistance in AML, leading to successful clinical translation of new, more effective drug combination strategies.

项目摘要/摘要：项目1 急性髓样白血病（AML）是一种复杂而遗传异质疾病，也是最常见的疾病之一 血液系统恶性肿瘤。在没有新疗法的30 - 40年后，已批准了一些最近的药物 对于AML，包括FLT3，IDH1/2和BCL2的抑制剂。尽管初始答复率提高了，但这些都没有 方案导致持久的恢复。由于不同的机制而产生的对这些试剂的抵抗力发展 其中包括通常由微环境信号驱动的肿瘤细胞适应。在过去的十年中，我们 协作团队已经为学习的研究提供了许多技术，模型和分析方法 AML的耐药性 - 部分是药物敏感性和抗药性网络（DRSN）的中心 - Artnet的前身。我们已经开发了初级AML的最早到期的功能基因组平台 患者样品并实施了全基因组CRISPR筛查。这些数据集的计算集成 已经为耐药性机制和提名的合理选择的药物产生了许多预测 组合，其中一些是在临床试验中。这种分析导致了肿瘤的中心假设 固有的生物学可以在治疗压力的情况下适应，通常在细胞外部的支持下 信号，要经历多步过程，其中通过与 免疫和基质细胞，导致事件迟到，细胞自动抗性状态，特征的特征 克隆进化。对于这个项目，我们的长期目标是优化和翻译最有效的药物 AML患者的诊所组合。我们的直接目标是了解肿瘤 获得的耐药性的内在机制。为了实现这些目标，提出了三个目标：1） 关键获得的抵抗情景的下一代全基因组审讯 - 我们创建了AML面板 获得的耐药性模型。这些模型是通过长期药物暴露而产生的， 有时在外部细胞因子的支持下。我们将使这些抗药性细胞对全基因组的细胞进行 CRISPR屏幕具有药物或药物组合的覆盖层。 2）获得的抗性的表观基因组进化 - 我们将使用方案来扩展髓样祖细胞从初级AML患者样品中进行研究 表观遗传适应。使用与AIM 1相同的药物和药物组合列表，我们将在 使用单细胞测序的表观遗传景观。 3）AML中固有耐药性的地图集 - 我们有 具有广泛数据集成和建模方法的专业知识。我们将使用这些策略来利用我们的 现有的功能基因组数据集结合了目标1和2中生成的新数据以生成Atlas AML中获得的耐药性的肿瘤固有机制。累积地，我们期望这些创新 分析对我们对AML中获得的药物耐药性的理解产生重大影响，从而成功 新的，更有效的药物组合策略的临床翻译。