Core regulatory circuitries in defining cancer cell identity across the malignant spectrum
Author(s) -
Leila Jahangiri,
Loukia Tsaprouni,
Ricky M. Trigg,
John A. Williams,
Georgios V. Gkoutos,
Suzanne D. Turner,
João D. Pereira
Publication year - 2020
Publication title -
open biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.078
H-Index - 53
ISSN - 2046-2441
DOI - 10.1098/rsob.200121
Subject(s) - biology , chromatin , enhancer , transcription factor , computational biology , cancer , genetics , cancer research , gene
Gene expression programmes driving cell identity are established by tightly regulated transcription factors that auto- and cross-regulate in a feed-forward manner, forming core regulatory circuitries (CRCs). CRC transcription factors create and engage super-enhancers by recruiting acetylation writers depositing permissive H3K27ac chromatin marks. These super-enhancers are largely associated with BET proteins, including BRD4, that influence higher-order chromatin structure. The orchestration of these events triggers accessibility of RNA polymerase machinery and the imposition of lineage-specific gene expression. In cancers, CRCs drive cell identity by superimposing developmental programmes on a background of genetic alterations. Further, the establishment and maintenance of oncogenic states are reliant on CRCs that drive factors involved in tumour development. Hence, the molecular dissection of CRC components driving cell identity and cancer state can contribute to elucidating mechanisms of diversion from pre-determined developmental programmes and highlight cancer dependencies. These insights can provide valuable opportunities for identifying and re-purposing drug targets. In this article, we review the current understanding of CRCs across solid and liquid malignancies and avenues of investigation for drug development efforts. We also review techniques used to understand CRCs and elaborate the indication of discussed CRC transcription factors in the wider context of cancer CRC models.
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