Bayesian network analysis of immune signaling networks FACS data

Cancer immunotherapy, specifically immune checkpoint blockade therapy, has been found to be effective in the treatment of metastatic cancers. However, many patients do not show marked clinical response. Consequently, elucidating immune system-related pre-treatment biomarkers that are predictive with respect to sustained clinical response is a major research priority. Another research priority is evaluating changes in immune signaling networks before and after treatment in responders and non-responders. High-dimensional flow cytometry data (FACS, Fluorescence-activated cell sorting) characterizing immune signaling network markers in gastrointestinal (GI) cancer patients was used by us to perform such analyses. We developed a novel computational pipeline to perform secondary analyses of FACS data using systems biology / machine learning / information-theoretic techniques and concepts, namely Bayesian networks and maximum entropy. Application of the pipeline resulted in elucidation of immu...