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Predictive biomarkers for the responsiveness of recurrent glioblastomas to activated killer cell immunotherapy

BACKGROUND: Recurrent glioblastoma multiforme (GBM) is a highly aggressive primary malignant brain tumor that is resistant to existing treatments. Recently, we reported that activated autologous natural killer (NK) cell therapeutics induced a marked increase in survival of some patients with recurre...

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Autores principales: Hwang, Sohyun, Lim, Jaejoon, Kang, Haeyoun, Jeong, Ju-Yeon, Joung, Je-Gun, Heo, Jinhyung, Jung, Daun, Cho, Kyunggi, An, Hee Jung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9875464/
https://www.ncbi.nlm.nih.gov/pubmed/36694264
http://dx.doi.org/10.1186/s13578-023-00961-4
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author Hwang, Sohyun
Lim, Jaejoon
Kang, Haeyoun
Jeong, Ju-Yeon
Joung, Je-Gun
Heo, Jinhyung
Jung, Daun
Cho, Kyunggi
An, Hee Jung
author_facet Hwang, Sohyun
Lim, Jaejoon
Kang, Haeyoun
Jeong, Ju-Yeon
Joung, Je-Gun
Heo, Jinhyung
Jung, Daun
Cho, Kyunggi
An, Hee Jung
author_sort Hwang, Sohyun
collection PubMed
description BACKGROUND: Recurrent glioblastoma multiforme (GBM) is a highly aggressive primary malignant brain tumor that is resistant to existing treatments. Recently, we reported that activated autologous natural killer (NK) cell therapeutics induced a marked increase in survival of some patients with recurrent GBM. METHODS: To identify biomarkers that predict responsiveness to NK cell therapeutics, we examined immune profiles in tumor tissues using NanoString nCounter analysis and compared the profiles between 5 responders and 7 non-responders. Through a three-step data analysis, we identified three candidate biomarkers (TNFRSF18, TNFSF4, and IL12RB2) and performed validation with qRT-PCR. We also performed immunohistochemistry and a NK cell migration assay to assess the function of these genes. RESULTS: Responders had higher expression of many immune-signaling genes compared with non-responders, which suggests an immune-active tumor microenvironment in responders. The random forest model that identified TNFRSF18, TNFSF4, and IL12RB2 showed a 100% accuracy (95% CI 73.5–100%) for predicting the response to NK cell therapeutics. The expression levels of these three genes by qRT-PCR were highly correlated with the NanoString levels, with high Pearson’s correlation coefficients (0.419 (TNFRSF18), 0.700 (TNFSF4), and 0.502 (IL12RB2)); their prediction performance also showed 100% accuracy (95% CI 73.54–100%) by logistic regression modeling. We also demonstrated that these genes were related to cytotoxic T cell infiltration and NK cell migration in the tumor microenvironment. CONCLUSION: We identified TNFRSF18, TNFSF4, and IL12RB2 as biomarkers that predict response to NK cell therapeutics in recurrent GBM, which might provide a new treatment strategy for this highly aggressive tumor. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13578-023-00961-4.
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spelling pubmed-98754642023-01-26 Predictive biomarkers for the responsiveness of recurrent glioblastomas to activated killer cell immunotherapy Hwang, Sohyun Lim, Jaejoon Kang, Haeyoun Jeong, Ju-Yeon Joung, Je-Gun Heo, Jinhyung Jung, Daun Cho, Kyunggi An, Hee Jung Cell Biosci Research BACKGROUND: Recurrent glioblastoma multiforme (GBM) is a highly aggressive primary malignant brain tumor that is resistant to existing treatments. Recently, we reported that activated autologous natural killer (NK) cell therapeutics induced a marked increase in survival of some patients with recurrent GBM. METHODS: To identify biomarkers that predict responsiveness to NK cell therapeutics, we examined immune profiles in tumor tissues using NanoString nCounter analysis and compared the profiles between 5 responders and 7 non-responders. Through a three-step data analysis, we identified three candidate biomarkers (TNFRSF18, TNFSF4, and IL12RB2) and performed validation with qRT-PCR. We also performed immunohistochemistry and a NK cell migration assay to assess the function of these genes. RESULTS: Responders had higher expression of many immune-signaling genes compared with non-responders, which suggests an immune-active tumor microenvironment in responders. The random forest model that identified TNFRSF18, TNFSF4, and IL12RB2 showed a 100% accuracy (95% CI 73.5–100%) for predicting the response to NK cell therapeutics. The expression levels of these three genes by qRT-PCR were highly correlated with the NanoString levels, with high Pearson’s correlation coefficients (0.419 (TNFRSF18), 0.700 (TNFSF4), and 0.502 (IL12RB2)); their prediction performance also showed 100% accuracy (95% CI 73.54–100%) by logistic regression modeling. We also demonstrated that these genes were related to cytotoxic T cell infiltration and NK cell migration in the tumor microenvironment. CONCLUSION: We identified TNFRSF18, TNFSF4, and IL12RB2 as biomarkers that predict response to NK cell therapeutics in recurrent GBM, which might provide a new treatment strategy for this highly aggressive tumor. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13578-023-00961-4. BioMed Central 2023-01-24 /pmc/articles/PMC9875464/ /pubmed/36694264 http://dx.doi.org/10.1186/s13578-023-00961-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Hwang, Sohyun
Lim, Jaejoon
Kang, Haeyoun
Jeong, Ju-Yeon
Joung, Je-Gun
Heo, Jinhyung
Jung, Daun
Cho, Kyunggi
An, Hee Jung
Predictive biomarkers for the responsiveness of recurrent glioblastomas to activated killer cell immunotherapy
title Predictive biomarkers for the responsiveness of recurrent glioblastomas to activated killer cell immunotherapy
title_full Predictive biomarkers for the responsiveness of recurrent glioblastomas to activated killer cell immunotherapy
title_fullStr Predictive biomarkers for the responsiveness of recurrent glioblastomas to activated killer cell immunotherapy
title_full_unstemmed Predictive biomarkers for the responsiveness of recurrent glioblastomas to activated killer cell immunotherapy
title_short Predictive biomarkers for the responsiveness of recurrent glioblastomas to activated killer cell immunotherapy
title_sort predictive biomarkers for the responsiveness of recurrent glioblastomas to activated killer cell immunotherapy
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9875464/
https://www.ncbi.nlm.nih.gov/pubmed/36694264
http://dx.doi.org/10.1186/s13578-023-00961-4
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