Dissecting the mechanisms by which chromosomal instability impacts anti-Disialoganglioside responses in neuroblastoma

剖析染色体不稳定性影响神经母细胞瘤抗双唾液酸神经节苷脂反应的机制

基本信息

批准号：
10654574
负责人：
Ryan Rebernick
金额：
$ 5.27万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10654574
关键词：
11q Address Affect Age Antibodies Biological Markers CD44 gene Cancer Model Cells Child Chromosomal Instability Chromosome Arm Clinical Data Data Set Diagnostic Disease Dryness Effectiveness Genes Genomic Instability Genomics Glycolipids Goals Immune Immunotherapy Institution Knowledge Life Link MYCN gene Mediating Mission Modeling Molecular Natural Killer Cells Neural Crest Neuroblastoma Patient Selection Patients Process Public Health Research Role Sampling Statistical Models Surface Survival Rate System Techniques Testing Tissues Toxic effect Training antibody-dependent cell cytotoxicity biomarker identification cancer therapy cell type chromosome loss cohort del(11q)exome sequencing experience fetal genetic signature high risk immune cell infiltrate immune checkpoint immunohistochemical markers immunoregulation improved innovation insight mouse model neoplastic cell new therapeutic target novel novel marker precursor cell predicting response predictive marker prognostic signature responders and non-responders response response biomarker risk prediction sialogangliosides side effect single cell sequencing single-cell RNA sequencing skills standard of care statistics transcriptome sequencing transcriptomics treatment response treatment stratification tumor tumor microenvironment

项目摘要

ABSTRACT Although anti-Disialoganglioside (anti-GD2) therapy has significantly improved the survival rates of children with High-Risk Neuroblastoma (HR-NBL), its clinical utility is severely limited by its life-threatening side effects and variable response rates. Despite being standard of care for HR-NBL for over 10 years, there are currently no existing mechanisms to predict whether a child will respond to anti-GD2 therapy. The long-term goal is to identify predictive biomarkers for response to anti-GD2 therapy and establish a comprehensive understanding of the therapeutic response mechanism. Our overall objective is to 1) develop a predictive statistical model for anti-GD2 response using genomic and transcriptomic biomarkers and 2) experimentally characterize the mechanism underlying this model. The central hypothesis is that genomic changes drive tumor cell subpopulations with variable immune infiltration and mixed anti-GD2 responses. The rationale for this project is that identifying predictive biomarkers for anti-GD2 response in Neuroblastoma will improve patient treatment stratification and help identify strategies for increasing the effectiveness of anti-GD2 therapy. The central hypothesis will be tested by pursuing 3 specific aims: 1) Define the role of genomic changes in Neuroblastoma tumor subpopulations; 2) Characterize the role of tumor subpopulations in immune modulation and anti-GD2 response; and 3) Generate a predictive multivariate model for anti-GD2 response in Neuroblastoma. To assist with these aims, an institutional single cell expression dataset will be prepared for 20 Neuroblastoma patients. Diagnostic samples from anti-GD2 responders and non-responders will be sequenced. Under the first aim, cellular-level genomic changes will be quantified in Neuroblastoma subpopulations using publicly available and institutional single cell expression data. The second aim has 2 parts. For part one, spatial transcriptomics will be used to analyze 8 patients (4 responders; 4 non-responders) for well-defined intratumoral tissue states known as sub-tumor microenvironments. For part two, syngeneic mouse models will be used to assess the immunomodulatory role of the immune checkpoint related gene CD44 in Neuroblastoma. Finally, the third aim will develop a multivariate model comprising genomic, transcriptomic, and IHC-based features for anti-GD2 response prediction in HR-NBL. The model will be applicable to bulk sequencing cohorts and validated in two external cohorts. The research proposed in this application is innovative because it identifies novel genomic/transcriptomic biomarkers for anti-GD2 response in Neuroblastoma and seeks to characterize a novel mechanism that explains response. The proposed research is significant because it is expected to improve patient selection for anti-GD2 therapy and provide much needed insight into mechanisms underlying anti-GD2 response in Neuroblastoma. Ultimately, such knowledge has the potential to improve survival rates and uncover novel adjunct treatments.

抽象的尽管抗二唾液酸神经节苷脂（抗GD2）治疗显着提高了儿童的生存率对于高风险神经母细胞瘤 (HR-NBL)，其危及生命的副作用严重限制了其临床应用和可变的响应率。尽管 10 多年来一直是 HR-NBL 的护理标准，但目前目前还没有机制可以预测儿童是否会对抗 GD2 治疗产生反应。长期目标是确定抗 GD2 治疗反应的预测生物标志物并建立全面的了解的治疗反应机制。我们的总体目标是 1) 开发预测统计模型使用基因组和转录组生物标志物进行抗 GD2 反应，2) 通过实验表征该模型的机制。中心假设是基因组变化驱动肿瘤细胞具有可变免疫浸润和混合抗 GD2 反应的亚群。该项目的理由确定神经母细胞瘤抗 GD2 反应的预测生物标志物将改善患者治疗分层并帮助确定提高抗 GD2 治疗有效性的策略。中央假设将通过追求 3 个具体目标来检验：1）定义基因组变化在神经母细胞瘤肿瘤亚群； 2) 表征肿瘤亚群在免疫调节中的作用和抗GD2反应； 3) 生成抗 GD2 反应的预测多变量模型成神经细胞瘤。为了帮助实现这些目标，将准备一个机构单细胞表达数据集，用于 20 神经母细胞瘤患者。来自抗 GD2 应答者和非应答者的诊断样本将被已测序。第一个目标是量化神经母细胞瘤中细胞水平的基因组变化使用公开可用的和机构单细胞表达数据的亚群。第二个目标有2 部分。第一部分将使用空间转录组学分析 8 名患者（4 名有反应者；4 名无反应者）用于定义明确的肿瘤内组织状态，称为肿瘤下微环境。对于第二部分，同基因小鼠模型将用于评估免疫检查点相关基因的免疫调节作用神经母细胞瘤中的 CD44。最后，第三个目标将开发一个包含基因组的多变量模型， HR-NBL 中抗 GD2 反应预测的转录组和基于 IHC 的特征。该模型将是适用于批量测序队列并在两个外部队列中进行了验证。本文提出的研究该应用具有创新性，因为它识别了抗 GD2 反应的新型基因组/转录组生物标志物在神经母细胞瘤中，并试图描述一种解释反应的新机制。拟议的研究意义重大，因为它有望改善抗 GD2 治疗的患者选择并提供神经母细胞瘤中抗 GD2 反应的机制亟需深入了解。最终，这样的知识有可能提高生存率并发现新的辅助治疗方法。