Phosphoproteomics and proteomic phenotyping to assess signal transduction in cancer cells

Abstract

This thesis applies quantitative mass spectrometry to research topics in relation to cancer. Proteome-wide quantification at the protein expression level and phosphorylation level were achieved. The technologies developed and used here cover the latest improvements in instrumentation in mass spectrometry, strategies in phosphopeptide enrichment in large scale, algorithms in data analysis and their streamlined implementation, and data mining in downstream bioinformatics. For each of the projects described in this thesis, proteome mapping routinely resulted in identification and quantitation of around 4,000 proteins and phosphoproteome mapping often lead to quantitation of more than 5,000 phosphorylation sites. This ‘systems-wide’ quantitation of the proteome and phosphoproteome is a completely novel development, which has not been used in cancer related topics before. Three major biology topics are studied in this thesis. In the first project, the phosphoproteome of a mouse liver cancer cell line Hepa1-6 was analyzed in-depth, by using phosphatase inhibitors (calyculin A, deltamethrin, and Na-pervanadate) to boost phosphorylation. The characterization of the phosphoproteome revealed a broad spectrum of cellular compartmentalization and biological functions. Quantitation of phosphatase inhibitor treatment using the Stable Isotope Labeling by Amino Acids in Cell culture (SILAC) method revealed the quantitative effects of these inhibitor compounds on the whole phosphoproteome. To our surprise, these three broadband phosphatase inhibitors displayed very different efficiency, with tyrosine phosphorylation significantly boosted but serine/threonine phosphorylation much less affected. Additionally, a method to estimate an upper bound of the stoichiometry of phosphorylation was introduced by comparing phosphorylation in three SILAC conditions: non-treated cells, stimulated cells (e.g. with insulin), and only phosphatase inhibitor treated cells. The methods developed here can be used directly in development of drugs directed against kinases and phosphatases, key regulators in cancer and other diseases. The second project continues with the application of phosphoproteomics techniques. Kinase inhibitors influence cellular signal transduction processes and therefore are of great potential in rescuing aberrant cellular signaling in tumors. In fact they constitute a significant portion of drug developing programs in pharmaceutical industry. With the aim of quantifying the effect of kinase inhibitors over the entire signaling network, the second project first set out to study two very commonly used kinase inhibitor compounds for MAPKs: U0126 and SB202190. Their effect on epidermal growth factor (EGF) signal transduction was quantified and compared using the HeLa cell system. The study confirmed that the MAPK cascades are the predominant signaling branches for propagating the EGF signaling at early time points of stimulation. These large scale examinations also suggest that U0126 and SB202190 are quite specific inhibitors for MAPKs as the majority of regulated phosphopeptides appears to belong to the MAPK pathways. In the second part of the project, the effect on phosphoproteome changes of the chemical compound dasatinib, which was demonstrated to effectively inhibit the constitutively activated fusion protein BCR-ABL and was recently approved for chronic myelogenous leukemia (CML) therapy, was quantified in the human CML cell line K562. Bioinformatic analysis revealed that the most influenced signal transduction branch was the Erk1/2 cascade. Overall more than 500 phosphorylation sites were found to be regulated by dasatinib, the vast majority not described in the literature yet. The third project compared the proteomes of mouse hepatoma cell line Hepa1-6 with the non-transformed mouse primary hepatocytes. This was performed by combining the SILAC heavy labeled form of Hepa1-6 with the primary hepatocytes. To characterize the features of these two proteomes, quantitation information (i.e. protein ratios between the two cell types) was used to divide all proteins into five quantiles. Each quantile was clustered according to the Gene Ontology and KEGG pathway databases to assess their enriched functional groups and signaling pathways. To integrate this information at a higher level, hierarchical clustering based on the p-value from the first Gene Ontology and KEGG clustering was performed. Using this improved bioinformatic algorithm for data mining, the proteomic phenotypes of the primary cells and transformed cells are immediately apparent. Primary hepatocytes are enriched in mitochondrial functions such as metabolic regulation and detoxification, as well as liver functions with tissue context such as secretion of plasma and low-density lipoprotein (LDL). In contrast, the transformed cancer cell line Hepa1-6 is enriched in cell cycle and growth functions. Interestingly, several aspects of the molecular basis of the “Warburg effect” described in many cancer cells became apparent in Hepa1-6, such as increased expression of glycolysis markers and decreased expression of markers for tricarboxylic acid (TCA) cycle. Studies in this thesis only provide examples of the application of mass spectrometry-based quantitative proteomics and phosphoproteomics in cancer research. The connection to clinical research, especially the assessment of drug effects on a proteome wide scale, is a specific feature of this thesis. Although this development is only in its infancy, it reflects a trend in the quantitative mass spectrometry field. We believe that more and more clinical related topics can and will be studied by these powerful methods