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Journal ArticleDOI

Toward improvement of screening through mass spectrometry-based proteomics: ovarian cancer as a case study.

04 Aug 2021-International Journal of Mass Spectrometry (Int J Mass Spectrom)-Vol. 469, pp 116679
TL;DR: This work discusses and provides examples for several workflows employing mass spectrometry-based proteomics towards protein biomarker discovery and characterization in the context of ovarian cancer, and discusses the opportunities to merge these workflows for a multiplexed approach for biomarkers.
About: This article is published in International Journal of Mass Spectrometry.The article was published on 2021-08-04. It has received 4 citations till now. The article focuses on the topics: Biomarker discovery.
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Journal ArticleDOI
TL;DR: The potential perspective for understanding and identifying the unique alterations in protein expressions that could prove beneficial in discovering new robust biomarkers to detect PDAC at an early stage and ascertain prognosis of patients with the disease are explored.
Abstract: Pancreatic ductal adenocarcinoma (PDAC), a highly aggressive malignancy with a poor prognosis is usually detected at the advanced stage of the disease. The only US Food and Drug Administration-approved biomarker that is available for PDAC, CA 19-9, is most useful in monitoring treatment response among PDAC patients rather than for early detection. Moreover, when CA 19-9 is solely used for diagnostic purposes, it has only a recorded sensitivity of 79% and specificity of 82% in symptomatic individuals. Therefore, there is an urgent need to identify reliable biomarkers for diagnosis (specifically for the early diagnosis), ascertain prognosis as well as to monitor treatment response and tumour recurrence of PDAC. In recent years, proteomic technologies are growing exponentially at an accelerated rate for a wide range of applications in cancer research. In this review, we discussed the current status of biomarker research for PDAC using various proteomic technologies. This review will explore the potential perspective for understanding and identifying the unique alterations in protein expressions that could prove beneficial in discovering new robust biomarkers to detect PDAC at an early stage, ascertain prognosis of patients with the disease in addition to monitoring treatment response and tumour recurrence of patients.

1 citations

Posted ContentDOI
04 Jan 2022-bioRxiv
TL;DR: An untargeted mass spectrometry microprotein profiling method is developed and identified a signature of cystatin A, validated this protein in an animal model, and sought to overcome the limits of detection inherent to mass Spectrometry by demonstrating that cystatar A is present at 100 pM concentrations using a label-free microtoroid resonator.
Abstract: Ovarian cancer, a leading cause of cancer related deaths among women, has been notoriously difficult to routinely screen for and diagnose early. Researchers and clinicians continue to seek routinely usable, non-invasive, screening methods as early detection significantly improves survival. Biomarker screening is ideal; however, currently available ovarian cancer biomarkers lack desirable sensitivity and specificity. Furthermore, the most fatal forms, high grade serous cancers often originate in the fallopian tube; therefore, sampling from the vaginal environment provides more proximal sources for tumor detection. To address these shortcomings and leverage proximal sampling, we developed an untargeted mass spectrometry microprotein profiling method and identified a signature of cystatin A, validated this protein in an animal model, and sought to overcome the limits of detection inherent to mass spectrometry by demonstrating that cystatin A is present at 100 pM concentrations using a label-free microtoroid resonator. The findings highlight the potential utility for early-stage detection where cystatin A levels would be low. Significance Statement It is now clear that high-grade serous ovarian cancer can originate in the fallopian tube epithelium. These tumors colonize the ovary and then metastasize throughout the peritoneum. This discovery has raised important, and yet unaddressed, questions how we might be able to detect and screen for this deadly disease for which there is no routine screening. We have leveraged vaginal lavages from a murine model of the disease as a complex biological fluid for untargeted discovery of microproteins using mass. We improved our limits of detection by conjugating a cystatin A antibody to the surface of a microtoroid resonator to allow us to specifically detect cystatin A from vaginal lavages at early time points across biological replicates.
Journal ArticleDOI
TL;DR: In this article , the authors reviewed over 2500 interventional clinical trials of ovarian cancers since 1990 and cataloged 22 types of interventions adopted in these trials, including PARP, VEGFR, conventional anti-cancer agents, and the remaining on sex hormones, MEK1/2, PD-L1, ERBB, and FRα.
Journal ArticleDOI
TL;DR: In this article , an untargeted mass spectrometry microprotein profiling method was used to identify cystatin A, which was validated in an animal model, and demonstrated that the protein is present at 100 pM concentrations using a label-free microtoroid resonator.
References
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Journal ArticleDOI
TL;DR: MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data, detects peaks, isotope clusters and stable amino acid isotope–labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space and achieves mass accuracy in the p.p.b. range.
Abstract: Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.

12,340 citations

Journal ArticleDOI
TL;DR: A new computer program, Mascot, is presented, which integrates all three types of search for protein identification by searching a sequence database using mass spectrometry data, and the scoring algorithm is probability based.
Abstract: Several algorithms have been described in the literature for protein identification by searching a sequence database using mass spectrometry data. In some approaches, the experimental data are peptide molecular weights from the digestion of a protein by an enzyme. Other approaches use tandem mass spectrometry (MS/MS) data from one or more peptides. Still others combine mass data with amino acid sequence data. We present results from a new computer program, Mascot, which integrates all three types of search. The scoring algorithm is probability based, which has a number of advantages: (i) A simple rule can be used to judge whether a result is significant or not. This is particularly useful in guarding against false positives. (ii) Scores can be compared with those from other types of search, such as sequence homology. (iii) Search parameters can be readily optimised by iteration. The strengths and limitations of probability-based scoring are discussed, particularly in the context of high throughput, fully automated protein identification.

8,195 citations

Journal ArticleDOI
TL;DR: The approach described in this manuscript provides a convenient method to interpret tandem mass spectra with known sequences in a protein database.

6,317 citations

Journal ArticleDOI
TL;DR: It is found that inactivation of Upf1p and Xrn1p causes common as well as unique effects on protein expression, and the use of 4-fold multiplexing to enable relative protein measurements simultaneously with determination of absolute levels of a target protein using synthetic isobaric peptide standards.

4,411 citations

Journal ArticleDOI
Alex Bateman, Maria Jesus Martin, Sandra Orchard, Michele Magrane, Rahat Agivetova, Shadab Ahmad, Emanuele Alpi, Emily H Bowler-Barnett, Ramona Britto, Borisas Bursteinas, Hema Bye-A-Jee, Ray Coetzee, Austra Cukura, Alan Wilter Sousa da Silva, Paul Denny, Tunca Doğan, ThankGod Ebenezer, Jun Fan, Leyla Jael Garcia Castro, Penelope Garmiri, George Georghiou, Leonardo Gonzales, Emma Hatton-Ellis, Abdulrahman Hussein, Alexandr Ignatchenko, Giuseppe Insana, Rizwan Ishtiaq, Petteri Jokinen, Vishal Joshi, Dushyanth Jyothi, Antonia Lock, Rodrigo Lopez, Aurelien Luciani, Jie Luo, Yvonne Lussi, Alistair MacDougall, Fábio Madeira, Mahdi Mahmoudy, Manuela Menchi, Alok Mishra, Katie Moulang, Andrew Nightingale, Carla Susana Oliveira, Sangya Pundir, Guoying Qi, Shriya Raj, Daniel Rice, Milagros Rodriguez Lopez, Rabie Saidi, Joseph Sampson, Tony Sawford, Elena Speretta, Edward Turner, Nidhi Tyagi, Preethi Vasudev, Vladimir Volynkin, Kate Warner, Xavier Watkins, Rossana Zaru, Hermann Zellner, Alan Bridge, Sylvain Poux, Nicole Redaschi, Lucila Aimo, Ghislaine Argoud-Puy, Andrea H. Auchincloss, Kristian B. Axelsen, Parit Bansal, Delphine Baratin, Marie-Claude Blatter, Jerven Bolleman, Emmanuel Boutet, Lionel Breuza, Cristina Casals-Casas, Edouard de Castro, Kamal Chikh Echioukh, Elisabeth Coudert, Béatrice A. Cuche, M Doche, Dolnide Dornevil, Anne Estreicher, Maria Livia Famiglietti, Marc Feuermann, Elisabeth Gasteiger, Sebastien Gehant, Vivienne Baillie Gerritsen, Arnaud Gos, Nadine Gruaz-Gumowski, Ursula Hinz, Chantal Hulo, Nevila Hyka-Nouspikel, Florence Jungo, Guillaume Keller, Arnaud Kerhornou, Vicente Lara, Philippe Le Mercier, Damien Lieberherr, Thierry Lombardot, Xavier D. Martin, Patrick Masson, Anne Morgat, Teresa Batista Neto, Salvo Paesano, Ivo Pedruzzi, Sandrine Pilbout, Lucille Pourcel, Monica Pozzato, Manuela Pruess, Catherine Rivoire, Christian J. A. Sigrist, K Sonesson, Andre Stutz, Shyamala Sundaram, Michael Tognolli, Laure Verbregue, Cathy H. Wu, Cecilia N. Arighi, Leslie Arminski, Chuming Chen, Yongxing Chen, John S. Garavelli, Hongzhan Huang, Kati Laiho, Peter B. McGarvey, Darren A. Natale, Karen E. Ross, C. R. Vinayaka, Qinghua Wang, Yuqi Wang, Lai-Su L. Yeh, Jian Zhang, Patrick Ruch, Douglas Teodoro 
TL;DR: The UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal and a credit-based publication submission interface was developed.
Abstract: Abstract The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this article, we describe significant updates that we have made over the last two years to the resource. The number of sequences in UniProtKB has risen to approximately 190 million, despite continued work to reduce sequence redundancy at the proteome level. We have adopted new methods of assessing proteome completeness and quality. We continue to extract detailed annotations from the literature to add to reviewed entries and supplement these in unreviewed entries with annotations provided by automated systems such as the newly implemented Association-Rule-Based Annotator (ARBA). We have developed a credit-based publication submission interface to allow the community to contribute publications and annotations to UniProt entries. We describe how UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.

4,001 citations