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Spatial cellular architecture predicts prognosis in glioblastoma

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Abstract
Intra-tumoral heterogeneity and cell-state plasticity are key drivers for the therapeutic resistance of glioblastoma. Here, we investigate the association between spatial cellular organization and glioblastoma prognosis. Leveraging single-cell RNA-seq and spatial transcriptomics data, we develop a deep learning model to predict transcriptional subtypes of glioblastoma cells from histology images. Employing this model, we phenotypically analyze 40 million tissue spots from 410 patients and identify consistent associations between tumor architecture and prognosis across two independent cohorts. Patients with poor prognosis exhibit higher proportions of tumor cells expressing a hypoxia-induced transcriptional program. Furthermore, a clustering pattern of astrocyte-like tumor cells is associated with worse prognosis, while dispersion and connection of the astrocytes with other transcriptional subtypes correlate with decreased risk. To validate these results, we develop a separate deep learning model that utilizes histology images to predict prognosis. Applying this model to spatial transcriptomics data reveal survival-associated regional gene expression programs. Overall, our study presents a scalable approach to unravel the transcriptional heterogeneity of glioblastoma and establishes a critical connection between spatial cellular architecture and clinical outcomes. Intra-tumoral heterogeneity and cell-state plasticity contribute to the development of therapeutic resistance in glioblastoma (GBM). Here the authors use two deep learning models to predict spatial transcriptional programs and prognosis from histology images in GBM.
Keywords
General Physics and Astronomy, General Biochemistry, Genetics and Molecular Biology, General Chemistry, Multidisciplinary

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MLA
Zheng, Yuanning, et al. “Spatial Cellular Architecture Predicts Prognosis in Glioblastoma.” NATURE COMMUNICATIONS, vol. 14, no. 1, 2023, doi:10.1038/s41467-023-39933-0.
APA
Zheng, Y., Carrillo-Perez, F., Pizurica, M., Heiland, D. H., & Gevaert, O. (2023). Spatial cellular architecture predicts prognosis in glioblastoma. NATURE COMMUNICATIONS, 14(1). https://doi.org/10.1038/s41467-023-39933-0
Chicago author-date
Zheng, Yuanning, Francisco Carrillo-Perez, Marija Pizurica, Dieter Henrik Heiland, and Olivier Gevaert. 2023. “Spatial Cellular Architecture Predicts Prognosis in Glioblastoma.” NATURE COMMUNICATIONS 14 (1). https://doi.org/10.1038/s41467-023-39933-0.
Chicago author-date (all authors)
Zheng, Yuanning, Francisco Carrillo-Perez, Marija Pizurica, Dieter Henrik Heiland, and Olivier Gevaert. 2023. “Spatial Cellular Architecture Predicts Prognosis in Glioblastoma.” NATURE COMMUNICATIONS 14 (1). doi:10.1038/s41467-023-39933-0.
Vancouver
1.
Zheng Y, Carrillo-Perez F, Pizurica M, Heiland DH, Gevaert O. Spatial cellular architecture predicts prognosis in glioblastoma. NATURE COMMUNICATIONS. 2023;14(1).
IEEE
[1]
Y. Zheng, F. Carrillo-Perez, M. Pizurica, D. H. Heiland, and O. Gevaert, “Spatial cellular architecture predicts prognosis in glioblastoma,” NATURE COMMUNICATIONS, vol. 14, no. 1, 2023.
@article{01HAN4X0A7TFHGFSYVSVNDGY7A,
  abstract     = {{Intra-tumoral heterogeneity and cell-state plasticity are key drivers for the therapeutic resistance of glioblastoma. Here, we investigate the association between spatial cellular organization and glioblastoma prognosis. Leveraging single-cell RNA-seq and spatial transcriptomics data, we develop a deep learning model to predict transcriptional subtypes of glioblastoma cells from histology images. Employing this model, we phenotypically analyze 40 million tissue spots from 410 patients and identify consistent associations between tumor architecture and prognosis across two independent cohorts. Patients with poor prognosis exhibit higher proportions of tumor cells expressing a hypoxia-induced transcriptional program. Furthermore, a clustering pattern of astrocyte-like tumor cells is associated with worse prognosis, while dispersion and connection of the astrocytes with other transcriptional subtypes correlate with decreased risk. To validate these results, we develop a separate deep learning model that utilizes histology images to predict prognosis. Applying this model to spatial transcriptomics data reveal survival-associated regional gene expression programs. Overall, our study presents a scalable approach to unravel the transcriptional heterogeneity of glioblastoma and establishes a critical connection between spatial cellular architecture and clinical outcomes.

Intra-tumoral heterogeneity and cell-state plasticity contribute to the development of therapeutic resistance in glioblastoma (GBM). Here the authors use two deep learning models to predict spatial transcriptional programs and prognosis from histology images in GBM.}},
  articleno    = {{4122}},
  author       = {{Zheng, Yuanning and Carrillo-Perez, Francisco and Pizurica, Marija and Heiland, Dieter Henrik and Gevaert, Olivier}},
  issn         = {{2041-1723}},
  journal      = {{NATURE COMMUNICATIONS}},
  keywords     = {{General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry,Multidisciplinary}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{16}},
  title        = {{Spatial cellular architecture predicts prognosis in glioblastoma}},
  url          = {{http://doi.org/10.1038/s41467-023-39933-0}},
  volume       = {{14}},
  year         = {{2023}},
}

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