Open AccessModeling glioblastoma heterogeneity as a dynamic network of cell states
Author(s) -
Larsson Ida,
Dalmo Erika,
Elgendy Ramy,
Niklasson Mia,
Doroszko Milena,
Segerman Anna,
Jörnsten Rebecka,
Westermark Bengt,
Nelander Sven
Publication year - 2021
Publication title -
molecular systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 8.523
H-Index - 148
ISSN - 1744-4292
DOI - 10.15252/msb.202010105
Subject(s) - biology , glioblastoma , neuroscience , cell , systems biology , computational biology , bioinformatics , cancer research , genetics
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.