3D enhancer dynamics dictate treatment response in T-cell acute lymphoblastic leukemia

Background Cellular plasticity frequently contributes to treatment resistance in cancer, yet the 3D chromatin dynamics driving this plasticity are unknown. To investigate how enhancer remodeling maintains transcriptional heterogeneity and modulates treatment response, we use T-cell acute lymphoblastic leukemia (T-ALL) patient-derived xenograft (PDX) models carrying activating NOTCH1 mutations treated with Notch-inhibitor as a model of therapeutic resistance. Methods Here, we assessed alterations in 3D chromatin structures differentiating responder and non-responder models by evaluating enhancer rewiring and mapping of the associated loops using H3K27ac HiChIP. After performing full-length single-cell RNA sequencing of 3,129 leukemic cells from both models, before and after treatment with a NOTCH inhibitor, we characterized transcriptional states by assessing developmental potential using CytoTRACE and identified state-specific gene-regulatory networks (GRN) driving state transitions using SCENIC. Apoptotic priming and functional validation of anti-apoptotic dependencies were determined by BH3 profiling. Results Analyzing 3D chromatin architecture and single-cell transcriptomics from responder and non-responder models, we show that gain of short enhancer-promoter interactions underlies increased developmental potential in non-responders. Moreover, this extensive enhancer rewiring is associated with the loss of active transcriptional regulators and a state transition towards more immature states co-expressing multi-lineage markers allowing for the emergence of novel dependencies such as the transcription factor ATF4 and its downstream anti-apoptotic target MCL1 Conclusions: Resistance to NOTCH inhibition remains a therapeutic challenge in T-ALL. We revealed that 3D enhancer mapping and detailed characterization of developmental states present a novel approach to develop biomarkers that predict treatment response and may allow to identify subsets of T-ALL patients resistant to therapy upfront.