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

项目摘要/摘要 儿童克罗恩病是一种慢性、复发性胃肠道炎症状态， 导致吸收不良、贫血和社会心理衰退。克罗恩病的发病率一直是 在10至18岁年龄组中成长。克罗恩病存在一系列临床严重程度，范围从 对标准抗肿瘤坏死因子ɑ治疗有反应的轻度疾病对具有狭窄的严重、治疗耐药的疾病有效 (B2)或穿透性(B3)并发症，通常需要手术治疗。区分哪些患者将 在诊断时需要最小干预的患者进展为更严重的疾病 未得到满足的紧急需求。对疾病结果的准确和自动化预测将显著改善患者 通过为个别患者提供个性化的干预措施来促进健康。之前尝试生成预测性数据 仅依靠疾病的临床特征和患者生物数据的克罗恩病模型 证明了有希望的，但不够准确的临床实践应用。这项提案涉及 这些限制是通过利用大量的档案和预期的患者临床元数据、组学和 机器学习派生出组织特征，以建立和测试预测特定克罗恩病的机器学习模型 疾病后果。在目标1中，我们将使用以下工具构建、测试和验证克罗恩病的预测模型 金标准活检组织病理切片的计算机图像分析。我们将使用显著图和 基因相关性分析通过可视化对我们的预测具有重要意义的组织特征来验证我们的模型 建立模型，并确定与这些功能相关的特定转录变化。在目标2中，我们将生成 整合组织学深层特征的克罗恩病临床相关性预测模型 图像分析与其他患者元数据收集作为标准临床护理的一部分。此外，我们还将整理 一份已发表的克罗恩病预测模型的完整清单，以基准我们建议的 以及未来的预测模型。最后，在目标3中，我们将使用尖端的单细胞RNA测序和空间 用转录组学方法阐明克罗恩病的转录特征并表征其特异性 与病变组织的标志性形态变化相关的基因图谱。这些数据将提供 克罗恩病和其他胃肠道疾病的亚型和临床转归研究框架 疾病，从而推动个性化治疗和精准医学的临床适应。这项建议 研究将提高诊断和预后信息的分辨率，以更好地管理克罗恩病 患者的疾病和显著的SHFT临床管理转向个体化治疗范例。

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.