Modeling glioblastoma heterogeneity as a dynamic network of cell states.
Mol Syst Biol
; 17(9): e10105, 2021 09.
Article
en En
| MEDLINE
| ID: mdl-34528760
ABSTRACT
Tumor cell heterogeneity is a crucial characteristic of malignant brain tumors and underpins phenomena such as therapy resistance and tumor recurrence. Advances in single-cell analysis have enabled the delineation of distinct cellular states of brain tumor cells, but the time-dependent changes in such states remain poorly understood. Here, we construct quantitative models of the time-dependent transcriptional variation of patient-derived glioblastoma (GBM) cells. We build the models by sampling and profiling barcoded GBM cells and their progeny over the course of 3 weeks and by fitting a mathematical model to estimate changes in GBM cell states and their growth rates. Our model suggests a hierarchical yet plastic organization of GBM, where the rates and patterns of cell state switching are partly patient-specific. Therapeutic interventions produce complex dynamic effects, including inhibition of specific states and altered differentiation. Our method provides a general strategy to uncover time-dependent changes in cancer cells and offers a way to evaluate and predict how therapy affects cell state composition.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Neoplasias Encefálicas
/
Glioblastoma
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Mol Syst Biol
Asunto de la revista:
BIOLOGIA MOLECULAR
/
BIOTECNOLOGIA
Año:
2021
Tipo del documento:
Article
País de afiliación:
Suecia