1
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Dai C, Pfeuffer J, Wang H, Zheng P, Käll L, Sachsenberg T, Demichev V, Bai M, Kohlbacher O, Perez-Riverol Y. quantms: a cloud-based pipeline for quantitative proteomics enables the reanalysis of public proteomics data. Nat Methods 2024; 21:1603-1607. [PMID: 38965444 PMCID: PMC11399091 DOI: 10.1038/s41592-024-02343-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 06/03/2024] [Indexed: 07/06/2024]
Abstract
The volume of public proteomics data is rapidly increasing, causing a computational challenge for large-scale reanalysis. Here, we introduce quantms ( https://quant,ms.org/ ), an open-source cloud-based pipeline for massively parallel proteomics data analysis. We used quantms to reanalyze 83 public ProteomeXchange datasets, comprising 29,354 instrument files from 13,132 human samples, to quantify 16,599 proteins based on 1.03 million unique peptides. quantms is based on standard file formats improving the reproducibility, submission and dissemination of the data to ProteomeXchange.
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Affiliation(s)
- Chengxin Dai
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, Beijing, China
| | - Julianus Pfeuffer
- Algorithmic Bioinformatics, Freie Universität Berlin, Berlin, Germany
| | - Hong Wang
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Ping Zheng
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Lukas Käll
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Timo Sachsenberg
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
| | | | - Mingze Bai
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, Beijing, China
| | - Oliver Kohlbacher
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
- Institute for Translational Bioinformatics, University Hospital Tübingen, Tübingen, Germany
| | - Yasset Perez-Riverol
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK.
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2
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Stelloo S, Alejo-Vinogradova MT, van Gelder CAGH, Zijlmans DW, van Oostrom MJ, Valverde JM, Lamers LA, Rus T, Sobrevals Alcaraz P, Schäfers T, Furlan C, Jansen PWTC, Baltissen MPA, Sonnen KF, Burgering B, Altelaar MAFM, Vos HR, Vermeulen M. Deciphering lineage specification during early embryogenesis in mouse gastruloids using multilayered proteomics. Cell Stem Cell 2024; 31:1072-1090.e8. [PMID: 38754429 DOI: 10.1016/j.stem.2024.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 01/10/2024] [Accepted: 04/19/2024] [Indexed: 05/18/2024]
Abstract
Gastrulation is a critical stage in embryonic development during which the germ layers are established. Advances in sequencing technologies led to the identification of gene regulatory programs that control the emergence of the germ layers and their derivatives. However, proteome-based studies of early mammalian development are scarce. To overcome this, we utilized gastruloids and a multilayered mass spectrometry-based proteomics approach to investigate the global dynamics of (phospho) protein expression during gastruloid differentiation. Our findings revealed many proteins with temporal expression and unique expression profiles for each germ layer, which we also validated using single-cell proteomics technology. Additionally, we profiled enhancer interaction landscapes using P300 proximity labeling, which revealed numerous gastruloid-specific transcription factors and chromatin remodelers. Subsequent degron-based perturbations combined with single-cell RNA sequencing (scRNA-seq) identified a critical role for ZEB2 in mouse and human somitogenesis. Overall, this study provides a rich resource for developmental and synthetic biology communities endeavoring to understand mammalian embryogenesis.
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Affiliation(s)
- Suzan Stelloo
- Department of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Oncode Institute, Radboud University Nijmegen, 6525 GA Nijmegen, the Netherlands.
| | - Maria Teresa Alejo-Vinogradova
- Department of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Oncode Institute, Radboud University Nijmegen, 6525 GA Nijmegen, the Netherlands
| | - Charlotte A G H van Gelder
- Molecular Cancer Research, Center for Molecular Medicine, Oncode Institute, University Medical Center Utrecht, Utrecht University, 3584 CG Utrecht, the Netherlands
| | - Dick W Zijlmans
- Department of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Oncode Institute, Radboud University Nijmegen, 6525 GA Nijmegen, the Netherlands
| | - Marek J van Oostrom
- Hubrecht Institute, KNAW (Royal Netherlands Academy of Arts and Sciences), University Medical Center Utrecht, 3584 CT Utrecht, the Netherlands
| | - Juan Manuel Valverde
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, 3584 CA Utrecht, the Netherlands; Netherlands Proteomics Center, 3584 CH Utrecht, the Netherlands
| | - Lieke A Lamers
- Department of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Oncode Institute, Radboud University Nijmegen, 6525 GA Nijmegen, the Netherlands
| | - Teja Rus
- Department of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Oncode Institute, Radboud University Nijmegen, 6525 GA Nijmegen, the Netherlands
| | - Paula Sobrevals Alcaraz
- Molecular Cancer Research, Center for Molecular Medicine, Oncode Institute, University Medical Center Utrecht, Utrecht University, 3584 CG Utrecht, the Netherlands
| | - Tilman Schäfers
- Department of Molecular Developmental Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University Nijmegen, 6525 GA Nijmegen, the Netherlands
| | - Cristina Furlan
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, 6708 WE Wageningen, the Netherlands
| | - Pascal W T C Jansen
- Department of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Oncode Institute, Radboud University Nijmegen, 6525 GA Nijmegen, the Netherlands
| | - Marijke P A Baltissen
- Department of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Oncode Institute, Radboud University Nijmegen, 6525 GA Nijmegen, the Netherlands
| | - Katharina F Sonnen
- Hubrecht Institute, KNAW (Royal Netherlands Academy of Arts and Sciences), University Medical Center Utrecht, 3584 CT Utrecht, the Netherlands
| | - Boudewijn Burgering
- Molecular Cancer Research, Center for Molecular Medicine, Oncode Institute, University Medical Center Utrecht, Utrecht University, 3584 CG Utrecht, the Netherlands
| | - Maarten A F M Altelaar
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, 3584 CA Utrecht, the Netherlands; Netherlands Proteomics Center, 3584 CH Utrecht, the Netherlands
| | - Harmjan R Vos
- Molecular Cancer Research, Center for Molecular Medicine, Oncode Institute, University Medical Center Utrecht, Utrecht University, 3584 CG Utrecht, the Netherlands
| | - Michiel Vermeulen
- Department of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Oncode Institute, Radboud University Nijmegen, 6525 GA Nijmegen, the Netherlands; Division of Molecular Genetics, Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands.
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3
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Wang S, Collins A, Prakash A, Fexova S, Papatheodorou I, Jones AR, Vizcaíno JA. Integrated Proteomics Analysis of Baseline Protein Expression in Pig Tissues. J Proteome Res 2024; 23:1948-1959. [PMID: 38717300 PMCID: PMC11165573 DOI: 10.1021/acs.jproteome.3c00741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 02/16/2024] [Accepted: 04/18/2024] [Indexed: 06/13/2024]
Abstract
The availability of an increasingly large amount of public proteomics data sets presents an opportunity for performing combined analyses to generate comprehensive organism-wide protein expression maps across different organisms and biological conditions. Sus scrofa, a domestic pig, is a model organism relevant for food production and for human biomedical research. Here, we reanalyzed 14 public proteomics data sets from the PRIDE database coming from pig tissues to assess baseline (without any biological perturbation) protein abundance in 14 organs, encompassing a total of 20 healthy tissues from 128 samples. The analysis involved the quantification of protein abundance in 599 mass spectrometry runs. We compared protein expression patterns among different pig organs and examined the distribution of proteins across these organs. Then, we studied how protein abundances were compared across different data sets and studied the tissue specificity of the detected proteins. Of particular interest, we conducted a comparative analysis of protein expression between pig and human tissues, revealing a high degree of correlation in protein expression among orthologs, particularly in brain, kidney, heart, and liver samples. We have integrated the protein expression results into the Expression Atlas resource for easy access and visualization of the protein expression data individually or alongside gene expression data.
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Affiliation(s)
- Shengbo Wang
- European
Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Andrew Collins
- Institute
of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Ananth Prakash
- European
Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
- Open
Targets, Wellcome Genome
Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Silvie Fexova
- European
Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Irene Papatheodorou
- European
Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
- Open
Targets, Wellcome Genome
Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Andrew R. Jones
- Institute
of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Juan Antonio Vizcaíno
- European
Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
- Open
Targets, Wellcome Genome
Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
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4
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Ma T, Wang J. GraphPath: a graph attention model for molecular stratification with interpretability based on the pathway-pathway interaction network. Bioinformatics 2024; 40:btae165. [PMID: 38530778 PMCID: PMC11007237 DOI: 10.1093/bioinformatics/btae165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 02/22/2024] [Accepted: 03/22/2024] [Indexed: 03/28/2024] Open
Abstract
MOTIVATION Studying the molecular heterogeneity of cancer is essential for achieving personalized therapy. At the same time, understanding the biological processes that drive cancer development can lead to the identification of valuable therapeutic targets. Therefore, achieving accurate and interpretable clinical predictions requires paramount attention to thoroughly characterizing patients at both the molecular and biological pathway levels. RESULTS Here, we present GraphPath, a biological knowledge-driven graph neural network with multi-head self-attention mechanism that implements the pathway-pathway interaction network. We train GraphPath to classify the cancer status of patients with prostate cancer based on their multi-omics profiling. Experiment results show that our method outperforms P-NET and other baseline methods. Besides, two external cohorts are used to validate that the model can be generalized to unseen samples with adequate predictive performance. We reduce the dimensionality of latent pathway embeddings and visualize corresponding classes to further demonstrate the optimal performance of the model. Additionally, since GraphPath's predictions are interpretable, we identify target cancer-associated pathways that significantly contribute to the model's predictions. Such a robust and interpretable model has the potential to greatly enhance our understanding of cancer's biological mechanisms and accelerate the development of targeted therapies. AVAILABILITY AND IMPLEMENTATION https://github.com/amazingma/GraphPath.
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Affiliation(s)
- Teng Ma
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 41083, Hunan, China
| | - Jianxin Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 41083, Hunan, China
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5
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Teng PN, Schaaf JP, Abulez T, Hood BL, Wilson KN, Litzi TJ, Mitchell D, Conrads KA, Hunt AL, Olowu V, Oliver J, Park FS, Edwards M, Chiang A, Wilkerson MD, Raj-Kumar PK, Tarney CM, Darcy KM, Phippen NT, Maxwell GL, Conrads TP, Bateman NW. ProteoMixture: A cell type deconvolution tool for bulk tissue proteomic data. iScience 2024; 27:109198. [PMID: 38439970 PMCID: PMC10910246 DOI: 10.1016/j.isci.2024.109198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 11/04/2023] [Accepted: 02/07/2024] [Indexed: 03/06/2024] Open
Abstract
Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations, and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript-level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome and transcriptome data generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co-quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture (r ≥ 0.63) in two commonly used deconvolution algorithms, ESTIMATE or ConsensusTME. We further developed and optimized protein-based signatures estimating cell admixture proportions and benchmarked these using bulk tumor proteomic data from over 150 patients with HGSOC. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://lmdomics.org/ProteoMixture/.
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Affiliation(s)
- Pang-ning Teng
- Gynecologic Cancer Center of Excellence and the Women’s Health Integrated Research Center, Annandale, VA 22003, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD 20817, USA
| | - Joshua P. Schaaf
- Gynecologic Cancer Center of Excellence and the Women’s Health Integrated Research Center, Annandale, VA 22003, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD 20817, USA
| | - Tamara Abulez
- Gynecologic Cancer Center of Excellence and the Women’s Health Integrated Research Center, Annandale, VA 22003, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD 20817, USA
| | - Brian L. Hood
- Gynecologic Cancer Center of Excellence and the Women’s Health Integrated Research Center, Annandale, VA 22003, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD 20817, USA
| | - Katlin N. Wilson
- Gynecologic Cancer Center of Excellence and the Women’s Health Integrated Research Center, Annandale, VA 22003, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD 20817, USA
| | - Tracy J. Litzi
- Gynecologic Cancer Center of Excellence and the Women’s Health Integrated Research Center, Annandale, VA 22003, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD 20817, USA
| | - David Mitchell
- Gynecologic Cancer Center of Excellence and the Women’s Health Integrated Research Center, Annandale, VA 22003, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD 20817, USA
| | - Kelly A. Conrads
- Gynecologic Cancer Center of Excellence and the Women’s Health Integrated Research Center, Annandale, VA 22003, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD 20817, USA
| | - Allison L. Hunt
- Gynecologic Cancer Center of Excellence and the Women’s Health Integrated Research Center, Annandale, VA 22003, USA
- Women’s Health Integrated Research Center, Women’s Service Line, Inova Health System, Falls Church, VA 22042, USA
| | - Victoria Olowu
- Gynecologic Cancer Center of Excellence and the Women’s Health Integrated Research Center, Annandale, VA 22003, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD 20817, USA
| | - Julie Oliver
- Gynecologic Cancer Center of Excellence and the Women’s Health Integrated Research Center, Annandale, VA 22003, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD 20817, USA
| | - Fred S. Park
- Gynecologic Cancer Center of Excellence and the Women’s Health Integrated Research Center, Annandale, VA 22003, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD 20817, USA
| | - Marshé Edwards
- Gynecologic Cancer Center of Excellence and the Women’s Health Integrated Research Center, Annandale, VA 22003, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD 20817, USA
| | - AiChun Chiang
- Gynecologic Cancer Center of Excellence and the Women’s Health Integrated Research Center, Annandale, VA 22003, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD 20817, USA
| | - Matthew D. Wilkerson
- Center for Military Precision Health, Department of Anatomy, Physiology and Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA
| | | | - Christopher M. Tarney
- Gynecologic Cancer Center of Excellence and the Women’s Health Integrated Research Center, Annandale, VA 22003, USA
- The John P. Murtha Cancer Center, Department of Surgery, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, MD 20889, USA
| | - Kathleen M. Darcy
- Gynecologic Cancer Center of Excellence and the Women’s Health Integrated Research Center, Annandale, VA 22003, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD 20817, USA
- The John P. Murtha Cancer Center, Department of Surgery, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, MD 20889, USA
| | - Neil T. Phippen
- Gynecologic Cancer Center of Excellence and the Women’s Health Integrated Research Center, Annandale, VA 22003, USA
- The John P. Murtha Cancer Center, Department of Surgery, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, MD 20889, USA
| | - G. Larry Maxwell
- Gynecologic Cancer Center of Excellence and the Women’s Health Integrated Research Center, Annandale, VA 22003, USA
- Women’s Health Integrated Research Center, Women’s Service Line, Inova Health System, Falls Church, VA 22042, USA
- The John P. Murtha Cancer Center, Department of Surgery, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, MD 20889, USA
| | - Thomas P. Conrads
- Gynecologic Cancer Center of Excellence and the Women’s Health Integrated Research Center, Annandale, VA 22003, USA
- Women’s Health Integrated Research Center, Women’s Service Line, Inova Health System, Falls Church, VA 22042, USA
- The John P. Murtha Cancer Center, Department of Surgery, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, MD 20889, USA
| | - Nicholas W. Bateman
- Gynecologic Cancer Center of Excellence and the Women’s Health Integrated Research Center, Annandale, VA 22003, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD 20817, USA
- The John P. Murtha Cancer Center, Department of Surgery, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, MD 20889, USA
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6
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Ahmed S, Tabish M. Phytocompounds screening of Nigella sativa in terms of human cancer by targeting sphingosine kinase-1 and pyruvate kinase-M2: a study based on in silico analysis. J Biomol Struct Dyn 2024; 42:1544-1558. [PMID: 37194426 DOI: 10.1080/07391102.2023.2212773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 04/03/2023] [Indexed: 05/18/2023]
Abstract
Cancer is a multifactorial disease that can cause morbidity and mortality in humans. An altered gene expression in cancer leads to a change in the overall activity of the human cell. Overexpression of cancer protein may give a piece of wide information about the specific type of tumor. Sphingosine kinase-1 (SK-1) is a metabolic enzyme that is mainly overexpressed in several types of cancer and other inflammatory diseases. Similarly, pyruvate kinase-M2 (PK-M2) is an important oncogenic ATP-producing glycolytic enzyme that is upregulated in most cancer cells. The phytocompound of medicinal plants such as Nigella sativa contains a variety of micronutrients that inhibit the proliferation and activity of tumor cells. In this study, the role of phytocompounds in combating cancer was studied against the model kinase proteins, that is, PK-M2 and SK-1. In silico tool like the PASS-Way2Drug server was used to predict the anticancer properties of phytocompounds. Moreover, the CLC-Pred web server provided the cytotoxicity prediction of chemical compounds against several human cancer cell lines. The pharmacokinetics and toxicity profiles were predicted by the SwissADME and pkCSM software. The binding energies were obtained by molecular docking to confirm the intermolecular interaction of selected phytocompounds with proteins. Consequently, molecular dynamics (MD) simulation confirmed the stability, conformational changes, and dynamic behavior of the kinase proteins complexed with the lead phytocompounds, that is, epicatechin, apigenin, and kaempferol.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Shahbaz Ahmed
- Department of Biochemistry, Faculty of Life Sciences, A.M.U, Aligarh, Uttar Pradesh, India
| | - Mohammad Tabish
- Department of Biochemistry, Faculty of Life Sciences, A.M.U, Aligarh, Uttar Pradesh, India
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7
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Webel H, Perez-Riverol Y, Nielsen AB, Rasmussen S. Mass spectrometry-based proteomics data from thousands of HeLa control samples. Sci Data 2024; 11:112. [PMID: 38263211 PMCID: PMC10806275 DOI: 10.1038/s41597-024-02922-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 01/05/2024] [Indexed: 01/25/2024] Open
Abstract
Here we provide a curated, large scale, label free mass spectrometry-based proteomics data set derived from HeLa cell lines for general purpose machine learning and analysis. Data access and filtering is a tedious task, which takes up considerable amounts of time for researchers. Therefore we provide machine based metadata for easy selection and overview along the 7,444 raw files and MaxQuant search output. For convenience, we provide three filtered and aggregated development datasets on the protein groups, peptides and precursors level. Next to providing easy to access training data, we provide a SDRF file annotating each raw file with instrument settings allowing automated reprocessing. We encourage others to enlarge this data set by instrument runs of further HeLa samples from different machine types by providing our workflows and analysis scripts.
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Affiliation(s)
- Henry Webel
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark
| | - Yasset Perez-Riverol
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Annelaura Bach Nielsen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark
| | - Simon Rasmussen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark.
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
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8
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George N, Fexova S, Fuentes AM, Madrigal P, Bi Y, Iqbal H, Kumbham U, Nolte N, Zhao L, Thanki A, Yu I, Marugan Calles J, Erdos K, Vilmovsky L, Kurri S, Vathrakokoili-Pournara A, Osumi-Sutherland D, Prakash A, Wang S, Tello-Ruiz M, Kumari S, Ware D, Goutte-Gattat D, Hu Y, Brown N, Perrimon N, Vizcaíno JA, Burdett T, Teichmann S, Brazma A, Papatheodorou I. Expression Atlas update: insights from sequencing data at both bulk and single cell level. Nucleic Acids Res 2024; 52:D107-D114. [PMID: 37992296 PMCID: PMC10767917 DOI: 10.1093/nar/gkad1021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/13/2023] [Accepted: 10/30/2023] [Indexed: 11/24/2023] Open
Abstract
Expression Atlas (www.ebi.ac.uk/gxa) and its newest counterpart the Single Cell Expression Atlas (www.ebi.ac.uk/gxa/sc) are EMBL-EBI's knowledgebases for gene and protein expression and localisation in bulk and at single cell level. These resources aim to allow users to investigate their expression in normal tissue (baseline) or in response to perturbations such as disease or changes to genotype (differential) across multiple species. Users are invited to search for genes or metadata terms across species or biological conditions in a standardised consistent interface. Alongside these data, new features in Single Cell Expression Atlas allow users to query metadata through our new cell type wheel search. At the experiment level data can be explored through two types of dimensionality reduction plots, t-distributed Stochastic Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP), overlaid with either clustering or metadata information to assist users' understanding. Data are also visualised as marker gene heatmaps identifying genes that help confer cluster identity. For some data, additional visualisations are available as interactive cell level anatomograms and cell type gene expression heatmaps.
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Affiliation(s)
- Nancy George
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Silvie Fexova
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Alfonso Munoz Fuentes
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Pedro Madrigal
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Yalan Bi
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Haider Iqbal
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Upendra Kumbham
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Nadja Francesca Nolte
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Lingyun Zhao
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Anil S Thanki
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Iris D Yu
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Jose C Marugan Calles
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Karoly Erdos
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Liora Vilmovsky
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Sandeep R Kurri
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | | | - David Osumi-Sutherland
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Ananth Prakash
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Shengbo Wang
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Marcela K Tello-Ruiz
- Cold Spring Harbour Laboratory, One Bungtown Road, Cold Spring Harbor, NY 11724, USA
| | - Sunita Kumari
- Cold Spring Harbour Laboratory, One Bungtown Road, Cold Spring Harbor, NY 11724, USA
| | - Doreen Ware
- Cold Spring Harbour Laboratory, One Bungtown Road, Cold Spring Harbor, NY 11724, USA
- USDA ARS NEA, Plant Soil & Nutrition Laboratory Research Unit, Ithaca, NY 14853, USA
| | - Damien Goutte-Gattat
- FlyBase-Cambridge, Department of Physiology, Development and Neuroscience, University of Cambridge Downing Street, Cambridge CB2 3DY, UK
| | - Yanhui Hu
- Perrimon Lab, Department of Genetics, Harvard Medical School, Boston MA 02115, USA
| | - Nick Brown
- FlyBase-Cambridge, Department of Physiology, Development and Neuroscience, University of Cambridge Downing Street, Cambridge CB2 3DY, UK
| | - Norbert Perrimon
- Perrimon Lab, Department of Genetics, Harvard Medical School, Boston MA 02115, USA
- FlyBase-Harvard Biological Laboratories, Harvard University, 16 Divinity Avenue, Cambridge, MA 02138, USA
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Tony Burdett
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Sarah Teichmann
- Wellcome Trust Sanger Institute. Wellcome Genome Campus, Hinxton CB10 1SA, UK
| | - Alvis Brazma
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Irene Papatheodorou
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
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9
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Robles J, Prakash A, Vizcaíno JA, Casal JI. Integrated meta-analysis of colorectal cancer public proteomic datasets for biomarker discovery and validation. PLoS Comput Biol 2024; 20:e1011828. [PMID: 38252632 PMCID: PMC10833860 DOI: 10.1371/journal.pcbi.1011828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 02/01/2024] [Accepted: 01/15/2024] [Indexed: 01/24/2024] Open
Abstract
The cancer biomarker field has been an object of thorough investigation in the last decades. Despite this, colorectal cancer (CRC) heterogeneity makes it challenging to identify and validate effective prognostic biomarkers for patient classification according to outcome and treatment response. Although a massive amount of proteomics data has been deposited in public data repositories, this rich source of information is vastly underused. Here, we attempted to reuse public proteomics datasets with two main objectives: i) to generate hypotheses (detection of biomarkers) for their posterior/downstream validation, and (ii) to validate, using an orthogonal approach, a previously described biomarker panel. Twelve CRC public proteomics datasets (mostly from the PRIDE database) were re-analysed and integrated to create a landscape of protein expression. Samples from both solid and liquid biopsies were included in the reanalysis. Integrating this data with survival annotation data, we have validated in silico a six-gene signature for CRC classification at the protein level, and identified five new blood-detectable biomarkers (CD14, PPIA, MRC2, PRDX1, and TXNDC5) associated with CRC prognosis. The prognostic value of these blood-derived proteins was confirmed using additional public datasets, supporting their potential clinical value. As a conclusion, this proof-of-the-concept study demonstrates the value of re-using public proteomics datasets as the basis to create a useful resource for biomarker discovery and validation. The protein expression data has been made available in the public resource Expression Atlas.
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Affiliation(s)
- Javier Robles
- Department of Molecular Biomedicine, Centro de Investigaciones Biológicas Margarita Salas, Consejo Superior de Investigaciones Científicas, Madrid, Spain
- Protein Alternatives SL, Tres Cantos, Madrid, Spain
| | - Ananth Prakash
- European Molecular Biology Laboratory—European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory—European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - J. Ignacio Casal
- Department of Molecular Biomedicine, Centro de Investigaciones Biológicas Margarita Salas, Consejo Superior de Investigaciones Científicas, Madrid, Spain
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10
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Payne K, Brooks J, Batis N, Khan N, El-Asrag M, Nankivell P, Mehanna H, Taylor G. Feasibility of mass cytometry proteomic characterisation of circulating tumour cells in head and neck squamous cell carcinoma for deep phenotyping. Br J Cancer 2023; 129:1590-1598. [PMID: 37735243 PMCID: PMC10645808 DOI: 10.1038/s41416-023-02428-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 09/01/2023] [Accepted: 09/05/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND Circulating tumour cells (CTCs) are a potential cancer biomarker, but current methods of CTC analysis at single-cell resolution are limited. Here, we describe high-dimensional single-cell mass cytometry proteomic analysis of CTCs in HNSCC. METHODS Parsortix microfluidic-enriched CTCs from 14 treatment-naïve HNSCC patients were analysed by mass cytometry analysis using 41 antibodies. Immune cell lineage, epithelial-mesenchymal transition (EMT), stemness, proliferation and immune checkpoint expression was assessed alongside phosphorylation status of multiple signalling proteins. Patient-matched tumour gene expression and CTC EMT profiles were compared. Standard bulk CTC RNAseq was performed as a baseline comparator to assess mass cytometry data. RESULTS CTCs were detected in 13/14 patients with CTC counts of 2-24 CTCs/ml blood. Unsupervised clustering separated CTCs into epithelial, early EMT and advanced EMT groups that differed in signalling pathway activation state. Patient-specific CTC cluster patterns separated into immune checkpoint low and high groups. Patient tumour and CTC EMT profiles differed. Mass cytometry outperformed bulk RNAseq to detect CTCs and characterise cell phenotype. DISCUSSION We demonstrate mass cytometry allows high-plex proteomic characterisation of CTCs at single-cell resolution and identify common CTC sub-groups with potential for novel biomarker development and immune checkpoint inhibitor treatment stratification.
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Affiliation(s)
- Karl Payne
- Institute of Head and Neck Studies and Education, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Jill Brooks
- Institute of Head and Neck Studies and Education, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Nikolaos Batis
- School of Biomedical Sciences, Institute of Clinical Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Naeem Khan
- Clinical Immunology Service, Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK
| | - Mohammed El-Asrag
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Paul Nankivell
- Institute of Head and Neck Studies and Education, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Hisham Mehanna
- Institute of Head and Neck Studies and Education, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Graham Taylor
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK.
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11
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Ahmed S, Nadeem M, Hussain I, Fatima S, Anwar S, Rizvi MA, Hassan MI, Tabish M. Preparation of nanoformulation of 5-fluorouracil to improve anticancer efficacy: integrated spectroscopic, docking, and MD simulation approaches. J Biomol Struct Dyn 2023:1-14. [PMID: 37850451 DOI: 10.1080/07391102.2023.2270704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 10/08/2023] [Indexed: 10/19/2023]
Abstract
Nanoformulations (NFs) can be used as a novel drug delivery system to treat all cancer types. One of the major drawbacks of conventional anticancer drugs is that they have poor specificity and higher toxicity towards normal cells. 5-fluorouracil (5-FU) is a well-studied anticancer drug that has a significant role in various cancers, specifically colorectal cancer therapy. This study was performed to determine the functional groups, particle size, surface charge, heterogeneity, and stability of the NF. The NFs of 5-FU were prepared through the ultrasonication technique by increasing the surfactant (Tween-80) concentrations. Among all three NFs, nanoformulated 5-FU (n5-FU) showed the most effective particle size (10.72 nm) with a zeta potential of (-4.57 mV). The cytotoxicity and apoptosis profiles confirmed that n5-FU enhanced the anticancer effect of the pure drug in HCT-116 cells, as evident from MTT assay, fluorescence microscopy, and FACS analysis. In HCT-116 cells, the IC50 values of pure and n5-FU were obtained as 41.3 μM and 18.8 μM, respectively, indicating that n5-FU was more effective against the cancer cell line. The cellular uptake study was performed to check the intake of NF in cancer cells. However, the microtubule-affinity regulating kinase-4 (MARK-4), a cancer-target protein, was purified to study the inhibition and interaction studies. The inhibition assay confirmed the inhibitory potential of 5-FU against MARK-4 protein. the multi-spectroscopic, molecular docking and MD simulation studies were performed to analyse the conformational changes, binding studies, intermolecular interactions, and stability of MARK-4 protein upon binding 5-FU. This demonstrates that NF can enhance the effectiveness of anticancer drugs.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Shahbaz Ahmed
- Department of Biochemistry, Faculty of Life Sciences, Aligarh Muslim University, Aligarh, UP, India
| | - Masood Nadeem
- Department of Biosciences, Jamia Milia Islamia, New Delhi, India
| | - Irfan Hussain
- Department of Biochemistry, Faculty of Life Sciences, Aligarh Muslim University, Aligarh, UP, India
| | - Sana Fatima
- Department of Biochemistry, Faculty of Life Sciences, Aligarh Muslim University, Aligarh, UP, India
| | - Saleha Anwar
- Center for Interdisciplinary Research in Basic Sciences, Jamia Milia Islamia, New Delhi, India
| | | | - Md Imtaiyaz Hassan
- Center for Interdisciplinary Research in Basic Sciences, Jamia Milia Islamia, New Delhi, India
| | - Mohammad Tabish
- Department of Biochemistry, Faculty of Life Sciences, Aligarh Muslim University, Aligarh, UP, India
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12
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Feng S, Sanford JA, Weber T, Hutchinson-Bunch CM, Dakup PP, Paurus VL, Attah K, Sauro HM, Qian WJ, Wiley HS. A Phosphoproteomics Data Resource for Systems-level Modeling of Kinase Signaling Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.03.551714. [PMID: 37577496 PMCID: PMC10418157 DOI: 10.1101/2023.08.03.551714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Building mechanistic models of kinase-driven signaling pathways requires quantitative measurements of protein phosphorylation across physiologically relevant conditions, but this is rarely done because of the insensitivity of traditional technologies. By using a multiplexed deep phosphoproteome profiling workflow, we were able to generate a deep phosphoproteomics dataset of the EGFR-MAPK pathway in non-transformed MCF10A cells across physiological ligand concentrations with a time resolution of <12 min and in the presence and absence of multiple kinase inhibitors. An improved phosphosite mapping technique allowed us to reliably identify >46,000 phosphorylation sites on >6600 proteins, of which >4500 sites from 2110 proteins displayed a >2-fold increase in phosphorylation in response to EGF. This data was then placed into a cellular context by linking it to 15 previously published protein databases. We found that our results were consistent with much, but not all previously reported data regarding the activation and negative feedback phosphorylation of core EGFR-ERK pathway proteins. We also found that EGFR signaling is biphasic with substrates downstream of RAS/MAPK activation showing a maximum response at <3ng/ml EGF while direct substrates, such as HGS and STAT5B, showing no saturation. We found that RAS activation is mediated by at least 3 parallel pathways, two of which depend on PTPN11. There appears to be an approximately 4-minute delay in pathway activation at the step between RAS and RAF, but subsequent pathway phosphorylation was extremely rapid. Approximately 80 proteins showed a >2-fold increase in phosphorylation across all experiments and these proteins had a significantly higher median number of phosphorylation sites (~18) relative to total cellular phosphoproteins (~4). Over 60% of EGF-stimulated phosphoproteins were downstream of MAPK and included mediators of cellular processes such as gene transcription, transport, signal transduction and cytoskeletal arrangement. Their phosphorylation was either linear with respect to MAPK activation or biphasic, corresponding to the biphasic signaling seen at the level of the EGFR. This deep, integrated phosphoproteomics data resource should be useful in building mechanistic models of EGFR and MAPK signaling and for understanding how downstream responses are regulated.
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Affiliation(s)
- Song Feng
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352 USA
| | - James A. Sanford
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352 USA
| | - Thomas Weber
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352 USA
| | | | - Panshak P. Dakup
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352 USA
| | - Vanessa L. Paurus
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352 USA
| | - Kwame Attah
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352 USA
| | - Herbert M. Sauro
- Department of Bioengineering, University of Washington, Seattle, WA
| | - Wei-Jun Qian
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352 USA
| | - H. Steven Wiley
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99352 USA
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13
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Park SW, Kang J, Kim HS, Yoon S, Kim BS, Lim C, Lee D, Kim YH. Predicting prognosis through the discovery of specific biomarkers according to colorectal cancer lymph node metastasis. Am J Cancer Res 2023; 13:3221-3233. [PMID: 37559990 PMCID: PMC10408476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 07/08/2023] [Indexed: 08/11/2023] Open
Abstract
Colorectal cancer (CRC) is a prevalent cancer worldwide, ranking as the third most common cancer and the second leading cause of cancer-related deaths. The presence or absence of lymph node metastases is one of the representative markers for predicting CRC prognosis, but often yields heterogeneous results. In this study, we conducted an integrative molecular analysis of CRC using publicly available data from The Cancer Genome Atlas database and NCBI's Gene Expression Omnibus. Through our analysis, we identified 372 upregulated genes that were differentially expressed in CRC patients. Additionally, Kyoto Encyclopedia of Genes and Genomes analysis revealed five significant pathways, including Hippo, FC-gamma, and forkhead box O signaling pathways, which are known to be associated with cancer. Survival analysis of 28 genes involved in these pathways led to the identification of 13 genes with prognostic significance (P < 0.05). To validate our findings, logistic regression models were generated and tested in multiple cohorts, demonstrating significant accuracy. Moreover, we identified six genes (BNIP3, CD63, RDX, RGCC, WASF1, and WASF3) whose combination predicted the best prognosis based on survival analysis. This predictive model holds promise as a potential biomarker for prognosis, survival, and treatment efficacy. In conclusion, our study provides valuable insights into the molecular characteristics of CRC and identifies prognostic biomarkers. The combination of differentially expressed genes and their involvement in cancer-related pathways enhances our understanding of CRC pathogenesis and opens avenues for personalized treatment approaches and improved patient outcomes.
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Affiliation(s)
- Sung Won Park
- Department of Convergence Medical Science, School of Medicine, Pusan National UniversityYangsan-si, Gyeongsangnam-do 50612, Republic of Korea
| | - Junho Kang
- Medical Research Institute, Pusan National UniversityBusan 46241, Republic of Korea
| | - Hyung-Sik Kim
- Department of Life Science in Dentistry, School of Dentistry, Pusan National UniversityYangsan-si, Gyeongsangnam-do 50612, Republic of Korea
| | - Sik Yoon
- Department of Anatomy, School of Medicine, Pusan National UniversityYangsan-si, Gyeongsangnam-do 50612, Republic of Korea
| | - Byoung Soo Kim
- School of Biomedical Convergence Engineering, Pusan National UniversityYangsan-si, Gyeongsangnam-do 50612, Republic of Korea
| | - Chaeseong Lim
- Occupational and Environmental Medicine, Kosin University Gospel HospitalBusan 46241, Republic of Korea
| | - Dongjun Lee
- Department of Convergence Medical Science, School of Medicine, Pusan National UniversityYangsan-si, Gyeongsangnam-do 50612, Republic of Korea
| | - Yun Hak Kim
- Department of Biomedical Informatics, School of Medicine, Pusan National UniversityYangsan-si, Gyeongsangnam-do 50612, Republic of Korea
- Department of Anatomy, School of Medicine, Pusan National UniversityYangsan-si, Gyeongsangnam-do 50612, Republic of Korea
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14
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Whitworth IT, Henke KB, Yang B, Scalf M, Frey BL, Jarrard DF, Smith LM. Elucidating the RNA-Protein Interactomes of Target RNAs in Tissue. Anal Chem 2023; 95:7087-7092. [PMID: 37093976 PMCID: PMC10234431 DOI: 10.1021/acs.analchem.2c05635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023]
Abstract
RNA-protein interactions are key to many aspects of cellular homeostasis and their identification is important to understanding cellular function. Multiple strategies have been developed for the RNA-centric characterization of RNA-protein complexes. However, these studies have all been done in immortalized cell lines that do not capture the complexity of heterogeneous tissue samples. Here, we develop hybridization purification of RNA-protein complexes followed by mass spectrometry (HyPR-MS) for use in tissue samples. We isolated both polyadenylated RNA and the specific long noncoding RNA MALAT1 and characterized their protein interactomes. These results demonstrate the feasibility of HyPR-MS in tissue for the multiplexed characterization of specific RNA-protein complexes.
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Affiliation(s)
- Isabella T Whitworth
- Department of Chemistry, University of Wisconsin, Madison, Wisconsin 53706, United States
| | - Katherine B Henke
- Department of Chemistry, University of Wisconsin, Madison, Wisconsin 53706, United States
| | - Bing Yang
- Department of Urology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin 53705, United States
| | - Mark Scalf
- Department of Chemistry, University of Wisconsin, Madison, Wisconsin 53706, United States
| | - Brian L Frey
- Department of Chemistry, University of Wisconsin, Madison, Wisconsin 53706, United States
| | - David F Jarrard
- Department of Urology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin 53705, United States
- Carbone Comprehensive Cancer Center, University of Wisconsin, Madison, Wisconsin 53705, United States
- Molecular and Environmental Toxicology Program, University of Wisconsin, Madison, Wisconsin 53706, United States
| | - Lloyd M Smith
- Department of Chemistry, University of Wisconsin, Madison, Wisconsin 53706, United States
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15
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Sisakhtnezhad S, Rahimi M, Mohammadi S. Biomedical applications of MnO 2 nanomaterials as nanozyme-based theranostics. Biomed Pharmacother 2023; 163:114833. [PMID: 37150035 DOI: 10.1016/j.biopha.2023.114833] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 04/30/2023] [Accepted: 05/01/2023] [Indexed: 05/09/2023] Open
Abstract
Manganese dioxide (MnO2) nanoenzymes/nanozymes (MnO2-NEs) are 1-100 nm nanomaterials that mimic catalytic, oxidative, peroxidase, and superoxide dismutase activities. The oxidative-like activity of MnO2-NEs makes them suitable for developing effective and low-cost colorimetric detection assays of biomolecules. Interestingly, MnO2-NEs also demonstrate scavenging properties against reactive oxygen species (ROS) in various pathological conditions. In addition, due to the decomposition of MnO2-NEs in the tumor microenvironment (TME) and the production of Mn2+, they can act as a contrast agent for improving clinical imaging diagnostics. MnO2-NEs also can use as an in situ oxygen production system in TME, thereby overcoming hypoxic conditions and their consequences in the progression of cancer. Furthermore, MnO2-NEs as a shell and coating make the nanosystems smart and, therefore, in combination with other nanomaterials, the MnO2-NEs can be used as an intelligent nanocarrier for delivering drugs, photosensitizers, and sonosensitizers in vivo. Moreover, these capabilities make MnO2-NEs a promising candidate for the detection and treatment of different human diseases such as cancer, metabolic, infectious, and inflammatory pathological conditions. MnO2-NEs also have ROS-scavenging and anti-bacterial properties against Gram-positive and Gram-negative bacterial strains, which make them suitable for wound healing applications. Given the importance of nanomaterials and their potential applications in biomedicine, this review aimed to discuss the biochemical properties and the theranostic roles of MnO2-NEs and recent advances in their use in colorimetric detection assays of biomolecules, diagnostic imaging, drug delivery, and combinatorial therapy applications. Finally, the challenges of MnO2-NEs applications in biomedicine will be discussed.
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Affiliation(s)
| | - Matin Rahimi
- Department of Biology, Faculty of Science, Razi University, Kermanshah, Iran
| | - Soheila Mohammadi
- Pharmaceutical Sciences Research Center, Health Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran
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16
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Kennedy BM, Harris RE. Cyclooxygenase and Lipoxygenase Gene Expression in the Inflammogenesis of Colorectal Cancer: Correlated Expression of EGFR, JAK STAT and Src Genes, and a Natural Antisense Transcript, RP11-C67.2.2. Cancers (Basel) 2023; 15:cancers15082380. [PMID: 37190308 DOI: 10.3390/cancers15082380] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/13/2023] [Accepted: 04/18/2023] [Indexed: 05/17/2023] Open
Abstract
We examined the expression of major inflammatory genes, cyclooxygenase-1, 2 (COX1, COX2), arachidonate-5-lipoxygenase (ALOX5), and arachidonate-5-lipoxygenase activating protein (ALOX5AP) among 469 tumor specimens of colorectal cancer in The Cancer Genome Atlas (TCGA). Among 411 specimens without mutations in mismatch repair (MMR) genes, the mean expression of each of the inflammatory genes ranked above the 80th percentile, and the overall mean cyclooxygenase expression (COX1+COX2) ranked in the upper 99th percentile of all genes. Similar levels were observed for 58 cases with MMR mutations. Pearson correlation coefficients exceeding r = 0.70 were observed between COX and LOX mRNA levels with genes of major cell-signaling pathways involved in tumorigenesis (Src, JAK STAT, MAPK, PI3K). We observed a novel association (r = 0.78) between ALOX5 expression and a natural antisense transcript (NAT), RP11-67C2.2, a long non-coding mRNA gene, 462 base pairs in length that is located within the terminal intron of the ALOX5 gene on chromosome 10q11.21. Tumor-promoting genes highly correlated with the expression of COX1, COX2, ALOX5 and ALOX5AP are known to increase mitogenesis, mutagenesis, angiogenesis, cell survival, immunosuppression and metastasis in the inflammogenesis of colorectal cancer. These genes and the novel NAT, RP1167C2.2 are potential molecular targets for chemoprevention and therapy of colorectal cancer.
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Affiliation(s)
- Brian M Kennedy
- Colleges of Public Health and Medicine, The Ohio State University Comprehensive Cancer Center, The Ohio State University, 1841 Neil Avenue, Columbus, OH 43210-1351, USA
| | - Randall E Harris
- Colleges of Public Health and Medicine, The Ohio State University Comprehensive Cancer Center, The Ohio State University, 1841 Neil Avenue, Columbus, OH 43210-1351, USA
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17
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Claeys T, Menu M, Bouwmeester R, Gevaert K, Martens L. Machine Learning on Large-Scale Proteomics Data Identifies Tissue and Cell-Type Specific Proteins. J Proteome Res 2023; 22:1181-1192. [PMID: 36963412 PMCID: PMC10088018 DOI: 10.1021/acs.jproteome.2c00644] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2023]
Abstract
Using data from 183 public human data sets from PRIDE, a machine learning model was trained to identify tissue and cell-type specific protein patterns. PRIDE projects were searched with ionbot and tissue/cell type annotation was manually added. Data from physiological samples were used to train a Random Forest model on protein abundances to classify samples into tissues and cell types. Subsequently, a one-vs-all classification and feature importance were used to analyze the most discriminating protein abundances per class. Based on protein abundance alone, the model was able to predict tissues with 98% accuracy, and cell types with 99% accuracy. The F-scores describe a clear view on tissue-specific proteins and tissue-specific protein expression patterns. In-depth feature analysis shows slight confusion between physiologically similar tissues, demonstrating the capacity of the algorithm to detect biologically relevant patterns. These results can in turn inform downstream uses, from identification of the tissue of origin of proteins in complex samples such as liquid biopsies, to studying the proteome of tissue-like samples such as organoids and cell lines.
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Affiliation(s)
- Tine Claeys
- VIB-UGent Center for Medical Biotechnology, VIB, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, 9052 Ghent, Belgium
| | - Maxime Menu
- VIB-UGent Center for Medical Biotechnology, VIB, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, 9052 Ghent, Belgium
| | - Robbin Bouwmeester
- VIB-UGent Center for Medical Biotechnology, VIB, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, 9052 Ghent, Belgium
| | - Kris Gevaert
- VIB-UGent Center for Medical Biotechnology, VIB, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, 9052 Ghent, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, 9052 Ghent, Belgium
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18
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Prakash A, García-Seisdedos D, Wang S, Kundu DJ, Collins A, George N, Moreno P, Papatheodorou I, Jones AR, Vizcaíno JA. Integrated View of Baseline Protein Expression in Human Tissues. J Proteome Res 2023; 22:729-742. [PMID: 36577097 PMCID: PMC9990129 DOI: 10.1021/acs.jproteome.2c00406] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
The availability of proteomics datasets in the public domain, and in the PRIDE database, in particular, has increased dramatically in recent years. This unprecedented large-scale availability of data provides an opportunity for combined analyses of datasets to get organism-wide protein abundance data in a consistent manner. We have reanalyzed 24 public proteomics datasets from healthy human individuals to assess baseline protein abundance in 31 organs. We defined tissue as a distinct functional or structural region within an organ. Overall, the aggregated dataset contains 67 healthy tissues, corresponding to 3,119 mass spectrometry runs covering 498 samples from 489 individuals. We compared protein abundances between different organs and studied the distribution of proteins across these organs. We also compared the results with data generated in analogous studies. Additionally, we performed gene ontology and pathway-enrichment analyses to identify organ-specific enriched biological processes and pathways. As a key point, we have integrated the protein abundance results into the resource Expression Atlas, where they can be accessed and visualized either individually or together with gene expression data coming from transcriptomics datasets. We believe this is a good mechanism to make proteomics data more accessible for life scientists.
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Affiliation(s)
- Ananth Prakash
- European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CambridgeCB10 1SD, United Kingdom.,Open Targets, Wellcome Genome Campus, Hinxton, CambridgeCB10 1SD, United Kingdom
| | - David García-Seisdedos
- European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CambridgeCB10 1SD, United Kingdom
| | - Shengbo Wang
- European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CambridgeCB10 1SD, United Kingdom
| | - Deepti Jaiswal Kundu
- European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CambridgeCB10 1SD, United Kingdom
| | - Andrew Collins
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, LiverpoolL69 7ZB, United Kingdom
| | - Nancy George
- European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CambridgeCB10 1SD, United Kingdom
| | - Pablo Moreno
- European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CambridgeCB10 1SD, United Kingdom
| | - Irene Papatheodorou
- European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CambridgeCB10 1SD, United Kingdom.,Open Targets, Wellcome Genome Campus, Hinxton, CambridgeCB10 1SD, United Kingdom
| | - Andrew R Jones
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, LiverpoolL69 7ZB, United Kingdom
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CambridgeCB10 1SD, United Kingdom.,Open Targets, Wellcome Genome Campus, Hinxton, CambridgeCB10 1SD, United Kingdom
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19
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Deutsch EW, Bandeira N, Perez-Riverol Y, Sharma V, Carver J, Mendoza L, Kundu DJ, Wang S, Bandla C, Kamatchinathan S, Hewapathirana S, Pullman B, Wertz J, Sun Z, Kawano S, Okuda S, Watanabe Y, MacLean B, MacCoss M, Zhu Y, Ishihama Y, Vizcaíno J. The ProteomeXchange consortium at 10 years: 2023 update. Nucleic Acids Res 2023; 51:D1539-D1548. [PMID: 36370099 PMCID: PMC9825490 DOI: 10.1093/nar/gkac1040] [Citation(s) in RCA: 222] [Impact Index Per Article: 222.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/20/2022] [Accepted: 10/23/2022] [Indexed: 11/13/2022] Open
Abstract
Mass spectrometry (MS) is by far the most used experimental approach in high-throughput proteomics. The ProteomeXchange (PX) consortium of proteomics resources (http://www.proteomexchange.org) was originally set up to standardize data submission and dissemination of public MS proteomics data. It is now 10 years since the initial data workflow was implemented. In this manuscript, we describe the main developments in PX since the previous update manuscript in Nucleic Acids Research was published in 2020. The six members of the Consortium are PRIDE, PeptideAtlas (including PASSEL), MassIVE, jPOST, iProX and Panorama Public. We report the current data submission statistics, showcasing that the number of datasets submitted to PX resources has continued to increase every year. As of June 2022, more than 34 233 datasets had been submitted to PX resources, and from those, 20 062 (58.6%) just in the last three years. We also report the development of the Universal Spectrum Identifiers and the improvements in capturing the experimental metadata annotations. In parallel, we highlight that data re-use activities of public datasets continue to increase, enabling connections between PX resources and other popular bioinformatics resources, novel research and also new data resources. Finally, we summarise the current state-of-the-art in data management practices for sensitive human (clinical) proteomics data.
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Affiliation(s)
| | - Nuno Bandeira
- Center for Computational Mass Spectrometry, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Dept. Computer Science and Engineering, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
| | - Yasset Perez-Riverol
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | | | - Jeremy J Carver
- Center for Computational Mass Spectrometry, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Dept. Computer Science and Engineering, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
| | - Luis Mendoza
- Institute for Systems Biology, Seattle WA 98109, USA
| | - Deepti J Kundu
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Shengbo Wang
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Chakradhar Bandla
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Selvakumar Kamatchinathan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Suresh Hewapathirana
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Benjamin S Pullman
- Center for Computational Mass Spectrometry, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Dept. Computer Science and Engineering, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
| | - Julie Wertz
- Center for Computational Mass Spectrometry, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Dept. Computer Science and Engineering, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
| | - Zhi Sun
- Institute for Systems Biology, Seattle WA 98109, USA
| | - Shin Kawano
- Faculty of Contemporary Society, Toyama University of International Studies, Toyama 930-1292, Japan
- Database Center for Life Science (DBCLS), Joint Support-Center for Data Science Research, Research Organization of Information and Systems, Chiba 277-0871, Japan
- School of Frontier Engineering, Kitasato University, Sagamihara 252-0373, Japan
| | - Shujiro Okuda
- Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8510, Japan
| | - Yu Watanabe
- Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8510, Japan
| | | | | | - Yunping Zhu
- Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Lifeomics, Beijing 102206, China
| | - Yasushi Ishihama
- Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto 606-8501, Japan
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
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20
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Desai N, Morris JS, Baladandayuthapani V. NetCellMatch: Multiscale Network-Based Matching of Cancer Cell Lines to Patients Using Graphical Wavelets. Chem Biodivers 2022; 19:e202200746. [PMID: 36279370 PMCID: PMC10066864 DOI: 10.1002/cbdv.202200746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 10/21/2022] [Indexed: 12/27/2022]
Abstract
Cancer cell lines serve as model in vitro systems for investigating therapeutic interventions. Recent advances in high-throughput genomic profiling have enabled the systematic comparison between cell lines and patient tumor samples. The highly interconnected nature of biological data, however, presents a challenge when mapping patient tumors to cell lines. Standard clustering methods can be particularly susceptible to the high level of noise present in these datasets and only output clusters at one unknown scale of the data. In light of these challenges, we present NetCellMatch, a robust framework for network-based matching of cell lines to patient tumors. NetCellMatch first constructs a global network across all cell line-patient samples using their genomic similarity. Then, a multi-scale community detection algorithm integrates information across topologically meaningful (clustering) scales to obtain Network-Based Matching Scores (NBMS). NBMS are measures of cluster robustness which map patient tumors to cell lines. We use NBMS to determine representative "avatar" cell lines for subgroups of patients. We apply NetCellMatch to reverse-phase protein array data obtained from The Cancer Genome Atlas for patients and the MD Anderson Cell Line Project for cell lines. Along with avatar cell line identification, we evaluate connectivity patterns for breast, lung, and colon cancer and explore the proteomic profiles of avatars and their corresponding top matching patients. Our results demonstrate our framework's ability to identify both patient-cell line matches and potential proteomic drivers of similarity. Our methods are general and can be easily adapted to other'omic datasets.
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Affiliation(s)
- Neel Desai
- Division of Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Jeffrey S Morris
- Division of Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
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21
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Upadhya SR, Ryan CJ. Experimental reproducibility limits the correlation between mRNA and protein abundances in tumor proteomic profiles. CELL REPORTS METHODS 2022; 2:100288. [PMID: 36160043 PMCID: PMC9499981 DOI: 10.1016/j.crmeth.2022.100288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 07/14/2022] [Accepted: 08/16/2022] [Indexed: 11/21/2022]
Abstract
Large-scale studies of human proteomes have revealed only a moderate correlation between mRNA and protein abundances. It is unclear to what extent this moderate correlation reflects post-transcriptional regulation and to what extent it reflects measurement error. Here, by analyzing replicate profiles of tumors and cell lines, we show that there is considerable variation in the reproducibility of measurements of transcripts and proteins from individual genes. Proteins with more reproducible measurements tend to have a higher mRNA-protein correlation, suggesting that measurement reproducibility accounts for a substantial fraction of the unexplained variation between mRNA and protein abundances. The reproducibility of individual proteins is somewhat consistent across studies, and we exploit this to develop an aggregate reproducibility score that explains a substantial amount of the variation in mRNA-protein correlations across multiple studies. Finally, we show that pathways previously reported to have a higher-than-average mRNA-protein correlation may simply contain members that can be more reproducibly quantified.
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Affiliation(s)
- Swathi Ramachandra Upadhya
- School of Computer Science, University College Dublin, Dublin, Ireland
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
| | - Colm J. Ryan
- School of Computer Science, University College Dublin, Dublin, Ireland
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
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22
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Integrative, In Silico and Comparative Analysis of Breast Cancer Secretome Highlights Invasive-Ductal-Carcinoma-Grade Progression Biomarkers. Cancers (Basel) 2022; 14:cancers14163854. [PMID: 36010848 PMCID: PMC9406168 DOI: 10.3390/cancers14163854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/05/2022] [Accepted: 08/05/2022] [Indexed: 11/16/2022] Open
Abstract
Globally, BC is the most frequently diagnosed cancer in women. The aim of this study was to identify novel secreted biomarkers that may indicate progression to high-grade BC malignancies and therefore predict metastatic potential. A total of 33 studies of breast cancer and 78 of other malignancies were screened via a systematic review for eligibility, yielding 26 datasets, 8 breast cancer secretome datasets, and 18 of other cancers that were included in the comparative secretome analysis. Sequential bioinformatic analysis using online resources enabled the identification of enriched GO_terms, overlapping clusters, and pathway reconstruction. This study identified putative predictors of IDC grade progression and their association with breast cancer patient mortality outcomes, namely, HSPG2, ACTG1, and LAMA5 as biomarkers of in silico pathway prediction, offering a putative approach by which the abovementioned proteins may mediate their effects, enabling disease progression. This study also identified ITGB1, FBN1, and THBS1 as putative pan-cancer detection biomarkers. The present study highlights novel, putative secretome biomarkers that may provide insight into the tumor biology and could inform clinical decision making in the context of IDC management in a non-invasive manner.
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23
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Perez-Riverol Y. Proteomic repository data submission, dissemination, and reuse: key messages. Expert Rev Proteomics 2022; 19:297-310. [PMID: 36529941 PMCID: PMC7614296 DOI: 10.1080/14789450.2022.2160324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 12/07/2022] [Indexed: 12/23/2022]
Abstract
INTRODUCTION The creation of ProteomeXchange data workflows in 2012 transformed the field of proteomics, consisting of the standardization of data submission and dissemination and enabling the widespread reanalysis of public MS proteomics data worldwide. ProteomeXchange has triggered a growing trend toward public dissemination of proteomics data, facilitating the assessment, reuse, comparative analyses, and extraction of new findings from public datasets. By 2022, the consortium is integrated by PRIDE, PeptideAtlas, MassIVE, jPOST, iProX, and Panorama Public. AREAS COVERED Here, we review and discuss the current ecosystem of resources, guidelines, and file formats for proteomics data dissemination and reanalysis. Special attention is drawn to new exciting quantitative and post-translational modification-oriented resources. The challenges and future directions on data depositions including the lack of metadata and cloud-based and high-performance software solutions for fast and reproducible reanalysis of the available data are discussed. EXPERT OPINION The success of ProteomeXchange and the amount of proteomics data available in the public domain have triggered the creation and/or growth of other protein knowledgebase resources. Data reuse is a leading, active, and evolving field; supporting the creation of new formats, tools, and workflows to rediscover and reshape the public proteomics data.
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Affiliation(s)
- Yasset Perez-Riverol
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
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24
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Walzer M, García-Seisdedos D, Prakash A, Brack P, Crowther P, Graham RL, George N, Mohammed S, Moreno P, Papatheodorou I, Hubbard SJ, Vizcaíno JA. Implementing the reuse of public DIA proteomics datasets: from the PRIDE database to Expression Atlas. Sci Data 2022; 9:335. [PMID: 35701420 PMCID: PMC9197839 DOI: 10.1038/s41597-022-01380-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 05/12/2022] [Indexed: 11/14/2022] Open
Abstract
The number of mass spectrometry (MS)-based proteomics datasets in the public domain keeps increasing, particularly those generated by Data Independent Acquisition (DIA) approaches such as SWATH-MS. Unlike Data Dependent Acquisition datasets, the re-use of DIA datasets has been rather limited to date, despite its high potential, due to the technical challenges involved. We introduce a (re-)analysis pipeline for public SWATH-MS datasets which includes a combination of metadata annotation protocols, automated workflows for MS data analysis, statistical analysis, and the integration of the results into the Expression Atlas resource. Automation is orchestrated with Nextflow, using containerised open analysis software tools, rendering the pipeline readily available and reproducible. To demonstrate its utility, we reanalysed 10 public DIA datasets from the PRIDE database, comprising 1,278 SWATH-MS runs. The robustness of the analysis was evaluated, and the results compared to those obtained in the original publications. The final expression values were integrated into Expression Atlas, making SWATH-MS experiments more widely available and combining them with expression data originating from other proteomics and transcriptomics datasets.
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Affiliation(s)
- Mathias Walzer
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom.
| | - David García-Seisdedos
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Ananth Prakash
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Paul Brack
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Oxford Road, Manchester, M13 9PT, United Kingdom
| | - Peter Crowther
- Melandra Limited, 16 Brook Road, Urmston, Manchester, M41 5RY, United Kingdom
| | - Robert L Graham
- School of Biological Sciences, Chlorine Gardens, Queen's University Belfast, Belfast, BT9 5DL, United Kingdom
| | - Nancy George
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Suhaib Mohammed
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Pablo Moreno
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Irene Papatheodorou
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Simon J Hubbard
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Oxford Road, Manchester, M13 9PT, United Kingdom
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom.
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25
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Wang S, García-Seisdedos D, Prakash A, Kundu DJ, Collins A, George N, Fexova S, Moreno P, Papatheodorou I, Jones AR, Vizcaíno JA. Integrated view and comparative analysis of baseline protein expression in mouse and rat tissues. PLoS Comput Biol 2022; 18:e1010174. [PMID: 35714157 PMCID: PMC9246241 DOI: 10.1371/journal.pcbi.1010174] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 06/30/2022] [Accepted: 05/05/2022] [Indexed: 11/19/2022] Open
Abstract
The increasingly large amount of proteomics data in the public domain enables, among other applications, the combined analyses of datasets to create comparative protein expression maps covering different organisms and different biological conditions. Here we have reanalysed public proteomics datasets from mouse and rat tissues (14 and 9 datasets, respectively), to assess baseline protein abundance. Overall, the aggregated dataset contained 23 individual datasets, including a total of 211 samples coming from 34 different tissues across 14 organs, comprising 9 mouse and 3 rat strains, respectively. In all cases, we studied the distribution of canonical proteins between the different organs. The number of canonical proteins per dataset ranged from 273 (tendon) and 9,715 (liver) in mouse, and from 101 (tendon) and 6,130 (kidney) in rat. Then, we studied how protein abundances compared across different datasets and organs for both species. As a key point we carried out a comparative analysis of protein expression between mouse, rat and human tissues. We observed a high level of correlation of protein expression among orthologs between all three species in brain, kidney, heart and liver samples, whereas the correlation of protein expression was generally slightly lower between organs within the same species. Protein expression results have been integrated into the resource Expression Atlas for widespread dissemination.
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Affiliation(s)
- Shengbo Wang
- European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - David García-Seisdedos
- European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Ananth Prakash
- European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Deepti Jaiswal Kundu
- European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Andrew Collins
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Nancy George
- European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Silvie Fexova
- European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Pablo Moreno
- European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Irene Papatheodorou
- European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Andrew R. Jones
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
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26
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Wang J, Liu J, Wang J, Wang S, Li F, Li R, Liu P, Li M, Wang C. Identification of proteomic markers for prediction of the response to 5-Fluorouracil based neoadjuvant chemoradiotherapy in locally advanced rectal cancer patients. Cancer Cell Int 2022; 22:117. [PMID: 35292026 PMCID: PMC8922748 DOI: 10.1186/s12935-022-02530-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 02/21/2022] [Indexed: 11/10/2022] Open
Abstract
Background Neoadjuvant chemoradiotherapy (nCRT) prior to surgery is the standard treatment for patients with locally advanced rectal cancer (LARC), while parts of them show poor therapeutic response accompanied by therapy adverse effects. Predictive biomarkers for nCRT response could facilitate the guidance on treatment decisions but are still insufficient until now, which limits the clinical applications of nCRT in LARC patients. Methods In our study, 37 formalin-fixed paraffin-embedded tumor biopsies were obtained from patients with LARC before receiving 5-fluorouracil based nCRT. Proteomics analyses were conducted to identify the differentially expressed proteins (DEPs) between total responders (TR) and poor responders (PR). The DEPs were validated via ROC plotter web tool and their predictive performance was evaluated by receiver operating characteristic analysis. Functional enrichment analyses were performed to further explore the potential mechanisms underlying nCRT response. Results Among 3,998 total proteins, 91 DEPs between TR and PR were screened out. HSPA4, NIPSNAP1, and SPTB all with areas under the curve (AUC) ~ 0.8 in the internal discovery cohort were independently validated by the external mRNA datasets (AUC ~ 0.7), and their protein levels were linearly correlated with the graded responses to nCRT in the internal cohort. The combination of HSPA4 and SPTB could distinctly discriminate the TR and PR groups (AUC = 0.980, p < 0.0001). Moreover, multiple combinations of the three proteins realized increased specificity and/or sensitivity, while achieving favorable predictive value when moderate responders were introduced into the ROC analysis. Pathways including DNA damage repair, cell cycle, and epithelial mesenchymal transition were involved in nCRT response according to the enrichment analysis results. Conclusions HSPA4, SPTB and NIPSNAP1 in tumor biopsies and/or their optional combinations might be potential predictive markers for nCRT response in patients with LARC. The DEPs and their related functions have implications for the potential mechanisms of treatment response to nCRT in patients with LARC. Supplementary Information The online version contains supplementary material available at 10.1186/s12935-022-02530-0.
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Affiliation(s)
- Jianan Wang
- Department of Laboratory Medicine, The First Medical Centre of Chinese PLA General Hospital, Beijing, 100853, China.,Medical School of Chinese PLA, Beijing, 100853, China
| | - Jiayu Liu
- Department of Laboratory Medicine, The First Medical Centre of Chinese PLA General Hospital, Beijing, 100853, China
| | - Jinyang Wang
- Department of Laboratory Medicine, The First Medical Centre of Chinese PLA General Hospital, Beijing, 100853, China.,School of Laboratory Medicine, Weifang Medical College, Weifang, 261053, Shandong, China
| | - Shijian Wang
- Nankai University School of Medicine, Nankai University, Tianjin, 300071, China
| | - Feifei Li
- Department of Pathology, The First Medical Centre of Chinese PLA General Hospital, Beijing, 100853, China
| | - Ruibing Li
- Department of Laboratory Medicine, The First Medical Centre of Chinese PLA General Hospital, Beijing, 100853, China
| | - Peng Liu
- Department of Pathology, The First Medical Centre of Chinese PLA General Hospital, Beijing, 100853, China
| | - Mianyang Li
- Department of Laboratory Medicine, The First Medical Centre of Chinese PLA General Hospital, Beijing, 100853, China.
| | - Chengbin Wang
- Department of Laboratory Medicine, The First Medical Centre of Chinese PLA General Hospital, Beijing, 100853, China.
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27
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Moreno P, Fexova S, George N, Manning JR, Miao Z, Mohammed S, Muñoz-Pomer A, Fullgrabe A, Bi Y, Bush N, Iqbal H, Kumbham U, Solovyev A, Zhao L, Prakash A, García-Seisdedos D, Kundu DJ, Wang S, Walzer M, Clarke L, Osumi-Sutherland D, Tello-Ruiz MK, Kumari S, Ware D, Eliasova J, Arends MJ, Nawijn MC, Meyer K, Burdett T, Marioni J, Teichmann S, Vizcaíno JA, Brazma A, Papatheodorou I. Expression Atlas update: gene and protein expression in multiple species. Nucleic Acids Res 2022; 50:D129-D140. [PMID: 34850121 PMCID: PMC8728300 DOI: 10.1093/nar/gkab1030] [Citation(s) in RCA: 87] [Impact Index Per Article: 43.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 10/11/2021] [Accepted: 11/19/2021] [Indexed: 01/21/2023] Open
Abstract
The EMBL-EBI Expression Atlas is an added value knowledge base that enables researchers to answer the question of where (tissue, organism part, developmental stage, cell type) and under which conditions (disease, treatment, gender, etc) a gene or protein of interest is expressed. Expression Atlas brings together data from >4500 expression studies from >65 different species, across different conditions and tissues. It makes these data freely available in an easy to visualise form, after expert curation to accurately represent the intended experimental design, re-analysed via standardised pipelines that rely on open-source community developed tools. Each study's metadata are annotated using ontologies. The data are re-analyzed with the aim of reproducing the original conclusions of the underlying experiments. Expression Atlas is currently divided into Bulk Expression Atlas and Single Cell Expression Atlas. Expression Atlas contains data from differential studies (microarray and bulk RNA-Seq) and baseline studies (bulk RNA-Seq and proteomics), whereas Single Cell Expression Atlas is currently dedicated to Single Cell RNA-Sequencing (scRNA-Seq) studies. The resource has been in continuous development since 2009 and it is available at https://www.ebi.ac.uk/gxa.
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Affiliation(s)
- Pablo Moreno
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Silvie Fexova
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Nancy George
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Jonathan R Manning
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Zhichiao Miao
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Suhaib Mohammed
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Alfonso Muñoz-Pomer
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Anja Fullgrabe
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Yalan Bi
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Natassja Bush
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Haider Iqbal
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Upendra Kumbham
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Andrey Solovyev
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Lingyun Zhao
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Ananth Prakash
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - David García-Seisdedos
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Deepti J Kundu
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Shengbo Wang
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Mathias Walzer
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Laura Clarke
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - David Osumi-Sutherland
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | | | - Sunita Kumari
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Doreen Ware
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
- USDA ARS NEA, Plant Soil & Nutrition Laboratory Research Unit, Ithaca, NY 14853, USA
| | - Jana Eliasova
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Mark J Arends
- Edinburgh Pathology, University of Edinburgh, Institute of Genetics & Cancer, Edinburgh, UK
| | - Martijn C Nawijn
- Department of Pathology and Medical Biology, GRIAC research institute, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Kerstin Meyer
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Tony Burdett
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - John Marioni
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Sarah Teichmann
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Alvis Brazma
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Irene Papatheodorou
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
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28
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Perez-Riverol Y, Bai J, Bandla C, García-Seisdedos D, Hewapathirana S, Kamatchinathan S, Kundu D, Prakash A, Frericks-Zipper A, Eisenacher M, Walzer M, Wang S, Brazma A, Vizcaíno J. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res 2022; 50:D543-D552. [PMID: 34723319 PMCID: PMC8728295 DOI: 10.1093/nar/gkab1038] [Citation(s) in RCA: 3068] [Impact Index Per Article: 1534.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 10/12/2021] [Accepted: 10/14/2021] [Indexed: 12/12/2022] Open
Abstract
The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world's largest data repository of mass spectrometry-based proteomics data. PRIDE is one of the founding members of the global ProteomeXchange (PX) consortium and an ELIXIR core data resource. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2019. The number of submitted datasets to PRIDE Archive (the archival component of PRIDE) has reached on average around 500 datasets per month during 2021. In addition to continuous improvements in PRIDE Archive data pipelines and infrastructure, the PRIDE Spectra Archive has been developed to provide direct access to the submitted mass spectra using Universal Spectrum Identifiers. As a key point, the file format MAGE-TAB for proteomics has been developed to enable the improvement of sample metadata annotation. Additionally, the resource PRIDE Peptidome provides access to aggregated peptide/protein evidences across PRIDE Archive. Furthermore, we will describe how PRIDE has increased its efforts to reuse and disseminate high-quality proteomics data into other added-value resources such as UniProt, Ensembl and Expression Atlas.
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Affiliation(s)
- Yasset Perez-Riverol
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Jingwen Bai
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Chakradhar Bandla
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - David García-Seisdedos
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Suresh Hewapathirana
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Selvakumar Kamatchinathan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Deepti J Kundu
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Ananth Prakash
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Anika Frericks-Zipper
- Ruhr University Bochum, Medical Faculty, Medizinisches Proteom-Center, D-44801 Bochum, Germany
- Ruhr University Bochum, Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, 44801 Bochum, Germany
| | - Martin Eisenacher
- Ruhr University Bochum, Medical Faculty, Medizinisches Proteom-Center, D-44801 Bochum, Germany
- Ruhr University Bochum, Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, 44801 Bochum, Germany
| | - Mathias Walzer
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Shengbo Wang
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Alvis Brazma
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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29
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Khoo A, Liu LY, Nyalwidhe JO, Semmes OJ, Vesprini D, Downes MR, Boutros PC, Liu SK, Kislinger T. Proteomic discovery of non-invasive biomarkers of localized prostate cancer using mass spectrometry. Nat Rev Urol 2021; 18:707-724. [PMID: 34453155 PMCID: PMC8639658 DOI: 10.1038/s41585-021-00500-1] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/30/2021] [Indexed: 02/08/2023]
Abstract
Prostate cancer is the second most frequently diagnosed non-skin cancer in men worldwide. Patient outcomes are remarkably heterogeneous and the best existing clinical prognostic tools such as International Society of Urological Pathology Grade Group, pretreatment serum PSA concentration and T-category, do not accurately predict disease outcome for individual patients. Thus, patients newly diagnosed with prostate cancer are often overtreated or undertreated, reducing quality of life and increasing disease-specific mortality. Biomarkers that can improve the risk stratification of these patients are, therefore, urgently needed. The ideal biomarker in this setting will be non-invasive and affordable, enabling longitudinal evaluation of disease status. Prostatic secretions, urine and blood can be sources of biomarker discovery, validation and clinical implementation, and mass spectrometry can be used to detect and quantify proteins in these fluids. Protein biomarkers currently in use for diagnosis, prognosis and relapse-monitoring of localized prostate cancer in fluids remain centred around PSA and its variants, and opportunities exist for clinically validating novel and complimentary candidate protein biomarkers and deploying them into the clinic.
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Affiliation(s)
- Amanda Khoo
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Lydia Y Liu
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
- Vector Institute for Artificial Intelligence, Toronto, Canada
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, USA
| | - Julius O Nyalwidhe
- Leroy T. Canoles Jr. Cancer Research Center, Eastern Virginia Medical School, Norfolk, VA, USA
- Department of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, VA, USA
| | - O John Semmes
- Leroy T. Canoles Jr. Cancer Research Center, Eastern Virginia Medical School, Norfolk, VA, USA
- Department of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, VA, USA
| | - Danny Vesprini
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Odette Cancer Research Program, Sunnybrook Research Institute, Toronto, Canada
| | - Michelle R Downes
- Division of Anatomic Pathology, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Paul C Boutros
- Department of Medical Biophysics, University of Toronto, Toronto, Canada.
- Vector Institute for Artificial Intelligence, Toronto, Canada.
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, Canada.
- Department of Urology, University of California, Los Angeles, Los Angeles, CA, USA.
- Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Stanley K Liu
- Department of Medical Biophysics, University of Toronto, Toronto, Canada.
- Department of Radiation Oncology, University of Toronto, Toronto, Canada.
- Odette Cancer Research Program, Sunnybrook Research Institute, Toronto, Canada.
| | - Thomas Kislinger
- Department of Medical Biophysics, University of Toronto, Toronto, Canada.
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.
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30
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Peckys DB, Gaa D, de Jonge N. Quantification of EGFR-HER2 Heterodimers in HER2-Overexpressing Breast Cancer Cells Using Liquid-Phase Electron Microscopy. Cells 2021; 10:cells10113244. [PMID: 34831465 PMCID: PMC8623301 DOI: 10.3390/cells10113244] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 11/10/2021] [Accepted: 11/15/2021] [Indexed: 12/25/2022] Open
Abstract
Currently, breast cancer patients are classified uniquely according to the expression level of hormone receptors, and human epidermal growth factor receptor 2 (HER2). This coarse classification is insufficient to capture the phenotypic complexity and heterogeneity of the disease. A methodology was developed for absolute quantification of receptor surface density ρR, and molecular interaction (dimerization), as well as the associated heterogeneities, of HER2 and its family member, the epidermal growth factor receptor (EGFR) in the plasma membrane of HER2 overexpressing breast cancer cells. Quantitative, correlative light microscopy (LM) and liquid-phase electron microscopy (LPEM) were combined with quantum dot (QD) labeling. Single-molecule position data of receptors were obtained from scanning transmission electron microscopy (STEM) images of intact cancer cells. Over 280,000 receptor positions were detected and statistically analyzed. An important finding was the subcellular heterogeneity in heterodimer shares with respect to plasma membrane regions with different dynamic properties. Deriving quantitative information about EGFR and HER2 ρR, as well as their dimer percentages, and the heterogeneities thereof, in single cancer cells, is potentially relevant for early identification of patients with HER2 overexpressing tumors comprising an enhanced share of EGFR dimers, likely increasing the risk for drug resistance, and thus requiring additional targeted therapeutic strategies.
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Affiliation(s)
- Diana B. Peckys
- Clinic of Operative Dentistry, Periodontology and Preventive Dentistry, University Hospital, Saarland University, 66421 Homburg, Germany;
| | - Daniel Gaa
- INM—Leibniz Institute for New Materials, 66123 Saarbrücken, Germany;
| | - Niels de Jonge
- INM—Leibniz Institute for New Materials, 66123 Saarbrücken, Germany;
- Department of Physics, Saarland University, 66123 Saarbrücken, Germany
- Correspondence:
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31
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Yang KC, Kalloger SE, Aird JJ, Lee MKC, Rushton C, Mungall KL, Mungall AJ, Gao D, Chow C, Xu J, Karasinska JM, Colborne S, Jones SJM, Schrader J, Morin RD, Loree JM, Marra MA, Renouf DJ, Morin GB, Schaeffer DF, Gorski SM. Proteotranscriptomic classification and characterization of pancreatic neuroendocrine neoplasms. Cell Rep 2021; 37:109817. [PMID: 34644566 DOI: 10.1016/j.celrep.2021.109817] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 07/16/2021] [Accepted: 09/20/2021] [Indexed: 12/13/2022] Open
Abstract
Pancreatic neuroendocrine neoplasms (PNENs) are biologically and clinically heterogeneous. Here, we use a multi-omics approach to uncover the molecular factors underlying this heterogeneity. Transcriptomic analysis of 84 PNEN specimens, drawn from two cohorts, is substantiated with proteomic profiling and identifies four subgroups: Proliferative, PDX1-high, Alpha cell-like and Stromal/Mesenchymal. The Proliferative subgroup, consisting of both well- and poorly differentiated specimens, is associated with inferior overall survival probability. The PDX1-high and Alpha cell-like subgroups partially resemble previously described subtypes, and we further uncover distinctive metabolism-related features in the Alpha cell-like subgroup. The Stromal/Mesenchymal subgroup exhibits molecular characteristics of YAP1/WWTR1(TAZ) activation suggestive of Hippo signaling pathway involvement in PNENs. Whole-exome sequencing reveals subgroup-enriched mutational differences, supported by activity inference analysis, and identifies hypermorphic proto-oncogene variants in 14.3% of sequenced PNENs. Our study reveals differences in cellular signaling axes that provide potential directions for PNEN patient stratification and treatment strategies.
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Affiliation(s)
- Kevin C Yang
- Canada's Michael Smith Genome Sciences Centre at BC Cancer, Vancouver, BC V5Z 1L3, Canada; Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Steve E Kalloger
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z7, Canada; School of Population and Public Health, University of British Columbia, Vancouver, BC V6T 1Z3, Canada; Division of Anatomical Pathology, Vancouver General Hospital, Vancouver, BC V5Z 1M9, Canada; Pancreas Centre BC, Vancouver, BC V5Z 1L8, Canada
| | - John J Aird
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z7, Canada; Division of Anatomical Pathology, Vancouver General Hospital, Vancouver, BC V5Z 1M9, Canada
| | - Michael K C Lee
- Division of Medical Oncology, BC Cancer, Vancouver, BC V5Z 4E6, Canada
| | - Christopher Rushton
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Karen L Mungall
- Canada's Michael Smith Genome Sciences Centre at BC Cancer, Vancouver, BC V5Z 1L3, Canada
| | - Andrew J Mungall
- Canada's Michael Smith Genome Sciences Centre at BC Cancer, Vancouver, BC V5Z 1L3, Canada
| | - Dongxia Gao
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z7, Canada; Genetic Pathology Evaluation Centre, Vancouver, BC V6H 3Z6, Canada
| | - Christine Chow
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z7, Canada; Genetic Pathology Evaluation Centre, Vancouver, BC V6H 3Z6, Canada
| | - Jing Xu
- Canada's Michael Smith Genome Sciences Centre at BC Cancer, Vancouver, BC V5Z 1L3, Canada; Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | | | - Shane Colborne
- Canada's Michael Smith Genome Sciences Centre at BC Cancer, Vancouver, BC V5Z 1L3, Canada
| | - Steven J M Jones
- Canada's Michael Smith Genome Sciences Centre at BC Cancer, Vancouver, BC V5Z 1L3, Canada; Department of Medical Genetics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Jörg Schrader
- Department of General, Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; I. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ryan D Morin
- Canada's Michael Smith Genome Sciences Centre at BC Cancer, Vancouver, BC V5Z 1L3, Canada; Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Jonathan M Loree
- Division of Medical Oncology, BC Cancer, Vancouver, BC V5Z 4E6, Canada
| | - Marco A Marra
- Canada's Michael Smith Genome Sciences Centre at BC Cancer, Vancouver, BC V5Z 1L3, Canada; Department of Medical Genetics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Daniel J Renouf
- Pancreas Centre BC, Vancouver, BC V5Z 1L8, Canada; Division of Medical Oncology, BC Cancer, Vancouver, BC V5Z 4E6, Canada
| | - Gregg B Morin
- Canada's Michael Smith Genome Sciences Centre at BC Cancer, Vancouver, BC V5Z 1L3, Canada; Department of Medical Genetics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - David F Schaeffer
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z7, Canada; Division of Anatomical Pathology, Vancouver General Hospital, Vancouver, BC V5Z 1M9, Canada; Pancreas Centre BC, Vancouver, BC V5Z 1L8, Canada
| | - Sharon M Gorski
- Canada's Michael Smith Genome Sciences Centre at BC Cancer, Vancouver, BC V5Z 1L3, Canada; Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada; Centre for Cell Biology, Development, and Disease, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.
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