Determine the mechanisms of acquired brain-tropism

确定获得性脑向性的机制

• 批准号: 10927525
• 负责人: Hanlee P Ji
• 金额: $ 14.42万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-21 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10927525
• 关键词: 3-Dimensional; Address; Advanced Malignant Neoplasm; Anti-CD47; Biological; Biological Models; Brain; Brain Neoplasms; Cells; Cellular Tropism; Characteristics; Clinical; Complement; Computer Models; Data; Data Set; Disease Progression; Early Diagnosis; Foundations; Genes; Genomic Instability; Genomics; Goals; Growth; Immune; Immunologic Surveillance; Incidence; Interruption; Link; Macrophage; Magnetic Resonance Imaging; Maintenance; Malignant Neoplasms; Malignant neoplasm of brain; Metastatic Neoplasm to the Central Nervous System; Metastatic malignant neoplasm to brain; Microglia; Modeling; Molecular; Morbidity - disease rate; Mutation; Neoplasm Metastasis; Neuroimmune; Neurologic; Operative Surgical Procedures; Organoids; Outcome; Pathway interactions; Patients; Peripheral; Play; Prediction of Response to Therapy; Primary Neoplasm; Prognostic Marker; Radiation; Radiation Tolerance; Radiation therapy; Recording of previous events; Resected; Resources; Risk; Role; Sampling; Selection for Treatments; Statistical Models; Testing; Treatment Efficacy; Treatment outcome; Tropism; biobank; brain cell; cancer genome; cancer type; cell type; differential expression; functional status; genomic biomarker; genomic data; genomic predictors; genomic signature; high risk; imaging modality; in vitro testing; insight; mortality; multiple omics; neuropathology; predictive marker; predictive modeling; pressure; programs; radioresistant; response; screening; standard of care; translational study; treatment response; tumor; tumor microenvironment; tumor-immune system interactions

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.

摘要 - 项目1 对于患有晚期癌症的患者，30％将遭受脑转移的折磨，这是造成毁灭性的原因 神经系统发病率和死亡率。结果，脑转移的事件为每个A的170,000例新病例 年。为了筛选，MRI是脑转移的首选成像方式，但被禁止 并且缺乏相关的分子信息。我们缺乏预测模型来识别患有大脑高风险的患者 作为治疗效率和发病率的筛查转移与早期检测有关。治疗涉及 手术和放射疗法，但缺乏疾病的预后或预测性生物标志物 进展或治疗。我们的中心假设是，肿瘤和 脑微环境的外在特征与癌症脑对流术和对疗法的反应有关。 我们将确定这些功能与小胶质细胞（项目2）和外周免疫检查的关联 （项目3），利用生物学模型来测试这些发现。我们将识别固有的细胞基因组 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 假设大脑中肿瘤细胞环境的外部特征有助于播种和 维护这些转移。 （1）为了确定外部细胞向潮流，我们将表征免疫 细胞类型的状态和在脑转移肿瘤微环境中的功能。使用单细胞基因组学，我们 将确定TME小胶质细胞和巨噬细胞在不同肿瘤上的分布和功能状态 具有CNS转移的类型。同时，使用三维器官，我们将确定TME- 基于巨噬细胞对抗CD47的反应，抗CD47是巨噬细胞功能的潜在调节剂。我们的结果将会 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）对于固有的向流因素，我们将分析原发性肿瘤的基因组特征 大脑的外在特征表明脑转移的前景增加了。我们将进行 对匹配的原发性和常规脑转移的基因组测序分析，并完全治疗 暴露患者病史。有了这些数据，我们将确定关键的癌症基因组特征，例如 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 与大脑转移风险有关的。我们的结果将确定指示增加的基因组生物标志物 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用于固有 基因组因子确定放射性脑转移的预测基因组特征。我们 假设原发性肿瘤的特定基因组特征是放射疗法预测因子。这些 结果可能会产生潜在的候选者，以提高对这种治疗方式的敏感性，可以在 体外。