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

乳腺癌健康差异中种族、Kaiso 与肿瘤微环境之间的联系

基本信息

批准号：
10445045
负责人：
KEVIN L. GARDNER
金额：
$ 58.03万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-24 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10445045
关键词：
African African ancestry Allografting Artificial Intelligence Autophagocytosis Biological Biological Markers Breast Breast Cancer Cell Breast Cancer Patient Carcinoma Cell Nucleus Cessation of life Color Computers Cytoplasm Data Scientist Development Disease Disease Outcome Disease Progression European Evaluation Fluorescence Frequencies Gene Expression Genes Genetic Engineering Genetic Transcription Genomics Goals Growth Health Services Accessibility Histopathology Hormone Receptor Hydroxychloroquine Immune Immunohistochemistry Immunosuppression Implant Knowledge Link Machine Learning Malignant Neoplasms Medical Oncologist Modality Modeling Morphology Mus Mutation Neoplasm Metastasis Nuclear Outcome Pathologist Pathway interactions Patients Pharmacology Population Prevalence Primary Neoplasm Prognostic Marker Property Race Recurrence Score Regulation Regulator Genes Regulatory Pathway Reporting Risk Role Sampling Scientist Slide Socioeconomic Factors Specimen Technology Tissue Microarray Tissues Tumor Biology United States Variant Visual Woman advanced disease base biomarker identification breast cancer diagnosis breast cancer progression breast cancer survival cancer health disparity cohort deep learning deep learning algorithm disparity reduction health disparity hormone receptor-negative hormone receptor-positive improved inhibition of autophagy inhibitor insight malignant breast neoplasm mortality multidisciplinary novel novel therapeutics predictive marker racial difference racial disparity racial diversity survival outcome transcription factor treatment response tumor tumor behavior tumor heterogeneity tumor microenvironment tumor progression tumor-immune system interactions

项目摘要

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.

与欧洲同行相比，非洲遗产的妇女乳腺癌死亡率更高。尽管这些差异的生物学基础仍然很少定义，但最近的研究表明了明确的作用用于控制肿瘤行为和肿瘤改变的基因表达途径的生物学变化微环境。转录因子kaiso（ZBTB33）是一个基因调节因子，在这两个中都发现乳腺癌细胞的细胞核和细胞质，在功能上与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在乳腺癌存活中有助于种族差异。我们通过使用机器来完成此操作学习和人工智能以表征空间和拓扑的Kaiso依赖性差异使用多重免疫荧光技术的肿瘤微环境的特征来介绍独特的乳腺癌健康差异队列（特定目的一）。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的药理阻滞的情况下，用于Kaiso耗竭的同种异体移植模型（特定目标两个）。然后，我们执行人工智能和深度学习的大规模应用介绍了901种种族多样的乳腺癌的肿瘤微环境的空间和拓扑特征通过多重免疫组织化学标本来定义Kaiso，自噬和肿瘤的详细作用乳腺癌预后人群特异性差异的微环境（特定目的三）。一起与乳腺癌病理学家，癌症生物学家，计算机密切相结合的多学科团队科学家，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性将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他们的关联因种族而异。