Artificial intelligence enhanced cancer cell classification based organelle morphology and topology

摘要 乳腺癌是一种高度异质性的疾病，在表型和基因上都是如此。数量和数量 癌蛋白生物标志物的亚细胞定位用于乳腺癌类型的分类。转录学， 多路成像或质量细胞术已被用来分类乳腺肿瘤细胞的异质性 成功。尽管基因组学和蛋白质组学已经成功地在鉴定肿瘤细胞群体方面取得了成功 参与转移进展，即确定患者肿瘤是否存在转移的能力 亚群仍然缺乏。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背景， 特别是它们的亚细胞位置和细胞器间的关系(拓扑)，以分类新颖和独特的 转移性癌细胞亚群。我们开发了一种基于器官拓扑的细胞分类流水线 (OTCCP)首次量化亚细胞细胞器的拓扑特征，定义为距离 在细胞内的每个细胞器对象和它的所有邻居之间。根据RFA-CA-21-013(开发 用于癌症研究和管理的创新信息学方法和算法)，我们将适应或开发 机器学习和深度学习方法，以加速和自动化基于OTCCP的器官- 基于拓扑学的癌细胞分类识别异质内转移细胞亚群 原发肿瘤具有潜在的诊断和预后价值。这种方法还将产生重大影响，因为 发现工具，在亚细胞水平上促进我们对癌细胞生物学的理解。

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.