Emerging roles of H3K9me3, SETDB1 and SETDB2 in therapy-induced cellular reprogramming - PubMed (original) (raw)

Review

Emerging roles of H3K9me3, SETDB1 and SETDB2 in therapy-induced cellular reprogramming

Joachim Torrano et al. Clin Epigenetics. 2019.

Abstract

Background: A multitude of recent studies has observed common epigenetic changes develop in tumour cells of multiple lineages following exposure to stresses such as hypoxia, chemotherapeutics, immunotherapy or targeted therapies. A significant increase in the transcriptionally repressive mark trimethylated H3K9 (H3K9me3) is becoming associated with treatment-resistant phenotypes suggesting upstream mechanisms may be a good target for therapy. We have reported that the increase in H3K9me3 is derived from the methyltransferases SETDB1 and SETDB2 following treatment in melanoma, lung, breast and colorectal cancer cell lines, as well as melanoma patient data. Other groups have observed a number of characteristics such as epigenetic remodelling, increased interferon signalling, cell cycle inhibition and apoptotic resistance that have also been reported by us suggesting these independent studies are investigating similar or identical phenomena.

Main body: Firstly, this review introduces reports of therapy-induced reprogramming in cancer populations with highly similar slow-cycling phenotypes that suggest a role for both IFN signalling and epigenetic remodelling in the acquisition of drug tolerance. We then describe plausible connections between the type 1 IFN pathway, slow-cycling phenotypes and these epigenetic mechanisms before reviewing recent evidence on the roles of SETDB1 and SETDB2, alongside their product H3K9me3, in treatment-induced reprogramming and promotion of drug resistance. The potential mechanisms for the activation of SETDB1 and SETDB2 and how they might arise in treatment is also discussed mechanistically, with a focus on their putative induction by inflammatory signalling. Moreover, we theorise their timely role in attenuating inflammation after their activation in order to promote a more resilient phenotype through homeostatic coordination of H3K9me3. We also examine the relatively uncharacterized functions of SETDB2 with some comparison to the more well-known qualities of SETDB1. Finally, an emerging overall mechanism for the epigenetic maintenance of this transient phenotype is outlined by summarising the collective literature herein.

Conclusion: A number of converging phenotypes outline a stress-responsive mechanism for SETDB1 and SETDB2 activation and subsequent increased survival, providing novel insights into epigenetic biology. A clearer understanding of how SETDB1/2-mediated transcriptional reprogramming can subvert treatment responses will be invaluable in improving length and efficacy of modern therapies.

Keywords: Adaptive resistance; H3K9me3; IFN signalling; SETDB1; SETDB2; Transcriptional reprogramming.

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The authors declare that they have no competing interests.

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Figures

Fig. 1

Fig. 1

Diagrammatic map of the SETDB1 (a) and SETDB2 (b) proteins. Illustration of the residual positions of the triple Tudor domain (T1, T2 and T3), methyl-CpG binding domains (MBD) and bifurcated SET multidomains

Fig. 2

Fig. 2

A simplified diagram of mitotic chromosomal remodelling by SETDB2. Model for putative chromosomal condensation and segregation during mitosis that is substantially contributed to via coordinated H3K9 methylation by SETDB2 and KDM4C. Other factors also contribute to this mechanism

Fig. 3

Fig. 3

A hypothetical mechanisms for type I IFN-mediated induction of SETDB1 and SETDB2 by STAT1 and Wnt5a respectively. Feedback attenuation proposed through inhibition of pro-IFN signalling cytokines and KAP1-SETDB1 interactions. Key at bottom

Fig. 4

Fig. 4

A speculative model for therapy-induced transcriptional remodelling. Detailed information of this model gathered using literature reviewed in the main text. Key at bottom

References

    1. Reyes J, Lahav G. Leveraging and coping with uncertainty in the response of individual cells to therapy. Curr Opin Biotechnol. 2018;51:109–115. - PMC - PubMed
    1. Ramirez M, Rajaram S, Steininger RJ, Osipchuk D, Roth MA, Morinishi LS, et al. Diverse drug-resistance mechanisms can emerge from drug-tolerant cancer persister cells. Nat Commun. 2016;7:10690. - PMC - PubMed
    1. Paek Andrew L, Liu Julia C, Loewer A, Forrester William C, Lahav G. Cell-to-cell variation in p53 dynamics leads to fractional killing. Cell. 2016;165(3):631–642. - PMC - PubMed
    1. Hammerlindl H, Schaider H. Tumor cell-intrinsic phenotypic plasticity facilitates adaptive cellular reprogramming driving acquired drug resistance. J Cell Commun Signal. 2018;12(1):133–141. - PMC - PubMed
    1. Ravindran Menon D, Das S, Krepler C, Vultur A, Rinner B, Schauer S, et al. A stress-induced early innate response causes multidrug tolerance in melanoma. Oncogene. 2014;34:4448–59. - PMC - PubMed

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