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Genome-wide methylation patterns predict clinical benefit of immunotherapy in lung cancer

BACKGROUND: It is crucial to unravel molecular determinants of responses to immune checkpoint blockade (ICB) therapy because only a small subset of advanced non-small cell lung cancer (NSCLC) patients responds to ICB therapy. Previous studies were concentrated on genomic and transcriptomic markers (...

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Autores principales: Kim, Jeong Yeon, Choi, Jung Kyoon, Jung, Hyunchul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7410160/
https://www.ncbi.nlm.nih.gov/pubmed/32762727
http://dx.doi.org/10.1186/s13148-020-00907-4
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author Kim, Jeong Yeon
Choi, Jung Kyoon
Jung, Hyunchul
author_facet Kim, Jeong Yeon
Choi, Jung Kyoon
Jung, Hyunchul
author_sort Kim, Jeong Yeon
collection PubMed
description BACKGROUND: It is crucial to unravel molecular determinants of responses to immune checkpoint blockade (ICB) therapy because only a small subset of advanced non-small cell lung cancer (NSCLC) patients responds to ICB therapy. Previous studies were concentrated on genomic and transcriptomic markers (e.g., mutation burden and immune gene expression). However, these markers are not sufficient to accurately predict a response to ICB therapy. RESULTS: Here, we analyzed DNA methylomes of 141 advanced NSCLC samples subjected to ICB therapy (i.e., anti-programmed death-1) from two independent cohorts (60 and 81 patients from our and IDIBELL cohorts). Integrative analysis of patients with matched transcriptome data in our cohort (n = 28) at pathway level revealed significant overlaps between promoter hypermethylation and transcriptional repression in nonresponders relative to responders. Fifteen immune-related pathways, including interferon signaling, were identified to be enriched for both hypermethylation and repression. We built a reliable prognostic risk model based on eight genes using LASSO model and successfully validated the model in independent cohorts. Furthermore, we found 30 survival-associated molecular interaction networks, in which two or three hypermethylated genes showed significant mutual exclusion across nonresponders. CONCLUSIONS: Our study demonstrates that methylation patterns can provide insight into molecular determinants underlying the clinical benefit of ICB therapy.
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spelling pubmed-74101602020-08-10 Genome-wide methylation patterns predict clinical benefit of immunotherapy in lung cancer Kim, Jeong Yeon Choi, Jung Kyoon Jung, Hyunchul Clin Epigenetics Research BACKGROUND: It is crucial to unravel molecular determinants of responses to immune checkpoint blockade (ICB) therapy because only a small subset of advanced non-small cell lung cancer (NSCLC) patients responds to ICB therapy. Previous studies were concentrated on genomic and transcriptomic markers (e.g., mutation burden and immune gene expression). However, these markers are not sufficient to accurately predict a response to ICB therapy. RESULTS: Here, we analyzed DNA methylomes of 141 advanced NSCLC samples subjected to ICB therapy (i.e., anti-programmed death-1) from two independent cohorts (60 and 81 patients from our and IDIBELL cohorts). Integrative analysis of patients with matched transcriptome data in our cohort (n = 28) at pathway level revealed significant overlaps between promoter hypermethylation and transcriptional repression in nonresponders relative to responders. Fifteen immune-related pathways, including interferon signaling, were identified to be enriched for both hypermethylation and repression. We built a reliable prognostic risk model based on eight genes using LASSO model and successfully validated the model in independent cohorts. Furthermore, we found 30 survival-associated molecular interaction networks, in which two or three hypermethylated genes showed significant mutual exclusion across nonresponders. CONCLUSIONS: Our study demonstrates that methylation patterns can provide insight into molecular determinants underlying the clinical benefit of ICB therapy. BioMed Central 2020-08-06 /pmc/articles/PMC7410160/ /pubmed/32762727 http://dx.doi.org/10.1186/s13148-020-00907-4 Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver (http://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
Kim, Jeong Yeon
Choi, Jung Kyoon
Jung, Hyunchul
Genome-wide methylation patterns predict clinical benefit of immunotherapy in lung cancer
title Genome-wide methylation patterns predict clinical benefit of immunotherapy in lung cancer
title_full Genome-wide methylation patterns predict clinical benefit of immunotherapy in lung cancer
title_fullStr Genome-wide methylation patterns predict clinical benefit of immunotherapy in lung cancer
title_full_unstemmed Genome-wide methylation patterns predict clinical benefit of immunotherapy in lung cancer
title_short Genome-wide methylation patterns predict clinical benefit of immunotherapy in lung cancer
title_sort genome-wide methylation patterns predict clinical benefit of immunotherapy in lung cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7410160/
https://www.ncbi.nlm.nih.gov/pubmed/32762727
http://dx.doi.org/10.1186/s13148-020-00907-4
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