Artificial intelligence enhanced cancer cell classification based organelle morphology and topology

人工智能增强基于细胞器形态和拓扑的癌细胞分类

基本信息

批准号：
10528867
负责人：
Margarida Barroso
金额：
$ 23.02万
依托单位：
ALBANY MEDICAL COLLEGE
依托单位国家：
美国
项目类别：
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/10528867
关键词：
3-Dimensional Address Affect Algorithms Artificial Intelligence Biology Brain Breast Cancer Cell Breast Cancer Model Breast Cancer cell line Cancer Biology Cell Culture Techniques Cell Line Cells Cellular biology Classification Computer software Consumption Cytometry Data Development Discriminant Analysis Disease Disseminated Malignant Neoplasm Genomics Heterogeneity Human Image In Vitro Individual Informatics Location Lung MDA MB 231 Machine Learning Malignant Neoplasms Measures Metastatic breast cancer Methodology Methods Morphology Neoplasm Metastasis Neural Network Simulation Organ Organelles Patients Performance Phenotype Population Primary Neoplasm Proteomics Research Personnel Sampling Spatial Distribution Supervision System Time Tissues Visual analysis pipeline anticancer research artificial intelligence algorithm base cancer cell cancer site cancer type classification algorithm convolutional neural network deep learning deep learning algorithm deep learning model diagnostic value feature extraction innovation learning network learning strategy machine learning algorithm machine learning model machine learning pipeline malignant breast neoplasm microscopic imaging multiplexed imaging neoplastic cell novel prognostic value protein biomarkers random forest success supervised learning tool transcriptomics tumor tumor xenograft

3-Dimensional Address Affect Algorithms Artificial Intelligence Biology Brain Breast Cancer Cell Breast Cancer Model Breast Cancer cell line Cancer Biology Cell Culture Techniques Cell Line Cells Cellular biology Classification Computer software Consumption Cytometry Data Development Discriminant Analysis Disease Disseminated Malignant Neoplasm Genomics Heterogeneity Human Image In Vitro Individual Informatics Location Lung MDA MB 231 Machine Learning Malignant Neoplasms Measures Metastatic breast cancer Methodology Methods Morphology Neoplasm Metastasis Neural Network Simulation Organ Organelles Patients Performance Phenotype Population Primary Neoplasm Proteomics Research Personnel Sampling Spatial Distribution Supervision System Time Tissues Visual analysis pipeline anticancer research artificial intelligence algorithm base cancer cell cancer site cancer type classification algorithm convolutional neural network deep learning deep learning algorithm deep learning model diagnostic value feature extraction innovation learning network learning strategy machine learning algorithm machine learning model machine learning pipeline malignant breast neoplasm microscopic imaging multiplexed imaging neoplastic cell novel prognostic value protein biomarkers random forest success supervised learning tool transcriptomics tumor tumor xenograft

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项目摘要

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.

抽象的乳腺癌在表型和遗传上都是一种高度异质疾病。数量和癌蛋白生物标志物的亚细胞位置用于对乳腺癌类型进行分类。转录组学，多路复用成像或质量细胞仪已被用来对乳腺肿瘤细胞异质性进行分类成功。尽管基因组学和蛋白质组学在识别肿瘤细胞群体方面已经成功参与转移性进展，确定患者肿瘤是否含有转移性的能力仍然缺乏亚群。最近，细胞器的形态和功能已被用作直接读数单个癌细胞的功能表型状态。我们建议使用细胞器的空间环境，特别是它们的亚细胞位置和轨道间关系（拓扑），以对新颖而独特的分类进行分类转移性癌细胞亚群。我们开发了一个基于细胞器拓扑的细胞分类管道（OTCCP）首次量化亚细胞细胞器的拓扑特征，定义为距离在每个细胞器的对象及其在单元中的所有邻居之间。根据RFA-CA-21-013（开发癌症研究和管理的创新信息学方法和算法），我们将适应或发展机器学习和深度学习方法，以加速和自动化基于OTCCP的细胞器 - 基于拓扑的癌细胞分类，以鉴定异质内转移细胞的亚群具有潜在诊断和预后价值的原发性肿瘤。这种方法也将产生重大影响发现在亚细胞水平上促进我们对癌细胞生物学的理解的发现工具。