Oral Presentation Melbourne Immunotherapy Network Winter Symposium 2021

Computational painting of glioblastoma tumour microenvironment using DCNN. (#24)

Amin Zadeh-Shirazi 1 , Mark McDonnell 2 , Eric Fornaciari 3 , Narjes Sadat Bagherian 4 , Kaitlin Scheer 1 , Michael Samuel 1 , Mahdi Yaghoobi 5 , Rebecca Ormsby 6 , Santosh Poonnoose 6 , Damon Tumes 1 , Guillermo Gomez 1
  1. Centre for Cancer Biology, University of South Australia, Adelaide, SA, Australia
  2. Computational Learning Systems Laboratory, UniSA STEM, University of South Australia,, Mawson Lakes, SA, Australia
  3. Department of Mathematics of Computation, University of California, , Los Angeles, CA, USA
  4. Mashhad University of Medical Sciences, Mashhad, Iran
  5. Electrical and Computer Engineering Department, Department of Artificial Intelligence, Islamic Azad University, Mashhad Branch, Mashhad, Iran
  6. Flinders Health and Medical Research Institute, College of Medicine & Public Health, Flinders University, Adelaide, SA, Australia

Glioblastoma is the most aggressive type of brain cancer with high levels of intra- and inter-tumour heterogeneity. However, a spatial characterization of gene signatures and the cell types expressing these in different tumour locations is still lacking. We have used deep convolutional neural networks (DCNN) as a semantic segmentation model to segment tumour regions in glioblastoma histopathological slides. We combined these results with RNA gene expression data to characterize the cellular composition of the tumour microenvironment in different tumour regions. Validation with single-cell RNA sequencing data from resected glioblastoma tissue samples further confirms that different cells in the tumour microenvironment drive gene signatures that are involved in tumour-stromal interactions, which were correlated with survival. These results pointed to a key role for interactions between stromal cells and tumour cells, which may contribute to poor survival in glioblastoma.

  1. Zadeh Shirazi A, McDonnell MD, Fornaciari E, Bagherian NS, Scheer KG, Samuel MS, Yaghoobi M, Ormsby RJ, Poonnoose S, Tumes DJ, Gomez GA. 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. Br J Cancer. 2021 Apr 29. doi: 10.1038/s41416-021-01394-x.