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Data-driven learning how oncogenic gene expression locally alters heterocellular networks

Author

Listed:
  • David J. Klinke

    (West Virginia University
    West Virginia University
    WVU Cancer Institute, West Virginia University)

  • Audry Fernandez

    (West Virginia University
    WVU Cancer Institute, West Virginia University)

  • Wentao Deng

    (West Virginia University
    WVU Cancer Institute, West Virginia University)

  • Atefeh Razazan

    (West Virginia University
    WVU Cancer Institute, West Virginia University)

  • Habibolla Latifizadeh

    (West Virginia University)

  • Anika C. Pirkey

    (West Virginia University)

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.

Suggested Citation

  • David J. Klinke & Audry Fernandez & Wentao Deng & Atefeh Razazan & Habibolla Latifizadeh & Anika C. Pirkey, 2022. "Data-driven learning how oncogenic gene expression locally alters heterocellular networks," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29636-3
    DOI: 10.1038/s41467-022-29636-3
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    References listed on IDEAS

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