bioRxivCancer BiologyPreprint
LDB1-dependent enhancer connectivity defines T-cell leukemia identities and masks metabolic vulnerabilities
Rahul S. BhansaliiD, Juan Sebastian Long, Petri PölöneniD, Tomoya IsobeiD, Siqing WangiD, Shuo Zhang, Zhuangzhuang GengiD, Ahnaf Tausif, Mariah C. AntopiaiD, Nicholas G. AboredeniD, Sarah J. SkuliiD, Belinda M. GiardineiD, Cheryl A. KelleriD, Ross C. HardisoniD, Kai TaniD, Charles Grenfell MullighaniD, Gerd A. BlobeliD
Abstract
Spatial enhancer connectivity is fundamental to proper gene regulation. Enhancer dysregulation has emerged as a hallmark of cancers, including T-cell acute lymphoblastic leukemias (T-ALL). T-ALL are aggressive malignancies characterized by marked transcriptional heterogeneity driven by distinct stages of developmental arrest and diverse noncoding alterations. How these cancers co-opt nuclear architecture to rewire enhancer connectivity remains poorly understood. Here, we report that the LDB1 chromatin architectural complex is an essential mediator of enhancer-oncogene looping that sustains oncogenic transcriptional programs across multiple T-ALL subtypes. Integrating bulk and single-cell transcriptomic data from patients with T-ALL and healthy hematopoietic controls, we show that the LDB1-dependent regulatory circuitry defines the molecular identities of distinct T-ALL subtypes while restricting plasticity toward alternative cell states. LDB1 loss dismantles chromatin looping among cell state-defining enhancers liberating them to form promiscuous interactions with nearby genes. This enhancer rewiring stimulates expression of key metabolic genes, creating a mevalonate pathway dependency exploitable with statin treatment. Our study establishes LDB1 as a central executor of T-ALL regulatory circuitry and more broadly illustrates chromatin rewiring as a source of targetable dependencies in cancer.
- organism: human
- assay: bulk RNA-seq
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 0
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