ArticleFrontiers in genetics2019
DNA Methylation Biomarkers Predict Objective Responses to PD-1/PD-L1 Inhibition Blockade.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Characterization of the immune response in patients with cancer of the oral cavity after neoadjuvant immunotherapy with the IRX-2 regimen.Oral oncology · 2021Trial
- Genomic and Epigenomic Biomarkers of Immune Checkpoint Immunotherapy Response in Melanoma: Current and Future Perspectives.International journal of molecular sciences · 2024Review
- Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy.Frontiers in immunology · 2024Review
- From rough to precise: PD-L1 evaluation for predicting the efficacy of PD-1/PD-L1 blockades.Frontiers in immunology · 2022Review
- Integration of Epigenetic Mechanisms into Non-Genotoxic Carcinogenicity Hazard Assessment: Focus on DNA Methylation and Histone Modifications.International journal of molecular sciences · 2021Review
- Metabolic Reprogramming in the Tumor Microenvironment With Immunocytes and Immune Checkpoints.Frontiers in oncology · 2021Review
- Identification of tumor mutation burden-related hub genes and the underlying mechanism in melanoma.Journal of Cancer · 2021Article
- Article
- Epigenetic Mechanisms of Resistance to Immune Checkpoint Inhibitors.Biomolecules · 2020Review
- Progress Toward Identifying Exact Proxies for Predicting Response to Immunotherapies.Frontiers in cell and developmental biology · 2020Review
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Immune checkpoint inhibitor (ICI) treatment could bring long-lasting clinical benefits to patients with metastatic cancer. However, only a small proportion of patients respond to PD-1/PD-L1 blockade, so predictive biomarkers are needed. Here, based on DNA methylation profiles and the objective response rates (ORRs) of PD-1/PD-L1 inhibition therapy, we identified 269 CpG sites and developed an initial CpG-based model by Lasso to predict ORRs. Notably, as measured by the area under the receiver operating characteristic curve (AUC), our model can produce better performance (AUC = 0.92) than both a model based on tumor mutational burden (TMB) (AUC = 0.77) and a previously reported TMB model (AUC = 0.71). In addition, most CpGs also have additional synergies with TMB, which can achieve a higher prediction accuracy when joined with TMB. Furthermore, we identified CpGs that are associated with TMB at the individual level. DNA methylation modules defined by protein networks, Kyoto Encylopedia of Genes and Genomes (KEGG) pathways, and ligand-receptor gene pairs are also associated with ORRs. This method suggested novel immuno-oncology targets that might be beneficial when combined with PD-1/PD-L1 blockade. Thus, DNA methylation studies might hold great potential for individualized PD1/PD-L1 blockade or combinatory therapy.
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