Functional Genomic Discovery of Pathway Targeted and Immune Modulatory Therapeutic Combinations in Hematologic Malignancies

项目总结 靶向治疗是近期治疗急性髓系白血病(AML)和 慢性淋巴细胞白血病(CLL)，但大多数患者最终即使对这些药物也会产生耐药性 新药。因此，迫切需要更好地了解耐药的潜在途径。 找出能有效抑制这些途径的新药或药物组合。通过我们的 BEAT AML计划以及我们实验室中面向CLL的其他计划的领导力，我们 正在积累大量具有相应基因组、功能、临床和免疫功能的患者样本 注释。我们正在开发新的计算工具，以便从这些庞大的 数据集。这项建议的总体目标是利用我们现有的队列、高通量筛查 工具和数据集，用于预测和临床前测试最终将被 转化为临床试验。该项目的具体目标是：(1)使用全基因组CRISPR筛查 和质量细胞术来创建500个初级样本的基因组和免疫图谱的发现资源 (2)开发一种集成的计算框架(称为戒律)来推断 驱动对扰动的抵抗力的细胞过程和预测可以克服的组合目标 抗药性；(3)通过单一药物的体外试验和联合应用，确定协同作用的药物组合 CRISPR/CAS计算预测优先基因综合致死性筛选 通过使用RNAseq来描述任何剩余的抗性细胞的抗性途径；以及(4)使用(3)到 识别和测试药物组合。这项拟议的项目将对以下三个研究领域做出贡献 CTD2通过提高我们对药物敏感性和 白血病中的耐药性，开发预测这些过程中的标志物和靶点的算法，以及 确定将使药物敏感性最大化并将耐药性降至最低的药物和/或组合。这个 建议的研究在为临床试验选择新的治疗策略方面具有直接的翻译相关性， 并将通过生成大规模数据集和提供新型计算工具使CTD2受益 将应用于未来的研究，并扩展到白血病之外。

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.