bioRxivCancer BiologyPreprint
Single-cell multi-omic analyses resolve the cellular diversity of ALK/ROS1/MET/NTRK-fused gliomas in infants and older children
Andrea Joseph De MicheliiD, Carlos Alberto Oliveira de BiagiiD, Costanza Lo CascioiD, Charbel MachaalaniiD, Andreas Postlmayr, Shashank KatiyariD, Roeltje R. MaasiD, Venkatesh Kancherla, Regina ReimanniD, Michal ZápotockýiD, Liana Figueiredo NobreiD, Sebastian K. EderiD, Jacob S RozowskyiD, Théo RibierreiD, Fides Lea ZenkiD, Adam C Resnick, Matthew ClarkeiD, Johannes GojoiD, Uri Y. TaboriiD, Cynthia E HawkinsiD, Chris JonesiD, Florence M.G. CavalliiD, Mariella G. FilbiniD, Ana Guerreiro StückliniD
Abstract
Pediatric cancers are thought to arise from dysregulation of developmental programs, otherwise tightly regulated in time and space. Infant-type hemispheric gliomas (IHGs) arise in early childhood, driven by characteristic ALK/ROS1/MET/NTRK receptor tyrosine kinase (RTK) gene fusions. We dissected the cellular hierarchies of 24 fusion-positive gliomas, spanning infants through adolescents, using single-cell and single-nucleus RNA/ATAC-seq, and spatial transcriptomics. We identified five cancer cell states, with radial glia-like cells at the apex of a neoplastic hierarchy resembling neuronal- and glial-like trajectories. Neuronal-like cells were enriched in most IHGs but diminished in ROS1-fused IHGs and older patients. Integration of chromatin profiling revealed FOS/JUN-driven oncogenic programs and high inferred plasticity across all cancer cell populations. Myeloid cells, the most abundant non-neoplastic population, comprised distinct subgroups, suggesting context-dependent functions. Despite lacking high-order structure, spatial transcriptomics revealed discrete cellular niches within IHGs. Collectively, our findings elucidate the cellular states and developmental programs underlying IHGs and RTK-fused gliomas in older patients, opening new avenues for research and therapy innovation.
- organism: human
- assay: scRNA-seq
- assay: scATAC-seq
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: clustering
- research focus: spatial domain detection
- approach: deep learning
- approach: statistical model
- Citations
- 0
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