Silencing of genes by promoter hypermethylation shapes tumor microenvironment and resistance to immunotherapy in clear-cell renal cell carcinomas

Abstract

The efficacy of immune checkpoint inhibitors varies in clear-cell renal cell carcinoma (ccRCC), with notable primary resistance among patients. Here, we integrate epigenetic (DNA methylation) and transcriptome data to identify a ccRCC subtype characterized by cancer-specific promoter hypermethylation and epigenetic silencing of Polycomb targets. We develop and validate an index of methylation-based epigenetic silencing (iMES) that predicts primary resistance to immune checkpoint inhibition (ICI) in the BIONIKK trial. High iMES is associated with VEGF pathway silencing, endothelial cell depletion, immune activation/suppression, EZH2 activation, BAP1/SETD2 deficiency, and resistance to ICI. Combination therapy with hypomethylating agents or tyrosine kinase inhibitors may benefit patients with high iMES. Intriguingly, tumors with low iMES exhibit increased endothelial cells and improved ICI response, suggesting the importance of angiogenesis in ICI treatment. We also develop a transcriptome-based analogous system for extended applicability of iMES. Our study underscores the interplay between epigenetic alterations and tumor microenvironment in determining immunotherapy response.

Keywords: DNA methylation; angiogenesis; biomarker; clear-cell renal cell carcinoma; epigenetic silencing; immune checkpoint inhibitors; tumor microenvironment.

Graphical abstract

Figure 1

Flow diagram of the study Flow diagram illustrating the study’s logical progression from initial epigenetic silencing to the development of the epigenome-based iMES and transcriptome-based systems.

Figure 2

Association between epigenetic silencing by DNA methylation and tumor aggressiveness of ccRCC (A) Heatmap of methylation and gene expression profiles in TCGA cohort, including annotations for adjacent normal tissues and clinical features. (B) Kaplan-Meier curves depicting OS rates of epigenetic subtypes. (C) Bar plots (actual sample size shown within parentheses below the percentage) and pie charts showing the association between two epigenetic subtypes and tumor grade. (D) Same as (C) but for tumor stage.

Figure 3

Epigenetic silencing by DNA methylation and its role in primary resistance to ICI treatment of ccRCC (A) Violin plot of EZH2 expression between epigenetic subtypes of TCGA cohort. (B) Violin plot of BAP1-LCR levels between epigenetic subtypes of TCGA cohort. (C) GSEA of activated pathways in EPI-C1 of TCGA cohort. (D) Violin plot of TIDE scores between epigenetic subtypes of TCGA cohort. (E) Stacked bar plot of TIDE-predicted ICI responder fractions in two epigenetic subtypes of TCGA cohort. (F) Venn diagram of the intersection of epigenetically silenced genes between TCGA-KIRC and BIONIKK’s ICI arms. (G) DNA methylation landscape of epigenetically silenced genes in two epigenetic subtypes in BIONIKK’s ICI arms. (H) Kaplan-Meier curves depicting OS rates of epigenetic subtypes in BIONIKK’s ICI arms.

Figure 4

Development and validation of an iMES (A) Flow chart. (B) Selection of λ in the adaLASSO model. The partial likelihood deviance was plotted vs. log(λ) (top panel), and a coefficient parole plot was produced against the log(λ) sequence (bottom panel). (C) Kaplan-Meier survival curve depicting OS rates of two iMES groups in TCGA cohort. (D) Time-dependent ROC curves of iMES in TCGA cohort. TPR, true positive rate; FPR, false positive rate. (E) Kaplan-Meier curves of PFS for patients stratified by iMES in BIONIKK’s ICI arms. (F) Same as (E) but for TST. (G) Same as (E) but for OS. (H) Time-dependent ROC curves of iMES within 6 months after treatment in BIONIKK’s ICI arms. (I) Forest plot displaying hazard ratios in univariate (above the dashed line) and multivariate (below the dashed line) analyses. CS, complete separation—outcome variable completely separates a predictor variable with inaccurate estimation.

Figure 5

Biological relevance of iMES (A) GSEA panels displaying activated Hallmark pathways in patients/cell lines with high iMES. (B) TME landscape of TCGA cohort, with samples sorted in ascending order based on iMES. Dot plots, positioned alongside the heatmaps, display the correlation between iMES and the expression or enrichment levels of immune-related factors. (C) Same as (B) but for BIONIKK’s ICI arms. (D) Association between iMES and cell fractions.

Figure 6

iMES association with VEGF and chromatin remodeling (A) Network illustrating the association between iMES (blue dot) and methylation status of VEGF pathway genes (green pie chart), and regulon activity of cancerous chromatin remodelers (purple dots), as well as internal correlation among chromatin remodels, mutual exclusivity status of VEGF pathway genes (green lines connecting VEGF genes), and their self-correlation between gene-level DNA methylation and gene expression (directed bended lines around pie charts). (B) Correlation between gene-level methylation and expression of the four VEGF pathway genes. (C) Correlation between iMES and regulon activity of two chromatin remodelers (EZH2 and KMT5A), and between iMES and single-sample GSEA score of STED2 loss signature.

Figure 7

Regulon phenotypes and prognostic relevance in ccRCC (A) K-mode clustering of genes corresponding to model-selected probes based on their regulon activity status in TCGA cohort. (B) Violin plot of iMES between two regulon subtypes. (C) Kaplan-Meier survival curves depicting OS rates of regulon subtypes in TCGA cohort. (D) K-mode clustering using regulon activity status in BIONIKK’s ICI arms. (E) Kaplan-Meier curves differentiate TST of regulon subtypes in BIONIKK’s ICI arms. (F) Same as (E) but for PFS. (G) K-mode clustering using regulon activity status (top panel), the TME landscape (middle panel) with deconvolution of cell fractions, and the regulon activity distribution of EZH2, KMT5A, and SETD2 as well as the single-sample GSEA score of SETD2 loss signature (bottom panel) in CheckMate’s ICI arm. (H) Kaplan-Meier survival curves depicting OS rates of regulon subtypes in CheckMate’s ICI arm. (I) Characterization of regulon phenotypes in JAVELIN’s ICI/TKI arm. (J) Kaplan-Meier survival curves depicting PFS rates of regulon subtypes in JAVELIN’s ICI/TKI arm.