Modeling Glioblastoma Heterogeneity As a Dynamic Network of Cell States

Overview

Journal Mol Syst Biol

Specialty Molecular Biology

Date 2021 Sep 16

PMID 34528760

Citations 26

Authors

Ida Larsson

Erika Dalmo

Ramy Elgendy

Mia Niklasson

Milena Doroszko

Anna Segerman

Rebecka Jornsten

Bengt Westermark

Sven Nelander

Affiliations

Soon will be listed here.

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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