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

• 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
• 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

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 尽管抗疾病胶质固醇（抗GD2）疗法已显着提高了儿童的存活率 使用高危神经母细胞瘤（HR-NBL），其临床实用性受到威胁生命的副作用的严重限制 和可变响应率。尽管已有10多年的HR-NBL护理标准 没有现有的机制来预测儿童是否会对抗GD2治疗做出反应。长期目标是 识别预测性生物标志物来反应抗GD2疗法并建立全面的理解 治疗反应机制。我们的总体目标是1）开发一个预测统计模型 对于使用基因组和转录组生物标志物的抗GD2反应，2）实验表征 该模型的基础机制。中心假设是基因组变化驱动肿瘤细胞 具有可变的免疫浸润和混合抗GD2反应的亚群。这个项目的理由 是确定神经母细胞瘤中抗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响应； 3）生成一个预测性多元模型，以用于抗GD2响应 成神经细胞瘤。为了协助这些目标，将准备一个机构单细胞表达数据集20 神经母细胞瘤患者。来自抗GD2响应者和非反应者的诊断样本将是 测序。在第一个目标下，将在神经母细胞瘤中定量细胞级基因组变化 使用公共可用和机构单细胞表达数据的亚群。第二个目标有2 部分。对于第一部分，空间转录组学将用于分析8例患者（4例响应者； 4个无反应者） 用于定义明确的肿瘤内组织态，称为亚肿瘤微环境。对于第二部分，同步 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 CD44在神经母细胞瘤中。最后，第三个目标将开发一个包括基因组的多元模型 HR-NBL中抗GD2响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验证。这项研究提出了 应用具有创新性，因为它确定了抗GD2响应的新型基因组/转录组生物标志物 在神经母细胞瘤中，试图表征一种解释反应的新机制。提议 研究很重要，因为预计将改善患者的抗GD2治疗并提供 急需了解神经母细胞瘤抗GD2反应的机制。最终，这样的 知识有可能提高生存率并发现新颖的辅助治疗方法。