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

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.

项目总结/摘要：项目1 急性髓系白血病（AML）是一种复杂的遗传异质性疾病，也是最常见的白血病之一， 血液恶性肿瘤经过30-40年没有新的治疗方法，最近几种药物已被批准 用于AML，包括FLT 3、IDH 1/2和BCL 2的抑制剂。尽管初始响应率有所提高，但这些 方案导致持久缓解。对这些药物的获得性耐药性是由于不同的机制而产生的 其中包括肿瘤细胞的适应，通常由微环境信号驱动。在过去的十年里，我们 一个协作小组采用了许多技术、模型和分析方法来研究获得的 AML耐药-部分作为药物敏感性和耐药网络（DRSN）的中心- ARTNet的前身。我们开发了迄今为止最大的原发性AML功能基因组学平台 患者样本，并实施全基因组CRISPR筛查。这些数据集的计算集成 已经产生了许多关于耐药机制的预测，并推荐了合理选择的药物 联合用药，其中一些正在进行临床试验。这一分析导致了一个中心假设，即肿瘤 内在生物学可以在面对治疗压力时适应，通常需要细胞外在的支持。 信号，经历一个多步骤的过程，其中早期耐药性是通过与 免疫和基质细胞，导致最终的晚期，细胞自主抵抗状态的特征， 克隆进化对于这个项目，我们的长期目标是优化和转化最有效的药物 联合用药进入临床治疗AML患者。我们的近期目标是了解肿瘤 获得性耐药的内在机制。为了实现这些目标，提出了三个目标：1） 下一代全基因组询问关键的获得性耐药情况-我们创建了一个AML小组 获得性耐药性的模型。这些模型是在长期药物暴露的情况下产生的， 有时还需要外源性细胞因子的支持。我们将对这些耐药细胞进行全基因组研究 CRISPR筛选与药物或药物组合的叠加。2)获得性抗性的表观基因组进化- 我们将使用从原发性AML患者样本中扩增髓系祖细胞的方案来研究 表观遗传适应使用与目标1中相同的药物和药物组合列表，我们将描述 使用单细胞测序的表观遗传景观。3)AML内在耐药性图谱-我们有 具有广泛的数据集成和建模方法的专业知识。我们将利用这些战略， 现有功能基因组数据集与目标1和2中生成的新数据相结合，以生成图谱 急性粒细胞白血病获得性耐药的肿瘤内在机制。累积起来，我们希望这些创新 分析对我们理解AML中获得性耐药性产生重大影响，导致成功的 新的、更有效的药物组合策略的临床转化。