The linkage between Race, Kaiso and the tumor microenvironment in breast cancer health disparities

非洲裔妇女的乳腺癌死亡率高于欧洲裔妇女。 虽然这些差异的生物学基础仍然不清楚，但最近的研究表明， 对于控制肿瘤行为的基因表达途径的生物学变异和肿瘤细胞的改变， 微环境转录因子Kaiso（ZBTB33）是一种基因调控因子，存在于两种细胞中， 乳腺癌细胞的细胞核和细胞质，这在功能上与生存的种族差异有关 几种上皮癌的结果。在这项研究中，我们利用机器学习和人工智能， 定义Kaiso，自噬和免疫肿瘤微环境之间的功能联系， 导致乳腺癌生存率的种族差异。我们通过应用机器来实现这一点 学习和人工智能来表征Kaiso依赖的空间和拓扑差异 使用多重免疫荧光技术分析肿瘤微环境的特征， 乳腺癌健康差异队列（具体目标1）。然后，我们应用这项技术来检查 Kaiso破坏对自噬和免疫肿瘤微环境的影响 在存在和不存在自噬药物阻断的情况下Kaiso耗竭的同种异体移植物模型 （具体目标二）。然后，我们执行人工智能和深度学习的大规模应用， 描述901例不同种族乳腺癌中肿瘤微环境的空间和拓扑特征 通过多重免疫组化来详细定义Kaiso、自噬和肿瘤的作用 微环境在乳腺癌预后中的人群特异性差异（具体目标三）。一起 与乳腺癌病理学家、癌症生物学家、计算机科学家、 科学家，生物统计学家，生物信息学家和数据科学家，我们将定义新的预后和预测 将Kaiso与肿瘤进展、免疫肿瘤微环境、乳腺癌结局联系起来的生物标志物 以及它们的关联如何因种族而异。

Women of African heritage suffer a higher breast cancer mortality compared to their European counterparts. Though the biologic basis for these disparities remains poorly defined, recent studies suggest definitive roles for biological variation in the gene expression pathways governing tumor behavior and alterations in the tumor microenvironment. The transcription factor Kaiso (ZBTB33) is a gene regulatory factor, found in both the nucleus and cytoplasm of breast cancer cells, that has been functionally linked to racial differences in survival outcome in several epithelial cancers. In this study we leverage machine learning and artificial intelligence to define functional linkages between Kaiso, autophagy and the immmune tumor microenvironment that contribute to racial differences in breast cancer survival. We accomplish this through application of machine learning and artificial intelligence to characterize the Kaiso dependent differences in spatial and topological features of the tumor microenvironment using multiplex immunofluorescent technologies to profile a unique breast cancer health disparities cohort (Specific Aim One). We then apply this technology to examine the impact of Kaiso disruption on autophagy and the immune tumor microenvironment using a murine orthotopic allograft model for Kaiso depletion in the presence and absence of pharmacologic blockade of autophagy (Specific Aim Two). We then perform a large-scale application of artificial intelligence and deep learning to profile the spatial and topological features of the tumor microenvironment in 901 racially diverse breast cancer specimens by multiplex immunohistochemistry to define the detailed role of Kaiso, autophagy and the tumor microenvironment in population-specific differences in breast cancer outcome (Specific Aim Three). Together with a closely integrated multi-disciplinary team of breast cancer pathologists, cancer biologists, computer scientists, biostatisticians, bioinformaticians and data scientists, we will define new prognostic and predictive biomarkers that link Kaiso to tumor progression, the immune tumor microenvironment, breast cancer outcome and how their association differs by race.