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

Abstract

Discovering and developing pharmaceutical drugs increasingly relies on mechanistic mathematical modeling and simulation. In immuno-oncology, models that capture causal relations among genetic drivers of oncogenesis, functional plasticity, and host immunity provide an important complement to wet experiments, given the cellular complexity and dynamics within tumors. Unfortunately, formulating such mechanistic cell-level models currently relies on hand curation by experts, which can bias how data is interpreted or the priority of drug targets. In modeling molecular-level networks, rules and algorithms have been developed to limit a priori biases in formulating mechanistic models. To realize an equivalent approach for cell-level networks, we combined digital cytometry with Bayesian network inference to generate causal models that link an increase in gene expression associated with onco-genesis with alterations in stromal and immune cell subsets directly from bulk transcriptomic datasets. To illustrate, we predicted how an increase in expression of Cell Communication Network factor 4 (CCN4/WISP1) altered the tumor microenvironment using data from patients diagnosed with breast cancer and melanoma. Digital cytometry and network inference predictions were then tested using two immunocompetent mouse models for melanoma, which provided consistent experimental results.