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Rappu P, Suwal U, Siljamäki E, Heino J. Inflammation-related citrullination of matrisome proteins in human cancer. Front Oncol 2022; 12:1035188. [PMID: 36531007 PMCID: PMC9753687 DOI: 10.3389/fonc.2022.1035188] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 11/14/2022] [Indexed: 10/03/2023] Open
Abstract
INTRODUCTION Protein arginine deiminases (PADs) are intracellular enzymes that may, especially in pathological conditions, also citrullinate extracellular substrates, including matrisome proteins such as structural proteins in extracellular matrix (ECM). PADs are abundantly expressed in human cancer cells. Citrullination of matrisome proteins has been reported in colon cancer but the phenomenon has never been systematically studied. METHODS To gain a broader view of citrullination of matrisome proteins in cancer, we analyzed cancer proteomics data sets in 3 public databases for citrullinated matrisome proteins. In addition, we used three-dimensional cell cocultures of fibroblasts and cancer cells and analyzed citrullination of ECM. RESULTS AND DISCUSSION Our new analysis indicate that citrullination of ECM occurs in human cancer, and there is a significant variation between tumors. Most frequently citrullinated proteins included fibrinogen and fibronectin, which are typically citrullinated in rheumatoid inflammation. We also detected correlation between immune cell marker proteins, matrix metalloproteinases and ECM citrullination, which suggests that in cancer, citrullination of matrisome proteins is predominantly an inflammation-related phenomenon. This was further supported by our analysis of three-dimensional spheroid co-cultures of nine human cancer cell lines and fibroblasts by mass spectrometry, which gave no evidence that cancer cells or fibroblasts could citrullinate matrisome proteins in tumor stroma. It also appears that in the spheroid cultures, matrisome proteins are protected from citrullination.
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Lautenbacher L, Samaras P, Muller J, Grafberger A, Shraideh M, Rank J, Fuchs ST, Schmidt TK, The M, Dallago C, Wittges H, Rost B, Krcmar H, Kuster B, Wilhelm M. ProteomicsDB: toward a FAIR open-source resource for life-science research. Nucleic Acids Res 2022; 50:D1541-D1552. [PMID: 34791421 PMCID: PMC8728203 DOI: 10.1093/nar/gkab1026] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 10/12/2021] [Accepted: 10/15/2021] [Indexed: 12/28/2022] Open
Abstract
ProteomicsDB (https://www.ProteomicsDB.org) is a multi-omics and multi-organism resource for life science research. In this update, we present our efforts to continuously develop and expand ProteomicsDB. The major focus over the last two years was improving the findability, accessibility, interoperability and reusability (FAIR) of the data as well as its implementation. For this purpose, we release a new application programming interface (API) that provides systematic access to essentially all data in ProteomicsDB. Second, we release a new open-source user interface (UI) and show the advantages the scientific community gains from such software. With the new interface, two new visualizations of protein primary, secondary and tertiary structure as well an updated spectrum viewer were added. Furthermore, we integrated ProteomicsDB with our deep-neural-network Prosit that can predict the fragmentation characteristics and retention time of peptides. The result is an automatic processing pipeline that can be used to reevaluate database search engine results stored in ProteomicsDB. In addition, we extended the data content with experiments investigating different human biology as well as a newly supported organism.
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Affiliation(s)
- Ludwig Lautenbacher
- Technical University of Munich, Computational Mass Spectrometry, 85354 Freising, Bavaria, Germany
| | - Patroklos Samaras
- Technical University of Munich, Chair of Proteomics and Bioanalytics, 85354 Freising, Bavaria, Germany
| | - Julian Muller
- Technical University of Munich, Chair of Proteomics and Bioanalytics, 85354 Freising, Bavaria, Germany
| | - Andreas Grafberger
- Technical University of Munich, Chair of Proteomics and Bioanalytics, 85354 Freising, Bavaria, Germany
| | - Marwin Shraideh
- Technical University of Munich, Chair for Information Systems, 85748 Garching, Bavaria, Germany
- Technical University of Munich, SAP University Competence Center, 85748 Garching, Bavaria, Germany
| | - Johannes Rank
- Technical University of Munich, Chair for Information Systems, 85748 Garching, Bavaria, Germany
- Technical University of Munich, SAP University Competence Center, 85748 Garching, Bavaria, Germany
| | - Simon T Fuchs
- Technical University of Munich, Chair for Information Systems, 85748 Garching, Bavaria, Germany
- Technical University of Munich, SAP University Competence Center, 85748 Garching, Bavaria, Germany
| | - Tobias K Schmidt
- Technical University of Munich, Chair of Proteomics and Bioanalytics, 85354 Freising, Bavaria, Germany
| | - Matthew The
- Technical University of Munich, Chair of Proteomics and Bioanalytics, 85354 Freising, Bavaria, Germany
| | - Christian Dallago
- Technical University of Munich, Department for Bioinformatics and Computational Biology, 85748 Garching, Bavaria, Germany
- Technical University of Munich, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), 85748 Garching, Bavaria, Germany
| | - Holger Wittges
- Technical University of Munich, Chair for Information Systems, 85748 Garching, Bavaria, Germany
- Technical University of Munich, SAP University Competence Center, 85748 Garching, Bavaria, Germany
| | - Burkhard Rost
- Technical University of Munich, Department for Bioinformatics and Computational Biology, 85748 Garching, Bavaria, Germany
- Technical University of Munich, Institute for Advanced Study (TUM-IAS), 85748 Freising, Bavaria, Germany
| | - Helmut Krcmar
- Technical University of Munich, Chair for Information Systems, 85748 Garching, Bavaria, Germany
- Technical University of Munich, SAP University Competence Center, 85748 Garching, Bavaria, Germany
| | - Bernhard Kuster
- Technical University of Munich, Chair of Proteomics and Bioanalytics, 85354 Freising, Bavaria, Germany
- Technical University of Munich, Bavarian Biomolecular Mass Spectrometry Center (BayBioMS), 85354 Freising, Bavaria, Germany
| | - Mathias Wilhelm
- Technical University of Munich, Computational Mass Spectrometry, 85354 Freising, Bavaria, Germany
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3
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Hernandez‐Alias X, Benisty H, Schaefer MH, Serrano L. Translational efficiency across healthy and tumor tissues is proliferation-related. Mol Syst Biol 2020; 16:e9275. [PMID: 32149479 PMCID: PMC7061310 DOI: 10.15252/msb.20199275] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 02/11/2020] [Accepted: 02/11/2020] [Indexed: 12/20/2022] Open
Abstract
Different tissues express genes with particular codon usage and anticodon tRNA repertoires. However, the codon-anticodon co-adaptation in humans is not completely understood, nor is its effect on tissue-specific protein levels. Here, we first validated the accuracy of small RNA-seq for tRNA quantification across five human cell lines. We then analyzed the tRNA abundance of more than 8,000 tumor samples from TCGA, together with their paired mRNA-seq and proteomics data, to determine the Supply-to-Demand Adaptation. We thereby elucidate that the dynamic adaptation of the tRNA pool is largely related to the proliferative state across tissues. The distribution of such tRNA pools over the whole cellular translatome affects the subsequent translational efficiency, which functionally determines a condition-specific expression program both in healthy and tumor states. Furthermore, the aberrant translational efficiency of some codons in cancer, exemplified by ProCCA and GlyGGT, is associated with poor patient survival. The regulation of these tRNA profiles is partly explained by the tRNA gene copy numbers and their promoter DNA methylation.
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Affiliation(s)
- Xavier Hernandez‐Alias
- Centre for Genomic Regulation (CRG)The Barcelona Institute of Science and TechnologyBarcelonaSpain
| | - Hannah Benisty
- Centre for Genomic Regulation (CRG)The Barcelona Institute of Science and TechnologyBarcelonaSpain
| | - Martin H Schaefer
- Centre for Genomic Regulation (CRG)The Barcelona Institute of Science and TechnologyBarcelonaSpain
- Department of Experimental OncologyIEO, European Institute of Oncology IRCCSMilanItaly
| | - Luis Serrano
- Centre for Genomic Regulation (CRG)The Barcelona Institute of Science and TechnologyBarcelonaSpain
- Universitat Pompeu Fabra (UPF)BarcelonaSpain
- ICREABarcelonaSpain
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4
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Wu P, Heins ZJ, Muller JT, Katsnelson L, de Bruijn I, Abeshouse AA, Schultz N, Fenyö D, Gao J. Integration and Analysis of CPTAC Proteomics Data in the Context of Cancer Genomics in the cBioPortal. Mol Cell Proteomics 2019; 18:1893-1898. [PMID: 31308250 PMCID: PMC6731080 DOI: 10.1074/mcp.tir119.001673] [Citation(s) in RCA: 102] [Impact Index Per Article: 20.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Revised: 07/14/2019] [Indexed: 11/06/2022] Open
Abstract
The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has produced extensive mass spectrometry-based proteomics data for selected breast, colon, and ovarian tumors from The Cancer Genome Atlas (TCGA). We have incorporated the CPTAC proteomics data into the cBioPortal to support easy exploration and integrative analysis of these proteomic datasets in the context of the clinical and genomics data from the same tumors. cBioPortal is an open source platform for exploring, visualizing, and analyzing multidimensional cancer genomics and clinical data. The public instance of the cBioPortal (http://cbioportal.org/) hosts more than 200 cancer genomics studies, including all of the data from TCGA. Its biologist-friendly interface provides many rich analysis features, including a graphical summary of gene-level data across multiple platforms, correlation analysis between genes or other data types, survival analysis, and per-patient data visualization. Here, we present the integration of the CPTAC mass spectrometry-based proteomics data into the cBioPortal, consisting of 77 breast, 95 colorectal, and 174 ovarian tumors that already have been profiled by TCGA for mutations, copy number alterations, gene expression, and DNA methylation. As a result, the CPTAC data can now be easily explored and analyzed in the cBioPortal in the context of clinical and genomics data. By integrating CPTAC data into cBioPortal, limitations of TCGA proteomics array data can be overcome while also providing a user-friendly web interface, a web API, and an R client to query the mass spectrometry data together with genomic, epigenomic, and clinical data.
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Affiliation(s)
- Pamela Wu
- ‡Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, NY 10016; §Institute for Systems Genetics, New York University School of Medicine, New York, NY 10016; ¶Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY 10016
| | - Zachary J Heins
- ‖Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - James T Muller
- ¶Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY 10016
| | - Lizabeth Katsnelson
- ¶Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY 10016
| | - Ino de Bruijn
- ‖Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Adam A Abeshouse
- ‖Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Nikolaus Schultz
- ‖Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065; **Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - David Fenyö
- ‡Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, NY 10016; §Institute for Systems Genetics, New York University School of Medicine, New York, NY 10016.
| | - Jianjiong Gao
- ‖Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065; **Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065.
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5
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Stewart PA, Welsh EA, Slebos RJC, Fang B, Izumi V, Chambers M, Zhang G, Cen L, Pettersson F, Zhang Y, Chen Z, Cheng CH, Thapa R, Thompson Z, Fellows KM, Francis JM, Saller JJ, Mesa T, Zhang C, Yoder S, DeNicola GM, Beg AA, Boyle TA, Teer JK, Ann Chen Y, Koomen JM, Eschrich SA, Haura EB. Proteogenomic landscape of squamous cell lung cancer. Nat Commun 2019; 10:3578. [PMID: 31395880 PMCID: PMC6687710 DOI: 10.1038/s41467-019-11452-x] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Accepted: 07/08/2019] [Indexed: 12/18/2022] Open
Abstract
How genomic and transcriptomic alterations affect the functional proteome in lung cancer is not fully understood. Here, we integrate DNA copy number, somatic mutations, RNA-sequencing, and expression proteomics in a cohort of 108 squamous cell lung cancer (SCC) patients. We identify three proteomic subtypes, two of which (Inflamed, Redox) comprise 87% of tumors. The Inflamed subtype is enriched with neutrophils, B-cells, and monocytes and expresses more PD-1. Redox tumours are enriched for oxidation-reduction and glutathione pathways and harbor more NFE2L2/KEAP1 alterations and copy gain in the 3q2 locus. Proteomic subtypes are not associated with patient survival. However, B-cell-rich tertiary lymph node structures, more common in Inflamed, are associated with better survival. We identify metabolic vulnerabilities (TP63, PSAT1, and TFRC) in Redox. Our work provides a powerful resource for lung SCC biology and suggests therapeutic opportunities based on redox metabolism and immune cell infiltrates. Squamous cell lung cancer has dismal prognosis due to the dearth of effective treatments. Here, the authors perform an integrated proteogenomic analysis of the disease, revealing three proteomics-based subtypes and suggesting potential therapeutic opportunities.
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Affiliation(s)
- Paul A Stewart
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA.,Biostatistics and Bioinformatics Shared Resource, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Eric A Welsh
- Biostatistics and Bioinformatics Shared Resource, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Robbert J C Slebos
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Bin Fang
- Proteomics Core Facility, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Victoria Izumi
- Proteomics Core Facility, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Matthew Chambers
- Biostatistics and Bioinformatics Shared Resource, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Guolin Zhang
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Ling Cen
- Biostatistics and Bioinformatics Shared Resource, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Fredrik Pettersson
- Biostatistics and Bioinformatics Shared Resource, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Yonghong Zhang
- Biostatistics and Bioinformatics Shared Resource, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Zhihua Chen
- Biostatistics and Bioinformatics Shared Resource, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Chia-Ho Cheng
- Biostatistics and Bioinformatics Shared Resource, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Ram Thapa
- Biostatistics and Bioinformatics Shared Resource, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Zachary Thompson
- Biostatistics and Bioinformatics Shared Resource, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Katherine M Fellows
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Jewel M Francis
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - James J Saller
- Department of Anatomical Pathology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Tania Mesa
- Molecular Genomics Core Facility, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Chaomei Zhang
- Molecular Genomics Core Facility, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Sean Yoder
- Molecular Genomics Core Facility, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Gina M DeNicola
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Amer A Beg
- Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Theresa A Boyle
- Department of Anatomical Pathology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Jamie K Teer
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Yian Ann Chen
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - John M Koomen
- Department of Molecular Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Steven A Eschrich
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Eric B Haura
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA.
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6
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Bramer LM, Stratton KG, White AM, Bleeker AH, Kobold MA, Waters KM, Metz TO, Rodland KD, Webb-Robertson BJM. P-Mart: Interactive Analysis of Ion Abundance Global Proteomics Data. J Proteome Res 2019; 18:1426-1432. [PMID: 30667224 DOI: 10.1021/acs.jproteome.8b00840] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The use of mass-spectrometry-based techniques for global protein profiling of biomedical or environmental experiments has become a major focus in research centered on biomarker discovery; however, one of the most important issues recently highlighted in the new era of omics data generation is the ability to perform analyses in a robust and reproducible manner. This has been hypothesized to be one of the issues hindering the ability of clinical proteomics to successfully identify clinical diagnostic and prognostic biomarkers of disease. P-Mart ( https://pmart.labworks.org ) is a new interactive web-based software environment that enables domain scientists to perform quality-control processing, statistics, and exploration of large-complex proteomics data sets without requiring statistical programming. P-Mart is developed in a manner that allows researchers to perform analyses via a series of modules, explore the results using interactive visualization, and finalize the analyses with a collection of output files documenting all stages of the analysis and a report to allow reproduction of the analysis.
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Affiliation(s)
- Lisa M Bramer
- Computing & Analytics Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States
| | - Kelly G Stratton
- Computing & Analytics Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States
| | - Amanda M White
- Computing & Analytics Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States
| | - Ameila H Bleeker
- Computing & Analytics Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States
| | - Markus A Kobold
- Computing & Analytics Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States
| | - Katrina M Waters
- Biological Sciences Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States
| | - Thomas O Metz
- Biological Sciences Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States
| | - Karin D Rodland
- Biological Sciences Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States.,Department of Cell, Developmental, and Cancer Biology , Oregon Health & Science University , Portland , Oregon 97221 , United States
| | - Bobbie-Jo M Webb-Robertson
- Computing & Analytics Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States
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7
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González-Gomariz J, Guruceaga E, López-Sánchez M, Segura V. Proteogenomics in the context of the Human Proteome Project (HPP). Expert Rev Proteomics 2019; 16:267-275. [PMID: 30654666 DOI: 10.1080/14789450.2019.1571916] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
INTRODUCTION The technological and scientific progress performed in the Human Proteome Project (HPP) has provided to the scientific community a new set of experimental and bioinformatic methods in the challenging field of shotgun and SRM/MRM-based Proteomics. The requirements for a protein to be considered experimentally validated are now well-established, and the information about the human proteome is available in the neXtProt database, while targeted proteomic assays are stored in SRMAtlas. However, the study of the missing proteins continues being an outstanding issue. Areas covered: This review is focused on the implementation of proteogenomic methods designed to improve the detection and validation of the missing proteins. The evolution of the methodological strategies based on the combination of different omic technologies and the use of huge publicly available datasets is shown taking the Chromosome 16 Consortium as reference. Expert commentary: Proteogenomics and other strategies of data analysis implemented within the C-HPP initiative could be used as guidance to complete in a near future the catalog of the human proteins. Besides, in the next years, we will probably witness their use in the B/D-HPP initiative to go a step forward on the implications of the proteins in the human biology and disease.
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Affiliation(s)
- José González-Gomariz
- a Bioinformatics Platform, Center for Applied Medical Research , University of Navarra , Pamplona , Spain.,b IdiSNA , Navarra Institute for Health Research , Pamplona , Spain
| | - Elizabeth Guruceaga
- a Bioinformatics Platform, Center for Applied Medical Research , University of Navarra , Pamplona , Spain.,b IdiSNA , Navarra Institute for Health Research , Pamplona , Spain
| | - Macarena López-Sánchez
- a Bioinformatics Platform, Center for Applied Medical Research , University of Navarra , Pamplona , Spain
| | - Victor Segura
- a Bioinformatics Platform, Center for Applied Medical Research , University of Navarra , Pamplona , Spain.,b IdiSNA , Navarra Institute for Health Research , Pamplona , Spain
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8
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Bittremieux W, Tabb DL, Impens F, Staes A, Timmerman E, Martens L, Laukens K. Quality control in mass spectrometry-based proteomics. MASS SPECTROMETRY REVIEWS 2018; 37:697-711. [PMID: 28802010 DOI: 10.1002/mas.21544] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2017] [Revised: 07/24/2017] [Accepted: 07/24/2017] [Indexed: 05/21/2023]
Abstract
Mass spectrometry is a highly complex analytical technique and mass spectrometry-based proteomics experiments can be subject to a large variability, which forms an obstacle to obtaining accurate and reproducible results. Therefore, a comprehensive and systematic approach to quality control is an essential requirement to inspire confidence in the generated results. A typical mass spectrometry experiment consists of multiple different phases including the sample preparation, liquid chromatography, mass spectrometry, and bioinformatics stages. We review potential sources of variability that can impact the results of a mass spectrometry experiment occurring in all of these steps, and we discuss how to monitor and remedy the negative influences on the experimental results. Furthermore, we describe how specialized quality control samples of varying sample complexity can be incorporated into the experimental workflow and how they can be used to rigorously assess detailed aspects of the instrument performance.
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Affiliation(s)
- Wout Bittremieux
- Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
- Biomedical Informatics Research Center Antwerp (Biomina), University of Antwerp/Antwerp University Hospital, Edegem, Belgium
| | - David L Tabb
- Division of Molecular Biology and Human Genetics, Stellenbosch University Faculty of Medicine and Health Sciences, Tygerberg Hospital, Cape Town, South Africa
| | - Francis Impens
- VIB Proteomics Core, Ghent, Belgium
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium
- Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium
| | - An Staes
- VIB Proteomics Core, Ghent, Belgium
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium
- Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium
| | - Evy Timmerman
- VIB Proteomics Core, Ghent, Belgium
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium
- Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium
- Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, Zwijnaarde, Belgium
| | - Kris Laukens
- Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
- Biomedical Informatics Research Center Antwerp (Biomina), University of Antwerp/Antwerp University Hospital, Edegem, Belgium
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9
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Lee PY, Chin SF, Low TY, Jamal R. Probing the colorectal cancer proteome for biomarkers: Current status and perspectives. J Proteomics 2018; 187:93-105. [PMID: 29953962 DOI: 10.1016/j.jprot.2018.06.014] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Revised: 06/13/2018] [Accepted: 06/23/2018] [Indexed: 02/07/2023]
Abstract
Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide. Biomarkers that can facilitate better clinical management of CRC are in high demand to improve patient outcome and to reduce mortality. In this regard, proteomic analysis holds a promising prospect in the hunt of novel biomarkers for CRC and in understanding the mechanisms underlying tumorigenesis. This review aims to provide an overview of the current progress of proteomic research, focusing on discovery and validation of diagnostic biomarkers for CRC. We will summarize the contributions of proteomic strategies to recent discoveries of protein biomarkers for CRC and also briefly discuss the potential and challenges of different proteomic approaches in biomarker discovery and translational applications.
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Affiliation(s)
- Pey Yee Lee
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, 56000 Kuala Lumpur, Malaysia.
| | - Siok-Fong Chin
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, 56000 Kuala Lumpur, Malaysia
| | - Teck Yew Low
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, 56000 Kuala Lumpur, Malaysia
| | - Rahman Jamal
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, 56000 Kuala Lumpur, Malaysia
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10
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Lim LC, Lim YM. Proteome Heterogeneity in Colorectal Cancer. Proteomics 2018; 18. [PMID: 29316255 DOI: 10.1002/pmic.201700169] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Revised: 12/17/2017] [Indexed: 01/26/2023]
Abstract
Tumor heterogeneity is an important feature of colorectal cancer (CRC) manifested by dynamic changes in gene expression, protein expression, and availability of different tumor subtypes. Recent publications in the past 10 years have revealed proteome heterogeneity between different colorectal tumors and within the same tumor site. This paper reviews recent research works on the proteome heterogeneity in CRC, which includes the heterogeneity within a single tumor (intratumor heterogeneity), between different anatomical sites at the same organ, and between primary and metastatic sites (intertumor heterogeneity). The potential use of proteome heterogeneity in precision medicine and its implications in biomarker discovery and therapeutic outcomes will be discussed. Identification of the unique proteome landscape between and within individual tumors is imperative for understanding cancer biology and the management of CRC patients.
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Affiliation(s)
- Lay Cheng Lim
- Centre for Cancer Research, Faculty of Medicine and Health Sciences, University of Tunku Abdul Rahman, Selangor, Malaysia
| | - Yang Mooi Lim
- Centre for Cancer Research, Faculty of Medicine and Health Sciences, University of Tunku Abdul Rahman, Selangor, Malaysia
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11
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Webb-Robertson BJM, Bramer LM, Jensen JL, Kobold MA, Stratton KG, White AM, Rodland KD. P-MartCancer-Interactive Online Software to Enable Analysis of Shotgun Cancer Proteomic Datasets. Cancer Res 2017; 77:e47-e50. [PMID: 29092938 PMCID: PMC5679244 DOI: 10.1158/0008-5472.can-17-0335] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 05/19/2017] [Accepted: 07/12/2017] [Indexed: 11/16/2022]
Abstract
P-MartCancer is an interactive web-based software environment that enables statistical analyses of peptide or protein data, quantitated from mass spectrometry-based global proteomics experiments, without requiring in-depth knowledge of statistical programming. P-MartCancer offers a series of statistical modules associated with quality assessment, peptide and protein statistics, protein quantification, and exploratory data analyses driven by the user via customized workflows and interactive visualization. Currently, P-MartCancer offers access and the capability to analyze multiple cancer proteomic datasets generated through the Clinical Proteomics Tumor Analysis Consortium at the peptide, gene, and protein levels. P-MartCancer is deployed as a web service (https://pmart.labworks.org/cptac.html), alternatively available via Docker Hub (https://hub.docker.com/r/pnnl/pmart-web/). Cancer Res; 77(21); e47-50. ©2017 AACR.
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Affiliation(s)
| | - Lisa M Bramer
- Pacific Northwest National Laboratory, Richland, Washington
| | | | | | | | - Amanda M White
- Pacific Northwest National Laboratory, Richland, Washington
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12
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Bittremieux W, Walzer M, Tenzer S, Zhu W, Salek RM, Eisenacher M, Tabb DL. The Human Proteome Organization-Proteomics Standards Initiative Quality Control Working Group: Making Quality Control More Accessible for Biological Mass Spectrometry. Anal Chem 2017; 89:4474-4479. [PMID: 28318237 DOI: 10.1021/acs.analchem.6b04310] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
To have confidence in results acquired during biological mass spectrometry experiments, a systematic approach to quality control is of vital importance. Nonetheless, until now, only scattered initiatives have been undertaken to this end, and these individual efforts have often not been complementary. To address this issue, the Human Proteome Organization-Proteomics Standards Initiative has established a new working group on quality control at its meeting in the spring of 2016. The goal of this working group is to provide a unifying framework for quality control data. The initial focus will be on providing a community-driven standardized file format for quality control. For this purpose, the previously proposed qcML format will be adapted to support a variety of use cases for both proteomics and metabolomics applications, and it will be established as an official PSI format. An important consideration is to avoid enforcing restrictive requirements on quality control but instead provide the basic technical necessities required to support extensive quality control for any type of mass spectrometry-based workflow. We want to emphasize that this is an open community effort, and we seek participation from all scientists with an interest in this field.
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Affiliation(s)
- Wout Bittremieux
- Department of Mathematics and Computer Science, University of Antwerp , Middelheimlaan 1, 2020 Antwerp, Belgium.,Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital , Wilrijkstraat 10, 2650 Edegem, Belgium
| | - Mathias Walzer
- Department of Computer Science, University of Tübingen , Tübingen 72076, Germany.,Center for Bioinformatics, University of Tübingen , Tübingen 72074, Germany
| | - Stefan Tenzer
- Institute for Immunology, University Medical Center of the Johannes-Gutenberg University Mainz D 55131, Germany
| | - Weimin Zhu
- National Center for Protein Science , No. 38, Science Park Road, Changping District, Beijing 102206, China
| | - Reza M Salek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) , Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Martin Eisenacher
- Medical Bioinformatics, Medizinisches Proteom-Center, Ruhr-University Bochum , Bochum 44801, Germany
| | - David L Tabb
- Division of Molecular Biology and Human Genetics, Stellenbosch University Faculty of Medicine and Health Sciences , Tygerberg Hospital, Francie Van Zijl Drive, Cape Town 7505, South Africa
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13
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Kistowski M, Dębski J, Karczmarski J, Paziewska A, Olędzki J, Mikula M, Ostrowski J, Dadlez M. A Strong Neutrophil Elastase Proteolytic Fingerprint Marks the Carcinoma Tumor Proteome. Mol Cell Proteomics 2016; 16:213-227. [PMID: 27927741 DOI: 10.1074/mcp.m116.058818] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2016] [Revised: 11/12/2016] [Indexed: 12/18/2022] Open
Abstract
Proteolytic cascades are deeply involved in critical stages of cancer progression. During the course of peptide-wise analysis of shotgun proteomic data sets representative of colon adenocarcinoma (AC) and ulcerative colitis (UC), we detected a cancer-specific proteolytic fingerprint composed of a set of numerous protein fragments cleaved C-terminally to V, I, A, T, or C residues, significantly overrepresented in AC. A peptide set linked by a common VIATC cleavage consensus was the only prominent cancer-specific proteolytic fingerprint detected. This sequence consensus indicated neutrophil elastase as a source of the fingerprint. We also found that a large fraction of affected proteins are RNA processing proteins associated with the nuclear fraction and mostly cleaved within their functionally important RNA-binding domains. Thus, we detected a new class of cancer-specific peptides that are possible markers of tumor-infiltrating neutrophil activity, which often correlates with the clinical outcome. Data are available via ProteomeXchange with identifiers: PXD005274 (Data set 1) and PXD004249 (Data set 2). Our results indicate the value of peptide-wise analysis of large global proteomic analysis data sets as opposed to protein-wise analysis, in which outlier differential peptides are usually neglected.
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Affiliation(s)
- Michał Kistowski
- From the ‡Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Pawińskiego 5A, 02-106 Warsaw
| | - Janusz Dębski
- From the ‡Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Pawińskiego 5A, 02-106 Warsaw
| | - Jakub Karczmarski
- §Department of Genetics, Maria Skłodowska-Curie Memorial Cancer Center and Institute of Oncology, Wilhelma Konrada Roentgena 5, 02-781 Warsaw, Poland
| | - Agnieszka Paziewska
- §Department of Genetics, Maria Skłodowska-Curie Memorial Cancer Center and Institute of Oncology, Wilhelma Konrada Roentgena 5, 02-781 Warsaw, Poland
| | - Jacek Olędzki
- From the ‡Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Pawińskiego 5A, 02-106 Warsaw
| | - Michał Mikula
- §Department of Genetics, Maria Skłodowska-Curie Memorial Cancer Center and Institute of Oncology, Wilhelma Konrada Roentgena 5, 02-781 Warsaw, Poland
| | - Jerzy Ostrowski
- ¶Department of Gastroenterology Hepatology and Clinical Oncology, Medical Center for Postgraduate Education, Warsaw, Poland
| | - Michał Dadlez
- From the ‡Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Pawińskiego 5A, 02-106 Warsaw;
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14
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Abstract
Label-free quantitative methods are advantageous in bottom-up (shotgun) proteomics because they are robust and can easily be applied to different workflows without additional cost. Both label-based and label-free approaches are routinely applied to discovery-based proteomics experiments and are widely accepted as semiquantitative. Label-free quantitation approaches are segregated into two distinct approaches: peak-abundance-based approaches and spectral counting (SpC). Peak abundance approaches like MaxLFQ, which is integrated into the MaxQuant environment, require precursor peak alignment that is computationally intensive and cannot be routinely applied to low-resolution data. Not limited by these constraints, SpC approaches simply use the number of peptide identifications corresponding to a given protein as a measurement of protein abundance. We show here that spectral counts from multidimensional proteomic data sets have a mean-dispersion relationship that can be modeled in edgeR. Furthermore, by simulating spectral counts, we show that this approach can routinely be applied to large-scale discovery proteomics data sets to determine differential protein expression.
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Affiliation(s)
- Owen E Branson
- The Ohio State Biochemistry Graduate Program, The Ohio State University , Columbus, Ohio 43210, United States.,Department of Molecular Virology, Immunology and Medical Genetics, The Ohio State University , Columbus, Ohio 43210, United States.,Comprehensive Cancer Center, The Ohio State University , Columbus, Ohio 43210, United States
| | - Michael A Freitas
- The Ohio State Biochemistry Graduate Program, The Ohio State University , Columbus, Ohio 43210, United States.,Department of Molecular Virology, Immunology and Medical Genetics, The Ohio State University , Columbus, Ohio 43210, United States.,Comprehensive Cancer Center, The Ohio State University , Columbus, Ohio 43210, United States
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15
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Bittremieux W, Valkenborg D, Martens L, Laukens K. Computational quality control tools for mass spectrometry proteomics. Proteomics 2016; 17. [DOI: 10.1002/pmic.201600159] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2016] [Revised: 07/28/2016] [Accepted: 08/19/2016] [Indexed: 12/30/2022]
Affiliation(s)
- Wout Bittremieux
- Department of Mathematics and Computer Science; University of Antwerp; Antwerp Belgium
- Biomedical Informatics Research Center Antwerp (biomina); University of Antwerp/Antwerp, University Hospital; Edegem Belgium
| | - Dirk Valkenborg
- Flemish Institute for Technological Research (VITO); Mol Belgium
- CFP; University of Antwerp; Antwerp Belgium
- I-BioStat; Hasselt University; Diepenbeek Belgium
| | - Lennart Martens
- Medical Biotechnology Center; VIB; Ghent Belgium
- Department of Biochemistry, Faculty of Medicine and Health Sciences; Ghent University; Ghent Belgium
- Bioinformatics Institute Ghent; Ghent University; Zwijnaarde Belgium
| | - Kris Laukens
- Department of Mathematics and Computer Science; University of Antwerp; Antwerp Belgium
- Biomedical Informatics Research Center Antwerp (biomina); University of Antwerp/Antwerp, University Hospital; Edegem Belgium
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16
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White FM, Wolf-Yadlin A. Methods for the Analysis of Protein Phosphorylation-Mediated Cellular Signaling Networks. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2016; 9:295-315. [PMID: 27049636 DOI: 10.1146/annurev-anchem-071015-041542] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Protein phosphorylation-mediated cellular signaling networks regulate almost all aspects of cell biology, including the responses to cellular stimulation and environmental alterations. These networks are highly complex and comprise hundreds of proteins and potentially thousands of phosphorylation sites. Multiple analytical methods have been developed over the past several decades to identify proteins and protein phosphorylation sites regulating cellular signaling, and to quantify the dynamic response of these sites to different cellular stimulation. Here we provide an overview of these methods, including the fundamental principles governing each method, their relative strengths and weaknesses, and some examples of how each method has been applied to the analysis of complex signaling networks. When applied correctly, each of these techniques can provide insight into the topology, dynamics, and regulation of protein phosphorylation signaling networks.
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Affiliation(s)
- Forest M White
- Department of Biological Engineering and David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139;
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17
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Bittremieux W, Meysman P, Martens L, Valkenborg D, Laukens K. Unsupervised Quality Assessment of Mass Spectrometry Proteomics Experiments by Multivariate Quality Control Metrics. J Proteome Res 2016; 15:1300-7. [PMID: 26974716 DOI: 10.1021/acs.jproteome.6b00028] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Despite many technological and computational advances, the results of a mass spectrometry proteomics experiment are still subject to a large variability. For the understanding and evaluation of how technical variability affects the results of an experiment, several computationally derived quality control metrics have been introduced. However, despite the availability of these metrics, a systematic approach to quality control is often still lacking because the metrics are not fully understood and are hard to interpret. Here, we present a toolkit of powerful techniques to analyze and interpret multivariate quality control metrics to assess the quality of mass spectrometry proteomics experiments. We show how unsupervised techniques applied to these quality control metrics can provide an initial discrimination between low-quality experiments and high-quality experiments prior to manual investigation. Furthermore, we provide a technique to obtain detailed information on the quality control metrics that are related to the decreased performance, which can be used as actionable information to improve the experimental setup. Our toolkit is released as open-source and can be downloaded from https://bitbucket.org/proteinspector/qc_analysis/ .
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Affiliation(s)
- Wout Bittremieux
- Department of Mathematics and Computer Science, University of Antwerp , 2020 Antwerp, Belgium.,Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital , 2650 Edegem, Belgium
| | - Pieter Meysman
- Department of Mathematics and Computer Science, University of Antwerp , 2020 Antwerp, Belgium.,Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital , 2650 Edegem, Belgium
| | - Lennart Martens
- Department of Medical Protein Research, VIB , 9000 Ghent, Belgium.,Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University , 9000 Ghent, Belgium.,Bioinformatics Institute Ghent, Ghent University , 9000 Ghent, Belgium
| | - Dirk Valkenborg
- Flemish Institute for Technological Research (VITO) , 2400 Mol, Belgium.,CFP, University of Antwerp , 2020 Antwerp, Belgium.,I-BioStat, Hasselt University , 3590 Diepenbeek, Belgium
| | - Kris Laukens
- Department of Mathematics and Computer Science, University of Antwerp , 2020 Antwerp, Belgium.,Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital , 2650 Edegem, Belgium
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18
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Subbannayya Y, Pinto SM, Gowda H, Prasad TSK. Proteogenomics for understanding oncology: recent advances and future prospects. Expert Rev Proteomics 2016; 13:297-308. [PMID: 26697917 DOI: 10.1586/14789450.2016.1136217] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The concept of proteogenomics has emerged rapidly as a valuable approach to integrate mass spectrometry-derived proteomic data with genomic and transcriptomic data. It is used to harness the full potential of the former dataset in the discovery of potential biomarkers, therapeutic targets and novel proteins associated with various biological processes including diseases. Proteogenomic strategies have been successfully utilized to identify novel genes and redefine annotation of existing gene models in various genomes. In recent years, this approach has been extended to the field of cancer biology to unravel complexities in the tumor genomes and proteomes. Standard proteomics workflows employing translated cancer genomes and transcriptomes can potentially identify peptides from mutant proteins, splice variants and fusion proteins in the tumor proteome, which in addition to the currently available biomarker panels can serve as potential diagnostic and prognostic biomarkers, besides having therapeutic utility. This review focuses on the role of proteogenomics to understand cancer biology.
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Affiliation(s)
- Yashwanth Subbannayya
- a YU-IOB Center for Systems Biology and Molecular Medicine , Yenepoya University , Mangalore, India.,b Institute of Bioinformatics , Bangalore , India
| | - Sneha M Pinto
- a YU-IOB Center for Systems Biology and Molecular Medicine , Yenepoya University , Mangalore, India.,b Institute of Bioinformatics , Bangalore , India
| | - Harsha Gowda
- a YU-IOB Center for Systems Biology and Molecular Medicine , Yenepoya University , Mangalore, India.,b Institute of Bioinformatics , Bangalore , India
| | - T S Keshava Prasad
- a YU-IOB Center for Systems Biology and Molecular Medicine , Yenepoya University , Mangalore, India.,b Institute of Bioinformatics , Bangalore , India.,c NIMHANS-IOB Proteomics and Bioinformatics Laboratory, Neurobiology Research Centre , National Institute of Mental Health and Neurosciences , Bangalore , India
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19
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Neri E, Guidi E, Pancrazi F, Castagna M, Castelluccio E, Balestri R, Buccianti P, Masi L, Falcone A, Manfredi B, Faggioni L, Bartolozzi C. MRI tumor volume reduction rate vs tumor regression grade in the pre-operative re-staging of locally advanced rectal cancer after chemo-radiotherapy. Eur J Radiol 2015; 84:2438-43. [PMID: 26462793 DOI: 10.1016/j.ejrad.2015.08.008] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2015] [Revised: 08/05/2015] [Accepted: 08/13/2015] [Indexed: 12/11/2022]
Abstract
OBJECTIVE To compare tumor volume reduction rate (TVRR) measured by MR volumetry after preoperative chemoradiotherapy (CRT) and pathological tumor regression grade (TRG) in locally advanced rectal cancer (LARC). MATERIAL AND METHODS In total, 20 patients with LARC (cT3-T4) treated with CRT followed by Total Mesorectal Excision (TME) between April 2011 and April 2013 were analyzed retrospectively. Pre- and post- CRT tumor volumes (MR volumetry) were measured on 3D MR sequences. TVRR was determined using the equation TVRR (%)=(pre-CRT tumor volume-post-CRT tumor volume)×100/pre-CRT tumor volume. The downstaging (defined as ypT0-T2) of tumor mass was evaluated and the correlation between TVRR and TRG was calculated with the method proposed by Dworak using the Spearman rank test. RESULTS The median TVRR was 77.3% (range, 26.4-99.3%); TVRR was >60% in 18 cases (90%) and in 8 of these patients (44.4 %) it was >80%. Downstaging of tumor lesions was obtained in 15 patients (75%). In 4 cases there was a complete tumor regression (TRG4) at histological examination and in the same patients there was also a TVRR>80% measured by MR volumetry. A statistically significant correlation between TVRR and TRG (r(s)=0.5466, p=0.0126) was observed. CONCLUSION TVRR after preoperative CRT correlates with TRG in LARC. The MR volumetry is a prognostic factor to estimate the tumor response after preoperative CRT. TVRR data may be an useful biomarker for tailoring surgery and postoperative adjuvant chemotherapy.
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Affiliation(s)
- Emanuele Neri
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Unit of Diagnostic and Interventional Radiology, Pisa University Hospital, Italy.
| | - Elisa Guidi
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Unit of Diagnostic and Interventional Radiology, Pisa University Hospital, Italy
| | - Francesca Pancrazi
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Unit of Diagnostic and Interventional Radiology, Pisa University Hospital, Italy
| | - Maura Castagna
- Unit of Anatomy and Pathology, Pisa University Hospital, Italy
| | | | | | | | - Luca Masi
- Unit of Oncology, Pisa University Hospital, Italy
| | | | | | - Lorenzo Faggioni
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Unit of Diagnostic and Interventional Radiology, Pisa University Hospital, Italy
| | - Carlo Bartolozzi
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Unit of Diagnostic and Interventional Radiology, Pisa University Hospital, Italy
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20
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Slebos RJ, Wang X, Wang X, Zhang B, Tabb DL, Liebler DC. Proteomic analysis of colon and rectal carcinoma using standard and customized databases. Sci Data 2015; 2:150022. [PMID: 26110064 PMCID: PMC4477697 DOI: 10.1038/sdata.2015.22] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2014] [Accepted: 02/17/2015] [Indexed: 12/17/2022] Open
Abstract
Understanding proteomic differences underlying the different phenotypic classes of colon and rectal carcinoma is important and may eventually lead to a better assessment of clinical behavior of these cancers. We here present a comprehensive description of the proteomic data obtained from 90 colon and rectal carcinomas previously subjected to genomic analysis by The Cancer Genome Atlas (TCGA). Here, the primary instrument files and derived secondary data files are compiled and presented in forms that will allow further analyses of the biology of colon and rectal carcinoma. We also discuss new challenges in processing these large proteomic datasets for relevant proteins and protein variants.
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Affiliation(s)
- Robbert J.C. Slebos
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Jim Ayers Institute for Precancer Detection and Diagnosis, Vanderbilt-Ingram Cancer Center, Nashville, TN 37232, USA
| | - Xia Wang
- Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH 45221, USA
| | - Xaojing Wang
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Bing Zhang
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - David L. Tabb
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Daniel C. Liebler
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Jim Ayers Institute for Precancer Detection and Diagnosis, Vanderbilt-Ingram Cancer Center, Nashville, TN 37232, USA
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