Author ORCID Identifier

https://orcid.org/0000-0003-3299-4938

https://orcid.org/0000-0002-5410-3094

https://orcid.org/0000-0002-5349-3561

Document Type

Article

Publication Date

4-13-2022

College/Unit

Statler College of Engineering and Mining Resources

Department/Program/Center

Chemical and Biomedical Engineering

Abstract

Developing drugs increasingly relies on mechanistic modeling and simulation. Models that capture causal relations among genetic drivers of oncogenesis, functional plasticity, and host immunity complement wet experiments. Unfortunately, formulating such mechanistic cell-level models currently relies on hand curation, which can bias how data is interpreted or the priority of drug targets. In modeling molecular-level networks, rules and algorithms are employed to limit a priori biases in formulating mechanistic models. Here we combine digital cytometry with Bayesian network inference to generate causal models of cell-level networks linking an increase in gene expression associated with oncogenesis with alterations in stromal and immune cell subsets from bulk transcriptomic datasets. We predict how increased Cell Communication Network factor 4, a secreted matricellular protein, alters the tumor microenvironment using data from patients diagnosed with breast cancer and melanoma. Predictions are then tested using two immunocompetent mouse models for melanoma, which provide consistent experimental results.

Source Citation

Klinke, D.J., Fernandez, A., Deng, W. et al. Data-driven learning how oncogenic gene expression locally alters heterocellular networks. Nat Commun 13, 1986 (2022). https://doi.org/10.1038/s41467-022-29636-3

Comments

© The Author(s) 2022, corrected publication 2022

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visithttp://creativecommons.org/licenses/by/4.0/.

This article received support from the WVU Libraries' Open Access Author Fund.

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