Dissecting Response to Cancer Immunotherapy by Applying Bayesian Network Analysis to Flow Cytometry Data
Cancer immunotherapy, specifically immune checkpoint blockade therapy, has been found to be effective in the treatment of metastatic cancers. However, only a subset of patients with certain cancer types achieve clinical responses. 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 the immune system before and after treatment in responders and non-responders. Specifically, our group has been studying immune signaling networks as an accurate reflection of the global immune state. Flow cytometry data (FACS, Fluorescence-activated cell sorting) characterizing immune signaling in peripheral blood mononuclear cells (PBMC) from gastroesophageal adenocarcinoma (GEA) patients were used to analyze changes in immune signaling networks in this setting. We developed a novel computational pipeline to perform secondary analyses of FACS data using systems biology / machine learning / information-theoretic techniques and concepts, primarily based on Bayesian network modeling. Application of this novel pipeline resulted in determination of immune markers, combinations / interactions thereof, and corresponding immune cell population types that are associated with clinical responses. Future studies are planned to generalize our analytical approach to different cancer types and corresponding datasets. ### Competing Interest Statement J.C. has received research funding (institutional) and consultant/advisory fees from Merck and serves on the speaker's bureau for Merck.
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