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Biomarkers for Predicting Response to Personalized Immunotherapy in Gastric Cancer

Despite advances in diagnostic imaging, surgical techniques, and systemic therapy, gastric cancer (GC) is the third leading cause of cancer-related death worldwide. Unfortunately, molecular heterogeneity and, consequently, acquired resistance in GC are the major causes of failure in the development...

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Detalles Bibliográficos
Autores principales: Kim, Moonsik, Jeong, Ji Yun, Seo, An Na
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10487043/
https://www.ncbi.nlm.nih.gov/pubmed/37685320
http://dx.doi.org/10.3390/diagnostics13172782
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author Kim, Moonsik
Jeong, Ji Yun
Seo, An Na
author_facet Kim, Moonsik
Jeong, Ji Yun
Seo, An Na
author_sort Kim, Moonsik
collection PubMed
description Despite advances in diagnostic imaging, surgical techniques, and systemic therapy, gastric cancer (GC) is the third leading cause of cancer-related death worldwide. Unfortunately, molecular heterogeneity and, consequently, acquired resistance in GC are the major causes of failure in the development of biomarker-guided targeted therapies. However, by showing promising survival benefits in some studies, the recent emergence of immunotherapy in GC has had a significant impact on treatment-selectable procedures. Immune checkpoint inhibitors (ICIs), widely indicated in the treatment of several malignancies, target inhibitory receptors on T lymphocytes, including the programmed cell death protein (PD-1)/programmed death-ligand 1 (PD-L1) axis and cytotoxic T-lymphocyte-associated protein 4 (CTLA4), and release effector T-cells from negative feedback signals. In this article, we review currently available predictive biomarkers (including PD-L1, microsatellite instability, Epstein–Barr virus, and tumor mutational burden) that affect the ICI treatment response, focusing on PD-L1 expression. We further briefly describe other potential biomarkers or mechanisms for predicting the response to ICIs in GC. This review may facilitate the expansion of the understanding of biomarkers for predicting the response to ICIs and help select the appropriate therapeutic approaches for patients with GC.
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spelling pubmed-104870432023-09-09 Biomarkers for Predicting Response to Personalized Immunotherapy in Gastric Cancer Kim, Moonsik Jeong, Ji Yun Seo, An Na Diagnostics (Basel) Review Despite advances in diagnostic imaging, surgical techniques, and systemic therapy, gastric cancer (GC) is the third leading cause of cancer-related death worldwide. Unfortunately, molecular heterogeneity and, consequently, acquired resistance in GC are the major causes of failure in the development of biomarker-guided targeted therapies. However, by showing promising survival benefits in some studies, the recent emergence of immunotherapy in GC has had a significant impact on treatment-selectable procedures. Immune checkpoint inhibitors (ICIs), widely indicated in the treatment of several malignancies, target inhibitory receptors on T lymphocytes, including the programmed cell death protein (PD-1)/programmed death-ligand 1 (PD-L1) axis and cytotoxic T-lymphocyte-associated protein 4 (CTLA4), and release effector T-cells from negative feedback signals. In this article, we review currently available predictive biomarkers (including PD-L1, microsatellite instability, Epstein–Barr virus, and tumor mutational burden) that affect the ICI treatment response, focusing on PD-L1 expression. We further briefly describe other potential biomarkers or mechanisms for predicting the response to ICIs in GC. This review may facilitate the expansion of the understanding of biomarkers for predicting the response to ICIs and help select the appropriate therapeutic approaches for patients with GC. MDPI 2023-08-28 /pmc/articles/PMC10487043/ /pubmed/37685320 http://dx.doi.org/10.3390/diagnostics13172782 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Kim, Moonsik
Jeong, Ji Yun
Seo, An Na
Biomarkers for Predicting Response to Personalized Immunotherapy in Gastric Cancer
title Biomarkers for Predicting Response to Personalized Immunotherapy in Gastric Cancer
title_full Biomarkers for Predicting Response to Personalized Immunotherapy in Gastric Cancer
title_fullStr Biomarkers for Predicting Response to Personalized Immunotherapy in Gastric Cancer
title_full_unstemmed Biomarkers for Predicting Response to Personalized Immunotherapy in Gastric Cancer
title_short Biomarkers for Predicting Response to Personalized Immunotherapy in Gastric Cancer
title_sort biomarkers for predicting response to personalized immunotherapy in gastric cancer
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10487043/
https://www.ncbi.nlm.nih.gov/pubmed/37685320
http://dx.doi.org/10.3390/diagnostics13172782
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