Determine the mechanisms of acquired brain-tropism

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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.