Perturbing Enhancer Activity in Cancer Therapy

Abstract

Tight regulation of gene transcription is essential for normal development, tissue homeostasis, and disease-free survival. Enhancers are distal regulatory elements in the genome that provide specificity to gene expression programs and are frequently misregulated in cancer. Recent studies examined various enhancer-driven malignant dependencies and identified different approaches to specifically target these programs. In this review, we describe numerous features that make enhancers good transcriptional targets in cancer therapy and discuss different approaches to overcome enhancer perturbation. Interestingly, a number of approved therapeutic agents, such as cyclosporine, steroid hormones, and thiazolidinediones, actually function by affecting enhancer landscapes by directly targeting very specific transcription factor programs. More recently, a broader approach to targeting deregulated enhancer programs has been achieved via Bromodomain and Extraterminal (BET) inhibition or perturbation of transcription-related cyclin-dependent kinases (CDK). One challenge to enhancer-targeted therapy is proper patient stratification. We suggest that monitoring of enhancer RNA (eRNA) expression may serve as a unique biomarker of enhancer activity that can help to predict and monitor responsiveness to enhancer-targeted therapies. A more thorough investigation of cancer-specific enhancers and the underlying mechanisms of deregulation will pave the road for an effective utilization of enhancer modulators in a precision oncology approach to cancer treatment.

Keywords: BET inhibitors; CDK7 inhibitors; HDAC inhibitors; cancer; eRNAs; enhancers; transcription factors.

Figure 1

Positive features rendering enhancers good transcriptional targets. (a) Enhancers can be pharmacologically manipulated using different small molecule inhibitors (indicated by green dots). They are also indispensable for cancer cells as they activate important oncogenes. (b) Enhancers are also context-specific. In this example, enhancers are activated by different transcription factors (TFs) in various tissues (A–C). The same inhibitor affects only a specific enhancer in a tissue if it is activated by a certain TF. Thus the illustrated inhibitor only affects Enhancer 1 in tissue A but not C. It also has no effect on the other active enhancers in tissue B. Gray enhancers are inactive, while orange ones are active. Bold arrows represent active transcription.

Figure 2

Schematic representation of putative targets to reprogram the enhancer landscape in cancer. (A) modulators of transcription factors, (B) HAT inhibitors, (C) HDAC inhibitors, (D) BET inhibitors, (E) CDK7/9 inhibitors. HAT: Histone acetyltransferase; HDAC: Histone deacetylase; BET: Bromodomain and extraterminal; CDK: Cyclin-dependent kinases.

Figure 3

Enhancer RNAs (eRNAs) are putative biomarkers for responsiveness and resistance in perturbation of enhancer activity. (a) In a responsive context, inhibiting an enhancer leads to a decrease in the activity of oncogenic target genes. In this case, high levels of eRNA can predict responsiveness to a specific inhibitor by providing a direct readout of enhancer activity. (b) In case of resistance, compensating mechanisms such as the activation of a different enhancer program can occur. Thereby, high levels of different eRNAs can predict resistance to a certain therapy. (c) To re-sensitize cells, compensatory mechanisms should also be targeted to ensure therapeutic success.