Neural network learning defines glioblastoma features to be of neural crest perivascular or radial glia lineages

• Published in: Science advances 2022-06, Vol.8 (23), p.eabm6340-eabm6340
• Main Authors: Hu, Yizhou, Jiang, Yiwen, Behnan, Jinan, Ribeiro, Mariana Messias, Kalantzi, Chrysoula, Zhang, Ming-Dong, Lou, Daohua, Häring, Martin, Sharma, Nilesh, Okawa, Satoshi, Del Sol, Antonio, Adameyko, Igor, Svensson, Mikael, Persson, Oscar, Ernfors, Patrik
• Format: Article
• Language: English
• Subjects: Biomedicine and Life Sciences; Cell Biology; Computer Science; Medicin och hälsovetenskap; SciAdv r-articles
• ISSN: 2375-2548; 2375-2548
• DOI: 10.1126/sciadv.abm6340

Glioblastoma is believed to originate from nervous system cells; however, a putative origin from vessel-associated progenitor cells has not been considered. We deeply single-cell RNA-sequenced glioblastoma progenitor cells of 18 patients and integrated 710 bulk tumors and 73,495 glioma single cells of 100 patients to determine the relation of glioblastoma cells to normal brain cell types. A novel neural network-based projection of the developmental trajectory of normal brain cells uncovered two principal cell-lineage features of glioblastoma, neural crest perivascular and radial glia, carrying defining methylation patterns and survival differences. Consistently, introducing tumorigenic alterations in naïve human brain perivascular cells resulted in brain tumors. Thus, our results suggest that glioblastoma can arise from the brains' vasculature, and patients with such glioblastoma have a significantly poorer outcome.