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

项目摘要 靶向治疗已经成为急性髓性白血病（AML）和白血病的药物开发的最近焦点。 慢性淋巴细胞白血病（CLL），但大多数患者最终甚至对这些药物产生耐药性 新药因此，迫切需要更好地了解耐药性的潜在途径， 确定可以有效抑制这些途径的新药或药物组合。通过我们 Beat AML计划以及我们实验室中面向CLL的其他计划的领导，我们 正在积累大量的患者样本，这些样本具有相应的基因组、功能、临床和免疫学特征。 注释。我们正在开发新的计算工具，从这些大的数据中提取有用的结论。 数据集。这项提案的总体目标是利用我们现有的队列，高通量筛选， 用于预测和临床前测试新型药物组合的工具和数据集， 转化为临床试验。该项目的具体目标是：（1）利用全基因组CRISPR筛选 和大规模细胞仪，以创建500个原始样本的基因组和免疫谱的发现资源 从白血病患者;（2）开发一个集成的计算框架（称为PRECEPTS），以推断 细胞过程驱动抗干扰剂和预测组合目标，可以克服 （3）通过将单一药物的离体测试与 CRISPR/Cas合成致死性筛选，使用通过计算预测优先排序的基因，并识别 通过使用RNAseq来分析任何残留的抗性细胞来分析抗性途径;以及（4）使用来自（3）的数据来 鉴定和测试药物组合。该项目将有助于所有3个研究领域， 通过提高我们对药物敏感性潜在分子过程的理解， 白血病的耐药性，开发算法来预测这些过程中的标志物和靶点， 鉴定将最大化药物敏感性和最小化耐药性的药物和/或组合。的 所提出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的计算工具， 应用于未来的研究，并扩展到白血病以外。

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.