What question did this study set out to answer?

This study aims to determine how tumor methylation subtypes influence the clinical outcomes of immunotherapy in pleural mesothelioma patients.

May 29, 2026

Tumor methylation subtypes as a predictor of clinical outcome to immunotherapy in pleural mesothelioma patients from the NIBIT-EPI-MESO study.

Key Points

This study aims to determine how tumor methylation subtypes influence the clinical outcomes of immunotherapy in pleural mesothelioma patients.
Evaluated tumor samples of 83 pleural mesothelioma patients using RRBS methylation analysis.
Conducted RNA-seq on 82 patients to assess molecular characteristics.
Performed multiplex immunofluorescence analysis on the tumor microenvironment of 35 patients.
Identified four tumor methylation subsets: demethylated, LOW, intermediate, and CpG island methylator phenotype (CIMP).
LOW/DEM methylation subtypes significantly associated with higher response rates to immunotherapy (p=0.002).
The LOW subset showed the longest median overall survival and highest 3-year survival rates.

Abstract

8052 Background: Pleural mesothelioma (PM) is an aggressive malignancy with a poor prognosis. The clinicalefficacy of standard therapy with immune checkpoint inhibitors (ICI) is limited and heterogeneous acrossPM subtypes. Tumor-intrinsic characteristics (i.e., inflammatory phenotype, molecular features, DNAmethylation) may influence immune responsiveness, but predictive biomarkers of ICI therapy efficacy inPM are still lacking. Methods: NIBIT-EPI-MESO is a retrospective, multicenter study, sponsored by theNIBIT Foundation, evaluating biological correlates of clinical outcomes in PM patients (pts) treated withICI (i.e., anti-CTLA-4 plus anti-PD-1, anti-CTLA-4 plus anti-PD-L1, or anti-CTLA-4 monotherapy). Pre-ICI therapy FFPE tumor samples were analyzed by RRBS methylation (n = 83 pts) and RNA-seq (n = 82pts), with methylation subtypes defined by consensus clustering of the top 1% most variable CpGs.Tumor microenvironment (TME) was characterized by multiplex immunofluorescence analysis of CD4,CD8, CD20, CD68, CD163 (n = 35 pts). Integrated multi-omics analyses were used to associate tumorbiology with clinical outcome of PM pts. Results: Unsupervised methylation profiling identified four PMsubsets with increasingly global DNA methylation levels: demethylated (DEM), LOW, intermediate (INT),and CpG island methylator phenotype (CIMP). Methylation subtypes were significantly associated withresponse to ICI, with LOW/DEM enriched among responder (R) pts and INT/CIMP in non-R pts (p =0.002); no association of response to ICI was found with PM histotype (p = 0.33). The LOW subsetexhibited the longest median overall survival (mOS) and the highest 3-year OS rate, expressed genesinvolved in pathways associated with innate and adaptive immune responses, and showed an “inflamed”TME (i.e., CD8+ T cells, CD20+ B cells). Conversely, the CIMP subtype had the shortest mOS and OSrate, was characterized by genes enriched in developmental, morphogenetic and cell cycle-relatedprocesses, along with a “desert” TME. Functional characterization of the identified methylation classes ofPM was validated in the MESOMICS dataset. Accordingly, a PM methylation subtype classifier wasdeveloped to predict response to ICI therapy. Conclusions: Tumor DNA methylation defines biologicallyand clinically distinct immune phenotypes in PM and robustly predicts clinical response and long-termsurvival in ICI-treated PM patients, regardless of tumor histology.

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