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

基本信息

  • 批准号:
    9912591
  • 负责人:
  • 金额:
    $ 39.5万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 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

项目摘要

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.
摘要 接受免疫检查点抑制剂(ICI)治疗的癌症患者中,超过80%的人经历了免疫 治疗后的相关不良事件,包括自身免疫。自身免疫性疾病的病原学研究进展 人们对人类知之甚少,有效的治疗方法也很有限。近交系小鼠是一种宝贵的工具 了解疾病的基本生物学机制,但对理解人类的有效性较低 自身免疫力。因此,开发微创人体模型以提供真实世界的洞察力是至关重要的 自身免疫的机制、发展和治疗。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深度数据,提出了一个重要的 有机会确定免疫治疗后自身免疫性疾病发展的风险因素。我们的 该项目汇集了一个由免疫学家、肿瘤学家、信息学家和机器学习专家组成的团队 在电子病历网络内工作,以确定接受ICI治疗的癌症患者队列。从… 我们将设计和实施一个广泛而深入的电子病历数据纵向数据库,包括 治疗和反应数据和实验室结果,以使表型图谱和 人类自身免疫性疾病发展的模型。本项目建议书的首要目标是测试 在硅/活体模型系统中结合机器学习的可行性和有效性 战略作为了解自身免疫性疾病病因的平台。在R61阶段,我们建议 确定在癌症存在或不存在的情况下发展为类风湿关节炎(RA)的患者,并进行对照 队列,使用医生验证的癌症患者队列和来自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表型特征的策略。在 R33阶段我们将开发和评估全球生物标记物耐受性破坏的表型特征 机器学习和确定家族史是否是ICI后自身免疫发展的预测因素 心理治疗。我们的建议是,开发一个基于电子计算机的模型来探索自身免疫的发生,这是一个飞跃 翻译免疫学与人类自身免疫性疾病发病机制的研究进展 通过利用在电子病历中收集的信息的力量来预测结果。表型 开发的配置文件可以显著加快使用ICIS的精准医学方法,从而将 基于个人遗传、环境和社会信息的潜在自身免疫性疾病。

项目成果

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ABEL N KHO其他文献

ABEL N KHO的其他文献

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{{ truncateString('ABEL N KHO', 18)}}的其他基金

HeartShare DeCODE-HF: Data translation center to Combine Omics, Deep phenotyping, and Electronic health records for Heart Failure subtypes and treatment targets
HeartShare DeCODE-HF:数据翻译中心,结合组学、深度表型分析和电子健康记录,了解心力衰竭亚型和治疗目标
  • 批准号:
    10678959
  • 财政年份:
    2021
  • 资助金额:
    $ 39.5万
  • 项目类别:
HeartShare DeCODE-HF: Data translation center to Combine Omics, Deep phenotyping, and Electronic health records for Heart Failure subtypes and treatment targets
HeartShare DeCODE-HF:数据翻译中心,结合组学、深度表型分析和电子健康记录,了解心力衰竭亚型和治疗目标
  • 批准号:
    10488276
  • 财政年份:
    2021
  • 资助金额:
    $ 39.5万
  • 项目类别:
HeartShare DeCODE-HF: Data translation center to Combine Omics, Deep phenotyping, and Electronic health records for Heart Failure subtypes and treatment targets
HeartShare DeCODE-HF:数据翻译中心,结合组学、深度表型分析和电子健康记录,了解心力衰竭亚型和治疗目标
  • 批准号:
    10327457
  • 财政年份:
    2021
  • 资助金额:
    $ 39.5万
  • 项目类别:
INtervention in Small Primary care practices to Implement Reduction in unhealthy alcohol usE (INSPIRE)
对小型初级保健实践进行干预,以减少不健康的饮酒行为(INSPIRE)
  • 批准号:
    10011808
  • 财政年份:
    2019
  • 资助金额:
    $ 39.5万
  • 项目类别:
INtervention in Small Primary care practices to Implement Reduction in unhealthy alcohol usE (INSPIRE)
对小型初级保健实践进行干预,以减少不健康的饮酒行为(INSPIRE)
  • 批准号:
    10260395
  • 财政年份:
    2019
  • 资助金额:
    $ 39.5万
  • 项目类别:
Midwest Small Practice Care Transformation Research Alliance
中西部小型实践护理转型研究联盟
  • 批准号:
    8885363
  • 财政年份:
    2015
  • 资助金额:
    $ 39.5万
  • 项目类别:

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IPP: AUTOIMMUNE DISEASES STATISTICAL AND CLINICAL COORDINATING CENTER (ADSCCC)
IPP:自身免疫性疾病统计和临床协调中心 (ADSCCC)
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系统性自身免疫性疾病中血管内皮功能障碍的生物标志物:循环 microRNA 分析
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针对半胱氨酸环受体的自身免疫性疾病的结构机制
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