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Modeling glioblastoma heterogeneity as a dynamic network of cell states

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 su...

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Autores principales: Larsson, Ida, Dalmo, Erika, Elgendy, Ramy, Niklasson, Mia, Doroszko, Milena, Segerman, Anna, Jörnsten, Rebecka, Westermark, Bengt, Nelander, Sven
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8444284/
https://www.ncbi.nlm.nih.gov/pubmed/34528760
http://dx.doi.org/10.15252/msb.202010105
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author Larsson, Ida
Dalmo, Erika
Elgendy, Ramy
Niklasson, Mia
Doroszko, Milena
Segerman, Anna
Jörnsten, Rebecka
Westermark, Bengt
Nelander, Sven
author_facet Larsson, Ida
Dalmo, Erika
Elgendy, Ramy
Niklasson, Mia
Doroszko, Milena
Segerman, Anna
Jörnsten, Rebecka
Westermark, Bengt
Nelander, Sven
author_sort Larsson, Ida
collection PubMed
description 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.
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spelling pubmed-84442842021-09-27 Modeling glioblastoma heterogeneity as a dynamic network of cell states Larsson, Ida Dalmo, Erika Elgendy, Ramy Niklasson, Mia Doroszko, Milena Segerman, Anna Jörnsten, Rebecka Westermark, Bengt Nelander, Sven Mol Syst Biol Articles 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. John Wiley and Sons Inc. 2021-09-16 /pmc/articles/PMC8444284/ /pubmed/34528760 http://dx.doi.org/10.15252/msb.202010105 Text en © 2021 The Authors. Published under the terms of the CC BY 4.0 license https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Articles
Larsson, Ida
Dalmo, Erika
Elgendy, Ramy
Niklasson, Mia
Doroszko, Milena
Segerman, Anna
Jörnsten, Rebecka
Westermark, Bengt
Nelander, Sven
Modeling glioblastoma heterogeneity as a dynamic network of cell states
title Modeling glioblastoma heterogeneity as a dynamic network of cell states
title_full Modeling glioblastoma heterogeneity as a dynamic network of cell states
title_fullStr Modeling glioblastoma heterogeneity as a dynamic network of cell states
title_full_unstemmed Modeling glioblastoma heterogeneity as a dynamic network of cell states
title_short Modeling glioblastoma heterogeneity as a dynamic network of cell states
title_sort modeling glioblastoma heterogeneity as a dynamic network of cell states
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8444284/
https://www.ncbi.nlm.nih.gov/pubmed/34528760
http://dx.doi.org/10.15252/msb.202010105
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