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

使用单细胞蛋白质组学预测小儿急性髓系白血病治疗后复发

• 批准号: 9758774
• 负责人: Timothy James Keyes
• 金额: $ 3.87万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-17 至 2021-09-16
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9758774
• 关键词: Acute Myelocytic Leukemia; Address; Adult; Aftercare; Age; Algorithms; Area; Automobile Driving; B-Cell Acute Lymphoblastic Leukemia; Biological Assay; Biology; Bone Marrow; Cancerous; Cell Cycle Regulation; Cells; Cessation of life; Characteristics; Child; Childhood; Childhood Acute Myeloid Leukemia; Clinical; Complex; Computing Methodologies; Cox Models; Cytogenetics; Cytometry; Data; Development; Developmental Process; Diagnosis; Diagnostic; Disease; Disease remission; Event; Fluorescence-Activated Cell Sorting; Future; Graph; Hematologic Neoplasms; Hematopoietic; Hematopoietic stem cells; In Vitro; Individual; Lead; Leukemic Cell; Light; Machine Learning; Maps; Mediating; Metadata; Methods; Modeling; Molecular; Molecular Profiling; Mus; Myelogenous; Myeloid Leukemia; Outcome; Pathway interactions; Patient-Focused Outcomes; Patients; Phenotype; Population; Probability; Prognostic Factor; Property; Proteomics; Recurrent disease; Relapse; Reporting; Resistance; STAT3 gene; Sampling; Signal Transduction; Surface; Testing; Therapeutic Intervention; Time; Treatment Efficacy; Treatment Protocols; United States; Work; Xenograft Model; acute myeloid leukemia cell; base; cancer cell; cell type; chemotherapy; design; differential expression; experience; high dimensionality; high risk; in vivo; individual patient; leukemia; leukemic stem cell; molecular subtypes; molecular targeted therapies; mortality; outcome forecast; pediatric patients; predictive modeling; progenitor; prognostic; programs; relapse patients; relapse prediction; relapse risk; response; self-renewal; stem-like cell; therapeutic target

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.

项目摘要 小儿急性髓系白血病（AML）是儿童期最致命的血液恶性肿瘤， 5年生存率只有60%大多数被诊断患有AML的儿童最初对标准治疗反应良好。 化疗;然而，近40%的患者最终会复发，对治疗反应不佳 并且在大多数患者中是致命的。尽管诊断时的年龄、对诱导化疗的反应以及 细胞遗传学状态已被确定为儿童AML的粗略预后因素，但仍不清楚 分子特征导致某些患者比其他患者复发。因此，加深对 儿童AML复发的潜在机制驱动因素代表了临床需求的重要领域。 许多研究表明，AML患者中存在罕见的造血干细胞样亚群 抵抗化疗并导致复发的细胞然而，这些复发相关的确切特征 细胞-通常称为“白血病干细胞”（LSC）-是一个有争议的问题，报告的表型跨越 许多已知的造血发育连续体和患者之间的显著差异， 在整个疾病过程中。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AML表型、临床表现和预后之间的关系。 60份经临床注释的儿科AML原代样本的结果和正常骨髓发育 患者，以确定复发相关的细胞亚型。为了实现这一目标，我们将利用 大规模细胞仪，40参数单细胞蛋白质组学平台，和机器学习， 研究了数百万白血病细胞的复杂表面和信号表型， 和复发骨髓样品。 中心假设：我们假设原发性AML细胞的高维分子谱将揭示 与复发驱动亚群相关的一致的功能表型， 健康造血发育的特定阶段，并代表未来治疗干预的要点。 目的1：开发计算对齐高维单细胞AML表型与其表型的方法。 最相似的发育状态沿着健康的骨髓连续体。 目的2：利用预测模型确定AML的表面、信号传导和功能表型 预测复发的亚群，并在体外和体内功能上验证这些特征。