Chapter

Epigenetic Alterations in Oncogenesis

Volume 754 of the series Advances in Experimental Medicine and Biology pp 313-338

Date:

Methods for Cancer Epigenome Analysis

  • Raman P. NagarajanAffiliated withUniversity of California
  • , Shaun D. FouseAffiliated withUniversity of California
  • , Robert J. A. BellAffiliated withUniversity of California
  • , Joseph F. CostelloAffiliated withUniversity of California Email author 

* Final gross prices may vary according to local VAT.

Get Access

Abstract

Accurate detection of epimutations in tumor cells is crucial for ­understanding the molecular pathogenesis of cancer. Alterations in DNA methylation in cancer are functionally important and clinically relevant, but even this well-studied area is continually re-evaluated in light of unanticipated results, such as the strong association between aberrant DNA methylation in adult tumors and polycomb group profiles in embryonic stem cells, cancer-associated genetic mutations in epigenetic regulators such as DNMT3A and TET family genes, and the discovery of altered 5-hydroxymethylcytosine, a product of TET proteins acting on 5-methylcytosine, in human tumors with TET mutations. The abundance and distribution of covalent histone modifications in primary cancer tissues relative to normal cells is an important but largely uncharted area, although there is good evidence for a mechanistic role of cancer-specific alterations in histone modifications in tumor etiology, drug response, and tumor progression. Meanwhile, the discovery of new epigenetic marks continues, and there are many useful methods for epigenome analysis applicable to primary tumor samples, in addition to cancer cell lines. For DNA methylation and hydroxymethylation, next-generation sequencing allows increasingly inexpensive and quantitative whole-genome profiling. Similarly, the refinement and maturation of chromatin immunoprecipitation with next-generation sequencing (ChIP-seq) has made possible genome-wide mapping of histone modifications, open chromatin, and transcription factor binding sites. Computational tools have been developed apace with these epigenome methods to better enable accurate interpretation of the profiling data.