Unknown,Transcriptomics,Genomics,Proteomics

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Genome-wide DNA methylation analysis of lung carcinoma

ABSTRACT: Lung cancer is the worldwide leading cause of death from cancer. DNA methylation in gene promoter regions is a major mechanism of gene expression regulation that may promote tumorigenesis. However, whether clinically relevant subgroups based on DNA methylation patterns exist in lung cancer is not well studied. We performed whole-genome methylation analysis using 450K Illumina BeadArrays on 124 tumors including 83 adenocarcinomas, 23 squamous cell carcinomas, one adenosquamous cancer, five large cell carcinomas, nine large cell neuroendocrine carcinomas (LCNEC), three small cell carcinomas (SCLC) and 12 normal lung tissues. Unsupervised class discovery was performed to identify DNA methylation subgroups with clinicopathological and molecular features. Subgroups were validated in two independent NSCLC cohorts. Unsupervised analysis identified five DNA methylation subgroups (epitypes). One epitype was distinctly associated with neuroendocrine tumors (LCNEC and SCLC). For adenocarcinoma, in both discovery and validation cohorts, remaining four epitypes were associated with differences in clinicopathological and molecular features, including global hypomethylation, promoter hypermethylation, copy number alterations, expression of proliferation-associated genes, association with unsupervised and supervised gene expression phenotypes, KRAS, TP53, KEAP1, SMARCA4, and STK11 mutations, smoking history, and patient outcome. Based on a multicohort approach we conducted a comprehensive survey of genome-wide DNA methylation in lung cancer, identifying a distinct neuroendocrine epitype and four adenocarcinoma epitypes associated with molecular and clinicopathological characteristics, and patient outcome. Our results bring further understanding of the epigenetic characteristics and molecular diversity in lung cancer generally and in adenocarcinoma specifically. Genome-wide DNA methylation analysis of 124 lung carcinomas and 12 normal lung tissues using Illumina Human Methylation 450K v1.0 Beadchips.

ORGANISM(S): Homo sapiens

SUBMITTER: Johan Staaf

PROVIDER: E-GEOD-56044 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Genome-wide DNA methylation analysis of lung carcinoma reveals one neuroendocrine and four adenocarcinoma epitypes associated with patient outcome.

Karlsson Anna A Jönsson Mats M Lauss Martin M Brunnström Hans H Jönsson Per P Borg Åke Å Jönsson Göran G Ringnér Markus M Planck Maria M Staaf Johan J

Clinical cancer research : an official journal of the American Association for Cancer Research 20141002 23

<h4>Purpose</h4>Lung cancer is the worldwide leading cause of death from cancer. DNA methylation in gene promoter regions is a major mechanism of gene expression regulation that may promote tumorigenesis. However, whether clinically relevant subgroups based on DNA methylation patterns exist in lung cancer remains unclear.<h4>Experimental design</h4>Whole-genome DNA methylation analysis using 450K Illumina BeadArrays was performed on 12 normal lung tissues and 124 tumors, including 83 adenocarcin ...[more]

PMID: 25278450

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Project description:Two prognostically significant subtypes of high-grade lung neuroendocrine tumors independent of small-cell and large-cell neuroendocrine carcinomas identified by gene expression profiles. BACKGROUND: Classification of high-grade neuroendocrine tumors (HGNT) of the lung currently recognises large-cell neuroendocrine carcinoma (LCNEC) and small-cell lung carcinoma (SCLC) as distinct groups. However, a similarity in histology for these two carcinomas and uncertain clinical course have led to suggestions that a single HGNT classification would be more appropriate. Gene expression profiling, which can reproduce histopathological classification, and often defines new subclasses with prognostic significance, can be used to resolve HGNT classification. METHODS: We used cDNA microarrays with 40386 elements to analyze the gene expression profiles of 38 surgically resected samples of lung neuroendocrine tumors and 11 SCLC cell lines. Samples of large-cell carcinoma, adenocarcinoma, and normal lung were also included to give a total of 105 samples analyzed. The data were subjected to filtering to yield informative genes before unsupervised hierarchical clustering that identified relatedness of tumor samples. FINDINGS: Distinct groups for carcinoids, large-cell carcinoma, adenocarcinoma, and normal lung were readily identified. However, we were unable to distinguish LCNEC from SCLC by gene expression profiling. Three independent rounds of unsupervised hierarchical clustering consistently divided SCLC samples into two main groups with LCNEC samples largely integrated with these groups. Furthermore, patients in one of the groups identified by clustering had a significantly better clinical outcome than the other (83% vs 12% survived for 5 years; p=0.0094. None of the highly proliferative SCLC cell lines subsequently analyzed clustered with this good-prognosis group. INTERPRETATION: Our findings show that HGNT of the lung can be classified into two groups independent of SCLC and LCNEC. To this end, we have identified many genes, some of which encode well-characterized markers of cancer that distinguish the HGNT groups. These results have implications for the diagnosis, classification, and treatment of lung neuroendocrine tumors, and provide important insights into their underlying biology. Keywords: other

Project description:Background Neuroendcrine carcinoma (NEC) of lung consists of small-cell lung carcinoma (SCLC) and large cell neuroendocrine carcinoma (LCNEC). Although long-term outcomes of SCLC patients are usually poor, a few patients achieve long-term survivals. Prognostic factors in SCLC have not been fully elucidated. microRNAs (miRNAs) are known to negatively regulate gene expression and to be relevant to tumorigenesis, tumor classification and prognosis. Methods 63 samples (46 neuroendocrine carcinoma including 35 SCLC and 11 LCNEC, 4 adenocarcinoma, 5 squamous cell carcinoma and 8 normal lung) were obtained through surgical resection. miRNA expression in each sample was comprehensively investigated using miRNA microarray. Results Unsupervised hierarchial clustering classified the all NEC into two subgroups, group 1 and 2. Group 1 was felt into an independent branch that consisted of only NEC, whereas group 2 was included in a branch that contained both NEC and non-NEC. Compared with SCLC of group 1 (SCLC 1), SCLC of group 2 (SCLC 2) was a distinct subgroup with surprisingly good prognosis (0% v 79% survived for 5 years; p=0.007). Serum proGRP level was significantly lower in SCLC 2 than in SCLC 1 despite of similar tumor stages between the two groups. Among 12 cases treated by presurgical chemotherapy, PR:NC ratio was 6:3 in SCLC 1 and 2:1 in SCLC 2, suggesting no apparent difference of chemosensitivity. Histologically, distinction between both groups was difficult. Cox regression analysis demonstrated that three miRNA signature was correlated with survival of SCLC patients. These prognostic miRNAs were differentially expressed between SCLC 1 and 2, which were validated by quantitative real time RT-PCR. Bioinformatics analysis predicted that these miRNAs targeted not only genes involving with tumorigenesis but also genes associated with neuroendocrine function. Conclusion miRNA expression profiling identified a distinct subgroup of SCLC with favorable prognosis. This subgroup might have less neuroendocrine character than usual SCLC. All samples were tissue samples obtained by surgical resection. 35 samples of small-cell lung cancer were investigated. 11 samples of larege cell neuroendocrine cancer, 4 samples of adenocarcinoma, 5 samples of squamous cell carcinoma and 8 samples of normal lung were also included as reference.

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Genome-wide DNA methylation analysis of lung carcinoma

Publications

Genome-wide DNA methylation analysis of lung carcinoma reveals one neuroendocrine and four adenocarcinoma epitypes associated with patient outcome.

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