IMAT-ITCR Collaboration: Artificial intelligence enhanced breast cancer dormancy cell classification-based organelle-morphology and topology

IMAT-ITCR 合作：人工智能增强乳腺癌休眠细胞分类的细胞器形态和拓扑

• 批准号: 10884759
• 负责人: Margarida Barroso
• 金额: $ 8.15万
• 依托单位: ALBANY MEDICAL COLLEGE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10884759
• 关键词: 3-Dimensional; 4T1; Acceleration; Algorithms; Artificial Intelligence; Biology; Biosensor; Breast Cancer Cell; Breast Cancer Model; Breast Cancer Patient; Breast Cancer cell line; Categories; Cell model; Cells; Cellular biology; Characteristics; Classification; Collaborations; Cytometry; Data; Development; Discriminant Analysis; Disease; Disseminated Malignant Neoplasm; Distant Metastasis; Endoplasmic Reticulum; Excision; Funding; Genetic; Genomics; Goals; Grant; Heterogeneity; Imaging Device; Immunofluorescence Immunologic; Individual; Informatics; Joints; Label; Location; Lysosomes; Machine Learning; Malignant Neoplasms; Metastatic breast cancer; Methodology; Methods; Mitochondria; Modeling; Morphology; Neoplasm Metastasis; Organelles; Patients; Pattern; Phenotype; Population; Primary Neoplasm; Proliferating; Proteomics; Recurrent Malignant Neoplasm; Regulator Genes; Resistance; Sampling; Signal Pathway; Spatial Distribution; Technology; Time; Tissues; analysis pipeline; analytical tool; anticancer research; cancer cell; cancer recurrence; cancer site; cancer type; classification algorithm; deep learning; diagnostic value; genetic manipulation; improved; in vivo; innovation; machine learning classifier; machine learning method; malignant breast neoplasm; monolayer; mortality; mouse model; multiplexed imaging; neoplastic cell; new technology; novel; novel strategies; prevent; prognostic value; protein biomarkers; random forest; rare cancer; success; synthetic biology; therapy development; tool; transcriptomics; tumor

ABSTRACT Breast cancer is a highly heterogenous disease, both phenotypically and genetically. The quantity and subcellular location of cancer protein biomarkers are used to classify breast cancer types. Transcriptomics, multiplexed imaging, or mass cytometry have been used to classify breast tumor cell heterogeneity with varying success. Although genomics and proteomics have been successful in the identification of tumor cell populations involved in metastatic progression, the ability to determine whether patient tumors contain metastatic subpopulations is still lacking. Recently, organelle morphology and function has been used as a direct readout of the functional phenotypic state of an individual cancer cell. We propose to use the spatial context of organelles, specifically their subcellular location and inter-organelle relationships (topology), to classify novel and distinct metastatic cancer cell subpopulations. We developed an Organelle Topology-based Cell Classification Pipeline (OTCCP) to quantify, for the first time, the topological features of subcellular organelles, defined as the distance between each organelle object and all its neighbors within a cell. Under RFA-CA-21-013 (Development of Innovative Informatics Methods and Algorithms for Cancer Research and Management), we will adapt or develop Machine learning and Deep Learning methodologies to accelerate and automate OTCCP-based organelle- based topology cancer cell classification to identify subpopulations of metastatic cells within heterogeneous primary tumors with potential diagnostic and prognostic value. This approach will also have major impact as a discovery tool to advance our understanding of cancer cell biology on a subcellular level.

摘要