Author ORCID Identifier
https://orcid.org/0000-0003-3299-4938
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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.
Digital Commons Citation
Klinke, David II; Fernandez, Audry; Deng, Wentao; Razazan, Atefeh; Latifizadeh, Habibolla; and Pirkey, Anika C., "Data-driven learning how oncogenic gene expression locally alters heterocellular networks" (2022). Faculty & Staff Scholarship. 3098.
https://researchrepository.wvu.edu/faculty_publications/3098
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
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This article received support from the WVU Libraries' Open Access Author Fund.