Systems approach for congruence and selection of cancer models towards precision medicine

Zou, Jian and Shah, Osama and Chiu, Yu-Chiao and Ma, Tianzhou and Atkinson, Jennifer M. and Oesterreich, Steffi and Lee, Adrian V. and Tseng, George C. and Mendes, Pedro (2024) Systems approach for congruence and selection of cancer models towards precision medicine. PLOS Computational Biology, 20 (1). e1011754. ISSN 1553-7358

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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.

Item Type: Article
Subjects: Research Scholar Guardian > Biological Science
Depositing User: Unnamed user with email
Date Deposited: 23 Mar 2024 09:37
Last Modified: 23 Mar 2024 09:37

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