Determine the mechanisms of acquired brain-tropism

确定获得性脑向性的机制

基本信息

批准号：
10706493
负责人：
Hanlee P Ji
金额：
$ 34.77万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-21 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10706493
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

项目摘要

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 对于晚期癌症患者来说，30%会出现脑转移，这是毁灭性的原因因此，每年脑转移的新发病例数为 170,000 例。对于筛查，MRI 是脑转移的首选成像方式，但价格昂贵。并且缺乏相关的分子信息来识别脑部高风险患者。筛查转移，因为治疗效果和发病率与早期检测有关。手术和放疗，但明显缺乏疾病的预后或预测生物标志物我们的中心假设是进展，即肿瘤有共同的内在特征，并且与癌症脑向性和治疗反应相关的大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，我们将识别内在的细胞基因组。与脑转移相关的特征可以推广到许多原发性肿瘤类型。大脑中肿瘤细胞环境的一些外在特征有利于肿瘤细胞的播种和生长。 (1) 为了确定外在细胞向性，我们将表征免疫。我们利用单细胞基因组学研究了脑转移肿瘤微环境中细胞类型的状态和功能。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同时，使用三维类器官，我们将确定 TME-。基于巨噬细胞对抗 CD47 的反应，CD47 是巨噬细胞功能的有效调节剂。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， (2)对于内在趋向性因素，我们将分析原发肿瘤的基因组特征我们将进行表明脑转移倾向增加的大脑外在特征。对匹配的原发性和异时性脑转移进行基因组测序分析，并进行完整治疗通过这些数据，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、瘤内克隆多样性、治疗选择压力和 TME 免疫细胞组成我们的结果将确定与脑转移风险相关的基因组生物标志物。 (3)最后，我们将使用重叠数据集进行内在分析。基因组因素来确定是否存在抗辐射脑转移的预测基因组特征。原发性肿瘤的特定基因组特征是放射治疗的预测因素。结果可能会产生潜在的候选药物，以提高对这种治疗模式的敏感性，可以在体外。