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Differentiation of lung adenocarcinoma, pleural mesothelioma, and nonmalignant pulmonary tissues using DNA methylation profiles.

ABSTRACT: Pathologic differentiation of tissue of origin in tumors found in the lung can be challenging, with differentiation of mesothelioma and lung adenocarcinoma emblematic of this problem. Indeed, proper classification is essential for determination of treatment regimen for these diseases, making accurate and early diagnosis critical. Here, we investigate the potential of epigenetic profiles of lung adenocarcinoma, mesothelioma, and nonmalignant pulmonary tissues (n = 285) as differentiation markers in an analysis of DNA methylation at 1413 autosomal CpG loci associated with 773 cancer-related genes. Using an unsupervised recursively partitioned mixture modeling technique for all samples, the derived methylation profile classes were significantly associated with sample type (P < 0.0001). In a similar analysis restricted to tumors, methylation profile classes significantly predicted tumor type (P < 0.0001). Random forests classification of CpG methylation of tumors--which splits the data into training and test sets--accurately differentiated mesothelioma from lung adenocarcinoma over 99% of the time (P < 0.0001). In a locus-by-locus comparison of CpG methylation between tumor types, 1266 CpG loci had significantly different methylation between tumors following correction for multiple comparisons (Q < 0.05); 61% had higher methylation in adenocarcinoma. Using the CpG loci with significant differential methylation in a pathway analysis revealed significant enrichment of methylated gene-loci in Cell Cycle Regulation, DNA Damage Response, PTEN Signaling, and Apoptosis Signaling pathways in lung adenocarcinoma when compared with mesothelioma. Methylation profile-based differentiation of lung adenocarcinoma and mesothelioma is highly accurate, informs on the distinct etiologies of these diseases, and holds promise for clinical application.

SUBMITTER: Christensen BC

PROVIDER: S-EPMC2755616 | biostudies-literature | 2009 Aug

REPOSITORIES: biostudies-literature

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Differentiation of lung adenocarcinoma, pleural mesothelioma, and nonmalignant pulmonary tissues using DNA methylation profiles.

Christensen Brock C BC Marsit Carmen J CJ Houseman E Andres EA Godleski John J JJ Longacker Jennifer L JL Zheng Shichun S Yeh Ru-Fang RF Wrensch Margaret R MR Wiemels Joseph L JL Karagas Margaret R MR Bueno Raphael R Sugarbaker David J DJ Nelson Heather H HH Wiencke John K JK Kelsey Karl T KT

Cancer research 20090728 15

Pathologic differentiation of tissue of origin in tumors found in the lung can be challenging, with differentiation of mesothelioma and lung adenocarcinoma emblematic of this problem. Indeed, proper classification is essential for determination of treatment regimen for these diseases, making accurate and early diagnosis critical. Here, we investigate the potential of epigenetic profiles of lung adenocarcinoma, mesothelioma, and nonmalignant pulmonary tissues (n = 285) as differentiation markers ...[more]

PMID: 19638575

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Project description:Pathologic differentiation of tissue of origin in tumors found in the lung can be challenging, with differentiation of mesothelioma and lung adenocarcinoma emblematic of this problem. Indeed, proper classification is essential for determination of treatment regimen for these diseases, making accurate and early diagnosis critical. Here we investigate the potential of epigenetic profiles of lung adenocarcinoma, mesothelioma, and non-malignant pulmonary tissues (n=285) as differentiation markers in an analysis of DNA methylation at 1413 autosomal CpG loci associated with 773 cancer-related genes. Using an unsupervised recursively-partitioned mixture modeling technique for all samples, the derived methylation profile classes were significantly associated with sample type (P < 0.0001). In a similar analysis restricted to tumors, methylation profile classes significantly predicted tumor type (P < 0.0001). Random forests classification of CpG methylation of tumors - which splits the data into training and test sets - accurately differentiated MPM from lung adenocarcinoma over 99% of the time (P < 0.0001). In a locus-by-locus comparison of CpG methylation between tumor types, 1266 CpG loci had significantly different methylation between tumors following correction for multiple comparisons (Q < 0.05); 61% had higher methylation in adenocarcinoma. Using the CpG loci with significant differential methylation in a pathways analysis revealed significant enrichment of methylated gene-loci in Cell Cycle Regulation, DNA Damage Response, PTEN Signaling, and Apoptosis Signaling pathways in lung adenocarcinoma when compared to mesothelioma. Methylation-profile-based differentiation of lung adenocarcinoma and mesothelioma is highly accurate, informs on the distinct etiologies of these diseases, and holds promise for clinical application. Mesotheliomas (n=158) and grossly non-tumorigenic parietal pleura (n=18) were obtained following surgical resection at Brigham and Womenâ??s Hospital through the International Mesothelioma Program from a pilot study conducted in 2002 (n=70) and an incident case series beginning in 2005 (n=88) with a participation rate of 85%. We used biopsy specimens from patients treated for NSCLC at the Massachusetts General Hospital from 1992 â?? 1996 (18) including lung adenocarcinomas (n=57) and non-malignant pulmonary tissues (n=48) (of which 22 (39%) were taken from the adenocarcinoma patients) (18). Additional normal lung tissues were obtained from the National Disease Research Interchange from donors free of lung malignancy (n=4).

Project description:Malignant pleural mesothelioma (MPM) is a rare and aggressive neoplasm. Patients are usually diagnosed when current treatments have limited benefits, highlighting the need for noninvasive tests aimed at an MPM risk assessment tool that might improve life expectancy. Three hundred asbestos-exposed subjects (163 MPM cases and 137 cancer-free controls), from the same geographical region in Italy, were recruited. The evaluation of asbestos exposure was conducted considering the frequency, the duration and the intensity of occupational, environmental and domestic exposure. A genome-wide methylation array was performed to identify novel blood DNA methylation (DNAm) markers of MPM. Multiple regression analyses adjusting for potential confounding factors and interaction between asbestos exposure and DNAm on the MPM odds ratio were applied. Epigenome-wide analysis (EWAS) revealed 12 single-CpGs associated with the disease. Two of these showed high statistical power (99%) and effect size (>0.05) after false discovery rate (FDR) multiple comparison corrections: (i) cg03546163 in FKBP5, significantly hypomethylated in cases (Mean Difference in beta values (MD) = -0.09, 95% CI = -0.12|-0.06, p = 1.2 × 10-7), and (ii) cg06633438 in MLLT1, statistically hypermethylated in cases (MD = 0.07, 95% CI = 0.04|0.10, p = 1.0 × 10-6). Based on the interaction analysis, asbestos exposure and epigenetic profile together may improve MPM risk assessment. Above-median asbestos exposure and hypomethylation of cg03546163 in FKBP5 (OR = 20.84, 95% CI = 8.71|53.96, p = 5.5 × 10-11) and hypermethylation of cg06633438 in MLLT1 (OR = 11.71, 95% CI = 4.97|29.64, p = 5.9 × 10-8) genes compared to below-median asbestos exposure and hyper/hypomethylation of single-CpG DNAm, respectively. Receiver Operation Characteristics (ROC) for Case-Control Discrimination showed a significant increase in MPM discrimination when DNAm information was added in the model (baseline model, BM: asbestos exposure, age, gender and white blood cells); area under the curve, AUC = 0.75; BM + cg03546163 at FKBP5. AUC = 0.89, 2.1 × 10-7; BM + cg06633438 at MLLT1. AUC = 0.89, 6.3 × 10-8. Validation and replication procedures, considering independent sample size and a different DNAm analysis technique, confirmed the observed associations. Our results suggest the potential application of DNAm profiles in blood to develop noninvasive tests for MPM risk assessment in asbestos-exposed subjects.

Project description:Objectives Our goal was to evaluate the diagnostic value of DNA methylation analysis in combination with machine learning to differentiate pleural mesothelioma (PM) from important histopathological mimics. Material and methods DNA methylation data of PM, lung adenocarcinomas, lung squamous cell carcinomas and chronic pleuritis was used to train a random forest as well as a support vector machine. These classifiers were validated using an independent validation cohort including pleural carcinosis and pleomorphic variants of lung adeno- and squamous cell carcinomas. Furthermore, we used a deconvolution method to estimate the composition of the tumor microenvironment. Results T-distributed stochastic neighbor embedding clearly separated PM from lung adenocarcinomas and squamous cell carcinomas, but there was a considerable overlap between chronic pleuritis specimens and PM with low tumor cell content. While both machine learning algorithms achieved comparable accuracies in a nested cross validation on the training cohort (random forest: 94.9%; support vector machine: 95.5%), the support vector machine outperformed the random forest in distinguishing PM from chronic pleuritis. Differential methylation analysis revealed promoter hypermethylation in PM specimens, including the tumor suppressor genes BCL11B, EBF1, FOXA1, and WNK2. Furthermore, we observed comparable accuracies for the support vector machine on the validation cohort (97.1%) while the random forest performed considerably worse (89.9%). Deconvolution of the stromal and immune cell composition revealed higher rates of regulatory T-cells and endothelial cells in tumor specimens and a heterogenous inflammation including macrophages, B-cells and natural killer cells in chronic pleuritis. Conclusion DNA methylation in combination with machine learning is a promising tool to reliably differentiate PM from chronic pleuritis and lung cancer, including pleomorphic carcinomas. Furthermore, our study highlights new candidate genes for PM carcinogenesis and shows that deconvolution of DNA methylation data can provide reasonable insights into the composition of the tumor microenvironment.

Project description:Malignant pleural mesothelioma (MPM) is a highly lethal, poorly understood neoplasm that is typically associated with asbestos exposure. We performed transcriptional profiling using high-density oligonucleotide microarrays containing approximately 22,000 genes to elucidate potential molecular and pathobiological pathways in MPM using discarded human MPM tumor specimens (n=40), normal lung specimens (n=4), normal pleura specimens (n=5), and MPM and SV40-immortalized mesothelial cell lines (n=5). In global expression analysis using unsupervised clustering techniques, we found two potential subclasses of mesothelioma which correlate loosely with tumor histology. We also identified sets of genes with expression levels that distinguish between multiple tumor subclasses, normal and tumor tissues, and tumors with different morphologies. Microarray gene expression data were confirmed using quantitative reverse transcriptase-polymerase chain reaction (RT-PCR) and protein analysis for three novel candidate oncogenes (NME2, CRI1, and PDGFC) and one candidate tumor suppressor (GSN). Finally, we used bioinformatics tools (i.e. software) to create and explore complex physiological pathways that may be relevant in mesothelioma tumorigenesis, pathobiology, or both. Tissues and cell lines profiled using microarrays. Discarded MPM surgical specimens (n=40), normal pleura specimens (n=5), and normal lung specimens (n=4) were freshly collected (and snap frozen) from patients who underwent surgery at Boston's Brigham and Women's Hospital (BWH) between October 1998 and August 2000. All of these patients underwent extrapleural pneumonectomy with heated intra-pleural cisplatin chemotherapy delivered after the specimens were removed. All normal specimens were obtained from patients who were never diagnosed with MPM. Two human MPM cell lines (MS589 and MS428) were kindly provided by Jonathan A. Fletcher, M.D., Department of Pathology, BWH. The JMN1B MPM cell line19,20 has been described previously. The SV40-immortalized, non-tumorigenic mesothelial cell line (Met-5A)21 and the MPM cell line MSTO-211H22 were purchased from the American Type Culture Collection. Normal tissues were obtained from additional consented patients undergoing treatment for diseases other than MPM. All MPM samples used in these studies contained relatively pure tumor (greater than 50% tumor cells per high power field examined in a section adjacent to the tissue used). The microscopic slides from the patients' resection specimens were reviewed by one of the authors (J.G.), and the diagnosis and histologic subclassification of MPM confirmed in all cases. Linked clinical and pathological data were obtained for all patients who contributed tumor specimens. Specimens and data were rendered anonymous to protect patient confidentiality. Studies utilizing human tissues were approved by and conducted in accordance with the policies of the Institutional Review Board at BWH.

Dataset Information

Differentiation of lung adenocarcinoma, pleural mesothelioma, and nonmalignant pulmonary tissues using DNA methylation profiles.

Publications

Differentiation of lung adenocarcinoma, pleural mesothelioma, and nonmalignant pulmonary tissues using DNA methylation profiles.

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