Department of Statistics, School of Public Health, Chongqing Medical University, Chongqing, China.
Women's Cancer Research Center, UPMC Hillman Cancer Center (HCC), Pittsburgh, Pennsylvania, United States of America.
PLoS Comput Biol. 2024 Jan 10;20(1):e1011754. doi: 10.1371/journal.pcbi.1011754. eCollection 2024 Jan.
Cancer models are instrumental as a substitute for human studies and to expedite basic, translational, and clinical cancer research. For a given cancer type, a wide selection of models, such as cell lines, patient-derived xenografts, organoids and genetically modified murine models, are often available to researchers. However, how to quantify their congruence to human tumors and to select the most appropriate cancer model is a largely unsolved issue. Here, we present Congruence Analysis and Selection of CAncer Models (CASCAM), a statistical and machine learning framework for authenticating and selecting the most representative cancer models in a pathway-specific manner using transcriptomic data. CASCAM provides harmonization between human tumor and cancer model omics data, systematic congruence quantification, and pathway-based topological visualization to determine the most appropriate cancer model selection. The systems approach is presented using invasive lobular breast carcinoma (ILC) subtype and suggesting CAMA1 followed by UACC3133 as the most representative cell lines for ILC research. Two additional case studies for triple negative breast cancer (TNBC) and patient-derived xenograft/organoid (PDX/PDO) are further investigated. CASCAM is generalizable to any cancer subtype and will authenticate cancer models for faithful non-human preclinical research towards precision medicine.
癌症模型是替代人体研究和加速基础、转化和临床癌症研究的重要工具。对于给定的癌症类型，研究人员通常可以选择多种模型，例如细胞系、患者来源的异种移植物、类器官和基因修饰的小鼠模型。然而，如何量化它们与人类肿瘤的一致性，并选择最合适的癌症模型，是一个尚未解决的问题。在这里，我们提出了 Congruence Analysis and Selection of CAncer Models（CASCAM），这是一种使用转录组数据以特定途径验证和选择最具代表性的癌症模型的统计和机器学习框架。CASCAM 提供了人类肿瘤和癌症模型组学数据之间的协调、系统的一致性量化以及基于途径的拓扑可视化，以确定最合适的癌症模型选择。该系统方法使用浸润性小叶乳腺癌（ILC）亚型进行了介绍，并建议选择 CAMA1 随后是 UACC3133 作为 ILC 研究最具代表性的细胞系。还进一步研究了另外两个三阴性乳腺癌（TNBC）和患者来源的异种移植物/类器官（PDX/PDO）的案例研究。CASCAM 具有通用性，可以为精准医学的忠实非人类临床前研究验证癌症模型。
