Speaker
Description
Introduction: Quantifying tumor plasticity is essential for understanding resistance in glioblastoma (GBM). A mathematical pipeline is presented for the joint analysis of time-resolved single-cell (sc) transcriptomics and lineage barcoding to characterize cancer cell states and quantify their dynamics (proliferation, death, state transitions) in control or temozolimide-treated conditions. This model is used to design interventions targeting specific cell states to maximize drug efficacy.
Methods: Cell states are identified through clustering and enrichment analysis of known signatures . An ordinary differential equation (ODE)-based model is utilized to infer cell state dynamics by integrating both sc barcoding and transcriptomics. Existing work \cite{1} was extended through the incorporation of exponential growth, improved parameter optimization via the CMA-ES algorithm, and relaxation of sparsity constraints.
Results: In GBM lines, lineages are reconstructed across three time points into a hierarchical tree that chieved a close fit to data. Estimating parameters in control or temozolomide-treated conditions revealed key resistance mechanisms . The pipeline was benchmarked against the original model. Strategies to maximize efficacy were designed bt predicting optimal interventions on cell state death or transition rates. Identifying corresponding molecular targets remains the next challenge to allow for clinical translation.
Bibliography
@article{1,
title={Modeling glioblastoma heterogeneity as a dynamic network of cell states},
author={Larsson, Ida and Dalmo, Erika and Elgendy, Ramy and Niklasson, Mia and Doroszko, Milena and Segerman, Anna and J{\"o}rnsten, Rebecka and Westermark, Bengt and Nelander, Sven},
journal={Molecular systems biology},
volume={17},
number={9},
pages={MSB202010105},
year={2021},
publisher={Springer}
}
