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: 10669357
• 负责人: Bin Chen
• 金额: $ 0.64万
• 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/10669357
• 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; Persons; 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.

项目摘要/摘要 许多疾病被研究不足，因为它们是罕见或很少的公共利益。每个研究的效果 疾病可能受到限制，但是所有这些疾病的累积作用可能是深远的。一个常见 这些疾病的研究挑战是分配给每个疾病的资源通常受到限制。例如，大 - 在小型实验室中，如果不可能的话，药物的比例筛查通常具有挑战性。下一步的降低成本 生成测序使研究疾病的基因表达谱的产生可能 样品。将这些表达概况与其他开放数据集成在一起，提供了巨大的机会 对疾病机制的见解，并确定针对正在研究的疾病的新疗法。我们利用了 采用疾病样品和药物诱导基因的基因表达谱的基于系统的方法 来自癌细胞系的表达谱预测肝细胞癌的新治疗候选物 尤因肉瘤和基底细胞癌。所有这些候选人在临床前模型中都得到了成功验证。 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，疾病特征评估和加权细胞系。有一个 公开可用但在不同的孤岛中孤立的大量相关数据集和分析模块， 在翻译研究中实施这种方法变得很乏味。集中信息系统 因此，研究人员允许预测治疗剂进行进一步的实验验证 研究研究疾病。因此，我们提出了四个具体目标：1）发展新颖的深度 学习方法以选择精确参考疾病签名创造的正常组织，2） 来自其他疾病预测模型的药物概况的计算方法，3）整合开放 功效数据以确定基于系统方法的新目标，以及4）开发集中式平台 并在科学界促进平台。该建议将重复使用几个大开放数据库（例如， TCGA，Target，GTEX，GEO，LINCS，CTRP，GDSC）并采用了尖端信息学方法（例如，深 学习）。为了证明系统的可伸缩性，我们将研究三个代表性的研究 疾病：多个器官功能障碍综合征（AIM 1），弥漫性固有的蓬托马瘤（AIM 2）和 肝细胞癌（AIM 3）。成功实施基于系统的方法可以用作 使用其他大型开放法（蛋白质，代谢物）来发现未经质量疾病的疗法的模型 需要。该建议将汇集来自信息学，统计，计算机科学和医生的专家 密歇根州立大学，斯坦福大学，加州大学伯克利分校和Spectrum Health。所有数据和代码将是 向公众发行以继续发展。该系统将部署到我们的Octad Portal （http://octad.org），一个开放的治疗工作场所。