Analysis of Paediatric Medulloblastoma cellular heterogeneity using Single-cell transcriptomic profiling for improved therapeutic strategies

脑肿瘤是儿童癌症相关死亡的主要原因。髓母细胞瘤（MB）是最常见的儿童恶性脑瘤，三分之一的患者在诊断时已经转移。Group3 MB亚型（G3）以婴幼儿为主，生存率最低，转移率最高（47%）。治疗后复发的g3患儿几乎100%有转移。目前，标准护理治疗是多模式和高度侵袭性的，使年轻的幸存者患有严重的长期认知和生长障碍以及继发性恶性肿瘤。从G3研究到开发治疗方法的进展受到两个主要因素的限制。缺乏合适的细胞模型和在分子水平上解开大脑细胞复杂性的能力。该建议利用下一代3D体外模型，准确概括大脑细胞结构，G3转移开始/进展和治疗后复发。对于G3反褶积，单细胞转录组学将用于发现驱动G3临床亚型细胞轨迹的基因和调控网络。此外，基于基因表达谱的高维统计建模将使我们能够开发生物标志物，在诊断时精确预测g3的进化和治疗敏感性，并突出有效的个性化治疗策略和改善生活质量的翻译目标。

Brain tumours are the leading cause of cancer-related death inchildren. Medulloblastoma (MB) is the most common malignant paediatric braintumour with a third of all patients already metastatic at diagnosis. Group3 MB subtype(G3) predominates in infants and young children, has the lowest survival rate andhighest incidence of metastasis (47%). Almost 100% of children with recurrent G3post-treatment have metastases. Currently, standard-of-care treatment is multi-modaland highly aggressive, leaving young survivors with severe long-term cognitive andgrowth impairments, and secondary malignancies. The advancement of G3 researchto develop therapies are limited by two major factors. The paucity of appropriate cellmodels and the ability to deconvolute the brain's cellular complexity at the molecularlevel. This proposal leverages next-generation 3D in vitro models that accuratelyrecapitulate the brain's cellular architecture, G3 metastatic initiation/progression andrelapse after therapy. For G3 deconvolution, single-cell transcriptomics will be utilizedto discover genes and regulatory networks driving cellular trajectories of G3 clinicalsubtypes. Additionally, high-dimensional statistical modelling based on geneexpression profiles will allow us to develop biomarkers to precisely predict G3evolution and treatment sensitivity at time of diagnosis and highlight translationaltargets for effective personalised therapeutic strategies and improved quality of life.