Epigenetic profiles distinguish pleural mesothelioma from normal pleura and predict lung asbestos burden and clinical outcome

Abstract

Mechanisms of action of nonmutagenic carcinogens such as asbestos remain poorly characterized. As pleural mesothelioma is known to have limited numbers of genetic mutations, we aimed to characterize the relationships among gene-locus-specific methylation alterations, disease status, asbestos burden, and survival in this rapidly fatal asbestos-associated tumor. Methylation of 1505 CpG loci associated with 803 cancer-related genes were studied in 158 pleural mesotheliomas and 18 normal pleura. After false-discovery rate correction, 969 CpG loci were independently associated with disease status (Q < 0.05). Classifying samples based on CpG methylation profile with a mixture model approach, methylation classes discriminated tumor from normal pleura (permutation P < 0.0001). In a random forests classification, the overall misclassification error rate was 3.4%, with <1% (n = 1) of tumors misclassified as normal (P < 0.0001). Among tumors, methylation class membership was significantly associated with lung tissue asbestos body burden (P < 0.03), and significantly predicted survival (likelihood ratio P < 0.01). Consistent with prior work, asbestos burden was associated with an increased risk of death (hazard ratio, 1.4; 95% confidence interval, 1.1-1.8). Our results have shown that methylation profiles powerfully differentiate diseased pleura from nontumor pleura and that asbestos burden and methylation profiles are independent predictors of mesothelioma patient survival. We have added to the growing body of evidence that cellular epigenetic dysregulation is a critical mode of action for asbestos in the induction of pleural mesothelioma. Importantly, these findings hold great promise for using epigenetic profiling in the diagnosis and prognosis of human cancers.

Figure 1. Unsupervised clustering of average beta values in tumor and non-tumor pleura

Using the R software package normal tissue sample average beta values were subjected to unsupervised hierarchical clustering based on Manhattan distance and average linkage. Each column represents a sample and each row represents a CpG locus (750 most variable autosomal loci). Above the heatmap blue indicates a tumor sample, and purple indicates a non-tumor pleural sample. In the heat map green = average beta of zero, or unmethylated, and red = average beta of one, or methylated

Figure 2. Beta mixture model of methylation profiles in mesothelioma and non-tumor pleura

Methylation average β is green for unmethylated and red for methylated. Methylation profile classes are stacked in rows separated by yellow lines, and class height corresponds to the number of samples in each class. Class methylation at each locus is a mean of methylation for all samples within a class. Bar charts display the proportion of tumors and non-tumor pleura samples in each class. Methylation profile classes differentiate tumor from non-tumor pleura (P < 0.0001).

Figure 3. Beta mixture model of methylation profiles in pleural mesothelioma

Methylation average β is green for unmethylated and red for methylated. Methylation profile classes are stacked in rows separated by yellow lines, and class height corresponds to the number of samples in each class. Class methylation at each locus is a mean of methylation for all samples within a class. On the left, bar charts show proportions for gender and tumor histology among samples within each class. On the right, box plots of log asbestos body counts for each class. Controlling for gender, methylation class membership predicts asbestos burden (P < 0.03).