1. Chromosomes and Gene Expression
  2. Cancer Biology
Download icon

Bromodomain inhibition of the transcriptional coactivators CBP/EP300 as a therapeutic strategy to target the IRF4 network in multiple myeloma

Research Article
  • Cited 54
  • Views 5,084
  • Annotations
Cite this article as: eLife 2016;5:e10483 doi: 10.7554/eLife.10483

Abstract

Pharmacological inhibition of chromatin co-regulatory factors represents a clinically validated strategy to modulate oncogenic signaling through selective attenuation of gene expression. Here, we demonstrate that CBP/EP300 bromodomain inhibition preferentially abrogates the viability of multiple myeloma cell lines. Selective targeting of multiple myeloma cell lines through CBP/EP300 bromodomain inhibition is the result of direct transcriptional suppression of the lymphocyte-specific transcription factor IRF4, which is essential for the viability of myeloma cells, and the concomitant repression of the IRF4 target gene c-MYC. Ectopic expression of either IRF4 or MYC antagonizes the phenotypic and transcriptional effects of CBP/EP300 bromodomain inhibition, highlighting the IRF4/MYC axis as a key component of its mechanism of action. These findings suggest that CBP/EP300 bromodomain inhibition represents a viable therapeutic strategy for targeting multiple myeloma and other lymphoid malignancies dependent on the IRF4 network.

Article and author information

Author details

  1. Andrew R Conery

    Constellation Pharmaceuticals, Cambridge, United States
    Competing interests
    Andrew R Conery, Employee of Constellation Pharmaceuticals.
  2. Richard C Centore

    Constellation Pharmaceuticals, Cambridge, United States
    Competing interests
    Richard C Centore, Employee of Constellation Pharmaceuticals.
  3. Adrianne Neiss

    Constellation Pharmaceuticals, Cambridge, United States
    Competing interests
    Adrianne Neiss, Employee of Constellation Pharmaceuticals.
  4. Patricia J Keller

    Constellation Pharmaceuticals, Cambridge, United States
    Competing interests
    Patricia J Keller, Employee of Constellation Pharmaceuticals.
  5. Shivangi Joshi

    Constellation Pharmaceuticals, Cambridge, United States
    Competing interests
    Shivangi Joshi, Employee of Constellation Pharmaceuticals.
  6. Kerry L Spillane

    Constellation Pharmaceuticals, Cambridge, United States
    Competing interests
    Kerry L Spillane, Employee of Constellation Pharmaceuticals.
  7. Peter Sandy

    Constellation Pharmaceuticals, Cambridge, United States
    Competing interests
    Peter Sandy, Employee of Constellation Pharmaceuticals.
  8. Charlie Hatton

    Constellation Pharmaceuticals, Cambridge, United States
    Competing interests
    Charlie Hatton, Employee of Constellation Pharmaceuticals.
  9. Eneida Pardo

    Constellation Pharmaceuticals, Cambridge, United States
    Competing interests
    Eneida Pardo, Employee of Constellation Pharmaceuticals.
  10. Laura Zawadzke

    Constellation Pharmaceuticals, Cambridge, United States
    Competing interests
    Laura Zawadzke, Employee of Constellation Pharmaceuticals.
  11. Archana Bommi-Reddy

    Constellation Pharmaceuticals, Cambridge, United States
    Competing interests
    Archana Bommi-Reddy, Employee of Constellation Pharmaceuticals.
  12. Karen E Gascoigne

    Genentech, South San Francisco, United States
    Competing interests
    Karen E Gascoigne, Employee of Genentech.
  13. Barbara M Bryant

    Constellation Pharmaceuticals, Cambridge, United States
    Competing interests
    Barbara M Bryant, Employee of Constellation Pharmaceuticals.
  14. Jennifer A Mertz

    Constellation Pharmaceuticals, Cambridge, United States
    Competing interests
    Jennifer A Mertz, Employee of Constellation Pharmaceuticals.
  15. Robert J Sims

    Constellation Pharmaceuticals, Cambridge, United States
    For correspondence
    robert.sims@constellationpharma.com
    Competing interests
    Robert J Sims, Employee of Constellation Pharmaceuticals.

Reviewing Editor

  1. Scott A Armstrong, Memorial Sloan Kettering Cancer Center, United States

Publication history

  1. Received: July 30, 2015
  2. Accepted: January 4, 2016
  3. Accepted Manuscript published: January 5, 2016 (version 1)
  4. Version of Record published: February 23, 2016 (version 2)
  5. Version of Record updated: July 14, 2016 (version 3)

Copyright

© 2016, Conery et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 5,084
    Page views
  • 1,118
    Downloads
  • 54
    Citations

Article citation count generated by polling the highest count across the following sources: Scopus, Crossref, PubMed Central.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Chromosomes and Gene Expression
    2. Genetics and Genomics
    Michelle C Ward et al.
    Research Article Updated

    One life-threatening outcome of cardiovascular disease is myocardial infarction, where cardiomyocytes are deprived of oxygen. To study inter-individual differences in response to hypoxia, we established an in vitro model of induced pluripotent stem cell-derived cardiomyocytes from 15 individuals. We measured gene expression levels, chromatin accessibility, and methylation levels in four culturing conditions that correspond to normoxia, hypoxia, and short- or long-term re-oxygenation. We characterized thousands of gene regulatory changes as the cells transition between conditions. Using available genotypes, we identified 1,573 genes with a cis expression quantitative locus (eQTL) in at least one condition, as well as 367 dynamic eQTLs, which are classified as eQTLs in at least one, but not in all conditions. A subset of genes with dynamic eQTLs is associated with complex traits and disease. Our data demonstrate how dynamic genetic effects on gene expression, which are likely relevant for disease, can be uncovered under stress.