Evaluation of Pan-Cancer Immune Heterogeneity Based on DNA Methylation

Abstract

Background/objectives: The heterogeneity of the tumor immune microenvironment is a key determinant of tumor oncogenesis. This study aims to evaluate the composition of seven immune cells across 5323 samples from 14 cancers using DNA methylation data.

Methods: A deconvolution algorithm was proposed to estimate the composition of seven immune cells using 1256 immune cell population-specific methylation genes. Based on the immune infiltration features of seven immune cell fractions, 42 subtypes of 14 tumors (2-5 subtypes per tumor) were identified.

Results: Significant differences in immune cells between subtypes were revealed for each cancer. The study found that the methylation values of the selected specific sites correlated with gene expression in most tumor subtypes. Immune infiltration results were integrated with phenotypic data, including survival data and tumor stages, revealing significant correlations between immune infiltration and phenotypes in some tumors. Subtypes with high proportions of CD4+ T cells, CD8+ T cells, CD56+ NK cells, CD19+ B cells, CD14+ monocytes, neutrophils, and eosinophils were identified, with subtype counts of 9, 24, 22, 13, 19, 9, and 11, respectively. Additionally, 2412 differentially expressed genes between these subtypes and normal tissues were identified. Pathway enrichment analysis revealed that these genes were mainly enriched in pathways related to drug response and chemical carcinogens. Differences in ESTIMATE scores for subtypes of seven tumors and TIDE scores for eight tumors were also observed.

Conclusions: This study demonstrates the intra-tumor and inter-tumor immune heterogeneity of pan-cancer through DNA methylation analysis, providing assistance for tumor diagnosis.

Keywords: DNA methylation; clustering; deconvolution algorithm; pan-cancer; tumor immune microenvironment.

Figure 1

Overview of the study. (A) Data collection and the identification of immune cell-specific methylation genes. (B) Development of a deconvolution algorithm to assess the immune infiltration fractions in pan-cancer. (C) Multi-omics integrative analysis.

Figure 2

The immune cell infiltration fractions in 14 tumors. (A) The results of the immune cell infiltration fractions in 14 tumors. (B) The differential immune cell infiltration fractions between tumor samples and normal samples in every tumor.

Figure 3

The immune cell infiltration fractions in tumor subtypes. The “cluster” in the legend represents the immune subtypes of the 14 cancers by clustering. (A) TSNE showed subtypes in lung adenocarcinoma, head and neck squamous cell carcinoma, liver hepatocellular carcinoma, and breast invasive carcinoma. (B) Comparison of the immune cell infiltration fractions between lung adenocarcinoma, head and neck squamous cell carcinoma, liver hepatocellular carcinoma, and breast invasive carcinoma subtypes. (C) The correlation between DNA methylation and gene expression of the 1256 sites in thyroid carcinoma, prostate adenocarcinoma, pancreatic adenocarcinoma, and kidney renal papillary cell carcinoma subtypes.

Figure 4

The phenotypic characteristics in tumor subtypes. The “cluster” in the legend represents the immune subtypes of the 14 cancers by clustering. (A) The survival analysis of the subtypes in colon adenocarcinoma, kidney renal clear cell carcinoma, pancreatic adenocarcinoma, and head and neck squamous cell carcinoma. (B) Comparison of the phenotypic characteristics and the immune cell infiltration fractions between subtypes in kidney renal clear cell carcinoma and pancreatic adenocarcinoma subtypes.

Figure 5

The analysis based on the transcriptome. (A,B) KEGG pathway of differential gene enrichment between subtypes with high proportions of seven immune cell types and normal samples. (C) The TIDE scores of 42 subtypes in 14 tumors. The meanings of the symbols in the figure are listed below: * indicates statistical significance with p ≤ 0.05, ** with p ≤ 0.01, *** with p ≤ 0.001, **** with p ≤ 0.0001, and ns indicates no statistical significance (p > 0.05). (D) The ESTIMATE scores of 42 subtypes in 14 tumors. The meanings of the symbols in the figure are listed below: * indicates statistical significance with p ≤ 0.05, ** with p ≤ 0.01, **** with p ≤ 0.0001, and ns indicates no statistical significance (p > 0.05). (E) The analysis of the immune checkpoint genes of 42 subtypes in 14 tumors. The “cluster” in the legend represents the immune subtypes of the 14 cancers by clustering.