bioRxivCancer BiologyPreprint
A connectivity signature for glioblastoma
L. Hai, D. C. Hoffmann, H. Mandelbaum, R. Xie, J. Ito, E. Jung, S. Weil, P. Sievers, V. Venkataramani, D. D. Azorin, K. Ernst, D. Reibold, R. Will, M. L. Suva, C. Herold-Mende, F. Sahm, F. Winkler, M. Schlesner, W. Wick, T. Kessler
Abstract
Tumor cell extensions called tumor microtubes (TMs) in glioma resemble neurites during neurodevelopment and connect glioma cells to a network that has considerable relevance for tumor progression and therapy resistance. The determination of interconnectivity in individual tumors has been challenging and the impact of tumor cell connectivity on patient survival remained unresolved so far. Here, a connectivity signature from single-cell RNA-sequenced (scRNA-Seq) xenografted primary glioblastoma (GB) cells was established and clinically validated. Thirty-four of 40 connectivity genes were related to neurogenesis, neural tube development or glioma progression, including the TM-network-relevant GAP43 gene. Astrocytic-like and mesenchymal-like GB cells had the highest connectivity signature scores in scRNA-Seq data of patient-derived xenografts and patient samples. In 230 human GBs, high connectivity correlated with the mesenchymal expression subtype, TP53 wildtype, and with dismal patient survival. CHI3L1 was identified as a robust molecular marker of connectivity. Thus, the connectivity signature allows novel insights into brain tumor biology, provides a proof-of-principle that tumor cell connectivity is relevant for patients prognosis, and serves as a robust biomarker that can be used for future clinical trials. Statement of significanceIntegration of GB cells into functional networks drives tumor progression and resistance. Here, we established and validated a novel connectivity gene expression signature of single GB cells and whole tumors that can be easily applied to clinical and preclinical samples. It is shown that connectivity is determining prognosis combining molecular, functional and clinical insights into the disease.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: protein structure prediction
- approach: statistical model
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