Penetrating the “Black box”: Prediction of early Bronchiolitis obliterans in Pediatric Hematopoietic Stem Cell Transplant Recipients

摘要 闭塞性细支气管炎综合征(BOS)是一种由多种炎症共同引起的阻塞性肺疾病。 以及在其晚期不可逆转的免疫反应。患有BOS的儿童通常诊断较晚 因为他们不能进行肺活量测定，发病率和死亡率都很高。这样做的长期目标是 工作是通过确定准确筛查和治疗的策略来提高存活率和降低BOS的发病率 预测儿童和年轻人HSCT后的BOS并利用这些工具识别新的药物靶点 用于早期干预或预防BOS。该应用程序的目的是验证新的预测等离子体 蛋白质生物标志物，建立BOS动态预测模型，用于早期诊断、危险分层和 造血干细胞移植后BOS的疾病轨迹预测。我们将通过以下具体措施实现这些目标 目的：1)验证新发现的BOS风险的血浆生物标志物的纵向预测性能 用质谱仪和酶联免疫吸附试验对儿童和成人进行的银行和前瞻性研究的样本 一群人。2)使用肺功能和临床数据优化和验证我们的动态预测算法 以及生物标志物水平(无法获得肺活量测定时需要)作为BOS和BOS项目风险的协变量 BOS肺功能快速下降，为治疗决策提供信息。目前没有生物标志物或预测性的 BOS的工具，所以这项工作是完全新颖的。动态预测算法在此临床环境中的使用是 创新，首次能够预测和诊断HSCT受试者的早期肺部疾病 对BOS的临床诊断有重要意义。BOS风险识别及筛查和治疗分层 根据风险和预测的病程进行的程序将使我们能够修改移植后的护理和 降低发病率和死亡率。我们将使用我们的数据为两个早期活跃的预期临床试验提供信息 在发生早期纤维化之前进行治疗，并测试新的预防疗法以减少 高危人群。我们的研究将提供可以在必要时频繁使用的血液生物标记物 而不需要年幼且往往病情严重的儿童的积极参与。我们的初步生物标志物和 HSCT特定算法数据显示最早在临床前2周至6个月检测到BOS BOS的诊断。这项工作既重要又至关重要，因为HSCT技术和 支持性护理提高了存活率。存活率的提高增加了晚发风险儿童的数量 HSCT的并发症与改变生活的发病率和晚期死亡率有关，并且有紧急情况 需要解决这些问题。这项工作将促进BOS的预测和早期诊断，并提供 未来预防和治疗试验的框架。

Abstract Bronchiolitis obliterans syndrome (BOS) is an obstructive lung disease caused by a combination of inflammation and immune response that is irreversible in its late stage. Children with BOS are typically diagnosed late because they are unable to perform spirometry, and morbidity and mortality are high. The long-term goal of this work is to improve survival and reduce morbidity from BOS by identifying strategies for accurate screening and prediction of BOS in children and young adults after HSCT and using these tools to identify novel drug targets for early intervention or prevention of BOS. The objective of this application is to validate novel predictive plasma protein biomarkers and establish a dynamic prediction model for BOS for early diagnosis, risk stratification and disease trajectory prediction for BOS after HSCT. We will achieve these goals through the following specific aims: 1) Validate longitudinal predictive performance of newly discovered plasma biomarkers of BOS risk in samples from banked and prospective studies by mass spectrometry and ELISA in both pediatric and adult cohorts. 2) Optimize and validate our dynamic prediction algorithm using pulmonary function and clinical data as well as biomarker levels (needed when no spirometry can be obtained) as covariates to project risks of BOS and rapid BOS lung-function decline to inform treatment decisions. There are currently no biomarkers or predictive tools for BOS so this work is entirely novel. The use of a dynamic prediction algorithm in this clinical setting is innovative allowing for the first time the ability to predict and diagnose early lung disease in HSCT subjects prior to the clinical diagnosis of BOS. Identification of BOS risk and stratification of screening and treatment procedures according to risk and predicted disease course would allow us to modify post-transplant care and reduce morbidity and mortality. We will use our data to inform prospective clinical trials of both early active treatment prior to development of early fibrosis and to test novel prophylactic therapies to reduce incidence in high risk individuals. Our studies will provide blood biomarkers that can be used as frequently as necessary without requiring active participation from small and often very sick children. Our preliminary biomarker and HSCT specific algorithm data demonstrate detection of BOS as soon as 2 weeks to 6 months prior to clinical diagnosis of BOS. This work is both significant and vital because improvements in HSCT techniques and supportive care have led to improved survival. Improved survival increases the number of children at risk for late complications of HSCT that are associated with life-changing morbidity and late mortality and there is urgent need to address these issues. This work will advance prediction and early diagnosis of BOS, as well as providing the framework for future prevention and treatment trials.