Single cell sequencing of mouse syngeneic tumor models
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE121861 | GEO | 2018/10/27
REPOSITORIES: GEO
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2022-12-12 | GSE212996 | GEO
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Project description:Hass2017-PanRTK model for single cell
line
The model structure comprises
heterodimerization and receptor trafficking as described in detail
in the article below. For ligand input, set a respective
event. The illustrated event sets the EGF concentration to 2.5 nMol
in the model file.
This model is described in the article:
Predicting ligand-dependent
tumors from multi-dimensional signaling features.
Hass H, Masson K, Wohlgemuth S,
Paragas V, Allen JE, Sevecka M, Pace E, Timmer J, Stelling J,
MacBeath G, Schoeberl B, Raue A.
NPJ Syst Biol Appl 2017; 3: 27
Abstract:
Targeted therapies have shown significant patient benefit in
about 5-10% of solid tumors that are addicted to a single
oncogene. Here, we explore the idea of ligand addiction as a
driver of tumor growth. High ligand levels in tumors have been
shown to be associated with impaired patient survival, but
targeted therapies have not yet shown great benefit in
unselected patient populations. Using an approach of applying
Bagged Decision Trees (BDT) to high-dimensional signaling
features derived from a computational model, we can predict
ligand dependent proliferation across a set of 58 cell lines.
This mechanistic, multi-pathway model that features receptor
heterodimerization, was trained on seven cancer cell lines and
can predict signaling across two independent cell lines by
adjusting only the receptor expression levels for each cell
line. Interestingly, for patient samples the predicted tumor
growth response correlates with high growth factor expression
in the tumor microenvironment, which argues for a co-evolution
of both factors in vivo.
This model is hosted on
BioModels Database
and identified by:
MODEL1708210000.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
| MODEL1708210000 | BioModels
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Project description:Tumors consist of a dynamic collection of interacting tumor cells, stromal cells and immune cells. In addition to the direct cell-cell interactions that are mediated by receptor-ligand interactions, cells in the tumor microenvironment (TME) influence each other through the secretion and sensing of soluble mediators, such as cytokines and chemokines. While signaling of the interferon γ (IFNγ) and tumor necrosis factor α (TNFα) cytokines has been shown to be critical in anti-tumor immune responses in a number of settings, our understanding of the spatiotemporal behavior of these cytokines in the TME is limited. Here, we describe a single cell transcriptome-based approach to infer which single or combined signals an individual cell has received and estimate the timing of such exposure. Using this technology, we demonstrate that, contrary to expectations, CD8+ T cell-derived IFNγ is the dominant modifier of the TME relative to TNFα. Furthermore, we demonstrate that cell pools that show abundant IFNγ sensing are characterized by decreased TGFβ signaling, consistent with IFNγ-mediated remodeling of the TME. Collectively, these data provide evidence that CD8+ T cell-secreted cytokines should be distinguished into local and more global modifiers of tumor tissue, and describe a broadly applicable approach to dissect cytokine and chemokine modulation of the tumor microenvironment.
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