Phosphoproteomics in cancer.
Ontology highlight
ABSTRACT:
SUBMITTER: Harsha HC
PROVIDER: S-EPMC3030978 | biostudies-other | 2010 Dec
REPOSITORIES: biostudies-other
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Project description:Phosphoproteomic analysis of tumor samples has the potential to uncover significant insights into kinase signaling networks present in late stage prostate cancer that are complementary to genomic and transcriptomic approaches. Phosphoproteomics could potentially aid drug development in clinical trial design as well as provide utility for oncologists in the personalized therapeutic management of individual cancers through identifying novel biomarkers and druggable targets. Rapid advancement of targeted mass spectrometry platforms is underway to integrate phosphoproteomic technology with genomic assays to soon translate this information into the cancer clinic.
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Project description:Cellular signaling, predominantly mediated by phosphorylation through protein kinases, is found to be deregulated in most cancers. Accordingly, protein kinases have been subject to intense investigations in cancer research, to understand their role in oncogenesis and to discover new therapeutic targets. Despite great advances, an understanding of kinase dysfunction in cancer is far from complete.A powerful tool to investigate phosphorylation is mass-spectrometry (MS)-based phosphoproteomics, which enables the identification of thousands of phosphorylated peptides in a single experiment. Since every phosphorylation event results from the activity of a protein kinase, high-coverage phosphoproteomics data should indirectly contain comprehensive information about the activity of protein kinases.In this chapter, we discuss the use of computational methods to predict kinase activity scores from MS-based phosphoproteomics data. We start with a short explanation of the fundamental features of the phosphoproteomics data acquisition process from the perspective of the computational analysis. Next, we briefly review the existing databases with experimentally verified kinase-substrate relationships and present a set of bioinformatic tools to discover novel kinase targets. We then introduce different methods to infer kinase activities from phosphoproteomics data and these kinase-substrate relationships. We illustrate their application with a detailed protocol of one of the methods, KSEA (Kinase Substrate Enrichment Analysis). This method is implemented in Python within the framework of the open-source Kinase Activity Toolbox (kinact), which is freely available at http://github.com/saezlab/kinact/ .
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Project description:TANK-binding kinase 1 (TBK1) has emerged as a novel therapeutic target for unspecified subset of lung cancers. TBK1 reportedly mediates prosurvival signaling by activating NF-?B and AKT. However, we observed that TBK1 knockdown also decreased viability of cells expressing constitutively active NF-?B and interferon regulatory factor 3. Basal phospho-AKT level was not reduced after TBK1 knockdown in TBK1-sensitive lung cancer cells, implicating that TBK1 mediates unknown survival mechanisms. To gain better insight into TBK1 survival signaling, we searched for altered phosphoproteins using mass spectrometry following RNAi-mediated TBK1 knockdown. In total, we identified 2,080 phosphoproteins (4,621 peptides), of which 385 proteins (477 peptides) were affected after TBK1 knockdown. A view of the altered network identified a central role of Polo-like kinase 1 (PLK1) and known PLK1 targets. We found that TBK1 directly phosphorylated PLK1 in vitro. TBK1 phosphorylation was induced at mitosis, and loss of TBK1 impaired mitotic phosphorylation of PLK1 in TBK1-sensitive lung cancer cells. Furthermore, lung cancer cell sensitivity to TBK1 was highly correlated with sensitivity to pharmacological PLK inhibition. We additionally found that TBK1 knockdown decreased metadherin phosphorylation at Ser-568. Metadherin was associated with poor outcome in lung cancer, and loss of metadherin caused growth inhibition and apoptosis in TBK1-sensitive lung cancer cells. These results collectively revealed TBK1 as a mitosis regulator through activation of PLK1 and also suggested metadherin as a putative TBK1 downstream effector involved in lung cancer cell survival.
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Project description:Our understanding of the molecular determinants of cancer is still inadequate because of cancer heterogeneity. Here, using epithelial ovarian cancer (EOC) as a model system, we analyzed a minute amount of patient-derived epithelial cells from either healthy or cancerous tissues by single-shot mass-spectrometry-based phosphoproteomics. Using a multi-disciplinary approach, we demonstrated that primary cells recapitulate tissue complexity and represent a valuable source of differentially expressed proteins and phosphorylation sites that discriminate cancer from healthy cells. Furthermore, we uncovered kinase signatures associated with EOC. In particular, CDK7 targets were characterized in both EOC primary cells and ovarian cancer cell lines. We showed that CDK7 controls cell proliferation and that pharmacological inhibition of CDK7 selectively represses EOC cell proliferation. Our approach defines the molecular landscape of EOC, paving the way for efficient therapeutic approaches for patients. Finally, we highlight the potential of phosphoproteomics to identify clinically relevant and druggable pathways in cancer.
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Project description:Prostate cancer is the most common malignancy in men in the United States. While androgen deprivation therapy results in tumor responses initially, there is relapse and progression to metastatic castration-resistant prostate cancer. Currently, all prostate cancer patients receive essentially the same treatment, and there is a need for clinically applicable technologies to provide predictive biomarkers toward personalized therapies. Genomic analyses of tumors are used for clinical applications, but with a paucity of obvious driver mutations in metastatic castration-resistant prostate cancer, other applications, such as phosphoproteomics, may complement this approach. Immunohistochemistry and reverse phase protein arrays are limited by the availability of reliable antibodies and evaluates a preselected number of targets. Mass spectrometry-based phosphoproteomics has been used to profile tumors consisting of thousands of phosphopeptides from individual patients after surgical resection or at autopsy. However, this approach is time consuming, and while a large number of candidate phosphopeptides are obtained for evaluation, limitations are reduced reproducibility, sensitivity, and precision. Targeted mass spectrometry can help eliminate these limitations and is more cost effective and less time consuming making it a practical platform for future clinical testing. In this review, we discuss the use of phosphoproteomics in prostate cancer and other clinical cancer tissues for target identification, hypothesis testing, and possible patient stratification. We highlight the majority of studies that have used phosphoproteomics in prostate cancer tissues and cell lines and propose ways forward to apply this approach in basic and clinical research. Overall, the implementation of phosphoproteomics via targeted mass spectrometry has tremendous potential to aid in the development of more rational, personalized therapies that will result in increased survival and quality of life enhancement in patients suffering from metastatic castration-resistant prostate cancer.
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Project description:Proteome-wide profiling of protein phosphorylation has been widely used to reveal the underlying mechanism of diverse cellular signaling events. Yet, characterizing subcellular phosphoproteome with high spatial-temporal resolution has remained challenging. Herein, we developed a subcellular-specific uncaging-assisted biotinylation and mapping of phosphoproteome (SubMAPP) strategy to monitor the phosphorylation dynamics of subcellular proteome in living cells and animals. Our method capitalizes on the genetically encoded bioorthogonal decaging strategy, which enables the rapid activation of subcellular localized proximity labeling biotin ligase through either light illumination or small-molecule triggers. By further adopting an integrated orthogonal pull-down strategy with quantitative mass spectrometry, SubMAPP allowed for the investigation of subcellular phosphoproteome dynamics, revealing the altered phosphorylation patterns of endoplasmic reticulum (ER) luminal proteins under ER stress. Finally, we further expanded the scope of the SubMAPP strategy to primary neuron culture and living mice.
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Project description:Phosphorylation of serine, threonine and tyrosine plays significant roles in cellular signal transduction and in modifying multiple protein functions. Phosphoproteins are coordinated and regulated by a network of kinases, phosphatases and phospho-binding proteins, which modify the phosphorylation states, recognize unique phosphopeptides, or target proteins for degradation. Detailed and complete information on the structure and dynamics of these networks is required to better understand fundamental mechanisms of cellular processes and diseases. High-throughput technologies have been developed to investigate phosphoproteomes in model organisms and human diseases. Among them, mass spectrometry (MS)-based technologies are the major platforms and have been widely applied, which has led to explosive growth of phosphoproteomic data in recent years. New bioinformatics tools are needed to analyze and make sense of these data. Moreover, most research has focused on individual phosphoproteins and kinases. To gain a more complete knowledge of cellular processes, systems biology approaches, including pathways and networks modeling, have to be applied to integrate all components of the phosphorylation machinery, including kinases, phosphatases, their substrates, and phospho-binding proteins. This review presents the latest developments of bioinformatics methods and attempts to apply systems biology to analyze phosphoproteomics data generated by MS-based technologies. Challenges and future directions in this field will be also discussed.
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Project description:Background:Ovarian cancer (OC) is the seventh most common cancer worldwide for women. However, there are no sufficient diagnostic methods and few treatment options available due to poor understanding of its pathogenic mechanisms. Methods:To comprehensively analyze the phosphoproteomic characterization for OC, we took advantage of a quantitative global phosphoproteomics method, titanium(IV) immobilized metal affinity chromatography (Ti4+-IMAC) coupled to nanoscale liquid chromatography and quadrupole time-of-flight tandem mass spectrometry (nanoLC/Q-TOF-MS/MS) on ovarian tissue samples obtained from five OC patients and five matched controls. Results:A total of 722 phosphorylated sites corresponding to 534 proteins were significantly different (fold change ? 2, p < 0.01) between OC patients and the controls. Among them, 83 transcription factors mainly consisted of transcription cofactors, zf-C2H2, and chromatin remodeling factors and 29 kinases were included. Further functional analysis suggested significantly biological processes were highly enriched and involved in the pathogenesis of OC, especially fructose and mannose metabolism. Moreover, the regulatory roles of modulated pathways, including MAPK, ErbB, and GnRH signaling pathways were also identified as critical processes involved in OC. The results here highlighted key phosphorylated proteins, particularly kinases, and the corresponding cancer-related metabolic and signal pathways that played important roles in the development of OC. Additionally, the expression levels of two kinases, phosphorylated CDK (T14) and phosphorylated PRKCQ (S695), were validated by Western blot analysis in the other group of ovarian tissue samples. Conclusion:Altogether, our data not only provided novel insights into the potential biomarkers and therapy options for OC but also extended our knowledge on its pathophysiological mechanism.
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