Project description:Driver tyrosine kinase mutations are rare in sarcomas, and patterns of tyrosine phosphorylation are poorly understood. To better understand the signaling pathways active in sarcoma, we examined global tyrosine phosphorylation in sarcoma cell lines and human tumor samples. Anti-phosphotyrosine antibodies were used to purify tyrosine phosphorylated peptides, which were then identified by liquid chromatography and tandem mass spectrometry. The findings were validated with RNA interference, rescue, and small-molecule tyrosine kinase inhibitors. We identified 1,936 unique tyrosine phosphorylated peptides, corresponding to 844 unique phosphotyrosine proteins. In sarcoma cells alone, peptides corresponding to 39 tyrosine kinases were found. Four of 10 cell lines showed dependence on tyrosine kinases for growth and/or survival, including platelet-derived growth factor receptor (PDGFR)?, MET, insulin receptor/insulin-like growth factor receptor signaling, and SRC family kinase signaling. Rhabdomyosarcoma samples showed overexpression of PDGFR? in 13% of examined cases, and sarcomas showed abundant tyrosine phosphorylation and expression of a number of tyrosine phosphorylated tyrosine kinases, including DDR2, EphB4, TYR2, AXL, SRC, LYN, and FAK. Together, our findings suggest that integrating global phosphoproteomics with functional analyses with kinase inhibitors can identify drivers of sarcoma growth and survival.
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:Acute myeloid leukemia (AML) is a clonal disorder arising from hematopoietic myeloid progenitors. Aberrantly activated tyrosine kinases (TK) are involved in leukemogenesis and are associated with poor treatment outcome. Kinase inhibitor (KI) treatment has shown promise in improving patient outcome in AML. However, inhibitor selection for patients is suboptimal.In a preclinical effort to address KI selection, we analyzed a panel of 16 AML cell lines using phosphotyrosine (pY) enrichment-based, label-free phosphoproteomics. The Integrative Inferred Kinase Activity (INKA) algorithm was used to identify hyperphosphorylated, active kinases as candidates for KI treatment, and efficacy of selected KIs was tested.Heterogeneous signaling was observed with between 241 and 2764 phosphopeptides detected per cell line. Of 4853 identified phosphopeptides with 4229 phosphosites, 4459 phosphopeptides (4430 pY) were linked to 3605 class I sites (3525 pY). INKA analysis in single cell lines successfully pinpointed driver kinases (PDGFRA, JAK2, KIT and FLT3) corresponding with activating mutations present in these cell lines. Furthermore, potential receptor tyrosine kinase (RTK) drivers, undetected by standard molecular analyses, were identified in four cell lines (FGFR1 in KG-1 and KG-1a, PDGFRA in Kasumi-3, and FLT3 in MM6). These cell lines proved highly sensitive to specific KIs. Six AML cell lines without a clear RTK driver showed evidence of MAPK1/3 activation, indicative of the presence of activating upstream RAS mutations. Importantly, FLT3 phosphorylation was demonstrated in two clinical AML samples with a FLT3 internal tandem duplication (ITD) mutation.Our data show the potential of pY-phosphoproteomics and INKA analysis to provide insight in AML TK signaling and identify hyperactive kinases as potential targets for treatment in AML cell lines. These results warrant future investigation of clinical samples to further our understanding of TK phosphorylation in relation to clinical response in the individual patient.
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>
Project description:Tumor classification based on their predicted responses to kinase inhibitors is a major goal for advancing targeted personalized therapies. Here, we used a phosphoproteomic approach to investigate biological heterogeneity across hematological cancer cell lines including acute myeloid leukemia, lymphoma, and multiple myeloma.Mass spectrometry was used to quantify 2,000 phosphorylation sites across three acute myeloid leukemia, three lymphoma, and three multiple myeloma cell lines in six biological replicates. The intensities of the phosphorylation sites grouped these cancer cell lines according to their tumor type. In addition, a phosphoproteomic analysis of seven acute myeloid leukemia cell lines revealed a battery of phosphorylation sites whose combined intensities correlated with the growth-inhibitory responses to three kinase inhibitors with remarkable correlation coefficients and fold changes (> 100 between the most resistant and sensitive cells). Modeling based on regression analysis indicated that a subset of phosphorylation sites could be used to predict response to the tested drugs. Quantitative analysis of phosphorylation motifs indicated that resistant and sensitive cells differed in their patterns of kinase activities, but, interestingly, phosphorylations correlating with responses were not on members of the pathway being targeted; instead, these mainly were on parallel kinase pathways.This study reveals that the information on kinase activation encoded in phosphoproteomics data correlates remarkably well with the phenotypic responses of cancer cells to compounds that target kinase signaling and could be useful for the identification of novel markers of resistance or sensitivity to drugs that target the signaling network.
Project description:There is a need for robust phosphopeptide enrichment methods to allow signaling network analysis in cancer cell lines and tissues with minimal fractionation. With recent instrument developments thousands of unique phosphopeptides can be detected by single-shot LC-MS/MS. However, successful phosphoproteomics experiments still rely on efficient phosphopeptide enrichment from a tryptic digest prior to LC-MS/MS analysis. Here we describe a performance assessment of HAMMOC (hydroxyl acid modified metal affinity chromatography) (Sugiyama MCP2007, Kyono, JPR 2008) combined with single shot label-free quantitation at 500 µg peptide input level. In a triplicate analysis we observe good phosphopeptide identification reproducibility (75.8%), depth of identification (6014-6150 phosphopeptides) and reproducibility of label-free quantification (CV 17.8%, Pearson r 0.87-0.98) by single-shot LC-MS/MS.