Determine the mechanisms of acquired brain-tropism

摘要-项目1 对于晚期癌症患者来说，30%的人会受到脑转移的折磨，这是毁灭性的原因。 神经系统发病率和死亡率。因此，脑转移的发病率为每年170，000例新发病例。 年对于筛查，MRI是脑转移瘤的首选成像方式，但过于昂贵 并且缺乏相关的分子信息。我们缺乏预测模型来识别大脑高风险患者 转移的筛查，因为治疗效果和发病率与早期发现有关。治疗包括 但明显缺乏疾病的预后或预测生物标志物 进展或治疗。我们的中心假设是肿瘤有共同的内在特征， 与癌症脑向性和对治疗的反应相关的脑微环境的外在特征。 我们将确定这些特征与小胶质细胞（项目2）和外周免疫监视的关联 （项目3），利用生物模型来测试这些发现。我们将识别内在细胞基因组 与脑转移相关的特征可以在许多原发性肿瘤类型中推广。同样我们 假设脑中肿瘤细胞环境的外在特征有助于接种， 维持这些转移。(1)为了确定外源性细胞向性，我们将描述免疫细胞的特性。 脑转移瘤微环境中细胞类型的状态和功能。利用单细胞基因组学， 将确定TME小胶质细胞和巨噬细胞在不同肿瘤中的分布和功能状态 有CNS转移的类型。同时，使用三维类器官，我们将确定TME- 基于巨噬细胞对抗CD 47的反应，抗CD 47是巨噬细胞功能的有效调节剂。我们的结果将 确定TME巨噬细胞/小胶质细胞的细胞基因组特征和功能状态， 他们的监管状态。(2)对于内在的向性因子，我们将分析原发肿瘤的基因组特征 以及表明脑转移倾向增加的脑的外在特征。我们会进行 对匹配的原发性和异时性脑转移瘤进行基因组测序分析，并进行完整治疗 患者暴露史。利用这些数据，我们将确定关键的癌症基因组特征，例如癌症的程度 基因组不稳定性、肿瘤内克隆多样性、治疗选择压力和TME免疫细胞组成 与脑转移风险相关的疾病我们的研究结果将确定基因组生物标志物指示增加 不同肿瘤类型的脑转移风险。(3)最后，我们将使用重叠的数据集进行内在分析。 基因组因素，以确定是否存在放射抗性脑转移的预测性基因组特征。我们 假设原发性肿瘤的特定基因组特征是放射治疗的预测因子。这些 结果可能产生增加对这种治疗模式的敏感性的潜在候选者， 体外

ABSTRACT – PROJECT 1 For patients with advanced cancer, 30% will be afflicted with brain metastases, the cause of devastating neurologic morbidity and mortality. As a result, the incidence of brain metastasis is 170,000 new cases per a year. For screening, MRI is the preferred imaging modality for brain metastasis, but is prohibitively expensive and lacks relevant molecular information. We lack predictive models to identify patients at high risk for brain metastases for screening, as treatment efficacy and morbidity are linked to early detection. Treatment involves surgery and radiotherapy but with a noticeable lack of prognostic or predictive biomarkers for disease progression or treatment. Our central hypothesis is that there are common intrinsic features to the tumor and extrinsic features to the brain microenvironment relevant for cancer brain tropism and response to therapies. We will determine these features’ association to microglia (Project 2) and peripheral immune surveillance (Project 3), leveraging biological models to test these discoveries. We will identify intrinsic cellular genomic features relevant for brain metastasis that can be generalized across many primary tumor types. Likewise, we hypothesize there are extrinsic features of the tumor cellular milieu in the brain that facilitate the seeding and maintenance of these metastases. (1) For determining extrinsic cellular tropism, we will characterize the immune cell types’ states and function in the brain metastasis tumor microenvironment. Using single cell genomics, we will determine the distribution and functional status of the TME microglia and macrophages across different tumor types that have CNS metastasis. In parallel, using three dimensional organoids, we will determine the TME- based macrophage response to anti-CD47, a potent modulator of macrophage function. Our results will determine the cellular genomic characteristics and functional status of TME macrophages/microglial cells and their regulatory states. (2) For intrinsic tropism factors, we will analyze genomic features of the primary tumor and extrinsic features of the brain that indicate increased propensity for brain metastasis. We will conduct genomic sequencing analysis on matched primary and metachronous brain metastasis, with complete treatment exposure patient history. With this data, we will determine critical cancer genome features such as the extent of genomic instability, intratumoral clonal diversity, treatment selection pressure, and TME immune cell composition that are associated with brain metastatic risk. Our results will identify genomic biomarkers indicative of increased risk of brain metastasis across different tumor types. (3) Finally, we will use the overlapping data set for intrinsic genomic factors to determine if there are predictive genomic signatures of radioresistant brain metastases. We hypothesize there are specific genomic features of primary tumors that are radiotherapy predictors. These results may yield potential candidates for increasing sensitivity to this mode of treatment that can be tested in vitro.