Abstract
Cancer immunotherapy, specifically immune checkpoint blockade, has been found to be effective in the treatment of metastatic cancers. However, only a subset of patients achieve clinical responses. Elucidating pretreatment biomarkers predictive of sustained clinical response is a major research priority. Another research priority is evaluating changes in the immune system before and after treatment in responders vs. nonresponders. Our group has been studying immune networks as an accurate reflection of the global immune state. Flow cytometry (FACS, fluorescence-activated cell sorting) data characterizing immune cell panels in peripheral blood mononuclear cells (PBMC) from gastroesophageal adenocarcinoma (GEA) patients were used to analyze changes in immune networks in this setting. Here, we describe a novel computational pipeline to perform secondary analyses of FACS data using systems biology/machine learning techniques and concepts. The pipeline is centered around comparative Bayesian network analyses of immune networks and is capable of detecting strong signals that conventional methods (such as FlowJo manual gating) might miss. Future studies are planned to validate and follow up the immune biomarkers (and combinations/interactions thereof) associated with clinical responses identified with this computational pipeline.
Highlights
The automated analysis of high-dimensional cytometry data is an active bioinformatics research area [1,2]
We introduce a comprehensive Bayesian network (BN)-centered analysis strategy aimed at FACS data analysis in the immuno-oncology context
Immunological changes in peripheral blood mononuclear cells (PBMC) samples could be correlated to clinical response to immunotherapy in these patients
Summary
The automated analysis of high-dimensional cytometry data is an active bioinformatics research area [1,2]. Recent works in the field [15,16,17,18,19] either reduce the markers’ deduction to classification and semimanual/pairwise combinatorics, or rely on ontological protein–protein interaction networks. These analyses are elegant and valid, their completeness and generalizability are uncertain. Such analyses are deficient in automatically inferring higher-order interactions from the data. This is where network-based approaches, such as Bayesian network (BN) modeling, may be useful
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