An In Silico, Medical Record-based Model for Understanding the INitiation of Autoimmune Events (IMMUNE)

用于了解自身免疫事件 (IMMUNE) 起始的基于医疗记录的计算机模型

• 批准号: 9912591
• 负责人: ABEL N KHO
• 金额: $ 39.5万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-17 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9912591
• 关键词: Adverse effects; Autoimmune Diseases; Autoimmune Process; Autoimmunity; Biological; Biological Markers; Biological Models; Cancer Control; Cancer Patient; Classification; Clinical Data; Cohort Studies; Computer Simulation; Data; Development; Disease; Electronic Health Record; Etiology; Event; Family; Funding; Genetic; Genomics; Goals; Health; Healthcare; Human; Hybrids; Immune Tolerance; Immune checkpoint inhibitor; Immunologist; Immunology; Immunotherapy; Inbred Mouse; Individual; Institution; Insulin-Dependent Diabetes Mellitus; Laboratories; Low Prevalence; Machine Learning; Malignant Neoplasms; Medical Records; Modeling; National Human Genome Research Institute; Oncologist; Patients; Performance; Phase; Phenotype; Physicians; Recording of previous events; Research; Rheumatoid Arthritis; Risk Factors; Supervision; System; Testing; Touch sensation; Use Effectiveness; base; checkpoint therapy; clinical care; cohort; data modeling; design; effective therapy; electronic data; experience; human model; immune-related adverse events; in vivo Model; insight; learning strategy; longitudinal database; minimally invasive; multidisciplinary; novel; outcome prediction; portability; precision medicine; profiles in patients; social; tool; treatment response; unsupervised learning

Abstract More than 80% of cancer patients who undergo immune checkpoint inhibitor (ICI) therapy experience immune related adverse events, including autoimmunity, following treatment. The etiology of autoimmune disease in humans is poorly understood and effective treatments are limited. Inbred mice are a valuable tool for understanding basic biological mechanisms of disease, but are less effective for understanding human autoimmunity. Therefore, it is critical to develop minimally invasive human models to provide real world insights into the mechanisms, development and therapy for autoimmunity. The widespread use of electronic health records (EHRs) in healthcare and the depth of data collected for cancer patients, presents an important opportunity to identify risk factors for the development of autoimmune disease following immunotherapy. Our project brings together a team of immunologists, oncologists, informaticists and machine learning experts working within an EHR network, to identify a cohort of cancer patients who have undergone ICI therapy. From this cohort we will design and implement a broad and deep longitudinal database of EHR data, including treatment and response data and laboratory results, to enable the development of phenotypic profiles and models for autoimmune disease development in humans. The overarching goal of this project proposal is to test the feasibility and effectiveness of using a hybrid in silico / in vivo model system combined with machine learning strategies as a platform for understanding the etiology of autoimmune disease. In the R61 Phase we propose to identify patients who develop rheumatoid arthritis (RA) in the in the presence or absence of cancer, and control cohort, using a physician-validated cohort of cancer patients and data from EHR, and use machine learning strategies to develop phenotypic profiles for RA in the presence or absence of cancer and ICI therapy. In the R33 Phase we will and develop and assess phenotypic profiles for global biomarkers tolerance disruption using machine learning and determine if family history is a predictor of the development of autoimmunity following ICI therapy. Our proposal, to develop an in silico based model for exploring the onset of autoimmunity, makes a leap forward for translational immunology and the exploration of mechanisms of human autoimmune disease development by leveraging the power of the information collected in EHR to predict outcomes. The phenotypic profiles developed could significantly accelerate precision medicine approaches for employing ICIs that minimize the potential autoimmune disease based on personal genetic, environmental and social information.

抽象的 接受免疫检查点抑制剂（ICI）治疗的癌症患者中有80％以上的患者经历免疫 治疗后，包括自身免疫性在内的相关不良事件。自身免疫性疾病的病因 人类的理解很少，有效的治疗受到限制。近交小鼠是一个有价值的工具 了解疾病的基本生物学机制，但对理解人的有效性较差 自身免疫性。因此，至关重要的是开发最低侵入性的人类模型以提供现实世界的见解 进入自身免疫的机制，开发和治疗。电子健康的广泛使用 医疗保健中的记录（EHR）和为癌症患者收集的数据深度提出了重要 免疫治疗后自身免疫性疾病发展的危险因素的机会。我们的 项目汇集了一个免疫学家，肿瘤学家，信息专家和机器学习专家团队 在EHR网络中工作，以确定接受ICI治疗的癌症患者的队列。从 我们将设计并实施EHR数据的广泛而深厚的纵向数据库，包括 治疗和响应数据和实验室结果，以使表型特征的发展和 人类自身免疫性疾病发展的模型。该项目建议的总体目标是测试 在计算机 /体内模型系统中使用混合动力车与机器学习相结合的可行性和有效性 策略是理解自身免疫性疾病病因的平台。在R61阶段，我们建议 在存在或不存在癌症的情况下确定发生类风湿关节炎（RA）的患者 同类，使用癌症患者的医师验证队列和EHR的数据，并使用机器学习 在存在或不存在癌症和ICI治疗的情况下开发RA的表型谱的策略。在 R33阶段我们将使用和评估使用的表型曲线，用于使用使用 机器学习并确定家族史是否是ICI后自身免疫发展发展的预测指标 治疗。我们的建议，开发一个基于硅的基于硅的模型，以探索自身免疫的发作，这是一个飞跃 转化免疫学和人类自身免疫性机制的探索 通过利用EHR中收集的信息来预测结果来开发。表型 开发的概况可能会大大加速精确医学方法，以采用最小化的ICI 基于个人遗传，环境和社会信息的潜在自身免疫性疾病。