Modeling glioblastoma heterogeneity as a dynamic network of cell states

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.

Reconstructing the regulatory programs underlying the phenotypic plasticity of neural cancers

“…One of the questions raised during our previous work on cell state transitions in GBM was how to successfully target an entire cell state, and not just individual target genes of these states. The question was prompted by our prediction that the minimal intervention needed to potentiate temozolomide (TMZ) treatment is to block transitions to what we call “state 5”, a state with an OPC-like, invasive profile (Larsson et al , 2021) (Figure 3A). To investigate this, we applied scRegClust to the previously mentioned scRNA-seq data from U3065MG cells (Xie et al , 2015) using either TFs or kinases as potential regulators (Figure 3B-C).…”

“… A Left panel: UMAP embedding of the scRNA-seq data from (Larsson et al , 2021). Colors according to cell state assignment, as defined in the original paper.…”

“… B-C The regulatory networks generated by scRegClust for scRNA-seq data from (Larsson et al , 2021), in transcription factor (B) and kinase (C) mode.…”

Transcription factor fluctuations underlie cell-to-cell variability in a signaling pathway response

“…A complementary measurement of the trajectory would involve using transcribing molecular barcodes to tag the cells prior to single-cell RNA sequencing (W. Kong et al 2020). This approach could reveal how fast cells transition between the slow and fast responder states (Hormoz et al 11/2016; Stumpf et al 2017; Larsson et al 2021). We have used overexpression experiments to infer the role of Prrx1 in the fast response.…”

Hydroxymethylation alterations in progenitor-like cell types of pediatric central nervous system tumors are associated with cell type-specific transcriptional changes

“…Numerous studies have established that brain tumors display intratumoral cellular heterogeneity 17,19,20,[75][76][77][78][79][80][81][82][83][84][85] . While it is known that both DNA methylation and hydroxymethylation patterns are tissue type and cell type dependent 52,53,[86][87][88][89][90] , limited research has addressed cell type-specific DNA cytosine modification alterations in these tumors.…”