1. A deep convolutional neural network for segmentation of whole-slide pathology images identifies novel tumour cell-perivascular niche interactions that are associated with poor survival in glioblastoma
- Author
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Mahdi Yaghoobi, Michael S. Samuel, Rebecca J. Ormsby, Damon J. Tumes, Guillermo A. Gomez, Kaitlin G. Scheer, Narjes Sadat Bagherian, Santosh Poonnoose, Eric Fornaciari, Mark D. McDonnell, Amin Zadeh Shirazi, Zadeh Shirazi, Amin, McDonnell, Mark D, Fornaciari, Eric, Bagherian, Narjes Sadast, Scheer, Kaitlin G, Samuel, Michael S, Yaghoobi, Mahdi, Ormsby, Rebecca J, Poonnoose, Santosh, Tumes, Damon J, and Gomez, Guillermo A
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Cancer microenvironment ,Cancer Research ,Cell type ,Pathology ,medicine.medical_specialty ,Necrosis ,neural network ,In silico ,Cell ,Biology ,Article ,03 medical and health sciences ,Deep Learning ,0302 clinical medicine ,Text mining ,pathology images ,Machine learning ,Tumor Microenvironment ,medicine ,Humans ,Gene Regulatory Networks ,Stem Cell Niche ,Gene ,030304 developmental biology ,brain cancer ,0303 health sciences ,Microglia ,Brain Neoplasms ,business.industry ,Gene Expression Profiling ,RNA ,Survival Analysis ,CNS cancer ,Gene Expression Regulation, Neoplastic ,medicine.anatomical_structure ,Oncology ,030220 oncology & carcinogenesis ,rapid growth ,Radiographic Image Interpretation, Computer-Assisted ,Neural Networks, Computer ,Single-Cell Analysis ,medicine.symptom ,Glioblastoma ,business - Abstract
BackgroundGlioblastoma 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.MethodsWe 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.ResultsWe 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.ConclusionsThis 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.
- Published
- 2021
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