Zadeh Shirazi, A.McDonnell, M.D.Fornaciari, E.Bagherian, N.S.Scheer, K.G.Samuel, M.S.Yaghoobi, M.Ormsby, R.J.Poonnoose, S.Tumes, D.J.Gomez, G.A.2021-06-012021-06-012021British Journal of Cancer, 2021; 125(3):337-3500007-09201532-1827http://hdl.handle.net/2440/130509Background: Glioblastoma is the most aggressive type of brain cancer with high-levels of intra- and inter-tumour heterogeneity that contribute to its rapid growth and invasion within the brain. However, a spatial characterisation of gene signatures and the cell types expressing these in different tumour locations is still lacking. Methods: We have used a deep convolutional neural network (DCNN) as a semantic segmentation model to segment seven different tumour regions including leading edge (LE), infiltrating tumour (IT), cellular tumour (CT), cellular tumour microvascular proliferation (CTmvp), cellular tumour pseudopalisading region around necrosis (CTpan), cellular tumour perinecrotic zones (CTpnz) and cellular tumour necrosis (CTne) in digitised glioblastoma histopathological slides from The Cancer Genome Atlas (TCGA). Correlation analysis between segmentation results from tumour images together with matched RNA expression data was performed to identify genetic signatures that are specific to different tumour regions. Results: We found that spatially resolved gene signatures were strongly correlated with survival in patients with defined genetic mutations. Further in silico cell ontology analysis along with single-cell RNA sequencing data from resected glioblastoma tissue samples showed that these tumour regions had different gene signatures, whose expression was driven by different cell types in the regional tumour microenvironment. Our results further pointed to a key role for interactions between microglia/pericytes/monocytes and tumour cells that occur in the IT and CTmvp regions, which may contribute to poor patient survival. Conclusions: This work identified key histopathological features that correlate with patient survival and detected spatially associated genetic signatures that contribute to tumour-stroma interactions and which should be investigated as new targets in glioblastoma. The source codes and datasets used are available in GitHub: https://github.com/amin20/GBM_WSSM.en© The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons. org/licenses/by/4.0/.HumansGlioblastomaBrain NeoplasmsRadiographic Image Interpretation, Computer-AssistedSurvival AnalysisGene Expression ProfilingGene Expression Regulation, NeoplasticGene Regulatory NetworksStem Cell NicheSingle-Cell AnalysisTumor MicroenvironmentDeep LearningNeural Networks, ComputerJournal article100004030410.1038/s41416-021-01394-x0006454895000012-s2.0-85105193320573879McDonnell, M.D. [0000-0002-7009-3869]Samuel, M.S. [0000-0001-7880-6379]Tumes, D.J. [0000-0001-5709-857X]