Predicting Clinical Phenotypes in Crohn's Disease Using Machine Learning and Single-Cell 'omics

使用机器学习和单细胞组学预测克罗恩病的临床表型

基本信息

批准号：
10586795
负责人：
Sana Syed
金额：
$ 71.24万
依托单位：
UNIVERSITY OF VIRGINIA
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-05-01 至 2027-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10586795
关键词：
Address Age Years Algorithms Anemia Autoimmune Automobile Driving Behavior Benchmarking Biopsy Cells Childhood Chromosome Mapping Chronic Classification Clinical Clinical Management Complex Complication Computer Models Crohn&apos s disease Data Data Set Development Diagnosis Diagnostic Disease Disease Management Disease Outcome Disease Progression Disease Resistance Early treatment Enrollment Evaluation Fistula Functional disorder Future Gastrointestinal Diseases Gastrointestinal tract structure Gene Expression Gene Expression Profile Genes Genetic Goals Health Histology Histopathology Image Analysis Incidence Inflammatory Intervention Knowledge Literature Machine Learning Malabsorption Syndromes Maps Metadata Methodology Methods Microscopic Modeling Molecular Morphology Mucous Membrane Newly Diagnosed Operative Surgical Procedures Outcome Pathologic Patients Pattern Pediatric Crohn&apos s disease Penetration Performance Phenotype Physical shape Predictive Value Prevention Prospective cohort Publishing Relapse Reporting Research Resolution Review Literature Risk Sensitivity and Specificity Severities Slide TNF gene Testing Therapeutic Intervention Time Tissue Extracts Tissues Training Translations Visualization Work accurate diagnostics adverse outcome age group bench to bedside biobank biomedical imaging clinical care clinical phenotype clinical practice clinical predictors clinically relevant cohort cost disease phenotype feature detection genetic signature improved individual patient individualized medicine innovation machine learning method machine learning model machine learning prediction model building novel outcome prediction personalized diagnostics personalized intervention personalized medicine precision medicine predictive modeling predictive tools prevent preventive intervention prognostic prospective psychosocial recruit response risk prediction model single-cell RNA sequencing spatial integration standard of care transcriptomics treatment response

项目摘要

PROJECT SUMMARY/ABSTRACT Pediatric Crohn's disease presents as a chronic, relapsing inflammatory condition of the gastrointestinal tract, leading to malabsorption, anemia, and psychosocial decline. The incidence rate of Crohn's disease has been growing in the 10- to 18-year age group. Crohn’s disease exists on a spectrum of clinical severity, ranging from mild disease responsive to standard anti-TNFɑ therapy to severe, treatment-resistant disease with stricturing (B2) or penetrating (B3) complications often requiring surgical intervention. Distinguishing which patients will progress to more severe disease from patients who will require minimal intervention at the time of diagnosis is an urgent unmet need. Accurate and automated prediction of disease outcomes will significantly improve patient health by informing personalized interventions for individual patients. Previous attempts at generating predictive models of Crohn’s disease relying solely on clinical features of the disease and patient biodata have demonstrated promising, yet inadequate accuracies for clinical practice applications. This proposal addresses these limitations by leveraging large cohorts of archival and prospective patient clinical metadata, ‘omics, and machine learning derived tissue features to build and test machine learning models for predicting specific Crohn's disease outcomes. In Aim 1, we will build, test, and validate predictive models of Crohn’s disease using computational image analysis of gold-standard biopsy histopathology slides. We will use saliency maps and gene correlations analysis to validate our models by visualizing the tissue features of importance to our predictive models and identify specific transcriptomic changes associated with these features. In Aim 2, we will generate a clinically-relevant predictive model of Crohn’s disease by integrating the deep features extracted from histology image analysis with other patient metadata collected as part of standard clincal care. Additionally, we will collate a thorough list of published predictive models of Crohn's disease to benchmark the performance of our proposed and future predictive models. Lastly, in Aim 3 we will use cutting edge single-cell RNA sequencing and spatial transcriptomics approaches to elucidate a transcriptomic signature of Crohn's disease and characterize specific genetic profiles associated with the hallmark morphological changes in diseased tissue. These data will provide a framework for studying the subtypes and clinical outcomes of Crohn’s disease and other gastrointestinal diseases, thus driving the clinical adaptation of personalized therapy and precision medicine. This proposed research will increase the resolution of both diagnostic and prognostic information to better manage Crohn’s disease in patients and significantly shft clinical management to an individualized treatment paradigm.

项目摘要/摘要小儿克罗恩病是胃肠道的慢性炎症状况，导致吸收不良，贫血和社会心理下降。克罗恩病的事件率已经在10至18岁的年龄组中生长。克罗恩病在各种临床严重程度上存在轻度疾病对标准抗TNF疗法有反应对严重的耐药性疾病的疾病（B2）或穿透性（B3）并发症通常需要手术干预。区分哪些患者在诊断时需要最少干预的患者的患者的进展是更严重的疾病紧急的未满足需求。对疾病预后的准确和自动预测将显着改善患者通过为个别患者提供个性化干预措施来了解健康。以前尝试产生预测性的尝试克罗恩病模型仅依赖于疾病的临床特征和患者生物的模型表现出希望，但对于临床实践应用的准确性不足。该提案解决了通过利用大量档案和潜在的患者临床元数据，'omics和机器学习派生的组织功能，用于构建和测试机器学习模型，以预测特定的克罗恩疾病结果。在AIM 1中，我们将使用，测试和验证克罗恩病的预测模型金标准活检组织病理学幻灯片的计算图像分析。我们将使用显着图，基因相关分析通过可视化对我们的预测的重要性组织特征来验证我们的模型模型并确定与这些特征相关的特定转录组变化。在AIM 2中，我们将产生通过整合从组织学中提取的深度特征，一种与临床相关的克罗恩病预测模型作为标准clincal护理的一部分收集的其他患者元数据的图像分析。此外，我们将合作克罗恩病的已发表预测模型的详尽清单，以基准我们提案的绩效和未来的预测模型。最后，在AIM 3中，我们将使用尖端单细胞RNA测序和空间转录组方法阐明克罗恩病的转录组特征并表征了特定的特征遗传特征与悬浮组织中标志性形态变化相关的遗传特征。这些数据将提供研究克罗恩病和其他胃肠道的亚型和临床结果的框架疾病，从而推动了个性化疗法和精度医学的临床适应。这提出了研究将增加诊断和预后信息的分辨率，以更好地管理克罗恩患者的疾病，以及对个性化治疗范式的SHFT临床管理。