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: 10819011
• 负责人: KEVIN L. GARDNER
• 金额: $ 7.09万
• 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/10819011
• 关键词: African; Allografting; Artificial Intelligence; Autophagocytosis; Biological; Breast Cancer Cell; Carcinoma; Cell Nucleus; Computers; Cytoplasm; Data Scientist; Disparity; European; Gene Expression; Genetic Transcription; Immunohistochemistry; Link; Machine Learning; Malignant Neoplasms; Modeling; Mus; Outcome; Pathologist; Pathway interactions; Population; Prognostic Marker; Race; Regulator Genes; Role; Scientist; Specimen; Technology; Variant; Woman; breast cancer survival; cancer health disparity; cohort; deep learning; malignant breast neoplasm; mortality; multidisciplinary; novel; pharmacologic; predictive marker; racial difference; racial disparity; racial diversity; survival outcome; transcription factor; tumor behavior; 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，非洲女性患乳腺癌的死亡率更高。

项目成果

An integrated LSTM-HeteroRGNN model for interpretable opioid overdose risk prediction.

• DOI: 10.1016/j.artmed.2022.102439
• 发表时间: 2023-01
• 期刊: ARTIFICIAL INTELLIGENCE IN MEDICINE
• 影响因子: 7.5
• 作者: Dong, Xinyu;Wong, Rachel;Lyu, Weimin;Abell-Hart, Kayley;Deng, Jianyuan;Liu, Yinan;Hajagos, Janos G.;Rosenthal, Richard N.;Chen, Chao;Wang, Fusheng
• 通讯作者: Wang, Fusheng