Spatial cellular architecture predicts prognosis in glioblastoma
Metadatos
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Springer Nature
Fecha
2023-07-11Referencia bibliográfica
Zheng, Y., Carrillo-Perez, F., Pizurica, M. et al. Spatial cellular architecture predicts prognosis in glioblastoma. Nat Commun 14, 4122 (2023). [https://doi.org/10.1038/s41467-023-39933-0]
Resumen
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 thismodel, 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.