Cross-linking breast tumor transcriptomic states and tissue histology.
Cell Rep Med
; 4(12): 101313, 2023 12 19.
Article
en En
| MEDLINE
| ID: mdl-38118424
ABSTRACT
Identification of the gene expression state of a cancer patient from routine pathology imaging and characterization of its phenotypic effects have significant clinical and therapeutic implications. However, prediction of expression of individual genes from whole slide images (WSIs) is challenging due to co-dependent or correlated expression of multiple genes. Here, we use a purely data-driven approach to first identify groups of genes with co-dependent expression and then predict their status from WSIs using a bespoke graph neural network. These gene groups allow us to capture the gene expression state of a patient with a small number of binary variables that are biologically meaningful and carry histopathological insights for clinical and therapeutic use cases. Prediction of gene expression state based on these gene groups allows associating histological phenotypes (cellular composition, mitotic counts, grading, etc.) with underlying gene expression patterns and opens avenues for gaining biological insights from routine pathology imaging directly.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Neoplasias de la Mama
/
Perfilación de la Expresión Génica
Límite:
Female
/
Humans
Idioma:
En
Revista:
Cell Rep Med
Año:
2023
Tipo del documento:
Article