Trajectory and Architecture of Tumor Intrinsic Drug Resistance in AML

AML 肿瘤内在耐药性的轨迹和结构

基本信息

批准号：
10684105
负责人：
Jeffrey Wallace Tyner
金额：
$ 30.41万
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：
2017-09-30 至 2027-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10684105
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 clinical translation clinically relevant cohort computer framework cytokine data integration data modeling drug response prediction drug sensitivity epigenomic profiling 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

项目摘要

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.

项目概要/摘要：项目 1 急性髓系白血病 (AML) 是一种复杂且具有遗传异质性的疾病，也是最常见的疾病之一在 30-40 年没有新疗法出现后，最近有几种药物已获得批准。对于 AML，包括 FLT3、IDH1/2 和 BCL2 抑制剂，尽管初始缓解率有所提高，但这些药物都没有改善。由于不同的机制，治疗方案可导致持久缓解。其中包括肿瘤细胞适应，通常由微环境信号驱动。协作团队采用了多种技术、模型和分析方法来研究获得的 AML 中的耐药性 – 部分作为药物敏感性和耐药性网络 (DRSN) 的中心 – 我们开发了迄今为止最大的原发性 AML 功能基因组学平台。患者样本并对这些数据集进行了全基因组 CRISPR 筛选。对耐药机制进行了许多预测并提名了合理选择的药物组合，其中一些正在进行临床试验。这项分析得出了一个中心假设：肿瘤。内在生物学可以在面对治疗压力时进行适应，通常需要细胞外在的支持信号，经历一个多步骤过程，其中早期耐药性是通过与免疫细胞和基质细胞最终导致晚期细胞自主抵抗状态，其特征为对于这个项目，我们的长期目标是优化和转化最有效的药物。我们的近期目标是了解肿瘤。为了实现这些目标，提出了三个目标：1) 对关键获得性耐药场景的下一代全基因组询问——我们创建了一组 AML 获得性耐药模型这些模型是通过长期药物暴露而产生的，有时在外源细胞因子的支持下，我们会将这些耐药细胞置于全基因组范围内。 CRISPR 筛选与药物或药物组合的叠加 2) 获得性耐药性的表观基因组进化。我们将使用从原发性 AML 患者样本中扩增骨髓祖细胞的方案来进行研究表观遗传适应。使用与目标 1 相同的药物和药物组合列表，我们将分析表观遗传适应的变化。使用单细胞测序的表观遗传景观 3) AML 内在耐药性图谱 – 我们有我们将利用这些策略来利用我们的广泛数据集成和建模方法方面的专业知识。现有的功能基因组数据集与目标 1 和 2 中生成的新数据相结合，生成图谱总的来说，我们期待这些创新的肿瘤内在机制。分析对我们对 AML 获得性耐药性的理解产生重大影响，从而导致成功新的、更有效的药物组合策略的临床转化。