Super enhancers as an important subset of enhancer elements

Since the recent identification of super enhancers in 2013 [123, 124], approximately 300 scientific papers discussing this subclass have been published. In this project, we endeavored to study the role of super enhancers as part of our general study of distal regulatory elements in cancer progression and disease. In chapter 2, we used publicly available data from the ER- positive cell line MCF7 to investigate if super enhancers have indeed higher efficacy compared to typical enhancers. This provided us with a system that is highly dependent on enhancers to mediate its effects. Our analysis failed to uncover a particular advantage of super enhancers over typical enhancers in the percentage of genes affected but identified major ER targets to be driven by adjacent super enhancers. This has also been observed in embryonic stem cells where deletion of various super enhancers had variable extent of effects on transcription of target genes [469]. However, a big limitation in our study is that we used simple gene associations based on proximity. A better approach would be to use newly available techniques that identify contact points between regions throughout the genome to ensure the accurate identification of target genes for super and typical enhancers [118]. As many neighboring genes may not be affected by an associated super enhancer, they may decrease the significant correlation seen when studying super enhancers and their target genes.

On the other hand, our studies of super enhancers in pancreatic cancer in the context of different molecular subtypes (chapter 4) and chemo-resistance (chapter 5) have identified a particularly important role for this subclass in driving gene regulation while applying the same association rule. It can be probable that super enhancers have diverse impacts in various contexts and that few systems are more dependent on super enhancers for activating particularly important programs. Future studies in different systems will uncover if super enhancers have such a preference in impact in certain contexts and settings.

Super enhancers represent a lucrative target as they have shown particular sensitivity when modulated by certain inhibitors leading to perceptible effects. For example, super enhancers in esophageal cancer showed high dependence on the CDK7 inhibitor, THZ1 [470]. Interestingly, a subset of super enhancers in esophageal cancer was observed to be highly enriched with TP63 [463]. As we have identified deltaNp63-dependent super enhancers driving the more aggressive squamous phenotype in pancreatic cancer, it would be of high interest to check the sensitivity of squamous cell lines to THZ1 in comparison to cell lines of other molecular subtypes. This can also be investigated in PDX from patients stratified into different subtypes and have a promising potential as a therapeutic agent in the squamous molecular subtype. Notably, combining of THZ1 and JQ1 has shown increased effects on super enhancer deactivation in osteosarcoma [471]. The effects of combining THZ1 and JQ1 in paclitaxel-resistant cells is also of particular interest given the implication of super enhancers in activating inflammatory and pro-migratory programs in resistant cells and the partial reprogramming of super enhancers in resistant cells. Additionally, analysis in acute myeloid leukemia uncovered a specific super enhancer in a subtype of patients that can be sensitive to an agonist leading to myeloid differentiation [472]. Accordingly, analysis should not only concentrate on super enhancers that can be silenced, but also on novel super enhancers that can be further activated leading to a better prognosis.

One of the challenges facing the study of super enhancers is the identification of less known enhancer subclasses which are not well defined. For example, stretch enhancers are sometimes used interchangeably with super enhancers [473, 474] while other studies indicate that stretch enhancers meet only the requirement of spanning long stretches of DNA and are not necessarily rich with transcription factors or cell-specific [475]. Shadow enhancers are a group of “secondary” enhancers that are redundant to an active enhancer and ensure the

precision of gene transcriptional regulation [476]. Such a concept which was first identified in Drosphila has also been reported in mammals [477]. This led to the sometimes imprecise use of shadow enhancers to describe typical enhancers which are not necessarily supportive of other enhancers. A clear definition of these new classifications will significantly help in controlling the confusion that is usually associated with super enhancers.

Testing and understanding the settings of the algorithms used in identifying super enhancers was a major focus of this project. In chapter 2, we have critically studied the various settings of the ROSE algorithm and questioned its biased approach for stitching enhancers at an already pre-defined distance. Interestingly, a new algorithm with a machine learning approach to define stitching distance was recently developed [130]. We have used both algorithms in identifying super enhancers and did not observe a big difference between them. To further optimize the process of identification of super enhancers, other types of data can be used. For example, occupancy data from more than one factor can be simultaneously incorporated to calculate the intensity at enhancers. By definition, super enhancers are highly enriched with transcription factors [248]. Thus, having high enrichment with more than one transcription or activating factor can uncover a highly effective subtype of super enhancers. Another optimizing possibility can be to include 3D genome data to account for the looping effect needed for enhancers to target their genes. While it is still possible that an enhancer can affect a target gene without direct interaction, e.g. eRNAs, it is still not studied if super enhancers particularly form a hub of interaction with many genes or other enhancers. The question arises if super enhancers will indeed have a higher interaction with many regions throughout the genome or if they exert their effects by simply controlling master upstream regulatory genes. Moreover, RNA-seq data from systems treated with inhibitors that preferentially target super enhances can be incorporated in the algorithms identifying super enhancers and may uncover new dependencies. Our main

findings throughout this project includes identifying of important ER effectors as downstream targets of super enhancers, uncovering of DeltaNp63-dependent super enhancers as drivers for a squamous molecular subtype in pancreatic cancer, in addition to detecting super enhancers driving genes leading to poorer prognosis in paclitaxel-resistant cell. All these observations underscore the importance of super enhancers in driving crucial programs in the cell and support the increased interest in this particularly important subset of enhancers.