دورية أكاديمية
Dissecting Response to Cancer Immunotherapy by Applying Bayesian Network Analysis to Flow Cytometry Data
| العنوان: | Dissecting Response to Cancer Immunotherapy by Applying Bayesian Network Analysis to Flow Cytometry Data |
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| المؤلفون: | Andrei S. Rodin, Grigoriy Gogoshin, Seth Hilliard, Lei Wang, Colt Egelston, Russell C. Rockne, Joseph Chao, Peter P. Lee |
| المصدر: | International Journal of Molecular Sciences, Vol 22, Iss 5, p 2316 (2021) |
| بيانات النشر: | MDPI AG, 2021. |
| سنة النشر: | 2021 |
| المجموعة: | LCC:Biology (General) LCC:Chemistry |
| مصطلحات موضوعية: | Bayesian networks, machine learning, flow cytometry, immuno-oncology, FACS, immune networks, Biology (General), QH301-705.5, Chemistry, QD1-999 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1422-0067 1661-6596 |
| العلاقة: | https://www.mdpi.com/1422-0067/22/5/2316Test; https://doaj.org/toc/1661-6596Test; https://doaj.org/toc/1422-0067Test |
| DOI: | 10.3390/ijms22052316 |
| الوصول الحر: | https://doaj.org/article/82806a6e06034fcd8ec8e79dc3863df0Test |
| رقم الانضمام: | edsdoj.82806a6e06034fcd8ec8e79dc3863df0 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 14220067 16616596 |
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| DOI: | 10.3390/ijms22052316 |