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A topic modeling approach reveals the dynamic T cell composition of peripheral blood during cancer immunotherapy
We present TopicFlow, a computational framework for flow cytometry data analysis of patient blood samples for the identification of functional and dynamic topics in circulating T cell population. This framework applies a Latent Dirichlet Allocation (LDA) model, adapting the concept of topic modeling...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10475788/ https://www.ncbi.nlm.nih.gov/pubmed/37671017 http://dx.doi.org/10.1016/j.crmeth.2023.100546 |
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author | Peng, Xiyu Lee, Jasme Adamow, Matthew Maher, Colleen Postow, Michael A. Callahan, Margaret K. Panageas, Katherine S. Shen, Ronglai |
author_facet | Peng, Xiyu Lee, Jasme Adamow, Matthew Maher, Colleen Postow, Michael A. Callahan, Margaret K. Panageas, Katherine S. Shen, Ronglai |
author_sort | Peng, Xiyu |
collection | PubMed |
description | We present TopicFlow, a computational framework for flow cytometry data analysis of patient blood samples for the identification of functional and dynamic topics in circulating T cell population. This framework applies a Latent Dirichlet Allocation (LDA) model, adapting the concept of topic modeling in text mining to flow cytometry. To demonstrate the utility of our method, we conducted an analysis of ∼17 million T cells collected from 138 peripheral blood samples in 51 patients with melanoma undergoing treatment with immune checkpoint inhibitors (ICIs). Our study highlights three latent dynamic topics identified by LDA: a T cell exhaustion topic that independently recapitulates the previously identified LAG-3(+) immunotype associated with ICI resistance, a naive topic and its association with immune-related toxicity, and a T cell activation topic that emerges upon ICI treatment. Our approach can be broadly applied to mine high-parameter flow cytometry data for insights into mechanisms of treatment response and toxicity. |
format | Online Article Text |
id | pubmed-10475788 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-104757882023-09-05 A topic modeling approach reveals the dynamic T cell composition of peripheral blood during cancer immunotherapy Peng, Xiyu Lee, Jasme Adamow, Matthew Maher, Colleen Postow, Michael A. Callahan, Margaret K. Panageas, Katherine S. Shen, Ronglai Cell Rep Methods Article We present TopicFlow, a computational framework for flow cytometry data analysis of patient blood samples for the identification of functional and dynamic topics in circulating T cell population. This framework applies a Latent Dirichlet Allocation (LDA) model, adapting the concept of topic modeling in text mining to flow cytometry. To demonstrate the utility of our method, we conducted an analysis of ∼17 million T cells collected from 138 peripheral blood samples in 51 patients with melanoma undergoing treatment with immune checkpoint inhibitors (ICIs). Our study highlights three latent dynamic topics identified by LDA: a T cell exhaustion topic that independently recapitulates the previously identified LAG-3(+) immunotype associated with ICI resistance, a naive topic and its association with immune-related toxicity, and a T cell activation topic that emerges upon ICI treatment. Our approach can be broadly applied to mine high-parameter flow cytometry data for insights into mechanisms of treatment response and toxicity. Elsevier 2023-08-02 /pmc/articles/PMC10475788/ /pubmed/37671017 http://dx.doi.org/10.1016/j.crmeth.2023.100546 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Peng, Xiyu Lee, Jasme Adamow, Matthew Maher, Colleen Postow, Michael A. Callahan, Margaret K. Panageas, Katherine S. Shen, Ronglai A topic modeling approach reveals the dynamic T cell composition of peripheral blood during cancer immunotherapy |
title | A topic modeling approach reveals the dynamic T cell composition of peripheral blood during cancer immunotherapy |
title_full | A topic modeling approach reveals the dynamic T cell composition of peripheral blood during cancer immunotherapy |
title_fullStr | A topic modeling approach reveals the dynamic T cell composition of peripheral blood during cancer immunotherapy |
title_full_unstemmed | A topic modeling approach reveals the dynamic T cell composition of peripheral blood during cancer immunotherapy |
title_short | A topic modeling approach reveals the dynamic T cell composition of peripheral blood during cancer immunotherapy |
title_sort | topic modeling approach reveals the dynamic t cell composition of peripheral blood during cancer immunotherapy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10475788/ https://www.ncbi.nlm.nih.gov/pubmed/37671017 http://dx.doi.org/10.1016/j.crmeth.2023.100546 |
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