Abstract Recent advances in high-resolution mapping of spatial interactions among regulatory elements support the existence of complex topological assemblies of enhancers and promoters known as enhancer-promoter hubs or cliques. Yet, organization principles of these multi-interacting enhancer-promoter hubs and their potential role in regulating gene expression in cancer remain unclear. Here, we systematically identify enhancer-promoter hubs in breast cancer, lymphoma, and leukemia. We find that highly interacting enhancer-promoter hubs form at key oncogenes and lineage-associated transcription factors potentially promoting oncogenesis of these diverse cancer types. Genomic and optical mapping of interactions among enhancer and promoter elements further show that topological alterations in hubs coincide with transcriptional changes underlying acquired resistance to targeted therapy in T cell leukemia and B cell lymphoma. Together, our findings suggest that enhancer-promoter hubs are dynamic and heterogeneous topological assemblies with the potential to control gene expression circuits promoting oncogenesis and drug resistance. Subject terms: Cancer genomics, Data integration, Cancer epigenetics __________________________________________________________________ The role of enhancer-promoter hubs in the regulation of gene expression in cancer remains to be explored. Here, the authors identify enhancer-promoter hubs in breast cancer, lymphoma, and leukemia and suggest their potential role in promoting oncogenesis and drug resistance. Introduction Genome spatial organization facilitates enhancer-promoter communication, which is crucial for control of oncogenic transcriptional programs^[37]1,[38]2. Emerging evidence from studies of cancer genome topology supports that multiple enhancers and promoters can spatially coalesce, forming topological assemblies that are variably referred to as enhancer-promoter hubs or cliques^[39]3,[40]4. Nevertheless, the fundamental properties of these topological assemblies and their potential role in promoting oncogenesis remain unclear. Investigation of oncogenic enhancer-promoter hubs has unique potential to advance our understanding of cancer given that enhancer dysregulation is a key hallmark of oncogenesis^[41]5. Furthermore, current models have yet to fully grasp how distal enhancers exert their regulatory functions across large genomic distances. Genome topology, which is organized at various length scales from megabase-scale compartments and topologically associating domains (TADs) to fine-scale chromatin loops, contributes to spatial positioning of enhancers and their target promoters, influencing their activity and specificity^[42]6–[43]9. Given that the number of active enhancers is 2–3 times more than active genes^[44]10, it is often possible that multiple enhancers control the expression of a single gene, giving rise to complex enhancer regulatory circuits^[45]11–[46]13. Although chromatin interaction data alone cannot capture the complexity of potential multi-enhancer regulation, its integration with chromatin activity datasets at a few oncogenes revealed that multiple distal enhancers can spatially cluster with promoters to form enhancer-promoter hubs in cancer genomes^[47]3,[48]14–[49]18. More recent studies have demonstrated that enhancer-promoter hubs facilitate enhancer cooperativity and target specificity to control gene expression dosage^[50]12,[51]14,[52]19. Despite these advances, a systematic understanding of enhancer-promoter hub prevalence, organization principles, and regulatory importance in mediating oncogenic enhancer function is lacking. In this work, we systematically identify enhancer-promoter hubs in T cell acute lymphoblastic leukemia (T-ALL), mantle cell lymphoma (MCL), and triple negative breast cancer (TNBC) to elucidate prevalence and organization principles of these topological assemblies across diverse cancer types. Examination of enhancer-promoter hubs reveals that they are ubiquitous and different from TADs and super-enhancers. Study of T-ALL, MCL and TNBC enhancer-promoter interactions further shows that hubs are heterogeneous with asymmetric distribution of interactions among enhancers and promoters. Notably, a small subset of enhancer-promoter hubs is hyperinteracting, exhibiting exceptionally high spatial interactivity among constituent enhancer and promoter elements. We demonstrate that hyperinteracting hubs are uniquely enriched for transcription, predominantly form around transcription factors and coregulators, and are more lineage associated than regular (i.e. non-hyperinteracting) hubs. To further substantiate the structure-function relationship of enhancer-promoter hubs, we examine their reorganization in Notch inhibitor resistant T-ALL and Bruton’s tyrosine kinase (BTK) inhibitor resistant MCL. Our population-based and single-cell resolution chromatin mapping studies reveal the role of enhancer-promoter hub reorganization in setting gene expression programs permissive to Notch inhibitor and BTK inhibitor resistance in T-ALL and MCL, respectively. Together, our data suggest that enhancer-promoter hub formation is an epigenetic mechanism which is potentially hijacked by cancer cells to set gene expression programs promoting oncogenesis and drug resistance. Results Interactions among enhancers and promoters are asymmetrically distributed in T leukemic cells Complex interactions among enhancers and promoters measured by high-resolution chromatin conformation capture assays such as in-situ Hi-C or HiChIP can be conceptualized as a network of connected nodes within nuclear space and modeled using undirected graph mathematical abstraction^[53]14,[54]20. To detect groups of highly interacting enhancers and promoters, known as enhancer-promoter hubs or cliques, from the graph of frequently interacting enhancers and promoters, we leveraged an efficient implementation of divisive hierarchical spectral clustering (see Methods)^[55]21. Using global information about the enhancer-enhancer, enhancer-promoter, and promoter-promoter interactions embedded in the interactivity graph, our clustering approach identifies a hierarchy of densely interacting enhancer and promoter groups with high intra-group and sparse inter-group interactions (Fig. [56]1a). Notably, our implementation of divisive hierarchical spectral clustering has tunable parameters (see Methods), enabling identification of hubs with granularity that matches user preferences. Given that enhancer-promoter hubs are dually defined by