Trajectory and Architecture of Tumor Intrinsic Drug Resistance in AML

项目摘要/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，是最常见的 恶性血液病。在三四十年没有新的治疗方法之后，最近的几种药物已经获得批准 对于AML，包括Flt3、IDH1/2和bcl2的抑制剂。尽管最初的应答率有所提高，但这些 养生法可带来持久的缓解。对这些药物的获得性耐药性是由于不同的机制造成的 这包括通常由微环境信号驱动的肿瘤细胞适应。在过去的十年里，我们 协作团队使用了许多技术、模型和分析方法来研究获得性 急性髓系白血病的耐药性-部分作为药物敏感性和耐药性网络(DRSN)的中心- Artnet的前身。我们开发了迄今为止最大的原发AML功能基因组学平台 患者样本和实施全基因组CRISPR筛查。这些数据集的计算积分 产生了许多对耐药机制的预测，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。这一分析导致了一个中心假设，即肿瘤 内在生物学可以适应治疗压力，通常是在细胞外源性的支持下。 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，具有以下特征 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 联合应用于急性髓系白血病患者的临床。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) 下一代全基因组对关键获得性耐药场景的询问-我们创建了一个AML小组 获得性耐药模型。这些模型是在长期接触毒品的情况下产生的， 有时在外源性细胞因子的支持下。我们将对这些耐药细胞进行全基因组测试 CRISPR筛选覆盖了药物或药物组合。2)获得性抗性的表观基因组进化- 我们将使用从原发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学图景。3)急性髓系白血病内在耐药图谱-我们有 具有广泛的数据集成和建模方法的专业知识。我们将使用这些策略来利用我们的 现有的功能基因组数据集与AIMS 1和AIMS 2中生成的新数据相结合，生成一份图谱 研究急性髓系白血病获得性耐药的肿瘤内在机制。总体而言，我们预计这些创新的 分析对我们理解急性髓细胞白血病获得性耐药有重大影响，导致成功 临床翻译新的、更有效的药物联合策略。

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.