Repurpose open data to discover therapeutics for understudied diseases

重新利用开放数据来发现尚未研究的疾病的治疗方法

• 批准号: 10231115
• 负责人: Bin Chen
• 金额: $ 41.63万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10231115
• 关键词: Address; Adult; Affect; Asia; Basal Cell; Basal cell carcinoma; Cancer cell line; Case Study; Cell Line; Code; Communities; Computing Methodologies; Data; Data Analyses; Data Set; Databases; Development; Diffuse intrinsic pontine glioma; Disease; Disease model; Drug Modelings; Drug Screening; Drug Targeting; Evaluation; Ewings sarcoma; Gene Expression; Gene Expression Profile; Generations; Genes; Genotype; Genotype-Tissue Expression Project; Goals; Health; Heterogeneity; Individual; Informatics; Information Systems; Malignant Childhood Neoplasm; Malignant Neoplasms; Methods; Michigan; Modeling; Molecular; Multiple Organ Failure; Normal tissue morphology; Pharmaceutical Preparations; Pharmacotherapy; Physicians; Pre-Clinical Model; Primary carcinoma of the liver cells; Procedures; Quality Control; Ramp; Rare Diseases; Research; Research Personnel; Resources; Sampling; Scientist; System; The Cancer Genome Atlas; Therapeutic; Therapeutic Agents; Tissues; Translating; Translational Research; Universities; Validation; Weight; Workplace; base; cancer gene expression; clinically relevant; computer science; cost; data modeling; deep learning; disease classification; disorder control; drug candidate; drug mechanism; high dimensionality; improved; insight; interest; large scale data; learning strategy; model development; mouse model; new therapeutic target; next generation sequencing; novel; novel therapeutics; open data; overexpression; pill; protein metabolite; statistics; success; therapeutic candidate; therapeutic target; transcriptome sequencing

Project Summary/Abstract Many diseases are understudied because they are rare or of little public interest. The effect of each understudied disease may be limited, but the cumulative effects of all these diseases could be profound. One common research challenge for these diseases is that the resources allocated to each is often limited. For instance, large- scale screening of drugs is often challenging, if not possible, in small labs. The decreasing costs of next generation sequencing make possible the generation of gene expression profiles of understudied disease samples. Integrating these expression profiles with other open data provides tremendous opportunities to gain insights into disease mechanisms and identify new therapeutics for understudied diseases. We have utilized a systems-based approach that employs gene expression profiles of disease samples and drug-induced gene expression profiles from cancer cell lines to predict new therapeutic candidates for hepatocellular carcinoma, Ewing sarcoma and basal cell carcinoma. All these candidates were successfully validated in preclinical models. The success of this approach relies on multiscale procedures, such as quality control of disease samples, selection of appropriate reference tissues, evaluation of disease signatures, and weighting cell lines. There is a plethora of relevant datasets and analysis modules that are publicly available, yet are isolated in distinct silos, making it tedious to implement this approach in translational research. A centralized informatics system that allows prediction of therapeutics for further experimental validation is thus of great interest to researchers working on understudied diseases. Accordingly, we propose four specific aims: 1) developing novel deep learning methods to select precise reference normal tissues for disease signature creation, 2) developing computational methods to reuse drug profiles from other disease models for drug prediction, 3) integrating open efficacy data to identify new targets from the systems-based approach, and 4) developing a centralized platform and promoting the platform in the scientific community. This proposal will reuse several big open databases (e.g., TCGA, TARGET, GTEx, GEO, LINCS, CTRP, GDSC) and employ cutting-edge informatics methods (e.g., deep learning). To demonstrate the scalability of the system, we will investigate three representative understudied diseases: multiple organ dysfunction syndrome (Aim 1), diffuse intrinsic pontine glioma (Aim 2) and hepatocellular carcinoma (Aim 3). Successful implementation of the systems-based approach can be used as a model for using other large open omics (proteins, metabolites) to discover therapeutics for diseases with unmet needs. This proposal will bring together experts in informatics, statistics, computer science, and physicians from Michigan State University, Stanford University, UC Berkeley and Spectrum Health. All data and code will be released to the public for continuing development. The system will be deployed to our OCTAD portal (http://octad.org), an open workplace for therapeutic discovery.

项目总结/摘要 许多疾病由于罕见或公众兴趣不大而未得到充分研究。每一个被研究对象的影响 疾病的影响可能有限，但所有这些疾病的累积影响可能是深远的。一个共同 这些疾病的研究挑战在于，分配给每种疾病的资源往往有限。例如，大- 在小型实验室中，药物的大规模筛选如果不可能的话，也常常是具有挑战性的。下一个成本下降 世代测序使得产生未研究疾病的基因表达谱成为可能 样品将这些表达谱与其他开放数据相结合， 深入了解疾病机制，并为未充分研究的疾病确定新的治疗方法。我们利用了一个 利用疾病样品和药物诱导基因的基因表达谱的系统方法 来自癌细胞系的表达谱以预测肝细胞癌的新治疗候选物， 尤文肉瘤和基底细胞癌。所有这些候选药物都在临床前模型中得到了成功验证。 这种方法的成功依赖于多尺度程序，例如疾病样本的质量控制， 选择适当的参考组织、评估疾病特征和加权细胞系。有一个 过多的相关数据集和分析模块是公开可用的，但被隔离在不同的筒仓中， 使得在翻译研究中实施这种方法是乏味的。一个集中的信息系统， 因此，研究人员对预测治疗方法进行进一步的实验验证非常感兴趣 研究未被充分研究的疾病据此，我们提出了四个具体目标：1）开发新的深度 学习选择用于疾病特征创建的精确参考正常组织的方法，2）开发 重新使用来自其他疾病模型的药物谱用于药物预测的计算方法，3）整合开放的 有效性数据，以确定基于系统的方法的新目标，以及4）开发集中平台 并在科学界推广该平台。该提案将重用几个大型开放数据库（例如， TCGA、TARGET、GTEX、GEO、LINCS、CTRP、GDSC），并采用尖端的信息学方法（例如，深 学习）。为了证明系统的可扩展性，我们将研究三个有代表性的未充分研究的 疾病：多器官功能障碍综合征（目的1），弥漫性内在脑桥胶质瘤（目的2）和 肝细胞癌（Aim 3）。成功地实施基于系统的方法可以作为 使用其他大型开放组学（蛋白质，代谢物）来发现未满足的疾病的治疗方法的模型 需求该提案将汇集信息学、统计学、计算机科学和医生方面的专家， 密歇根州立大学、斯坦福大学、加州大学伯克利分校和频谱健康。所有的数据和代码都将 向公众开放，以便继续发展。该系统将被部署到我们的OCCENTRAL门户网站 (http：octad.org），一个开放的工作场所，用于治疗发现。