Epigenetic profiling reveals etiologically distinct patterns of DNA methylation in head and neck squamous cell carcinoma
- 6 January 2009
- journal article
- Published by Oxford University Press (OUP) in Carcinogenesis: Integrative Cancer Research
- Vol. 30 (3) , 416-422
- https://doi.org/10.1093/carcin/bgp006
Abstract
Head and neck squamous cell carcinomas (HNSCCs) represent clinically and etiologically heterogeneous tumors affecting >40 000 patients per year in the USA. Previous research has identified individual epigenetic alterations and, in some cases, the relationship of these alterations with carcinogen exposure or patient outcomes, suggesting that specific exposures give rise to specific types of molecular alterations in HNSCCs. Here, we describe how different etiologic factors are reflected in the molecular character and clinical outcome of these tumors. In a case series of primary, incident HNSCC (n = 68), we examined the DNA methylation profile of 1413 autosomal CpG loci in 773 genes, in relation to exposures and etiologic factors. The overall pattern of epigenetic alteration could significantly distinguish tumor from normal head and neck epithelial tissues (P < 0.0001) more effectively than specific gene methylation events. Among tumors, there were significant associations between specific DNA methylation profile classes and tobacco smoking and alcohol exposures. Although there was a significant association between methylation profile and tumor stage (P < 0.01), we did not observe an association between these profiles and overall patient survival after adjustment for stage; although methylation of a number of specific loci falling in different cellular pathways was associated with overall patient survival. We found that the etiologic heterogeneity of HNSCC is reflected in specific patterns of molecular epigenetic alterations within the tumors and that the DNA methylation profiles may hold clinical promise worthy of further study.Keywords
This publication has 27 references indexed in Scilit:
- Model-based clustering of DNA methylation array data: a recursive-partitioning algorithm for high-dimensional data arising as a mixture of beta distributionsBMC Bioinformatics, 2008
- Examination of a CpG Island Methylator Phenotype and Implications of Methylation Profiles in Solid TumorsCancer Research, 2006
- High-throughput DNA methylation profiling using universal bead arraysGenome Research, 2006
- Applications of beta-mixture models in bioinformaticsBioinformatics, 2005
- Frequent promoter hypermethylation of RASSF1A and p16INK4a and infrequent allelic loss other than 9p21 in betel‐associated oral carcinoma in a Vietnamese non‐smoking/non‐drinking female populationJournal of Oral Pathology & Medicine, 2005
- Efficient quadratic regularization for expression arraysBiostatistics, 2004
- Invasiveness of breast carcinoma cells and transcript profile: Eph receptors and ephrin ligands as molecular markers of potential diagnostic and prognostic applicationBiochemical and Biophysical Research Communications, 2004
- A comparison of cluster analysis methods using DNA methylation dataBioinformatics, 2004
- Inactivation of the Fanconi anemia/BRCA pathway in lung and oral cancers: implications for treatment and survivalOncogene, 2003
- Model-Based Clustering, Discriminant Analysis, and Density EstimationJournal of the American Statistical Association, 2002