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Systems Approach for Congruence and Selection of Cancer Models Towards Precision Medicine

Jian Zou, Osama Shah, Yu-Chiao Chiu, Tianzhou Ma, Jennifer M. Atkinson, Steffi Oesterreich, Adrian V. Lee, George C. Tseng.


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.


Cancer models, inheriting genetic properties of the tumors of origin, are essential tools in cancer research for exploring carcinogenesis and developing drugs in basic, translational and clinical studies. For a given cancer subtype, a wide selection of models, such as cell lines, patient-derived xenografts (PDX), patient-derived organoids (PDO), and genetically modified murine models, are often available to researchers.

Materials and Methods:

The gene expression matrices from tumors and cell lines are not directly comparable. We evaluated three different approaches for normalization. Quantile normalization is a widely used method to achieve equal quantiles across all the samples (“normalize.quantiles” function in preprocessCore package).


Cancer models play a crucial role in cancer research for understanding carcinogenesis and drug development. However, how to best select the most congruent cancer model to faithfully represent a specific tumor subtype remains mostly unsolved, which is an urgent gap to fill given the increasing number of cell lines and PDOs being generated.

Citation: Zou J, Shah O, Chiu Y-C, Ma T, Atkinson JM, Oesterreich S, et al. (2024) Systems approach for congruence and selection of cancer models towards precision medicine. PLoS Comput Biol 20(1): e1011754.

Editor: Pedro Mendes, University of Connecticut School of Medicine, UNITED STATES

Received: May 4, 2023; Accepted: December 12, 2023; Published: January 10, 2024.

Copyright: © 2024 Zou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: An R package, CASCAM, with an interactive app is publicly available ( to facilitate the use of our proposed framework.

Funding: Research funding for this project was provided in part by Susan G. Komen Scholar awards (SAC110021 to AVL and SAC160073 to SO), the Breast Cancer Research Foundation (to AVL and SO), the Magee Foundation, and the National Cancer Institute (CA252378). JZ and GCT were funded by NIH grant R01LM014142 and R21LM012752. This research was supported in part by the University of Pittsburgh Center for Research Computing, RRID:SCR_022735, through the resources provided. Specifically, this work used the HTC cluster, which is supported by NIH award number S10OD028483. The study is in part funded by P30CA047904. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.


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