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

• 批准号: 10058193
• 负责人: KEVIN L. GARDNER
• 金额: $ 67.22万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 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/10058193
• 关键词: African; 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; Expression Profiling; 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/antagonist; insight; malignant breast neoplasm; mortality; multidisciplinary; novel; novel therapeutics; predictive marker; racial difference; racial diversity; response; survival outcome; transcription factor; tumor; tumor behavior; tumor heterogeneity; tumor microenvironment; tumor progression; tumor-immune system interactions

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.

与欧洲女性相比，非洲血统的女性患乳腺癌的死亡率更高。 尽管这些差异的生物学基础仍然不明确，但最近的研究表明，它们在其中扮演了明确的角色。 关于控制肿瘤行为和肿瘤变化的基因表达途径的生物学差异 微环境。转录因子Kaiso(ZBTB33)是一种基因调控因子，存在于 乳腺癌细胞的胞核和胞浆，在功能上与生存的种族差异有关 几种上皮性癌症的转归。在这项研究中，我们利用机器学习和人工智能 明确Kaiso、自噬和免疫肿瘤微环境之间的功能联系 导致乳腺癌存活率的种族差异。我们通过机器的应用来实现这一点 学习和人工智能表征Kaiso依赖在空间和拓扑上的差异 使用多重免疫荧光技术描述独特的肿瘤微环境的特征 乳腺癌健康差异队列(特定目标一)。然后我们应用这项技术来检查 Kaiso干扰对小鼠原位自噬和免疫肿瘤微环境的影响 自噬药物阻断前后Kaiso耗竭的同种异体移植模型 (具体目标二)。然后我们进行了人工智能和深度学习的大规模应用，以 901例不同种族乳腺癌患者肿瘤微环境的空间和拓扑特征 标本通过多重免疫组织化学方法确定Kaiso、自噬和肿瘤的详细作用 微环境在人群特定乳腺癌预后中的差异(具体目标三)。同舟共济 拥有一个紧密整合的多学科团队，包括乳腺癌病理学家、癌症生物学家、计算机 科学家、生物统计学家、生物信息学家和数据科学家，我们将定义新的预测和预测 将Kaiso与肿瘤进展、免疫肿瘤微环境、乳腺癌预后联系起来的生物标志物 以及他们的联系如何因种族而不同。