Project description:Mass spectrometry-based phosphoproteomics of tumor tissue or cell line lysates provides insight in aberrantly activated signaling pathways and potential drug targets. For improved understanding of the individual patient’s tumor biology, analysis of the phosphoproteome should be feasible and reproducible using tumor needle biopsies. We hereto scaled down a pTyr-phosphopeptide enrichment protocol to biopsy-level protein input and show its performance using colorectal cancer (CRC) cell line and needle biopsies from patients. In this study, the feasibility of label-free pTyr-phosphoproteomics at the biopsy level is demonstrated. Unsupervised cluster analysis shows that this approach can identify patient-specific profiles, which will improve our understanding of individual tumor biology and may enable future pTyr-phosphoproteomics-based TKI treatment selection.
Project description: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.
Project description:<p><strong>Background:</strong> Cell line specific, genome-scale metabolic models enable rigorous and systematic in silico investigation of cellular metabolism. Such models have recently become available for Chinese hamster ovary (CHO) too. However, a key ingredient, namely an experimentally validated biomass function that summarizes the cellular composition, was so far missing in all genome-scale metabolic models of CHO cell lines. Here, we close this gap by providing extensive experimental data on the biomass composition of 13 parental and producer CHO cell lines under various conditions.</p><p><strong>Results:</strong> We report protein, lipid, DNA, RNA and carbohydrate content, cell dry mass, and protein and lipid composition. Furthermore, we present meticulous data on exchange rates between cells and environment and provide detailed experimental protocols on how to determine all of the above. The biomass composition is converted into cell line and condition specific biomass functions for usage in cell line specific, genome-scale metabolic models of CHO. Finally, flux-balance analysis (FBA) is used to demonstrate consistency between in silico predictions and experimental analysis.</p><p><strong>Conclusions:</strong> Our study reveals a strong variability of the total protein content and cell dry mass across cell lines. Yet, the relative amino acid composition is independent of the cell lines and conditions and thus needs not be explicitly measured for each new cell line. In contrast, the lipid composition is strongly influenced by the growth media and needs to be determined for each case. These cell line specific variations in the biomass composition have a small impact on growth rate predictions with FBA, as inaccuracies in the predictions are typically dominated by inaccuracies in the exchange rate spectra. Cell specific biomass variations become only important if the experimental errors in the exchange rate spectra drop below twenty percent.</p><p><br></p><p><strong>Links:</strong></p><p>All data and code for data processing are available at <a href='http://dx.doi.org/10.17632/g3wz7spyc9.1' rel='noopener noreferrer' target='_blank'>Data Mendeley</a>.</p><p>Cell line specific metabolic models were deposited in <a href='https://www.ebi.ac.uk/biomodels/(Chelliah et al, 2014)' rel='noopener noreferrer' target='_blank'>BioModels</a>, identifiers are listed in the study sample table.</p>