Predicting Post-treatment Relapse in Pediatric Acute Myeloid Leukemia Using Single-cell Proteomics

项目摘要 儿童急性髓系白血病(AML)是儿童时期最致命的血液系统恶性肿瘤，具有 5年存活率仅为60%。大多数被诊断为急性髓细胞白血病的儿童最初对标准反应良好 化疗；然而，近40%的人最终会患上复发的疾病，对治疗的反应很差 在大多数患者中是致命的。虽然确诊时的年龄、对诱导化疗的反应，以及 细胞遗传学状态已被确认为儿童AML的粗略预后因素，目前仍不清楚 分子特征会导致某些患者比其他患者复发。因此，发展一个更好的理解 儿科AML复发的机制驱动因素代表了临床需要的一个重要领域。 许多报道表明，AML患者中存在罕见的造血样干细胞亚群。 抵抗化疗并导致复发的疾病。然而，这些与复发相关的确切特征 细胞--通常被称为“白血病干细胞”(LSCs)--是一个争论不休的问题，报道的表型包括 许多已知的造血发育连续体在患者和患者之间存在显著差异 在整个病程中。因此，这些复发相关细胞的身份和重要性也 因为它们与正常的造血发育过程的关系仍然是个谜。 拟议的项目将研究单细胞急性髓系白血病表型、临床表型和 60例经临床注释的儿童AML原发样本的预后和正常髓系发育 以确定与复发相关的细胞亚型。为了实现这一点，我们将利用 40参数单细胞蛋白质组学平台和机器学习的同时 百万白血病诊断细胞复杂表面及信号表型的研究 与健康对照组相比，复发的骨髓样本。 中心假设：我们假设原代AML细胞的高维分子图谱将揭示 与复发驱动亚群相关的一致的功能表型，在计算上与 这是健康造血发展的特定阶段，代表了未来治疗干预的要点。 目标1：开发计算将高维、单细胞AML表型与其 沿着健康的髓系连续体最相似的发育状态。 目的2：利用预测性建模来确定AML的表面、信号和功能表型 预测复发的亚群，并在体外和体内功能上验证这些特征。

Project Summary Pediatric Acute Myeloid Leukemia (AML) is the most lethal hematologic malignancy in childhood, with a probability of 5-year survival at only 60%. Most children diagnosed with AML initially respond well to standard chemotherapy; however, nearly 40% eventually develop relapsed disease, which responds poorly to treatment and is fatal in the majority of patients. Although age at diagnosis, response to induction chemotherapy, and cytogenetic status have been identified as coarse prognostic factors in pediatric AML, it is still unclear what molecular features lead certain patients to relapse over others. Thus, developing an enhanced understanding of the mechanistic drivers underlying relapse in pediatric AML represents a significant area of clinical need. Many reports indicate that there are rare, hematopoietic stem cell-like subpopulations in AML patients that resist chemotherapy and drive relapse. However, the exact characteristics of these relapse-associated cells—often called “leukemic stem cells” (LSCs)—are a matter of contention, with reported phenotypes spanning much of the known hematopoietic developmental continuum and differing significantly between patients and throughout the course of disease. As such, the identity and importance of these relapse-associated cells as well as their relationship to normal hematopoietic developmental processes remain mysterious. The proposed project will examine the relationship between single-cell AML phenotypes, clinical outcomes, and normal myeloid development in 60 clinically-annotated primary samples from pediatric AML patients in order to identify relapse-associated cellular subtypes. To achieve this, we will leverage the versatility of mass cytometry, a 40-parameter single-cell proteomics platform, and machine learning in simultaneously studying the complex surface and signaling phenotypes of millions of leukemic cells from patients’ diagnostic and relapse bone marrow samples relative to healthy controls. Central hypothesis: We hypothesize that high-dimensional molecular profiling of primary AML cells will reveal consistent, functional phenotypes associated with relapse-driving subpopulations that computationally align with particular stages of healthy hematopoietic development and represent points of future therapeutic intervention. Aim 1: Develop methods to computationally align high-dimensional, single-cell AML phenotypes with their most analogous developmental state along the healthy myeloid continuum. Aim 2: Utilize predictive modeling to determine the surface, signaling, and functional phenotype of AML subpopulations predicting relapse and functionally validate these characteristics in vitro and in vivo.