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Dissecting Response to Cancer Immunotherapy by Applying Bayesian Network Analysis to Flow Cytometry Data
Andrei S Rodin1, Grigoriy Gogoshin1, Seth Hilliard1
1City of Hope National Medical Center, Department of Computational and Quantitative Medicine, Beckman Research Institute, 1500 East Duarte Road, Duarte, CA 91010, USA.
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.
Insights
A new computational pipeline analyzes immune cell data to identify biomarkers for cancer immunotherapy response. This approach enhances understanding of immune networks in gastroesophageal adenocarcinoma patients, aiding in predicting treatment success.
Area of Science:
- Immunology
- Computational Biology
- Oncology
Background:
- Immune checkpoint blockade is a key cancer immunotherapy, but response rates vary.
- Identifying predictive biomarkers for sustained clinical response is crucial.
- Understanding immune system dynamics in responders versus non-responders is a research priority.
Purpose of the Study:
- To develop a novel computational pipeline for secondary analysis of flow cytometry data.
- To apply systems biology and machine learning techniques to analyze immune networks.
- To identify potential pretreatment biomarkers for cancer immunotherapy response.
Main Methods:
- Utilized flow cytometry (FACS) data from peripheral blood mononuclear cells (PBMC) of gastroesophageal adenocarcinoma (GEA) patients.
- Developed a computational pipeline centered around comparative Bayesian network analyses of immune networks.
- Applied machine learning techniques for secondary analysis of FACS data.
Main Results:
- The novel pipeline can detect subtle signals missed by conventional methods like manual gating.
- Identified potential immune network alterations associated with clinical response.
- Demonstrated the capability of the pipeline in analyzing complex immune cell data.
Conclusions:
- The developed computational pipeline offers a powerful tool for analyzing FACS data.
- This approach can reveal immune biomarkers predictive of immunotherapy response.
- Future studies will validate identified biomarkers for gastroesophageal adenocarcinoma treatment.
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