Larsson I, Dalmo E, Elgendy R, Niklasson M, Doroszko M, Segerman A, Jörnsten R, Westermark B, Nelander S. Modeling glioblastoma heterogeneity as a dynamic network of cell states.
Mol Syst Biol 2021;
17:e10105. [PMID:
34528760 PMCID:
PMC8444284 DOI:
10.15252/msb.202010105]
[Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 08/16/2021] [Accepted: 08/17/2021] [Indexed: 12/13/2022] Open
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.
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