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Raimundez-Alvarez, E. ; Keller, S.* ; Ebert, K.* ; Hug, S. ; Theis, F.J. ; Maier, D.* ; Luber, B.* ; Hasenauer, J.

Model-based analysis of response and resistance factors of cetuximab treatment in gastric cancer cell lines.

PLoS Comput. Biol. 16:e1007147 (2020)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Author summaryUnraveling the causal differences between drug responders and non-responders is an important challenge. The information can help to understand molecular mechanisms and to guide the selection and design of targeted therapies. Here, we approach this problem for cetuximab treatment for gastric cancer using mechanistic mathematical modeling. The proposed model describes responder and non-responder gastric cancer cell lines and can predict the response in several validation experiments. Our analysis provides a differentiated view on mutations and explains, for instance, the relevance of MET mutations and the insignificance of PIK3CA mutation in the considered cell lines. The model might potentially provide the basis for understanding the recent failure of several clinical studies.Targeted cancer therapies are powerful alternatives to chemotherapies or can be used complementary to these. Yet, the response to targeted treatments depends on a variety of factors, including mutations and expression levels, and therefore their outcome is difficult to predict. Here, we develop a mechanistic model of gastric cancer to study response and resistance factors for cetuximab treatment. The model captures the EGFR, ERK and AKT signaling pathways in two gastric cancer cell lines with different mutation patterns. We train the model using a comprehensive selection of time and dose response measurements, and provide an assessment of parameter and prediction uncertainties. We demonstrate that the proposed model facilitates the identification of causal differences between the cell lines. Furthermore, our study shows that the model provides predictions for the responses to different perturbations, such as knockdown and knockout experiments. Among other results, the model predicted the effect of MET mutations on cetuximab sensitivity. These predictive capabilities render the model a basis for the assessment of gastric cancer signaling and possibly for the development and discovery of predictive biomarkers.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Growth-factor Receptor; Parameter-estimation; Sensitivity; Mechanisms
ISSN (print) / ISBN 1553-734X
e-ISSN 1553-7358
Journal PLoS Computational Biology
Quellenangaben Volume: 16, Issue: 3, Pages: , Article Number: e1007147 Supplement: ,
Publisher Public Library of Science (PLoS)
Publishing Place 1160 Battery Street, Ste 100, San Francisco, Ca 94111 Usa
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)