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.

摘要