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）是一种阻塞性肺疾病， 和免疫反应在晚期是不可逆的。患有BOS的儿童通常诊断较晚 因为他们不能进行肺量测定，并且发病率和死亡率很高。长期目标是 工作是通过确定准确筛查的策略来提高生存率和降低BOS的发病率， 预测HSCT后儿童和年轻人的BOS，并使用这些工具识别新的药物靶点 早期干预或预防BOS。本申请的目的是验证新型预测血浆 蛋白质生物标志物，建立BOS的动态预测模型，用于早期诊断、危险分层和 HSCT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）新发现的BOS风险的血浆生物标志物的纵向预测性能， 通过质谱法和ELISA法在儿童和成人中进行的库存和前瞻性研究的样本 同伙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（当无法获得肺量测定时需要）作为预测BOS风险的协变量， 快速BOS肺功能下降，为治疗决策提供信息。目前还没有生物标志物或预测 所以这项工作是完全新颖的。在该临床环境中使用动态预测算法是可行的。 首次实现了预测和诊断HSCT受试者早期肺部疾病的能力， 对BOS的临床诊断有重要意义。BOS风险识别以及筛查和治疗分层 根据风险和预测的疾病过程的程序将允许我们修改移植后护理， 降低发病率和死亡率。我们将使用我们的数据为早期活性药物和非活性药物的前瞻性临床试验提供信息。 在发展早期纤维化之前进行治疗，并测试新的预防性治疗以降低 高危人群。我们的研究将提供血液生物标志物，可以根据需要频繁使用 而不需要小孩子和经常生病的孩子的积极参与。我们初步的生物标志物和 HSCT特定算法数据表明，在临床应用前2周至6个月即可检测到BOS。 BOS的诊断这项工作既重要又重要，因为HSCT技术的改进和 支持性治疗提高了存活率。生存率的提高增加了晚期风险儿童的数量 HSCT的并发症与改变生活的发病率和晚期死亡率相关， 需要解决这些问题。这项工作将推进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.