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

摘要 尽管抗二唾液酸神经节苷脂(抗GD2)治疗显著提高了儿童存活率 高危神经母细胞瘤(HR-NBL)的临床应用因其危及生命的副作用而受到严重限制 和可变的响应率。尽管作为HR-NBL的标准护理超过10年，目前有 没有现有的机制来预测儿童是否会对抗GD2治疗有反应。长期目标是 确定抗GD2治疗反应的预测性生物标志物并建立全面的理解 治疗反应机制。我们的总体目标是1)开发一种预测性统计模型 使用基因组和转录生物标记物进行抗GD2反应和2)实验表征 这一模型背后的机制。中心假设是基因组变化驱动肿瘤细胞 具有可变免疫渗透和混合抗GD2反应的亚群。这个项目的基本原理是 识别神经母细胞瘤中抗GD2反应的预测生物标志物将改善患者的治疗 分层，并帮助确定提高抗GD2治疗有效性的策略。中环 假说将通过追求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应答；以及3)生成用于抗GD2应答的预测多变量模型 神经母细胞瘤。为了帮助实现这些目标，将为20个人准备一个机构单细胞表达数据集 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 已排序。在第一个目标下，神经母细胞瘤细胞水平的基因组变化将被量化 使用公共可获得的和机构的单细胞表达数据的亚群。第二个目标有2个 零件。在第一部分，空间转录学将被用来分析8名患者(4名应答者，4名无应答者)。 用于定义明确的肿瘤内组织状态，称为亚肿瘤微环境。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 CD44在神经母细胞瘤中的表达最后，第三个目标将开发一个包含基因组、 转录学和基于IHC的特征用于预测HR-NBL的抗GD2反应。模型将是 适用于批量测序队列，并在两个外部队列中验证。这项研究中提出的 应用是创新的，因为它识别了抗GD2反应的新的基因组/转录生物标记物 并试图描述一种解释反应的新机制。建议数 这项研究意义重大，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反应的潜在机制亟需深入了解。最终，这样的 知识有可能提高存活率，并发现新的辅助治疗方法。

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.