Modeling glioblastoma heterogeneity as a dynamic network of cell states

• Main Authors: Dalmo, E. (Author), Doroszko, M. (Author), Elgendy, R. (Author), Jörnsten, R. (Author), Larsson, I. (Author), Nelander, S. (Author), Niklasson, M. (Author), Segerman, A. (Author), Westermark, B. (Author)
• Format: Article
• Language: English
• Published: John Wiley and Sons Inc 2021
• Subjects: Article; astrocyte; bone morphogenetic protein 4; Brain Neoplasms; brain tumor; cancer cell; CD24 antigen; cell differentiation; cell growth; cell heterogeneity; Cell Line, Tumor; cell lineage; cell proliferation; cell state; cellular barcoding; controlled study; cyclin dependent kinase inhibitor 1A; flow cytometry; fluorescence activated cell sorting; gene set enrichment analysis; genetics; glioblastoma; Glioblastoma; Hermes antigen; human; human cell; Humans; Markov chain; mathematical model; Neoplasm Recurrence, Local; neural stem cell; oligodendrocyte precursor cell; patient-derived brain tumor cells; RNA; single cell analysis; single cell RNA seq; Single-Cell Analysis; single-cell lineage tracing; stochastic model; temozolomide; time-dependent computational models; transcription factor Sox2; transcription factor Sox4; transcriptome; tumor cell line; tumor recurrence; upregulation
• Online Access: View Fulltext in Publisher
• ISBN: 17444292 (ISSN)
• DOI: 10.15252/msb.202010105

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. © 2021 The Authors. Published under the terms of the CC BY 4.0 license