Dataset Information

Integrative proteogenomic characterization of colorectal cancer cell lines and primary tumors

ABSTRACT: Solid tumors are complex organs comprising neoplastic cells and stroma, yet cancer cell lines remain widely used to study tumor biology, biomarkers and experimental therapy. Here, we performed a fully integrative analysis of global proteomic data comparing human colorectal cancer (CRC) cell lines to primary tumors and normal tissues. We found a significant, systematic difference between cell line and tumor proteomes, with a major contribution from tumor stroma proteomes. Nevertheless, cell lines overall mirrored the proteomic differences observed between tumors and normal tissues, in particular for genetic information processing and metabolic pathways, indicating that cell lines provide a system for the study of the intrinsic molecular programs in cancer cells. Intersection of cell line data with tumor data provided insights into tumor cell specific proteome alterations driven by genomic alterations. Our integration of cell line proteogenomic data with drug sensitivity data highlights the potential of proteomic data in predicting therapeutic response. We identified representative cell lines for the proteomic subtypes of primary tumors, and linked these to drug sensitivity data to identify subtype-specific drug candidates.

OTHER RELATED OMICS DATASETS IN: PXD013455PRJNA356046PRJNA356065

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Bing Zhang

PROVIDER: MSV000080374 | MassIVE | Thu Dec 01 19:06:00 GMT 2016

SECONDARY ACCESSION(S): PXD005483

REPOSITORIES: MassIVE

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Publications

Colorectal Cancer Cell Line Proteomes Are Representative of Primary Tumors and Predict Drug Sensitivity.

Wang Jing J Mouradov Dmitri D Wang Xiaojing X Jorissen Robert N RN Chambers Matthew C MC Zimmerman Lisa J LJ Vasaikar Suhas S Love Christopher G CG Li Shan S Lowes Kym K Leuchowius Karl-Johan KJ Jousset Helene H Weinstock Janet J Yau Christopher C Mariadason John J Shi Zhiao Z Ban Yuguang Y Chen Xi X Coffey Robert J C RJC Slebos Robbert J C RJC Burgess Antony W AW Liebler Daniel C DC Zhang Bing B Sieber Oliver M OM

Gastroenterology 20170616 4

<h4>Background and aims</h4>Proteomics holds promise for individualizing cancer treatment. We analyzed to what extent the proteomic landscape of human colorectal cancer (CRC) is maintained in established CRC cell lines and the utility of proteomics for predicting therapeutic responses.<h4>Methods</h4>Proteomic and transcriptomic analyses were performed on 44 CRC cell lines, compared against primary CRCs (n=95) and normal tissues (n=60), and integrated with genomic and drug sensitivity data.<h4>R ...[more]

PMID: 28625833

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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

Integrative proteogenomic characterization of colorectal cancer cell lines and primary tumors

Publications

Colorectal Cancer Cell Line Proteomes Are Representative of Primary Tumors and Predict Drug Sensitivity.

Similar Datasets

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