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.

项目摘要/摘要 许多疾病都没有得到充分的研究，因为它们很罕见，或者与公众兴趣不大。每一个未被研究的人的影响 疾病可能是有限的，但所有这些疾病的累积影响可能是深远的。一个共同之处 这些疾病的研究挑战在于，分配给每种疾病的资源往往是有限的。例如，大型- 在小型实验室中，大规模筛选药物往往是具有挑战性的，如果不可能的话。Next的成本不断降低 世代测序使未被研究的疾病的基因表达谱的生成成为可能 样本。将这些表情配置文件与其他开放数据集成可提供巨大的机会 对疾病机制的洞察和为未被研究的疾病确定新的治疗方法。我们已经利用了一个 基于系统的方法，利用疾病样本和药物诱导基因的基因表达谱 肿瘤细胞系的表达谱预测新的肝细胞癌治疗候选者 尤文肉瘤和基底细胞癌。所有这些候选者都在临床前模型中得到了成功的验证。 这种方法的成功依赖于多尺度的程序，如疾病样本的质量控制， 选择合适的参考组织，评估疾病特征，并对细胞系进行加权。有一个 过多的相关数据集和分析模块是公开可用的，但被隔离在不同的竖井中， 这使得在翻译研究中实施这种方法变得单调乏味。一个集中的信息学系统， 因此，允许进一步的实验验证的治疗预测是研究人员非常感兴趣的 致力于研究未被研究的疾病。据此，我们提出了四个具体目标：1)深度发展小说 选择精确参考正常组织的学习方法，用于创建疾病征兆，2)发展 重复使用其他疾病模型中的药物分布用于药物预测的计算方法，3)集成开放的 功效数据，以从基于系统的方法中确定新的目标，以及4)开发一个集中平台 并在科学界推广该平台。该提议将重复使用几个大型开放数据库(例如， TCGA、TARGET、GTEx、GEO、LINCS、CTRP、GDSC)，并采用尖端信息学方法(例如 学习)。为了证明系统的可扩展性，我们将调查三个正在研究中的有代表性的 疾病：多器官功能障碍综合征(AIM 1)、弥漫性固有桥脑胶质瘤(AIM 2)和 肝细胞癌(目标3)。成功实施基于系统的方法可以作为一种 使用其他大型开放组学(蛋白质、代谢物)发现治疗非MET疾病的方法的模型 需要。这项提案将把信息学、统计学、计算机科学方面的专家和来自 密歇根州立大学、斯坦福大学、加州大学伯克利分校和Spectrum Health。所有数据和代码都将 向公众发布，以供继续开发。该系统将部署到我们的OCTAD门户 (http://octad.org)，是一个开放的治疗发现工作场所。