bioRxivCancer BiologyPreprint
Functional spatial transcriptomics uncover LMO7 as a fusion-regulated and clinically relevant driver of metastasis in Ewing sarcoma
Veronika BuršićiD, Heng LuoiD, Clémence HenoniD, Lianghao MaoiD, Anna C. EhlersiD, Angelina Yershova, J-Ann M. Lego, Jing LiiD, Martha J. Carreno Gonzalez, Richard ArndtiD, Ana Sastre, Javier AlonsoiD, Uta DirkseniD, Wolfgang HartmanniD, Ashok Kumar JayaveluiD, Moritz GerstungiD, Thomas G. P. GrünewaldiD, Florencia Cidre‐AranaziD
Abstract
Metastatic dissemination represents the major determinant of poor clinical outcome across cancer entities. Yet, how driver oncogenes shape transcriptional programs facilitating metastasis is poorly understood. In Ewing sarcoma (EwS) - a highly aggressive pediatric bone and soft-tissue sarcoma driven by chimeric FET::ETS transcription factors - low activity of the fusion oncoproteins is thought to promote metastasis, but the underlying molecular mechanisms remain largely elusive. Here, using spatially resolved functional transcriptomics in EwS patient tumors, we identify a distinct transcriptional state at the invasive tumor front, that in contrast to the tumor core, is characterized by lower FET::ETS activity and induction of the multifunctional shuttle LIM domain only protein 7 (LMO7). Integrating these data with clinical information reveals that high LMO7 expression is associated with poor outcomes. Gene network analysis of patient tumors and integrated proteomic and transcriptomic profiling of EwS cell lines following inducible LMO7 silencing highlight LMO7 as a central regulatory hub orchestrating epithelial-mesenchymal transition (EMT) and cytoskeletal remodeling in EwS. Functional experiments demonstrate that LMO7 silencing decreases clonogenicity and migratory capacity in vitro and suppresses primary tumor growth and metastatic dissemination in vivo. Collectively, these findings identify LMO7 as a clinically relevant effector of FET::ETS fusions in EwS, and illustrate how integrating functional, spatial and clinical data can uncover oncogene-driven effectors of metastasis.
- organism: human
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
- Citations
- 0
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