The microscopic assessment of adenocarcinoma of the lung can resemble malignant pleural mesothelioma. There is no absolute standardized approach to differential diagnosis of these diseases, which can be challenging. As is the case with any disease, proper diagnosis is paramount; a rapid, accurate diagnosis has the potential to improve patient outcome. Using DNA methylation profiling we successfully differentiated these tumors, suggesting that this approach may be a useful adjunct in diagnosis.
All somatic cells in a given individual are genetically identical (excluding T and B-cells). However, different cell types form distinct anatomic structures and carry out a wide range of physiologic functions. This is made possible largely via control of gene expression. One approach for differentiating pleural mesothelioma and lung adenocarcinoma relies on the differential gene expression profiles of these tumors (11
). While this approach is sound, and has been reproduced in malignant pleural effusions (1
), the instability of mRNA transcripts makes methods relying upon RNA measures difficult to standardize and implement. DNA methylation profiles reflect phenotypically important differences in gene transcription and the molecular structure of DNA is inherently more stable than RNA, making assessment of DNA methylation profiles attractive as a highly accurate and reproducible diagnostic test.
Unsupervised clustering achieved excellent segregation of tumor tissues from each other and from non-tumor tissues, although there was indistinct clustering of non-tumorigenic lung and pleural samples. Similarly, some RPMM methylation classes contained a mixture of both non-tumor lung and non-tumor pleura samples, and in random forests classification, non-tumor pleura samples had the highest misclassification error. The most likely reason for pleura being misclassified as lung tissue is the potential contamination of the pleural sample with adjacent lung tissue. In addition, in this and other random forests classifications of methylation data from our group, we found a significant correlation between sample size and classification error. Therefore, some of the misclassification error for pleural samples may be attributable to small sample size. In the future, arrays with larger panels of CpG methylation markers may further increase the accuracy with which these tissue types can be differentiated.
In an analysis restricted to tumors, we demonstrated the great extent to which CpG methylation varies between mesothelioma and lung adenocarcinoma. Disparate CpG methylation profiles between these tumor types can be attributed in part to differential methylation profiles in the tissues of origin. Although there has been a general consensus that normal cells maintain CpG islands in an unmethylated state permissive to transcription (13
), tissue-specific methylation of CpG islands has been described in non-diseased cells (25
). In fact, data from the Human Epigenome Project have shown that there is tissue-specific methylation among 90 genes associated with the human major histocompatability complex (26
), and others have reported tissue-specific promoter-region methylation of monocytes, testis and brain tissues (27
). Consistent with these findings, our data show that, in general, normal lung and pleura have different basal methylation profiles.
The different etiologic factors associated with the induction of these tumors likely contribute to their differential methylation. While the majority of lung adenocarcinomas are related to smoking, smoking is not a risk factor for mesothelioma; rather, the vast majority of mesotheliomas are linked to asbestos exposure. Although asbestos is also a risk factor for lung adenocarcinoma, in our study population only one lung adenocarcinoma patient had occupational asbestos exposure, and this individual was also a smoker. Significant smoking-related and asbestos-related methylation-induced gene inactivation events have been described in lung adenocarcinoma and mesothelioma respectively (28
). It is possible that differences in carcinogen exposure result in differences in methylation profiles within and between tumor types.
In a locus-by-locus analysis of tumor samples, over one thousand CpG loci were differentially methylated between tumor types. Previously, with a combined sample of over 100 mesotheliomas and lung adenocarcinomas, Toyooka et al. reported significantly increased methylation in lung adenocarcinoma at APC, CDH13, CDKN2A, MGMT
, and RARB
). Consistent with these results, in our study, all 12 CpG loci examined among these five genes had significantly higher methylation in adenocarcinomas after correcting for multiple comparisons. In another study, methylation of CDH1, ESR1, PTGS2
, and RASSF1
had significantly different methylation among normal lung, mesothelioma and adenocarcinoma (total n=24), with all gene-loci exhibiting higher methylation in lung adenocarcinoma versus mesothelioma (17
). Similarly, in our results, at least one of the two CpG loci investigated in each of these genes had significantly higher methylation in lung adenocarcinoma and none of the CpG loci we examined in these genes had higher methylation in mesothelioma.
Pathways analysis of differentially methylated CpG loci suggested that there is significant, tumor-type-specific enrichment for methylation-based silencing of genes in specific pathways. As tumorigenesis requires somatic inactivation of several pathways, our observations suggest that either the differing etiologic factors or the differential response of the target cells to these factors is driving the mode of pathway inactivation (i.e. epigenetic vs. genetic). For example, the enrichment for methylation inactivation of differential cytokine signaling pathway genes (IL-6 Signaling
in lung adenocarcinoma and Fc Epsilon Signaling
in mesothelioma) could represent a differential immune-regulated inflammatory response to the primary carcinogens of tobacco smoke and asbestos for these tumors. Further, our group and others have shown that there is an increasing prevalence of DNA methylation of CDKN2A
with greater smoking duration in lung cancers (30
), while this gene is often inactivated through homozygous deletion in malignant mesothelioma (32
). These results suggest that a preferential mode of inactivation may not be occurring in a gene-specific pattern, but instead represents a broader selection of inactivation by exposure and/or target tissue. Alternatively, but not mutually exclusively, the epigenetic status of the genes in these pathways in the stem cells that give rise to these tissues could differ, contributing to the observed differences between these tumors. More complete detailing of the somatic alterations, including profiles of both genetic and epigenetic alterations would assist in characterizing the relationship between exposures and differential pathway inactivation in these cancers.
Future studies which include treatment and survival data for these patients in their respective diseases may identify specific markers of therapeutic value. Epigenetic alterations associated with overall prognosis could potentially contribute to treatment decisions.
In summary, using CpG methylation profiles we accurately differentiated mesothelioma from lung adenocarcinoma. This approach is DNA based, inexpensive, commercially available, and individual samples can be classified by simply comparing to existing RPMM data with an empirical Bayes estimator. Furthermore, random forest is a prediction-based algorithm and can, in principle, be used as the basis for diagnostic software. In addition to characterizing the methylation profiles of these tumors for potential diagnostic use, these data and those of the pathways analysis could aid in understanding variation in patients' response to treatment, and or the identification of novel, critical therapeutic targets. Finally, beyond the classification of lung adenocarcinoma and mesothelioma, this method may be useful for a range of other clinical scenarios.