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Sharon J. Pitteri

Bio: Sharon J. Pitteri is an academic researcher from Stanford University. The author has contributed to research in topics: Cancer & Prostate cancer. The author has an hindex of 39, co-authored 91 publications receiving 5585 citations. Previous affiliations of Sharon J. Pitteri include Ludwig Institute for Cancer Research & University of Washington.


Papers
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Journal ArticleDOI
02 Apr 2008-Nature
TL;DR: Systematic searches for plasma proteins that are biological indicators, or biomarkers, for cancer are underway and the expected outcome is the development of panels of biomarkers that will allow early detection of cancer and prediction of the probable response to therapy.
Abstract: Systematic searches for plasma proteins that are biological indicators, or biomarkers, for cancer are underway. The difficulties caused by the complexity of biological-fluid proteomes and tissue proteomes (which contribute proteins to plasma) and by the extensive heterogeneity among diseases, subjects and levels of sample procurement are gradually being overcome. This is being achieved through rigorous experimental design and in-depth quantitative studies. The expected outcome is the development of panels of biomarkers that will allow early detection of cancer and prediction of the probable response to therapy. Achieving these objectives requires high-quality specimens with well-matched controls, reagent resources, and an efficient process to confirm discoveries through independent validation studies.

827 citations

Journal ArticleDOI
TL;DR: This work frames central issues regarding determination of protein-level variation and PTMs, including some paradoxes present in the field today, and uses this framework to assess existing data and ask the question, "How many distinct primary structures of proteins (proteoforms) are created from the 20,300 human genes?"
Abstract: Despite decades of accumulated knowledge about proteins and their post-translational modifications (PTMs), numerous questions remain regarding their molecular composition and biological function O

516 citations

Journal ArticleDOI
TL;DR: The findings indicate that GEM models of cancer, in combination with in-depth proteomic analysis, provide a useful strategy to identify candidate markers applicable to human cancer with potential utility for early detection.
Abstract: Background The complexity and heterogeneity of the human plasma proteome have presented significant challenges in the identification of protein changes associated with tumor development. Refined genetically engineered mouse (GEM) models of human cancer have been shown to faithfully recapitulate the molecular, biological, and clinical features of human disease. Here, we sought to exploit the merits of a well-characterized GEM model of pancreatic cancer to determine whether proteomics technologies allow identification of protein changes associated with tumor development and whether such changes are relevant to human pancreatic cancer.

266 citations

Journal ArticleDOI
TL;DR: The three-dimensional (3-D) in vitro tumor models aim to closely mimic cancer microenvironments and have emerged as an alternative to routinely used methods for drug screening.
Abstract: The natural microenvironment of tumors is composed of extracellular matrix (ECM), blood vasculature, and supporting stromal cells. The physical characteristics of ECM as well as the cellular components play a vital role in controlling cancer cell proliferation, apoptosis, metabolism, and differentiation. To mimic the tumor microenvironment outside the human body for drug testing, two-dimensional (2-D) and murine tumor models are routinely used. Although these conventional approaches are employed in preclinical studies, they still present challenges. For example, murine tumor models are expensive and difficult to adopt for routine drug screening. On the other hand, 2-D in vitro models are simple to perform, but they do not recapitulate natural tumor microenvironment, because they do not capture important three-dimensional (3-D) cell–cell, cell–matrix signaling pathways, and multi-cellular heterogeneous components of the tumor microenvironment such as stromal and immune cells. The three-dimensional (3-D) in vitro tumor models aim to closely mimic cancer microenvironments and have emerged as an alternative to routinely used methods for drug screening. Herein, we review recent advances in 3-D tumor model generation and highlight directions for future applications in drug testing.

240 citations

Journal ArticleDOI
01 Nov 2017-ACS Nano
TL;DR: The ability of ExoTIC to efficiently isolate EVs from small sample volumes opens up avenues for preclinical studies in small animal tumor models and for point-of-care EV-based clinical testing from fingerprick quantities (10-100 μL of blood).
Abstract: Circulating tumor-derived extracellular vesicles (EVs) have emerged as a promising source for identifying cancer biomarkers for early cancer detection. However, the clinical utility of EVs has thus far been limited by the fact that most EV isolation methods are tedious, nonstandardized, and require bulky instrumentation such as ultracentrifugation (UC). Here, we report a size-based EV isolation tool called ExoTIC (exosome total isolation chip), which is simple, easy-to-use, modular, and facilitates high-yield and high-purity EV isolation from biofluids. ExoTIC achieves an EV yield ∼4–1000-fold higher than that with UC, and EV-derived protein and microRNA levels are well-correlated between the two methods. Moreover, we demonstrate that ExoTIC is a modular platform that can sort a heterogeneous population of cancer cell line EVs based on size. Further, we utilize ExoTIC to isolate EVs from cancer patient clinical samples, including plasma, urine, and lavage, demonstrating the device’s broad applicability to...

235 citations


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Journal ArticleDOI
TL;DR: The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions.
Abstract: We present a new update to MetaboAnalyst (version 4.0) for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since the last major update in 2015, MetaboAnalyst has continued to evolve based on user feedback and technological advancements in the field. For this year's update, four new key features have been added to MetaboAnalyst 4.0, including: (1) real-time R command tracking and display coupled with the release of a companion MetaboAnalystR package; (2) a MS Peaks to Pathways module for prediction of pathway activity from untargeted mass spectral data using the mummichog algorithm; (3) a Biomarker Meta-analysis module for robust biomarker identification through the combination of multiple metabolomic datasets and (4) a Network Explorer module for integrative analysis of metabolomics, metagenomics, and/or transcriptomics data. The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions. The underlying knowledgebases (compound libraries, metabolite sets, and metabolic pathways) have also been updated based on the latest data from the Human Metabolome Database (HMDB). A Docker image of MetaboAnalyst is also available to facilitate download and local installation of MetaboAnalyst. MetaboAnalyst 4.0 is freely available at http://metaboanalyst.ca.

2,857 citations

Journal ArticleDOI
TL;DR: The ProteoWizard Toolkit is developed, a robust set of open-source, software libraries and applications designed to facilitate proteomics research that implements the first-ever, non-commercial, unified data access interface for proteomics, bridging field-standard open formats and all common vendor formats.
Abstract: Mass-spectrometry-based proteomics has become an important component of biological research. Numerous proteomics methods have been developed to identify and quantify the proteins in biological and clinical samples1, identify pathways affected by endogenous and exogenous perturbations2, and characterize protein complexes3. Despite successes, the interpretation of vast proteomics datasets remains a challenge. There have been several calls for improvements and standardization of proteomics data analysis frameworks, as well as for an application-programming interface for proteomics data access4,5. In response, we have developed the ProteoWizard Toolkit, a robust set of open-source, software libraries and applications designed to facilitate proteomics research. The libraries implement the first-ever, non-commercial, unified data access interface for proteomics, bridging field-standard open formats and all common vendor formats. In addition, diverse software classes enable rapid development of vendor-agnostic proteomics software. Additionally, ProteoWizard projects and applications, building upon the core libraries, are becoming standard tools for enabling significant proteomics inquiries.

2,480 citations

Journal ArticleDOI
TL;DR: Peptide sequence analysis using a combination of gas-phase ion/ion chemistry and tandem mass spectrometry (MS/MS) is demonstrated and automated acquisition of high-quality, single-scan electron transfer dissociation MS/MS spectra of phosphopeptides separated by nanoflow HPLC is described.
Abstract: Peptide sequence analysis using a combination of gas-phase ion/ion chemistry and tandem mass spectrometry (MS/MS) is demonstrated. Singly charged anthracene anions transfer an electron to multiply protonated peptides in a radio frequency quadrupole linear ion trap (QLT) and induce fragmentation of the peptide backbone along pathways that are analogous to those observed in electron capture dissociation. Modifications to the QLT that enable this ion/ion chemistry are presented, and automated acquisition of high-quality, single-scan electron transfer dissociation MS/MS spectra of phosphopeptides separated by nanoflow HPLC is described.

2,349 citations

Journal ArticleDOI
15 Jul 2019-Cells
TL;DR: The purpose of this review is to not only introduce the different types of extracellular vesicles but also to summarize their differences and similarities, and discuss different methods of exosome isolation and analysis currently used.
Abstract: The use of extracellular vesicles, specifically exosomes, as carriers of biomarkers in extracellular spaces has been well demonstrated Despite their promising potential, the use of exosomes in the clinical setting is restricted due to the lack of standardization in exosome isolation and analysis methods The purpose of this review is to not only introduce the different types of extracellular vesicles but also to summarize their differences and similarities, and discuss different methods of exosome isolation and analysis currently used A thorough understanding of the isolation and analysis methods currently being used could lead to some standardization in the field of exosomal research, allowing the use of exosomes in the clinical setting to become a reality

1,366 citations

Journal ArticleDOI
TL;DR: The progress of proteomics has been driven by the development of new technologies for peptide/protein separation, mass spectrometry analysis, isotope labeling for quantification, and bioinformatics data analysis.
Abstract: According to Genome Sequencing Project statistics (http://www.ncbi.nlm.nih.gov/genomes/static/gpstat.html), as of Feb 16, 2012, complete gene sequences have become available for 2816 viruses, 1117 prokaryotes, and 36 eukaryotes.1–2 The availability of full genome sequences has greatly facilitated biological research in many fields, and has greatly contributed to the growth of proteomics. Proteins are important because they are the direct bio-functional molecules in the living organisms. The term “proteomics” was coined from merging “protein” and “genomics” in the 1990s.3–4 As a post-genomic discipline, proteomics encompasses efforts to identify and quantify all the proteins of a proteome, including expression, cellular localization, interactions, post-translational modifications (PTMs), and turnover as a function of time, space and cell type, thus making the full investigation of a proteome more challenging than sequencing a genome. There are possibly 100,000 protein forms encoded by the approximate 20,235 genes of the human genome,5 and determining the explicit function of each form will be a challenge. The progress of proteomics has been driven by the development of new technologies for peptide/protein separation, mass spectrometry analysis, isotope labeling for quantification, and bioinformatics data analysis. Mass spectrometry has emerged as a core tool for large-scale protein analysis. In the past decade, there has been a rapid advance in the resolution, mass accuracy, sensitivity and scan rate of mass spectrometers used to analyze proteins. In addition, hybrid mass analyzers have been introduced recently (e.g. Linear Ion Trap-Orbitrap series6–7) which have significantly improved proteomic analysis. “Bottom-up” protein analysis refers to the characterization of proteins by analysis of peptides released from the protein through proteolysis. When bottom-up is performed on a mixture of proteins it is called shotgun proteomics,8–10 a name coined by the Yates lab because of its analogy to shotgun genomic sequencing.11 Shotgun proteomics provides an indirect measurement of proteins through peptides derived from proteolytic digestion of intact proteins. In a typical shotgun proteomics experiment, the peptide mixture is fractionated and subjected to LC-MS/MS analysis. Peptide identification is achieved by comparing the tandem mass spectra derived from peptide fragmentation with theoretical tandem mass spectra generated from in silico digestion of a protein database. Protein inference is accomplished by assigning peptide sequences to proteins. Because peptides can be either uniquely assigned to a single protein or shared by more than one protein, the identified proteins may be further scored and grouped based on their peptides. In contrast, another strategy, termed ‘top-down’ proteomics, is used to characterize intact proteins (Figure 1). The top-down approach has some potential advantages for PTM and protein isoform determination and has achieved notable success. Intact proteins have been measured up to 200 kDa,12 and a large scale study has identified more than 1,000 proteins by multi-dimensional separations from complex samples.13 However, the top-down method has significant limitations compared with shotgun proteomics due to difficulties with protein fractionation, protein ionization and fragmentation in the gas phase. By relying on the analysis of peptides, which are more easily fractionated, ionized and fragmented, shotgun proteomics can be more universally adopted for protein analysis. In fact, a hybrid of bottom-up and top-down methodologies and instrumentation has been introduced as middle-down proteomics.14 Essentially, middle-down proteomics analyzes larger peptide fragments than bottom-up proteomics, minimizing peptide redundancy between proteins. Additionally the large peptide fragments yield similar advantages as top-down proteomics, such as gaining further insight into post-translational modifications, without the analytical challenges of analyzing intact proteins. Shotgun proteomics has become a workhorse for the analysis of proteins and their modifications and will be increasingly combined with top-down methods in the future. Figure 1 Proteomic strategies: bottom-up vs. top-down vs. middle-down. The bottom-up approach analyzes proteolytic peptides. The top-down method measures the intact proteins. The middle-down strategy analyzes larger peptides resulted from limited digestion or ... In the past decade shotgun proteomics has been widely used by biologists for many different research experiments, advancing biological discoveries. Some applications include, but are not limited to, proteome profiling, protein quantification, protein modification, and protein-protein interaction. There have been several reviews nicely summarizing mass spectrometry history,15 protein quantification with mass spectrometry,16 its biological applications,5,17–26 and many recent advances in methodology.27–32 In this review, we try to provide a full and updated survey of shotgun proteomics, including the fundamental techniques and applications that laid the foundation along with those developed and greatly improved in the past several years.

1,184 citations