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A 20-gene mutation signature predicts the efficacy of immune checkpoint inhibitor therapy in advanced non-small cell lung cancer patients
BACKGROUND: There is an unmet need to identify novel predictive biomarkers that enable more accurate identification of individuals who can benefit from immune checkpoint inhibitor (ICI) therapy. The US FDA recently approved tumor mutational burden (TMB) score of ≥ 10 mut/Mb as a threshold for pembro...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10288780/ https://www.ncbi.nlm.nih.gov/pubmed/37349743 http://dx.doi.org/10.1186/s12890-023-02512-6 |
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author | Hu, Xilin Guo, Jing Shi, Jianguang Li, Da Li, Xinjian Zhao, Weijun |
author_facet | Hu, Xilin Guo, Jing Shi, Jianguang Li, Da Li, Xinjian Zhao, Weijun |
author_sort | Hu, Xilin |
collection | PubMed |
description | BACKGROUND: There is an unmet need to identify novel predictive biomarkers that enable more accurate identification of individuals who can benefit from immune checkpoint inhibitor (ICI) therapy. The US FDA recently approved tumor mutational burden (TMB) score of ≥ 10 mut/Mb as a threshold for pembrolizumab treatment of solid tumors. Our study aimed to test the hypothesis that specific gene mutation signature may predict the efficacy of ICI therapy more precisely than high TMB (≥ 10). METHODS: We selected 20 candidate genes that may predict for the efficacy of ICI therapy by the analysis of data from a published cohort of 350 advanced non-small cell lung cancer (NSCLC) patients. Then, we compared the influences of various gene mutation signatures on the efficacy of ICI treatment. They were also compared with PD-L1 and TMB. The Kaplan-Meier method was utilized to evaluate the prognosis univariates, while selected univariates were adopted to develop a systematic nomogram. RESULTS: A high mutation signature, where three or more of the 20 selected genes were mutated, was associated with the significant benefits of ICI therapy. Specifically, patients with high mutation signature were confirmed to have better prognosis for ICI treatment, compared with those with wild type (the median PFS: 7.17 vs. 2.90 months, p = 0.0004, HR = 0.47 (95% [CI]:0.32–0.68); the median OS: unreached vs. 9 months, p = 1.8E-8, HR = 0.17 (95% [CI]:0.11–0.25)). Moreover, those patients with the high mutation signature achieved significant ICI treatment benefits, while there was no difference of OS and PFS between patients without the signature but TMB-H (≥ 10) and those without the signature and low TMB(< 10). Finally, we constructed a novel nomogram to evaluate the efficacy of ICI therapy. CONCLUSION: A high mutational signature with 3 or more of the 20-gene panel could provide more accurate predictions for the outcomes of ICI therapy than TMB ≥ 10 in NSCLC patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12890-023-02512-6. |
format | Online Article Text |
id | pubmed-10288780 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-102887802023-06-24 A 20-gene mutation signature predicts the efficacy of immune checkpoint inhibitor therapy in advanced non-small cell lung cancer patients Hu, Xilin Guo, Jing Shi, Jianguang Li, Da Li, Xinjian Zhao, Weijun BMC Pulm Med Research BACKGROUND: There is an unmet need to identify novel predictive biomarkers that enable more accurate identification of individuals who can benefit from immune checkpoint inhibitor (ICI) therapy. The US FDA recently approved tumor mutational burden (TMB) score of ≥ 10 mut/Mb as a threshold for pembrolizumab treatment of solid tumors. Our study aimed to test the hypothesis that specific gene mutation signature may predict the efficacy of ICI therapy more precisely than high TMB (≥ 10). METHODS: We selected 20 candidate genes that may predict for the efficacy of ICI therapy by the analysis of data from a published cohort of 350 advanced non-small cell lung cancer (NSCLC) patients. Then, we compared the influences of various gene mutation signatures on the efficacy of ICI treatment. They were also compared with PD-L1 and TMB. The Kaplan-Meier method was utilized to evaluate the prognosis univariates, while selected univariates were adopted to develop a systematic nomogram. RESULTS: A high mutation signature, where three or more of the 20 selected genes were mutated, was associated with the significant benefits of ICI therapy. Specifically, patients with high mutation signature were confirmed to have better prognosis for ICI treatment, compared with those with wild type (the median PFS: 7.17 vs. 2.90 months, p = 0.0004, HR = 0.47 (95% [CI]:0.32–0.68); the median OS: unreached vs. 9 months, p = 1.8E-8, HR = 0.17 (95% [CI]:0.11–0.25)). Moreover, those patients with the high mutation signature achieved significant ICI treatment benefits, while there was no difference of OS and PFS between patients without the signature but TMB-H (≥ 10) and those without the signature and low TMB(< 10). Finally, we constructed a novel nomogram to evaluate the efficacy of ICI therapy. CONCLUSION: A high mutational signature with 3 or more of the 20-gene panel could provide more accurate predictions for the outcomes of ICI therapy than TMB ≥ 10 in NSCLC patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12890-023-02512-6. BioMed Central 2023-06-22 /pmc/articles/PMC10288780/ /pubmed/37349743 http://dx.doi.org/10.1186/s12890-023-02512-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 Hu, Xilin Guo, Jing Shi, Jianguang Li, Da Li, Xinjian Zhao, Weijun A 20-gene mutation signature predicts the efficacy of immune checkpoint inhibitor therapy in advanced non-small cell lung cancer patients |
title | A 20-gene mutation signature predicts the efficacy of immune checkpoint inhibitor therapy in advanced non-small cell lung cancer patients |
title_full | A 20-gene mutation signature predicts the efficacy of immune checkpoint inhibitor therapy in advanced non-small cell lung cancer patients |
title_fullStr | A 20-gene mutation signature predicts the efficacy of immune checkpoint inhibitor therapy in advanced non-small cell lung cancer patients |
title_full_unstemmed | A 20-gene mutation signature predicts the efficacy of immune checkpoint inhibitor therapy in advanced non-small cell lung cancer patients |
title_short | A 20-gene mutation signature predicts the efficacy of immune checkpoint inhibitor therapy in advanced non-small cell lung cancer patients |
title_sort | 20-gene mutation signature predicts the efficacy of immune checkpoint inhibitor therapy in advanced non-small cell lung cancer patients |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10288780/ https://www.ncbi.nlm.nih.gov/pubmed/37349743 http://dx.doi.org/10.1186/s12890-023-02512-6 |
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