Spatial Transcriptomic Sequencing of a DIPG-infiltrated Brainstem reveals Key Invasion Markers and Novel Ligand-Receptor Pairs contributing to Tumour to TME Crosstalk
Kordowski, A.; Mulay, O.; Tan, X.; Vo, T.; Baumgartner, U.; Maybury, M. K.; Hassall, T. E. G.; Wainwright, B.; Harris, L.; Nguyen, Q.; Day, B. W.
Show abstract
Emerging spatially-resolved sequencing technologies offer unprecedented possibilities to study cellular functionality and organisation, transforming our understanding of health and disease. The necessity to understand healthy and diseased tissues in its entirety becomes even more evident for the human brain, the most complex organ in the body. The brains cellular architecture and corresponding functions are tightly regulated. However, when intercellular communications are altered by pathologies, such as brain cancer, these microenvironmental interactions are disrupted. DIPG is a brainstem high-grade glioma arising in young children and is universally fatal. Major disease obstacles include intratumoural genetic and cellular heterogeneity as well as a highly invasive phenotype. Recent breakthrough studies have highlighted the vital oncogenic capacity of brain cancer cells to functionally interact with the central nervous system (CNS). This CNS-crosstalk crucially contributes to tumour cell invasion and disease progression. Ongoing worldwide efforts seek to better understand these cancer-promoting CNS interactions to develop more effective DIPG anti-cancer therapies. In this study, we performed spatial transcriptomic analysis of a complete tumour-infiltrated brainstem from a single DIPG patient. Gene signatures from ten sequential tumour regions were analysed to assess disease progression and to study DIPG cell interactions with the tumour microenvironment (TME). We leveraged this unique DIPG dataset to evaluate genes significantly correlated with invasive tumour distal regions versus the proximal tumour initiation site. Furthermore, we assessed novel ligand-receptor pairs that actively promote DIPG tumour progression via crosstalk with endothelial, neuronal and immune cell communities, which can be utilised to support future research efforts in this area of high unmet need.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Ganglioglioma deep transcriptomics reveals primitive neuroectoderm neural precursor-like population 96%
- Distinct Tumor-TAM Interactions in IDH-Stratified Glioma Microenvironments unveiled by Single-Cell and Spatial Transcriptomics 96%
- Capture at the single cell level of metabolic modules distinguishing aggressive and indolent glioblastoma cells 94%
Similar papers in this journal
- Spatial transcriptomic analysis of Sonic Hedgehog Medulloblastoma identifies that the loss of heterogeneity and promotion of differentiation underlies the response to CDK4/6 inhibition 96%
- Retroelement co-option disrupts the cancer transcriptional programme 93%
- Glioblastoma-instructed microglia transition to heterogeneous phenotypic states with phagocytic and dendritic cell-like features in patient tumors and patient-derived orthotopic xenografts 92%
Similar papers in this journal
- Spatial profiling of longitudinal glioblastoma reveals consistent changes in cellular architecture, post-treatment 94%
- FYN tyrosine kinase, a downstream target of receptor tyrosine kinases, modulates anti-glioma immune responses 93%
- Triggering receptor expressed on myeloid cells 2 (TREM2) regulates phagocytosis in glioblastoma 93%
Similar papers in this journal
- Epigenetic landscape reorganization and reactivation of embryonic development genes are associated with malignancy in IDH-mutant astrocytoma 94%
- Primary and recurrent glioma patient-derived orthotopic xenografts (PDOX) represent relevant patient avatars for precision medicine 92%
- From methylation to myelination: epigenomic and transcriptomic profiling of chronic inactive demyelinated multiple sclerosis lesions 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.