Targeted Chemical-Genetic Screen Platform for Identifying Novel AML Therapeutics

项目摘要 超过93%的癌症患者没有从肿瘤学精准医学获得治疗益处。这可以是 部分归因于1)不了解假定的靶向治疗的遗传靶点和作用模式， (2)缺乏有特色的综合性药物化合物文库。为了应对这些挑战，转变 在药物发现范式中，从“靶标中心”到“化学中心”的方法是必要的。 其中一种方法是化学基因组学，它涉及到从一系列药物化合物中筛选出 定义了基因突变，以确定使药物效果敏感或抑制其效果的突变。这些化学成因 相互作用可以通过在存在感兴趣的化合物的情况下测量突变体的细胞活力来量化。 一种特定化合物的一整套突变活性测量，称为“轮廓”，然后可以用作 “指纹”，以了解药物的作用模式。这些筛选可以在人类细胞系中进行 使用汇集慢病毒CRISPR-CAS9方法。而目前的全基因组筛选(约70,000个sgRNA 靶向~18,000个基因)可以为药物开发的候选化合物提供信息，但许多实验室不能 有足够的资源进行这些大规模的筛选，而不仅仅是少量的化合物。近期 来自我们实验室的初步数据显示，带有一个小的靶向CRISPR文库(约3,000个sgRNA)的屏幕 以~1,000个基因为目标)可以1)在压缩的文库中恢复类似的生物信息 全基因组筛选，以及2)降低资源成本，以实现更高吞吐量的药物筛选。 TP53突变的急性髓系白血病(AML)患者目前的预后非常差(1-2年存活率 税率为0-10%)。目前还没有针对这一患者亚群的有效疗法。然而，最近的一次 研究表明，TP53 KO AML细胞系对一组小分子的敏感性增加 抑制剂。我们在这里开发的靶向化学-遗传筛选方法可以揭示 这些推定的药物有助于确定药物开发的先导化合物的优先顺序。 这项提案的总体目标是开发一种有针对性的CRISPR-Cas9化学遗传筛查方法和 开发一种根据化学-遗传相互作用数据预测药物作用模式的计算方法。这就做 追求以下目标：(1a)开发和验证人类细胞系中的靶向筛选，(1b)开发 化学-遗传相互作用评分、候选药物靶点排序和药物预测的计算方法 作用模式，以及(2)阐明可能的药物抑制剂对TP53突变AML的作用机制 这种有针对性的化学-遗传筛选方法。这项研究的完成将产生一种有效的方法 药物作用模式的发现，并对更好地理解癌症(AML) 治疗起作用了。这项工作还将增进我对计算工具开发和 在临床肿瘤学中的应用，以及进一步培训我成为一名计算内科科学家。

PROJECT ABSTRACT Over 93% of cancer patients do not receive therapeutic benefit from oncology precision medicine. This can be partly attributed to 1) not understanding the genetic targets and modes-of-action of putative targeted therapies, and 2) lack of a well-characterized comprehensive drug compound library. To address these challenges, a shift in the drug discovery paradigm from “target-centric” to “chemical-centric” approaches is necessary. One such approach is chemical genomics, which involves screening drug compounds against a collection of defined gene mutants to identify mutations that sensitize or suppress a drug’s effect. These chemical-genetic interactions can be quantified by measuring mutants’ cell viability in the presence of compounds of interest. The entire set of mutant viability measurements for a given compound, called a “profile”, can then be used as a “fingerprint” to understand a drug’s modes-of-action. These screens can be performed in human cell lines using a pooled lentiviral CRISPR-Cas9 approach. While current genome-wide screens (~70,000 sgRNAs targeting ~18,000 genes) can inform candidate chemical compounds for drug development, many labs do not have the resources to perform these large-scale screens for more than a small number of compounds. Recent preliminary data from our labs show that screens with a small targeted CRISPR library (~3,000 sgRNAs targeting ~1,000 genes) can 1) recover similar biological information in a compressed library compared to genome-wide screens, and 2) reduce resource costs to allow for higher-throughput drug screening. TP53-mutant acute myeloid leukemia (AML) patients currently have very poor prognosis (1-2 year survival rates of 0-10%). There are currently no effective therapeutics for this patient subpopulation. However, a recent study showed that a TP53 KO AML cell line showed increased sensitivity to a panel of small-molecule inhibitors. The targeted chemical-genetic screen approach we develop here can reveal the genetic targets of these putative drugs to help with prioritizing lead compounds for drug development. The overall goal of this proposal is to develop a targeted CRISPR-Cas9 chemical-genetic screen approach and to develop a computational method to predict drug mode-of-action from chemical-genetic interaction data. I will pursue the following aims: (1a) develop and validate targeted screens in human cell lines, (1b) develop a computational method to score chemical-genetic interactions, rank candidate drug targets, and predict drug mode-of-action, and (2) elucidate mechanism-of-action of putative drug inhibitors for TP53-mutant AML using this targeted chemical-genetic screen approach. Completion of this study will result in a method for efficient discovery of drug mode-of-action and has implications for better understanding of how cancer (AML) therapeutics work. This work will also advance my understanding of computational tool development and application in clinical oncology, as well as further my training to become a computational physician-scientist.