Accelerating drug development and repurposing using systematic genetic interactio

利用系统的遗传相互作用加速药物开发和重新利用

基本信息

批准号：
8757890
负责人：
MICHAEL C BASSIK
金额：
$ 240.75万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-15 至 2019-06-30
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): Major limitations in drug development are identification of cellular targets (and off-targets) for novel drugs, and discovery of effective drg combinations to combat rapidly evolving diseases such as cancer. Furthermore, drug screens require extensive handling, which limits throughput and increases variability. Finally, genetic background often determines drug susceptibility, complicating selection of optimal treatment regimens. This proposal describes a broad effort in my lab to begin to address these issues by (1) dramatically enhancing the speed, accuracy, and scalability of pooled genome-wide screens using bioreactors to automate cell culture, (2) developing tools to measure pairwise genetic interactions between drug targets in high-throughput, and (3) using these tools to identify synthetic lethal combination targets that are specific to stress and oncogene states. We have shown in pilot studies that our novel high-complexity shRNA libraries (25 shRNAs/gene) can be used to identify the target of a cancer-killing drug with remarkable specificity (Matheny et al., 2013). We have also developed a scalable, rapid strategy to create double-shRNA libraries to simultaneously measure genetic interactions between 100,000's of gene pairs (Bassik et al., 2013). We will adapt this platform to create novel, high-complexity gene modulation libraries composed of shRNA and CRISPR elements targeting pairs of FDA-approved drug targets, and then identify synthetic lethal interactions between these genes in the context of a panel of stresses and oncogenes. Elucidating these synergies will inform our understanding of the underlying biology of stress signaling and cell death regulation, and illuminate new therapeutic combinations using available drugs in diseases such as cancer where these stresses are prevalent. At the same time, the genetic interaction maps will allow us to directly investigate off target effects of drugs, which would streamline the identification of viable candidate molecules for further development. Together, we expect the tools developed here will be broadly useful for functional genomics screening and drug development efforts, and that this work will establish a new paradigm for empirical testing and identification of drug response biomarkers.

描述（由申请人提供）：药物开发的主要限制是新药的细胞靶标（和脱靶）的识别，以及发现有效的药物组合来对抗快速发展的疾病（例如癌症）。此外，药物筛选需要大量的处理，这限制了通量并增加了变异性。最后，遗传背景通常决定药物敏感性，使最佳治疗方案的选择变得复杂。该提案描述了我的实验室为解决这些问题所做的广泛努力：(1) 使用生物反应器实现细胞培养自动化，显着提高全基因组范围汇集筛选的速度、准确性和可扩展性，(2) 开发测量成对遗传的工具高通量药物靶标之间的相互作用，以及（3）使用这些工具来识别特定于应激和癌基因状态的合成致死组合靶标。我们在试点研究中表明，我们的新型高复杂性 shRNA 文库（25 个 shRNA/基因）可用于识别具有显着特异性的抗癌药物的靶标（Matheny 等，2013）。我们还开发了一种可扩展的快速策略来创建双 shRNA 文库，以同时测量 100,000 个基因对之间的遗传相互作用（Bassik 等，2013）。我们将采用该平台来创建新颖的、高复杂性的基因调节文库，该文库由 shRNA 和 CRISPR 元件组成，针对 FDA 批准的药物靶点对，然后在一组应激和癌基因的背景下识别这些基因之间的合成致死相互作用。阐明这些协同作用将有助于我们了解压力信号传导和细胞死亡调节的基础生物学，并阐明使用可用药物治疗这些压力普遍存在的疾病（例如癌症）的新治疗组合。同时，遗传相互作用图谱将使我们能够直接研究药物的目标作用，这将简化对进一步开发的可行候选分子的识别。我们共同期望这里开发的工具将广泛用于功能基因组学筛选和药物开发工作，并且这项工作将为药物反应生物标志物的经验测试和识别建立新的范例。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（1）

E2A-PBX1 Remodels Oncogenic Signaling Networks in B-cell Precursor Acute Lymphoid Leukemia.

DOI：
10.1158/0008-5472.can-16-1899
发表时间：
2016-12-01
期刊：
Cancer research
影响因子：
11.2
作者：
Duque-Afonso J;Lin CH;Han K;Wei MC;Feng J;Kurzer JH;Schneidawind C;Wong SH;Bassik MC;Cleary ML
通讯作者：
Cleary ML

Bithionol blocks pathogenicity of bacterial toxins, ricin, and Zika virus.

DOI：
10.1038/srep34475
发表时间：
2016-09-30
期刊：
Scientific reports
影响因子：
4.6
作者：
Leonardi W;Zilbermintz L;Cheng LW;Zozaya J;Tran SH;Elliott JH;Polukhina K;Manasherob R;Li A;Chi X;Gharaibeh D;Kenny T;Zamani R;Soloveva V;Haddow AD;Nasar F;Bavari S;Bassik MC;Cohen SN;Levitin A;Martchenko M
通讯作者：
Martchenko M