Modelling how the brain microenvironment influences therapy response and metastasis of the most common childhood brain tumour

髓母细胞瘤是儿童脑肿瘤中最常见的恶性类型，占儿童癌症死亡的10%。虽然近年来存活率有所提高，但转移性扩散几乎普遍是致命的。目前的治疗方法还会对周围大脑造成不可修复的损害，导致存活患者的生活质量下降，因此需要新的靶向治疗方案。肿瘤的转移扩散程度和对当前治疗方法的抵抗力受到肿瘤潜在分子亚型的影响。因此，新疗法的开发需要可靠的模型来反映亚群的特定行为(抵抗力和迁移能力)。髓母细胞瘤的研究传统上依赖于基本的二维(2D)培养或原位小鼠模型，这两种模型都不能准确地概括儿童的大脑。我们最近开发了一个3D肿瘤微环境模型(Linke等人，2020)，它概括了在患者中观察到的耐药和迁移模式。除了更具代表性外，这些研究只需一小部分时间，而且比动物密集型项目便宜得多。我们深入的分子(单细胞分析)和代谢(3D OrbiSIMs)分析(Linke等人正在准备中)使我们进一步深入了解髓母细胞瘤细胞如何在这些模型中与其微环境相互作用。因此，学生将利用这些发现来进一步定制我们的模型，使其与患者更相关。这将通过以下方式完成：-1.我们的数据表明，特定的细胞外基质(ECM)因素对髓母细胞瘤亚群的迁移有不同的影响。这将通过使用CRISPR Cas9敲除肿瘤细胞中的表达(通过定量PCR、Western blotting和测序分析来验证)或向模型中添加特定的ECM因子来进行研究。然后，将使用延时显微镜对迁徙进行成像，并在迁徙分析中进行量化。大脑微环境由几种独特的细胞类型组成，包括小胶质免疫细胞，我们的发现表明，这些细胞以一种亚群依赖的方式影响预后。这将通过将免疫细胞添加到我们的模型中并分析它们对迁移(使用时间推移显微镜对迁移分析中的量化进行成像)和治疗反应(细胞存活和细胞死亡分析)的影响来进行研究。研究结果将与患者数据相关联，从而能够验证关键的生物标记物/途径，然后在我们的模型系统中成为目标。靶向效果将通过细胞存活/死亡和迁移分析进行评估。到目前为止，我们的模型仅用于培养已建立的细胞系。为了使其与个性化医学方法相关，有必要优化原代细胞的生长条件。细胞活力分析将被用来测量增殖和生长将通过显微镜来确定。总体目标是深入了解肿瘤如何与周围正常细胞和组织相互作用，以产生有利于肿瘤细胞迁移和耐药的微环境。这些发现将有助于指导未来儿童髓母细胞瘤的临床试验，并有可能支持其他儿科脑肿瘤的研究。

Medulloblastoma is the most common type of malignant paediatric brain tumour, accounting for 10% of cancer deaths in children. While survival rates have improved in recent years, metastatic spread is almost universally fatal. Current therapies also cause irreparable damage to the surrounding brain leading to decreased quality of life in surviving patients, consequently there is a need for new targeted therapy options. The degree of metastatic spread and resistance to current therapies is influenced by the underlying molecular subtype of the tumour. Development of novel therapies therefore requires reliable models which reflect subgroup specific behaviour (resistance and migration capability).Medulloblastoma research has traditionally relied on either basic two-dimensional (2D) culture or orthotopic mouse models, neither of which accurately recapitulates a child's brain. We have recently developed a 3D tumour microenvironment model (Linke et al 2020), which recapitulates resistance and migration patterns observed in patients. In addition to being more representative, these studies take a fraction of the time and are far cheaper than animal-intensive projects. Our in depth molecular (single cell analyses) and metabolic (3D OrbiSIMs) analyses (Linke et al in preparation) have given us further insights into how medulloblastoma cells interact with their microenvironment within these models. The student will therefore use these findings to further tailor our model, making it even more relevant to patients. This will be done by:-1. Our data indicates that specific extracellular matrix (ECM) factors differentially influence migration of medulloblastoma subgroups. This will be investigated by either using CRISPR Cas9 to knockout expression in tumour cells (verified by quantitative PCR, western blotting and sequencing assays) or adding specific ECM factors to the model. Migration will then be imaged using time-lapse microscopy on quantified in migration assays.2. The brain microenvironment consists of several unique cells types, including microglial immune cells which our findings indicate influence outcome in a subgroup dependent manner. This will be investigated by adding immune cells to our model and analysing their effects on migration (imaged using time-lapse microscopy on quantified in migration assays) and therapy response (cell viability and cell death assays).3. Findings will be correlated with patient data allowing verification of key biomarkers/pathways which can then be targeted in our model system. Targeting efficacy will be assessed using cell viability/death and migration assays.4. To date our model has only been used to grow established cell lines. In order to make it relevant to personalised medicine approaches, it will be necessary to optimise conditions for growth of primary cells. Cell viability assays will be used to measure proliferation and growth will be determined using microscopy.The overall aim being to gain an in-depth understanding of how the tumour interacts with surrounding normal cells and tissue to produce a microenvironment that is conducive to tumour cell migration and resistance. These findings will help to guide future clinical trials in children with medulloblastoma and have the potential to support studies of other paediatric brain tumours.