Dataset Information

Sensitizing Resistant Cancer Cells to T cell-secreted Cytokines

ABSTRACT: Tumor heterogeneity is a major barrier to cancer therapy, including immunotherapy. Activated T cells can efficiently kill tumor cells following recognition of MHC class I (MHC-I) bound peptides, but this selection pressure favors outgrowth of MHC-I deficient tumor cells. We performed a genome-scale screen to discover alternative pathways for T cell-mediated killing of MHC-I deficient tumor cells. Autophagy and TNF signaling emerged as top pathways, and inactivation of Rnf31 (TNF signaling) and Atg5 (autophagy) sensitized MHC-I deficient tumor cells to apoptosis by T cell-derived cytokines. Mechanistic studies demonstrated that inhibition of autophagy amplified pro-apoptotic effects of cytokines in tumor cells. Antigens from apoptotic MHC-I deficient tumor cells were efficiently cross-presented by dendritic cells, resulting in heightened tumor infiltration by IFNg and TNFa-producing T cells. Tumors with a substantial population of MHC-I deficient cancer cells could be controlled by T cells when both pathways were targeted using genetic or pharmacological approaches.

ORGANISM(S): Mus musculus

PROVIDER: GSE213078 | GEO | 2022/09/13

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:This model is from the article: Heterogeneity Reduces Sensitivity of Cell Death for TNF-Stimuli Schliemann M, Bullinger E, Borchers S, Allgower F, Findeisen R, Scheurich P. BMC Syst Biol. 2011 Dec 28;5(1):204. 22204418 , Abstract: BACKGROUND: Apoptosis is a form of programmed cell death essential for the maintenance of homeostasis and the removal of potentially damaged cells in multicellular organisms. By binding its cognate membrane receptor, TNF receptor type 1 (TNF-R1), the proinflammatory cytokine Tumor Necrosis Factor (TNF) activates pro-apoptotic signaling via caspase activation, but at the same time also stimulates nuclear factor kappaB (NF-kappaB)-mediated survival pathways. Differential dose-response relationships of these two major TNF signaling pathways have been described experimentally and using mathematical modeling. However, the quantitative analysis of the complex interplay between pro- and anti-apoptotic signaling pathways is an open question as it is challenging for several reasons: the overall signaling network is complex, various time scales are present, and cells respond quantitatively and qualitatively in a heterogeneous manner. RESULTS: This study analyzes the complex interplay of the crosstalk of TNF-R1 induced pro- and anti-apoptotic signaling pathways based on an experimentally validated mathematical model. The mathematical model describes the temporal responses on both the single cell level as well as the level of a heterogeneous cell population, as observed in the respective quantitative experiments using TNF-R1 stimuli of different strengths and durations. Global sensitivity of the heterogeneous population was quantified by measuring the average gradient of time of death versus each population parameter. This global sensitivity analysis uncovers the concentrations of Caspase-8 and Caspase-3, and their respective inhibitors BAR and XIAP, as key elements for deciding the cell's fate. A simulated knockout of the NF-kappaB-mediated anti-apoptotic signaling reveals the importance of this pathway for delaying the time of death, reducing the death rate in the case of pulse stimulation and significantly increasing cell-to-cell variability. CONCLUSIONS: Cell ensemble modeling of a heterogeneous cell population including a global sensitivity analysis presented here allowed us to illuminate the role of the different elements and parameters on apoptotic signaling. The receptors serve to transmit the external stimulus; procaspases and their inhibitors control the switching from life to death, while NF-kappaB enhances the heterogeneity of the cell population. The global sensitivity analysis of the cell population model further revealed an unexpected impact of heterogeneity, i.e. the reduction of parametric sensitivity. Note: SBML model generated from Matlab system description on 12-July-2011 21:08:15 by exportSBML Copyright Eric Bullinger 2007-2011

Dataset Information

Sensitizing Resistant Cancer Cells to T cell-secreted Cytokines

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets