Personalized Rejection Risk Assessment in Cardiac Transplantation

心脏移植排斥反应（CAR）是移植医学中的一个严重问题，代表着 对移植物短期和长期存活的主要威胁。因此，CAR监测和预防是一个 移植后护理的主要重点，接受者接受频繁的，定期的，监测肌内膜- 针对组织学CAR分级的转盘活检（EMB）沿着频繁、计划的免疫抑制剂减量， （IS）。这种标准化的CAR缓解方法的统一性是无法使用 基于个人CAR风险的可靠、主动和量身定制的策略。因此，CAR风险低的患者 暴露于不必要的EMB程序和过量的IS治疗，而高风险患者经历 CAR监测不足和IS早期/不适当的断奶。这会使患者受到潜在的伤害， 并强调了对精确CAR风险评估工具的明确而未满足的需求。这一切的首要前提是 建议是包含在临床数据和EMB组织中，这些数据和组织已经作为常规护理的一部分收集， 移植中心存在提供可操作的CAR风险评估的手段。广泛的免疫学，诊断- 在移植中心的电子健康记录（EHR）中捕获了nostic和药理学数据， EMB组织学样本的集合被存储（并且通常被数字化）在病理学档案中。该提案寻求 利用先进的机器学习算法和原位诊断方法， 用于验证新的CAR风险预测模型的资源。在目标1中，我们将利用我们的经验- 与自动组织学分析管道合作，开发用于预测未来CAR的“形态学模型” 使用存档的H&E幻灯片。从所有EMB事件中生成苏木精-伊红（H&E）组织学切片 作为标准治疗的一部分在已发表和已获得专利的先前努力中，我们已经提取了定量形态学特征。 来自数字化H&E载玻片的特征，当建模时， 心肌损伤和CAR分级。在目标2中，我们将超越标准H&E，利用我们在以下方面的经验： 移植EMB的定量原位免疫分析，以开发用于预测的“形态分子”模型 未来汽车这一目标将在令人兴奋的试点工作基础上得到扩展，这些试点工作显示了 结合定量图像分析和多重免疫荧光。最后，在目标3中，我们将开发一个 通过整合来自目标1和2的数据与综合临床研究， 从EHR中提取的信息学数据。最终，作为这项工作的结果，我们希望验证一个新的预- 用于探索个性化CAR监测和预测的前瞻性研究的精确预测模型 预防战略。除了潜在的翻译影响，本研究计划将建立在申请人的 了解复杂的队列设计、综合数据建模和移植免疫诊断。沿着 计划的课程和多样化的指导，咨询和协作团队，该提案提供了最佳的， Peyster博士成熟为一名在多模态诊断方面具有公认专业知识的调查员。

Project Summary: Cardiac allograft rejection (CAR) is a serious concern in transplant medicine, representing the leading threat to short- and long-term allograft survival. As a result, CAR surveillance and prevention is a primary focus of post-transplant care, with recipients undergoing frequent, scheduled, surveillance endomyocar- dial biopsy (EMB) for histologic CAR grading along with frequent, scheduled de-escalation of immunosuppres- sion (IS). The uniformity of this standardized approach to CAR mitigation is the result of an inability to employ reliable, proactive, and tailored strategies based on individual CAR risk. Consequently, patients at low CAR risk are exposed to unnecessary EMB procedures and excess IS therapy, while patients at high risk experience inadequate CAR surveillance and early/inappropriate weaning of IS. This exposes patients to potential harm, and highlights the clear, unmet need for precision CAR risk-assessment tools. The overarching premise for this proposal is that contained within the clinical data and EMB tissues already collected as part of usual care at transplant centers exists the means to provide actionable CAR risk assessments. Extensive immunologic, diag- nostic, and pharmacologic data are captured in electronic health records (EHR) at transplant centers, while large collections of EMB histology samples are stored (and often digitized) in pathology archives. This proposal seeks to utilize advanced machine-learning algorithms and in-situ diagnostic methods to deeply mine these archival resources for the purpose of validating novel CAR risk-prediction models. In Aim 1, we will leverage our experi- ence with automated histologic analysis pipelines to develop a ‘morphologic model’ for predicting future CAR using archived H&E slides. Hematoxylin-and-Eosin (H&E) histology slides are generated from all EMB events as part of standard-of-care. In published and patented prior efforts, we have extracted quantitative morphologic features from digitized H&E slides which, when modeled, demonstrate excellent performance for diagnosing myocardial injury and CAR grades. In Aim 2, we will move beyond standard H&E, leveraging our experience with quantitative, in-situ immune-profiling of transplant EMBs to develop a ‘morpho-molecular’ model for predicting future CAR. This aim will expand upon exciting pilot work which showed the CAR risk-stratification potential of combining quantitative image-analysis with multiplex immunofluorescence. Finally, in Aim 3, we will develop a ‘histo-informatics’ model for predicting CAR by integrating data from Aims 1 & 2 with comprehensive clinical informatics data extracted from the EHR. Ultimately, as a result of this work, we expect to validate a novel pre- cision prediction model for use in prospective investigations exploring personalized CAR surveillance and pre- vention strategies. Beyond the potential translational impact, this research plan will build on the Applicant’s knowledge of complex cohort design, integrated data modeling, and transplant immunodiagnostics. Along with planned coursework and a diverse mentoring, advisory, and collaborative team, this proposal provides the opti- mal vehicle for Dr. Peyster’s maturation into an investigator with proven expertise in multi-modality diagnostics.