Modeling glioblastoma heterogeneity as a dynamic network of cell states. Issue 9 (16th September 2021)

• Record Type: Journal Article
• Authors: Larsson, Ida; Dalmo, Erika; Elgendy, Ramy; Niklasson, Mia; Doroszko, Milena; Segerman, Anna; Jörnsten, Rebecka; Westermark, Bengt; Nelander, Sven
• Is Part Of: Molecular systems biology. Volume 17:Issue 9(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-09-16
• Subjects: cell state -- cellular barcoding -- patient‐derived brain tumor cells -- single‐cell lineage tracing -- time‐dependent computational models; Molecular biology -- Periodicals; Systems biology -- Periodicals; 572.8
• DOI: 10.15252/msb.202010105
• Languages: English
• ISSNs: 1744-4292

Abstract

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. Synopsis: A single cell‐based strategy that tracks and models time‐dependent changes in brain tumor cells indicates that patient‐derived glioblastoma cells follow a near‐hierarchical organisation that can be altered by therapeutic agents. A general method is developed for de novo construction of quantitative network models of cancer cell State Transitions and Growth (STAG) from single‐cell measurements. Patient‐derived glioblastoma cells transit between transcriptional states, recapitulating normal neural cell types, in a hierarchical fashion. The STAG model can identify patient differences in cell state dynamics and define how therapeutic agents can alter the transition network. The long‐term cell population growth and cell state composition can be predicted by a mathematical eigendecomposition of the STAG network. Abstract : A single cell‐based strategy that tracks and models time‐dependent changes in brain tumor cells indicates that patient‐derived glioblastoma cells follow a near‐hierarchical organisation that can be altered by therapeutic agents.