Dataset Information

Single cell RNA sequencing of KPC1199 cell line [mouse scRNA-seq]

ABSTRACT: Single cell RNA sequencing of KPC1199 cell line [mouse scRNA-seq]

PROVIDER: PRJNA528899 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR8781530.fastq.gz	Fastqsanger.gz
	SRR8781540.fastq.gz	Fastqsanger.gz
	SRR8781550.fastq.gz	Fastqsanger.gz
	SRR8781560.fastq.gz	Fastqsanger.gz
	SRR8781570.fastq.gz	Fastqsanger.gz

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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.

Dataset Information

Single cell RNA sequencing of KPC1199 cell line [mouse scRNA-seq]

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