Dataset Information

PanRTK model for single cell line

ABSTRACT: 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.

SUBMITTER: Helge Hass

PROVIDER: MODEL1708210000 | BioModels |

REPOSITORIES: BioModels

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Publications

Predicting ligand-dependent tumors from multi-dimensional signaling features.

Hass Helge H Masson Kristina K Wohlgemuth Sibylle S Paragas Violette V Allen John E JE Sevecka Mark M Pace Emily E Timmer Jens J Stelling Joerg J MacBeath Gavin G Schoeberl Birgit B Raue Andreas A

NPJ systems biology and applications 20170920

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 ...[more]

PMID: 28944080

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Project description:This project describes the establishment and validation of a murine orthotopic xenograft model using fresh human tumor samples that recapitulates the critical components of human pancreatic adenocarcinoma. The authors discuss the proven and theoretical advantages of the model as well as future translational implications. Background: Relevant preclinical models that recapitulate the key features of human pancreatic ductal adenocarcinoma (PDAC) are needed in order to provide biologically tractable models to probe disease progression and therapeutic responses and ultimately improve patient outcomes for this disease. Here, we describe the establishment and clinical, pathological, molecular and genetic validation of a murine, orthotopic xenograft model of PDAC. Methods: Human PDACs were resected and orthotopically implanted and propagated in immunocompromised mice. Patient survival was correlated with xenograft growth and metastatic rate in mice. Human and mouse tumor pathology were compared. Tumors were analyzed for genetic mutations, gene expression, receptor tyrosine kinase (RTK) activation, and cytokine expression. Results: Fifteen human PDACs were propagated orthotopically in mice. Xenografts developed peritoneal and liver metastases. Time to growth and metastatic efficiency in mice each correlated with patient survival. Tumor architecture, nuclear grade and stromal content were similar in patient and xenografted tumors. Propagated tumors closely exhibited the genetic and molecular features known to characterize pancreatic cancer (e.g. high rate of KRAS, p53, SMAD4 mutation and EGFR activation). The correlation coefficient of gene expression between patient tumors and xenografts propagated through multiple generations was 93 to 99%. Analysis of gene expression demonstrated distinct differences between xenografts from fresh patient tumors versus commercially available PDAC cell lines. Conclusions: Our orthotopic xenograft model derived from fresh human PDACs closely recapitulates the clinical, pathologic, genetic and molecular aspects of human disease. This model has resulted in the identification of rational therapeutic strategies to be tested in clinical trials and will permit additional therapeutic approaches and identification of biomarkers of response to therapy. 47 Samples in total were generated for normal pancreatic tissue in patients, pancreatic tumors in patients, pancreatic tumors propagated in a mouse xenograft model, and pancreatic cancer cell lines in vitro. Clustering analysis was performed to evaluate the differences between patient tumors, xenograft tumors, established cancer cell lines, and cell lines derived from xenografts.

Dataset Information

PanRTK model for single cell line

Publications

Predicting ligand-dependent tumors from multi-dimensional signaling features.

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