Format

Send to

Choose Destination
Curr Opin Genet Dev. 2017 Apr;43:82-92. doi: 10.1016/j.gde.2017.01.003. Epub 2017 Jan 24.

Decoding transcriptional states in cancer.

Author information

1
Laboratory of Computational Biology, VIB Center for Brain & Disease Research, Leuven, Belgium; Department of Human Genetics, KU Leuven (University of Leuven), Leuven, Belgium.
2
Laboratory of Computational Biology, VIB Center for Brain & Disease Research, Leuven, Belgium; Department of Human Genetics, KU Leuven (University of Leuven), Leuven, Belgium. Electronic address: stein.aerts@kuleuven.vib.be.

Abstract

Gene regulatory networks determine cellular identity. In cancer, aberrations of gene networks are caused by driver mutations that often affect transcription factors and chromatin modifiers. Nevertheless, gene transcription in cancer follows the same cis-regulatory rules as normal cells, and cancer cells have served as convenient model systems to study transcriptional regulation. Tumours often show regulatory heterogeneity, with subpopulations of cells in different transcriptional states, which has important therapeutic implications. Here, we review recent experimental and computational techniques to reverse engineer cancer gene networks using transcriptome and epigenome data. New algorithms, data integration strategies, and increasing amounts of single cell genomics data provide exciting opportunities to model dynamic regulatory states at unprecedented resolution.

PMID:
28129557
DOI:
10.1016/j.gde.2017.01.003
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center