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

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., mutati...

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Bibliographic Details
Published in:Clinical epigenetics 2020-08, Vol.12 (1), p.119-119, Article 119
Main Authors: Kim, Jeong Yeon, Choi, Jung Kyoon, Jung, Hyunchul
Format: Article
Language:English
Subjects:
Apoptosis
Biological response modifiers
Biomarkers
Cancer genetics
Cancer research
Cancer treatment
Deoxyribonucleic acid
DNA
DNA methylation
Drug therapy
Epigenetics
Gene expression
Gene silencing
Genes
Genetic aspects
Genetic research
Genomes
Genomics
Immune checkpoint
Immunotherapy
Interferon
Lung cancer
Medical prognosis
Methylation
Mutation
Non-small cell lung cancer
Non-small cell lung carcinoma
Patients
PD-1 protein
RNA polymerase
Small cell lung carcinoma
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Summary: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. 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. Our study demonstrates that methylation patterns can provide insight into molecular determinants underlying the clinical benefit of ICB therapy.
ISSN:1868-7075
1868-7083
1868-7083
1868-7075
DOI:10.1186/s13148-020-00907-4