1
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Buljan M, Banaei-Esfahani A, Blattmann P, Meier-Abt F, Shao W, Vitek O, Tang H, Aebersold R. A computational framework for the inference of protein complex remodeling from whole-proteome measurements. Nat Methods 2023; 20:1523-1529. [PMID: 37749212 PMCID: PMC10555833 DOI: 10.1038/s41592-023-02011-w] [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: 12/30/2020] [Accepted: 08/16/2023] [Indexed: 09/27/2023]
Abstract
Protein complexes are responsible for the enactment of most cellular functions. For the protein complex to form and function, its subunits often need to be present at defined quantitative ratios. Typically, global changes in protein complex composition are assessed with experimental approaches that tend to be time consuming. Here, we have developed a computational algorithm for the detection of altered protein complexes based on the systematic assessment of subunit ratios from quantitative proteomic measurements. We applied it to measurements from breast cancer cell lines and patient biopsies and were able to identify strong remodeling of HDAC2 epigenetic complexes in more aggressive forms of cancer. The presented algorithm is available as an R package and enables the inference of changes in protein complex states by extracting functionally relevant information from bottom-up proteomic datasets.
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Affiliation(s)
- Marija Buljan
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
- EMPA, Swiss Federal Laboratories for Materials Science and Technology, St Gallen, Switzerland.
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
| | - Amir Banaei-Esfahani
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Department of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland
| | - Peter Blattmann
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Idorsia Pharmaceuticals, Allschwil, Switzerland
| | - Fabienne Meier-Abt
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Department of Medical Oncology and Hematology, University and University Hospital Zurich, Zurich, Switzerland
- Institute of Medical Genetics, University of Zurich, Zurich, Switzerland
| | - Wenguang Shao
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- State Key Laboratory of Microbial Metabolism, School of Life Science & Biotechnology, and Joint International Research Laboratory of Metabolic & Developmental Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Olga Vitek
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Hua Tang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
- Faculty of Science, University of Zurich, Zurich, Switzerland.
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2
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Li Y, Dou Y, Da Veiga Leprevost F, Geffen Y, Calinawan AP, Aguet F, Akiyama Y, Anand S, Birger C, Cao S, Chaudhary R, Chilappagari P, Cieslik M, Colaprico A, Zhou DC, Day C, Domagalski MJ, Esai Selvan M, Fenyö D, Foltz SM, Francis A, Gonzalez-Robles T, Gümüş ZH, Heiman D, Holck M, Hong R, Hu Y, Jaehnig EJ, Ji J, Jiang W, Katsnelson L, Ketchum KA, Klein RJ, Lei JT, Liang WW, Liao Y, Lindgren CM, Ma W, Ma L, MacCoss MJ, Martins Rodrigues F, McKerrow W, Nguyen N, Oldroyd R, Pilozzi A, Pugliese P, Reva B, Rudnick P, Ruggles KV, Rykunov D, Savage SR, Schnaubelt M, Schraink T, Shi Z, Singhal D, Song X, Storrs E, Terekhanova NV, Thangudu RR, Thiagarajan M, Wang LB, Wang JM, Wang Y, Wen B, Wu Y, Wyczalkowski MA, Xin Y, Yao L, Yi X, Zhang H, Zhang Q, Zuhl M, Getz G, Ding L, Nesvizhskii AI, Wang P, Robles AI, Zhang B, Payne SH. Proteogenomic data and resources for pan-cancer analysis. Cancer Cell 2023; 41:1397-1406. [PMID: 37582339 PMCID: PMC10506762 DOI: 10.1016/j.ccell.2023.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 11/15/2022] [Accepted: 06/27/2023] [Indexed: 08/17/2023]
Abstract
The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.
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Affiliation(s)
- Yize Li
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Yongchao Dou
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | | | - Yifat Geffen
- Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
| | - Anna P Calinawan
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - François Aguet
- Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
| | - Yo Akiyama
- Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
| | - Shankara Anand
- Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
| | - Chet Birger
- Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
| | - Song Cao
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | | | | | - Marcin Cieslik
- Department of Computational Medicine & Bioinformatics, Department of Pathology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Antonio Colaprico
- Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Daniel Cui Zhou
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Corbin Day
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | | | - Myvizhi Esai Selvan
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - David Fenyö
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Steven M Foltz
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | | | - Tania Gonzalez-Robles
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Zeynep H Gümüş
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - David Heiman
- Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
| | | | - Runyu Hong
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Yingwei Hu
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Eric J Jaehnig
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jiayi Ji
- Tisch Cancer Institute and Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Wen Jiang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Lizabeth Katsnelson
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | | | - Robert J Klein
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jonathan T Lei
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Wen-Wei Liang
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Yuxing Liao
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Caleb M Lindgren
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Weiping Ma
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Lei Ma
- ICF, Rockville, MD 20850, USA
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Fernanda Martins Rodrigues
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Wilson McKerrow
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | | | - Robert Oldroyd
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | | | - Pietro Pugliese
- Department of Sciences and Technologies, University of Sannio, Benevento 82100, Italy
| | - Boris Reva
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Paul Rudnick
- Spectragen Informatics, Bainbridge Island, WA 98110, USA
| | - Kelly V Ruggles
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Dmitry Rykunov
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sara R Savage
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Michael Schnaubelt
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Tobias Schraink
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Zhiao Shi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | | | - Xiaoyu Song
- Tisch Cancer Institute and Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Erik Storrs
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Nadezhda V Terekhanova
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | | | | | - Liang-Bo Wang
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Joshua M Wang
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Ying Wang
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Bo Wen
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yige Wu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Matthew A Wyczalkowski
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Yi Xin
- ICF, Rockville, MD 20850, USA
| | - Lijun Yao
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Xinpei Yi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Hui Zhang
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Qing Zhang
- Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
| | | | - Gad Getz
- Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA; Cancer Center and Department of Pathology, Mass. General Hospital, Boston, MA 02114, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Li Ding
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Genetics, Washington University in St. Louis, St. Louis, MO 63130, USA
| | | | - Pei Wang
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA.
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Samuel H Payne
- Department of Biology, Brigham Young University, Provo, UT 84602, USA.
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3
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Yates J, Gomes F, Durbin K, Schauer K, Nwachukwu J, Russo R, Njeri J, Saviola A, McClatchy D, Diedrich J, Garrett P, Papa A, Ciolacu I, Kelleher N, Nettles K. Native top-down proteomics reveals EGFR-ERα signaling crosstalk in breast cancer cells dissociates NUTF2 dimers to modulate ERα signaling and cell growth. RESEARCH SQUARE 2023:rs.3.rs-3097806. [PMID: 37546719 PMCID: PMC10402242 DOI: 10.21203/rs.3.rs-3097806/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Oligomerization of proteins and their modified forms (proteoforms) produces functional protein complexes 1,2. Complexoforms are complexes that consist of the same set of proteins with different proteoforms 3. The ability to characterize these assemblies within cells is critical to understanding the molecular mechanisms involved in disease and to designing effective drugs. An outstanding biological question is how proteoforms drive function and oligomerization of complexoforms. However, tools to define endogenous proteoform-proteoform/ligand interactions are scarce 4. Here, we present a native top-down proteomics (nTDP) strategy that combines size-exclusion chromatography, nano liquid-chromatography in direct infusion mode, field asymmetric ion mobility spectrometry, and multistage mass spectrometry to identify protein assemblies (≤70 kDa) in breast cancer cells and in cells that overexpress EGFR, a resistance model of estrogen receptor-α (ER-α) targeted therapies. By identifying ~104 complexoforms from 17 protein complexes, our nTDP approach revealed several molecular features of the breast cancer proteome, including EGFR-induced dissociation of nuclear transport factor 2 (NUTF2) assemblies that modulate ER activity. Our findings show that the K4 and K55 posttranslational modification sites discovered with nTDP differentially impact the effects of NUTF2 on the inhibition of the ER signaling pathway. By characterizing endogenous proteoform-proteoform/ligand interactions, we reveal the molecular diversity of complexoforms, which allows us to propose a model for ER drug discovery in the context of designing effective inhibitors to selectively bind and disrupt the actions of targeted ER complexoforms.
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4
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Reid XJ, Low JKK, Mackay JP. A NuRD for all seasons. Trends Biochem Sci 2023; 48:11-25. [PMID: 35798615 DOI: 10.1016/j.tibs.2022.06.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 06/02/2022] [Accepted: 06/08/2022] [Indexed: 12/27/2022]
Abstract
The nucleosome-remodeling and deacetylase (NuRD) complex is an essential transcriptional regulator in all complex animals. All seven core subunits of the complex exist as multiple paralogs, raising the question of whether the complex might utilize paralog switching to achieve cell type-specific functions. We examine the evidence for this idea, making use of published quantitative proteomic data to dissect NuRD composition in 20 different tissues, as well as a large-scale CRISPR knockout screen carried out in >1000 human cancer cell lines. These data, together with recent reports, provide strong support for the idea that distinct permutations of the NuRD complex with tailored functions might regulate tissue-specific gene expression programs.
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Affiliation(s)
- Xavier J Reid
- School of Life and Environmental Sciences, University of Sydney, NSW 2006, Australia
| | - Jason K K Low
- School of Life and Environmental Sciences, University of Sydney, NSW 2006, Australia
| | - Joel P Mackay
- School of Life and Environmental Sciences, University of Sydney, NSW 2006, Australia.
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5
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Wu S, Wagner G. Protocol to analyze dysregulation of the eIF4F complex in human cancers using R software and large public datasets. STAR Protoc 2022; 3:101880. [PMID: 36595939 PMCID: PMC9768376 DOI: 10.1016/j.xpro.2022.101880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 09/08/2022] [Accepted: 11/02/2022] [Indexed: 12/14/2022] Open
Abstract
Understanding dysregulation of the eukaryotic initiation factor 4F (eIF4F) complex across tumor types is critical to cancer treatment development. We present a protocol and accompanying R package "eIF4F.analysis". We describe analysis of copy number status, gene abundance and stoichiometry, survival probability, expression covariation, correlating genes, mRNA/protein correlation, and protein co-expression. Using publicly available large multi-omics data, eIF4F.analysis permits computationally derived and statistically powerful inferences regarding initiation factor regulation in human cancers and clinical relevance of protein interactions within the eIF4F complex. For complete details on the use and execution of this protocol, please refer to Wu and Wagner (2021).1.
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Affiliation(s)
- Su Wu
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA,Corresponding author
| | - Gerhard Wagner
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA,Corresponding author
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6
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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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7
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Gonçalves E, Poulos RC, Cai Z, Barthorpe S, Manda SS, Lucas N, Beck A, Bucio-Noble D, Dausmann M, Hall C, Hecker M, Koh J, Lightfoot H, Mahboob S, Mali I, Morris J, Richardson L, Seneviratne AJ, Shepherd R, Sykes E, Thomas F, Valentini S, Williams SG, Wu Y, Xavier D, MacKenzie KL, Hains PG, Tully B, Robinson PJ, Zhong Q, Garnett MJ, Reddel RR. Pan-cancer proteomic map of 949 human cell lines. Cancer Cell 2022; 40:835-849.e8. [PMID: 35839778 PMCID: PMC9387775 DOI: 10.1016/j.ccell.2022.06.010] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 03/29/2022] [Accepted: 06/21/2022] [Indexed: 12/12/2022]
Abstract
The proteome provides unique insights into disease biology beyond the genome and transcriptome. A lack of large proteomic datasets has restricted the identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types are analyzed by mass spectrometry. Deploying a workflow to quantify 8,498 proteins, these data capture evidence of cell-type and post-transcriptional modifications. Integrating multi-omics, drug response, and CRISPR-Cas9 gene essentiality screens with a deep learning-based pipeline reveals thousands of protein biomarkers of cancer vulnerabilities that are not significant at the transcript level. The power of the proteome to predict drug response is very similar to that of the transcriptome. Further, random downsampling to only 1,500 proteins has limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. This pan-cancer proteomic map (ProCan-DepMapSanger) is a comprehensive resource available at https://cellmodelpassports.sanger.ac.uk.
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Affiliation(s)
- Emanuel Gonçalves
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK; Instituto Superior Técnico (IST), Universidade de Lisboa, 1049-001 Lisboa, Portugal; INESC-ID, 1000-029 Lisboa, Portugal
| | - Rebecca C Poulos
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Zhaoxiang Cai
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Syd Barthorpe
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Srikanth S Manda
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Natasha Lucas
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Alexandra Beck
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Daniel Bucio-Noble
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Michael Dausmann
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Caitlin Hall
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Michael Hecker
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Jennifer Koh
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Howard Lightfoot
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Sadia Mahboob
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Iman Mali
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - James Morris
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Laura Richardson
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Akila J Seneviratne
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Rebecca Shepherd
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Erin Sykes
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Frances Thomas
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Sara Valentini
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Steven G Williams
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Yangxiu Wu
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Dylan Xavier
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Karen L MacKenzie
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Peter G Hains
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Brett Tully
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Phillip J Robinson
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia.
| | - Qing Zhong
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia.
| | - Mathew J Garnett
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK.
| | - Roger R Reddel
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia.
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8
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Li M, Li F, Chen J, Su H, Chen G, Cao J, Li J, Dong L, Yu Z, Wang Y, Zhou C, Zhu Y, Wei Q, Li Q, Chai K. Mechanistic insights on cytotoxicity of KOLR, Cinnamomum pauciflorum Nees leaf derived active ingredient, by targeting signaling complexes of phosphodiesterase 3B and rap guanine nucleotide exchange factor 3. Phytother Res 2022; 36:3540-3554. [PMID: 35703011 DOI: 10.1002/ptr.7521] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 04/04/2022] [Accepted: 04/23/2022] [Indexed: 12/17/2022]
Abstract
Protein signaling complexes play important roles in prevention of several cancer types and can be used for development of targeted therapy. The roles of signaling complexes of phosphodiesterase 3B (PDE3B) and Rap guanine nucleotide exchange factor 3 (RAPGEF3), which are two important enzymes of cyclic adenosine monophosphate (cAMP) metabolism, in cancer have not been fully explored. In the current study, a natural product Kaempferol-3-O-(3'',4''-di-E-p-coumaroyl)-α-L-rhamnopyranoside designated as KOLR was extracted from Cinnamomum pauciflorum Nees leaves. KOLR exhibited higher cytotoxic effects against BxCP-3 pancreatic cancer cell line. In BxPC-3 cells, the KOLR could enhance the formation of RAPGEF 3/ PDE3B protein complex to inhibit the activation of Rap-1 and PI3K-AKT pathway, thereby promoting cell apoptosis and inhibiting cell metastasis. Mutation of RAPGEF3 G557A or low expression of PDE3B inactivated the binding action of KOLR resulting in KOLR resistance. The findings of this study show that PDE3B/RAPGEF3 complex is a potential therapeutic cancer target.
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Affiliation(s)
- Mingqian Li
- Cancer Institute of Integrated tradition Chinese and Western Medicine, Zhejiang Academy of Traditional Chinese Medicine, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Fei Li
- College of Life Science, Sichuan Normal University, Chengdu, Sichuan, China
| | - Jiabin Chen
- Cancer Institute of Integrated tradition Chinese and Western Medicine, Zhejiang Academy of Traditional Chinese Medicine, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - He Su
- The second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Traditional Chinese Medicine, Guang zhou, Guangdong, China
| | - Guanping Chen
- Cancer Institute of Integrated tradition Chinese and Western Medicine, Zhejiang Academy of Traditional Chinese Medicine, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Jili Cao
- Cancer Institute of Integrated tradition Chinese and Western Medicine, Zhejiang Academy of Traditional Chinese Medicine, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Jiacheng Li
- College of Life Science, Sichuan Normal University, Chengdu, Sichuan, China
| | - Liyao Dong
- College of Life Science, Sichuan Normal University, Chengdu, Sichuan, China
| | - Zhihong Yu
- Cancer Institute of Integrated tradition Chinese and Western Medicine, Zhejiang Academy of Traditional Chinese Medicine, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Yifan Wang
- Cancer Institute of Integrated tradition Chinese and Western Medicine, Zhejiang Academy of Traditional Chinese Medicine, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Chun Zhou
- Nursing Department, People's Liberation Army Joint Logistic Support Force 903th Hospital, Hangzhou, Zhejiang, China
| | - Yongqiang Zhu
- Cancer Institute of Integrated tradition Chinese and Western Medicine, Zhejiang Academy of Traditional Chinese Medicine, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Qin Wei
- Key Laboratory of Fermentation Resources and Application in Universities of Sichuan Province, Yibin University, Yibin, Sichuan, China
| | - Qun Li
- College of Life Science, Sichuan Normal University, Chengdu, Sichuan, China
| | - Kequn Chai
- Cancer Institute of Integrated tradition Chinese and Western Medicine, Zhejiang Academy of Traditional Chinese Medicine, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
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9
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Guharoy M, Lazar T, Macossay-Castillo M, Tompa P. Degron masking outlines degronons, co-degrading functional modules in the proteome. Commun Biol 2022; 5:445. [PMID: 35545699 PMCID: PMC9095673 DOI: 10.1038/s42003-022-03391-z] [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: 01/19/2021] [Accepted: 04/22/2022] [Indexed: 11/28/2022] Open
Abstract
Effective organization of proteins into functional modules (networks, pathways) requires systems-level coordination between transcription, translation and degradation. Whereas the cooperation between transcription and translation was extensively studied, the cooperative degradation regulation of protein complexes and pathways has not been systematically assessed. Here we comprehensively analyzed degron masking, a major mechanism by which cellular systems coordinate degron recognition and protein degradation. For over 200 substrates with characterized degrons (E3 ligase targeting motifs, ubiquitination sites and disordered proteasomal entry sequences), we demonstrate that degrons extensively overlap with protein-protein interaction sites. Analysis of binding site information and protein abundance comparisons show that regulatory partners effectively outcompete E3 ligases, masking degrons from the ubiquitination machinery. Protein abundance variations between normal and cancer cells highlight the dynamics of degron masking components. Finally, integrative analysis of gene co-expression, half-life correlations and functional relationships between interacting proteins point towards higher-order, co-regulated degradation modules (‘degronons’) in the proteome. Systematic bioinformatics analysis of cooperative degradation of protein complexes indicates that degrons extensively overlap with protein-protein interaction sites, hiding degrons from ubiquitination machinery and suggesting the existence of co-degrading functional modules in the proteome.
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Affiliation(s)
- Mainak Guharoy
- VIB-VUB Center for Structural Biology, Pleinlaan 2, 1050, Brussels, Belgium. .,Structural Biology Brussels, Department of Bioengineering Sciences, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium. .,VIB Bioinformatics Core, Technologiepark-Zwijnaarde 75, 9052, Ghent, Belgium.
| | - Tamas Lazar
- VIB-VUB Center for Structural Biology, Pleinlaan 2, 1050, Brussels, Belgium.,Structural Biology Brussels, Department of Bioengineering Sciences, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - Mauricio Macossay-Castillo
- VIB-VUB Center for Structural Biology, Pleinlaan 2, 1050, Brussels, Belgium.,Structural Biology Brussels, Department of Bioengineering Sciences, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - Peter Tompa
- VIB-VUB Center for Structural Biology, Pleinlaan 2, 1050, Brussels, Belgium. .,Structural Biology Brussels, Department of Bioengineering Sciences, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium. .,Institute of Enzymology, Research Centre for Natural Sciences of the Hungarian Academy of Sciences, 1117, Budapest, Hungary.
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10
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Matsui Y, Abe Y, Uno K, Miyano S. RoDiCE: robust differential protein co-expression analysis for cancer complexome. Bioinformatics 2022; 38:1269-1276. [PMID: 34529752 DOI: 10.1093/bioinformatics/btab612] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 08/09/2021] [Accepted: 08/23/2021] [Indexed: 01/05/2023] Open
Abstract
MOTIVATION The full spectrum of abnormalities in cancer-associated protein complexes remains largely unknown. Comparing the co-expression structure of each protein complex between tumor and healthy cells may provide insights regarding cancer-specific protein dysfunction. However, the technical limitations of mass spectrometry-based proteomics, including contamination with biological protein variants, causes noise that leads to non-negligible over- (or under-) estimating co-expression. RESULTS We propose a robust algorithm for identifying protein complex aberrations in cancer based on differential protein co-expression testing. Our method based on a copula is sufficient for improving identification accuracy with noisy data compared to conventional linear correlation-based approaches. As an application, we use large-scale proteomic data from renal cancer to show that important protein complexes, regulatory signaling pathways and drug targets can be identified. The proposed approach surpasses traditional linear correlations to provide insights into higher-order differential co-expression structures. AVAILABILITY AND IMPLEMENTATION https://github.com/ymatts/RoDiCE. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yusuke Matsui
- Biomedical and Health Informatics Unit, Department of Integrated Health Science, Nagoya University Graduate School of Medicine, 461-8673 Nagoya, Aichi, Japan.,Institute for Glyco-core Research (iGCORE), Nagoya University, 461-8673 Nagoya, Aichi, Japan
| | - Yuichi Abe
- Division of Molecular Diagnostics, Aichi Cancer Center Research Institute, 464-0021 Nagoya, Aichi, Japan
| | - Kohei Uno
- Biomedical and Health Informatics Unit, Department of Integrated Health Science, Nagoya University Graduate School of Medicine, 461-8673 Nagoya, Aichi, Japan
| | - Satoru Miyano
- Department of Integrated Data Science, M&D Data Science Center, Tokyo Medical and Dental University, 113-8510 Tokyo, Japan
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11
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Kunowska N, Stelzl U. Decoding the cellular effects of genetic variation through interaction proteomics. Curr Opin Chem Biol 2022; 66:102100. [PMID: 34801969 DOI: 10.1016/j.cbpa.2021.102100] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/07/2021] [Accepted: 10/14/2021] [Indexed: 12/24/2022]
Abstract
It is often unclear how genetic variation translates into cellular phenotypes, including how much of the coding variation can be recovered in the proteome. Proteogenomic analyses of heterogenous cell lines revealed that the genetic differences impact mostly the abundance and stoichiometry of protein complexes, with the effects propagating post-transcriptionally via protein interactions onto other subunits. Conversely, large scale binary interaction analyses of missense variants revealed that loss of interaction is widespread and caused by about 50% disease-associated mutations, while deep scanning mutagenesis of binary interactions identified thousands of interaction-deficient variants per interaction. The idea that phenotypes arise from genetic variation through protein-protein interaction is therefore substantiated by both forward and reverse interaction proteomics. With improved methodologies, these two approaches combined can close the knowledge gap between nucleotide sequence variation and its functional consequences on the cellular proteome.
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Affiliation(s)
- Natalia Kunowska
- Institute of Pharmaceutical Sciences, Pharmaceutical Chemistry, University of Graz, Austria
| | - Ulrich Stelzl
- Institute of Pharmaceutical Sciences, Pharmaceutical Chemistry, University of Graz, Austria; BioTechMed-Graz, Austria; Field of Excellence BioHealth - University of Graz, Austria.
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12
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Senger G, Schaefer MH. Protein Complex Organization Imposes Constraints on Proteome Dysregulation in Cancer. FRONTIERS IN BIOINFORMATICS 2021; 1:723482. [PMID: 36303728 PMCID: PMC9580999 DOI: 10.3389/fbinf.2021.723482] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 08/16/2021] [Indexed: 01/07/2023] Open
Abstract
Protein assembly is a highly dynamic process and proteins can interact in different ways and stoichiometries within a complex. The importance of maintaining protein stoichiometry for complex function and avoiding aggregation of orphan subunits has been demonstrated. However, how exactly the organization of proteins into complexes constrains differential protein abundance in extreme cellular conditions like cancer, where a lot of protein abundance changes occur, has not been systematically investigated. To study this, we collected proteomic data made available by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) to quantify proteomic changes during carcinogenesis and systematically tested five interaction types in complexes to investigate which of these features impact on protein abundance correlation patterns in cancer. We found that higher than expected fraction of protein complex subunits does not show changes in their abundances compared to those in the normal samples. Furthermore, we found that the way proteins interact in complexes indeed constrains their co-abundance patterns. Our results highlight the role of the interactions between the proteins and the need of cancer cells to deal with aberrant changes in protein abundance.
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13
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Huttlin EL, Bruckner RJ, Navarrete-Perea J, Cannon JR, Baltier K, Gebreab F, Gygi MP, Thornock A, Zarraga G, Tam S, Szpyt J, Gassaway BM, Panov A, Parzen H, Fu S, Golbazi A, Maenpaa E, Stricker K, Guha Thakurta S, Zhang T, Rad R, Pan J, Nusinow DP, Paulo JA, Schweppe DK, Vaites LP, Harper JW, Gygi SP. Dual proteome-scale networks reveal cell-specific remodeling of the human interactome. Cell 2021; 184:3022-3040.e28. [PMID: 33961781 PMCID: PMC8165030 DOI: 10.1016/j.cell.2021.04.011] [Citation(s) in RCA: 370] [Impact Index Per Article: 123.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 01/05/2021] [Accepted: 04/07/2021] [Indexed: 12/16/2022]
Abstract
Thousands of interactions assemble proteins into modules that impart spatial and functional organization to the cellular proteome. Through affinity-purification mass spectrometry, we have created two proteome-scale, cell-line-specific interaction networks. The first, BioPlex 3.0, results from affinity purification of 10,128 human proteins-half the proteome-in 293T cells and includes 118,162 interactions among 14,586 proteins. The second results from 5,522 immunoprecipitations in HCT116 cells. These networks model the interactome whose structure encodes protein function, localization, and complex membership. Comparison across cell lines validates thousands of interactions and reveals extensive customization. Whereas shared interactions reside in core complexes and involve essential proteins, cell-specific interactions link these complexes, "rewiring" subnetworks within each cell's interactome. Interactions covary among proteins of shared function as the proteome remodels to produce each cell's phenotype. Viewable interactively online through BioPlexExplorer, these networks define principles of proteome organization and enable unknown protein characterization.
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Affiliation(s)
- Edward L Huttlin
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA.
| | - Raphael J Bruckner
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | | | - Joe R Cannon
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Kurt Baltier
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Fana Gebreab
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Melanie P Gygi
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Alexandra Thornock
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Gabriela Zarraga
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Stanley Tam
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - John Szpyt
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Brandon M Gassaway
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Alexandra Panov
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Hannah Parzen
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Sipei Fu
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Arvene Golbazi
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Eila Maenpaa
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Keegan Stricker
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | | | - Tian Zhang
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Ramin Rad
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Joshua Pan
- Broad Institute, Cambridge, MA 02142, USA
| | - David P Nusinow
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Joao A Paulo
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Devin K Schweppe
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | | | - J Wade Harper
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA.
| | - Steven P Gygi
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA.
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14
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Lindgren CM, Adams DW, Kimball B, Boekweg H, Tayler S, Pugh SL, Payne SH. Simplified and Unified Access to Cancer Proteogenomic Data. J Proteome Res 2021; 20:1902-1910. [PMID: 33560848 PMCID: PMC8022323 DOI: 10.1021/acs.jproteome.0c00919] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
![]()
Comprehensive cancer
data sets recently generated by the Clinical
Proteomic Tumor Analysis Consortium (CPTAC) offer great potential
for advancing our understanding of how to combat cancer. These data
sets include DNA, RNA, protein, and clinical characterization for
tumor and normal samples from large cohorts of many different cancer
types. The raw data are publicly available at various Cancer Research
Data Commons. However, widespread reuse of these data sets is also
facilitated by easy access to the processed quantitative data tables.
We have created a data application programming interface (API) to
distribute these processed tables, implemented as a Python package
called cptac. We implement it such that users
who prefer to work in R can easily use our package for data access
and then transfer the data into R for analysis. Our package distributes
the finalized processed CPTAC data sets in a consistent, up-to-date
format. This consistency makes it easy to integrate the data with
common graphing, statistical, and machine-learning packages for advanced
analysis. Additionally, consistent formatting across all cancer types
promotes the investigation of pan-cancer trends. The data API structure
of directly streaming data within a programming environment enhances
the reproducibility. Finally, with the accompanying tutorials, this
package provides a novel resource for cancer research education. View
the software documentation at https://paynelab.github.io/cptac/. View the GitHub repository at https://github.com/PayneLab/cptac.
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Affiliation(s)
- Caleb M Lindgren
- Biology Department, Brigham Young University, Provo, Utah 84602, United States
| | - David W Adams
- Biology Department, Brigham Young University, Provo, Utah 84602, United States
| | - Benjamin Kimball
- Biology Department, Brigham Young University, Provo, Utah 84602, United States
| | - Hannah Boekweg
- Biology Department, Brigham Young University, Provo, Utah 84602, United States
| | - Sadie Tayler
- Biology Department, Brigham Young University, Provo, Utah 84602, United States
| | - Samuel L Pugh
- Biology Department, Brigham Young University, Provo, Utah 84602, United States
| | - Samuel H Payne
- Biology Department, Brigham Young University, Provo, Utah 84602, United States
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15
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Chen YJ, Roumeliotis TI, Chang YH, Chen CT, Han CL, Lin MH, Chen HW, Chang GC, Chang YL, Wu CT, Lin MW, Hsieh MS, Wang YT, Chen YR, Jonassen I, Ghavidel FZ, Lin ZS, Lin KT, Chen CW, Sheu PY, Hung CT, Huang KC, Yang HC, Lin PY, Yen TC, Lin YW, Wang JH, Raghav L, Lin CY, Chen YS, Wu PS, Lai CT, Weng SH, Su KY, Chang WH, Tsai PY, Robles AI, Rodriguez H, Hsiao YJ, Chang WH, Sung TY, Chen JS, Yu SL, Choudhary JS, Chen HY, Yang PC, Chen YJ. Proteogenomics of Non-smoking Lung Cancer in East Asia Delineates Molecular Signatures of Pathogenesis and Progression. Cell 2021; 182:226-244.e17. [PMID: 32649875 DOI: 10.1016/j.cell.2020.06.012] [Citation(s) in RCA: 148] [Impact Index Per Article: 49.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 03/13/2020] [Accepted: 06/03/2020] [Indexed: 12/13/2022]
Abstract
Lung cancer in East Asia is characterized by a high percentage of never-smokers, early onset and predominant EGFR mutations. To illuminate the molecular phenotype of this demographically distinct disease, we performed a deep comprehensive proteogenomic study on a prospectively collected cohort in Taiwan, representing early stage, predominantly female, non-smoking lung adenocarcinoma. Integrated genomic, proteomic, and phosphoproteomic analysis delineated the demographically distinct molecular attributes and hallmarks of tumor progression. Mutational signature analysis revealed age- and gender-related mutagenesis mechanisms, characterized by high prevalence of APOBEC mutational signature in younger females and over-representation of environmental carcinogen-like mutational signatures in older females. A proteomics-informed classification distinguished the clinical characteristics of early stage patients with EGFR mutations. Furthermore, integrated protein network analysis revealed the cellular remodeling underpinning clinical trajectories and nominated candidate biomarkers for patient stratification and therapeutic intervention. This multi-omic molecular architecture may help develop strategies for management of early stage never-smoker lung adenocarcinoma.
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Affiliation(s)
- Yi-Ju Chen
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
| | - Theodoros I Roumeliotis
- Functional Proteomics Group, Chester Beatty Laboratories, The Institute of Cancer Research, London SW3 6JB, UK
| | - Ya-Hsuan Chang
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Ching-Tai Chen
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Chia-Li Han
- Master Program in Clinical Pharmacogenomics and Pharmacoproteomics, College of Pharmacy, Taipei Medical University, Taipei, Taiwan.
| | - Miao-Hsia Lin
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
| | - Huei-Wen Chen
- Graduate Institute of Toxicology, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Gee-Chen Chang
- Division of Chest Medicine, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan; Faculty of Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Yih-Leong Chang
- Department of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Chen-Tu Wu
- Department of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Mong-Wei Lin
- Division of Thoracic Surgery, Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan
| | - Min-Shu Hsieh
- Department of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Yu-Tai Wang
- National Applied Research Laboratories, National Center for High-performance Computing, Hsinchu, Taiwan
| | - Yet-Ran Chen
- Agricultural Biotechnology Research Center, Academia Sinica, Taipei, Taiwan
| | - Inge Jonassen
- Computational Biology Unit (CBU), Informatics Department, University of Bergen, Bergen, Norway
| | | | - Ze-Shiang Lin
- Department of Clinical Laboratory Sciences and Medical Biotechnology, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Kuen-Tyng Lin
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
| | - Ching-Wen Chen
- Department of Clinical Laboratory Sciences and Medical Biotechnology, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Pei-Yuan Sheu
- Department of Clinical Laboratory Sciences and Medical Biotechnology, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Chen-Ting Hung
- Department of Clinical Laboratory Sciences and Medical Biotechnology, College of Medicine, National Taiwan University, Taipei, Taiwan
| | | | - Hao-Chin Yang
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
| | - Pei-Yi Lin
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
| | - Ta-Chi Yen
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
| | - Yi-Wei Lin
- Division of Thoracic Surgery, Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan
| | - Jen-Hung Wang
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Lovely Raghav
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan; Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Bioinformatics Program, Taiwan International Graduate Program, Hsinchu, Taiwan
| | - Chien-Yu Lin
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Yan-Si Chen
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Pei-Shan Wu
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
| | - Chi-Ting Lai
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
| | | | - Kang-Yi Su
- Department of Clinical Laboratory Sciences and Medical Biotechnology, College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Laboratory Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Wei-Hung Chang
- Agricultural Biotechnology Research Center, Academia Sinica, Taipei, Taiwan
| | - Pang-Yan Tsai
- Agricultural Biotechnology Research Center, Academia Sinica, Taipei, Taiwan
| | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Yi-Jing Hsiao
- Department of Clinical Laboratory Sciences and Medical Biotechnology, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Wen-Hsin Chang
- Institute of Molecular Medicine, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Ting-Yi Sung
- Institute of Information Science, Academia Sinica, Taipei, Taiwan.
| | - Jin-Shing Chen
- Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan.
| | - Sung-Liang Yu
- Department of Clinical Laboratory Sciences and Medical Biotechnology, College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Laboratory Medicine, National Taiwan University Hospital, Taipei, Taiwan.
| | - Jyoti S Choudhary
- Functional Proteomics Group, Chester Beatty Laboratories, The Institute of Cancer Research, London SW3 6JB, UK.
| | - Hsuan-Yu Chen
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan; Ph.D. Program in Microbial Genomics, National Chung Hsing University, Taichung, Taiwan.
| | - Pan-Chyr Yang
- Department of Internal Medicine, National Taiwan University, Taipei, Taiwan; Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
| | - Yu-Ju Chen
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan; Department of Chemistry, National Taiwan University, Taipei, Taiwan.
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16
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Proteome-wide Systems Genetics to Identify Functional Regulators of Complex Traits. Cell Syst 2021; 12:5-22. [PMID: 33476553 DOI: 10.1016/j.cels.2020.10.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 09/15/2020] [Accepted: 10/07/2020] [Indexed: 02/08/2023]
Abstract
Proteomic technologies now enable the rapid quantification of thousands of proteins across genetically diverse samples. Integration of these data with systems-genetics analyses is a powerful approach to identify new regulators of economically important or disease-relevant phenotypes in various populations. In this review, we summarize the latest proteomic technologies and discuss technical challenges for their use in population studies. We demonstrate how the analysis of correlation structure and loci mapping can be used to identify genetic factors regulating functional protein networks and complex traits. Finally, we provide an extensive summary of the use of proteome-wide systems genetics throughout fungi, plant, and animal kingdoms and discuss the power of this approach to identify candidate regulators and drug targets in large human consortium studies.
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17
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Divergent organ-specific isogenic metastatic cell lines identified using multi-omics exhibit differential drug sensitivity. PLoS One 2020; 15:e0242384. [PMID: 33196681 PMCID: PMC7668614 DOI: 10.1371/journal.pone.0242384] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 11/01/2020] [Indexed: 12/19/2022] Open
Abstract
Background Monitoring and treating metastatic progression remains a formidable task due, in part, to an inability to monitor specific differential molecular adaptations that allow the cancer to thrive within different tissue types. Hence, to develop optimal treatment strategies for metastatic disease, an important consideration is the divergence of the metastatic cancer growing in visceral organs from the primary tumor. We had previously reported the establishment of isogenic human metastatic breast cancer cell lines that are representative of the common metastatic sites observed in breast cancer patients. Methods Here we have used proteomic, RNAseq, and metabolomic analyses of these isogenic cell lines to systematically identify differences and commonalities in pathway networks and examine the effect on the sensitivity to breast cancer therapeutic agents. Results Proteomic analyses indicated that dissemination of cells from the primary tumor sites to visceral organs resulted in cell lines that adapted to growth at each new site by, in part, acquiring protein pathways characteristic of the organ of growth. RNAseq and metabolomics analyses further confirmed the divergences, which resulted in differential efficacies to commonly used FDA approved chemotherapeutic drugs. This model system has provided data that indicates that organ-specific growth of malignant lesions is a selective adaptation and growth process. Conclusions The insights provided by these analyses indicate that the rationale of targeted treatment of metastatic disease may benefit from a consideration that the biology of metastases has diverged from the primary tumor biology and using primary tumor traits as the basis for treatment may not be ideal to design treatment strategies.
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Development of synthetic lethality in cancer: molecular and cellular classification. Signal Transduct Target Ther 2020; 5:241. [PMID: 33077733 PMCID: PMC7573576 DOI: 10.1038/s41392-020-00358-6] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 09/02/2020] [Accepted: 09/03/2020] [Indexed: 12/27/2022] Open
Abstract
Recently, genetically targeted cancer therapies have been a topic of great interest. Synthetic lethality provides a new approach for the treatment of mutated genes that were previously considered unable to be targeted in traditional genotype-targeted treatments. The increasing researches and applications in the clinical setting made synthetic lethality a promising anticancer treatment option. However, the current understandings on different conditions of synthetic lethality have not been systematically assessed and the application of synthetic lethality in clinical practice still faces many challenges. Here, we propose a novel and systematic classification of synthetic lethality divided into gene level, pathway level, organelle level, and conditional synthetic lethality, according to the degree of specificity into its biological mechanism. Multiple preclinical findings of synthetic lethality in recent years will be reviewed and classified under these different categories. Moreover, synthetic lethality targeted drugs in clinical practice will be briefly discussed. Finally, we will explore the essential implications of this classification as well as its prospects in eliminating existing challenges and the future directions of synthetic lethality.
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Giurgiu M, Reinhard J, Brauner B, Dunger-Kaltenbach I, Fobo G, Frishman G, Montrone C, Ruepp A. CORUM: the comprehensive resource of mammalian protein complexes-2019. Nucleic Acids Res 2020; 47:D559-D563. [PMID: 30357367 PMCID: PMC6323970 DOI: 10.1093/nar/gky973] [Citation(s) in RCA: 383] [Impact Index Per Article: 95.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Accepted: 10/18/2018] [Indexed: 12/17/2022] Open
Abstract
CORUM is a database that provides a manually curated repository of experimentally characterized protein complexes from mammalian organisms, mainly human (67%), mouse (15%) and rat (10%). Given the vital functions of these macromolecular machines, their identification and functional characterization is foundational to our understanding of normal and disease biology. The new CORUM 3.0 release encompasses 4274 protein complexes offering the largest and most comprehensive publicly available dataset of mammalian protein complexes. The CORUM dataset is built from 4473 different genes, representing 22% of the protein coding genes in humans. Protein complexes are described by a protein complex name, subunit composition, cellular functions as well as the literature references. Information about stoichiometry of subunits depends on availability of experimental data. Recent developments include a graphical tool displaying known interactions between subunits. This allows the prediction of structural interconnections within protein complexes of unknown structure. In addition, we present a set of 58 protein complexes with alternatively spliced subunits. Those were found to affect cellular functions such as regulation of apoptotic activity, protein complex assembly or define cellular localization. CORUM is freely accessible at http://mips.helmholtz-muenchen.de/corum/.
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Affiliation(s)
- Madalina Giurgiu
- Institute for Bioinformatics and Systems Biology (IBIS), Helmholtz Zentrum München-German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Julian Reinhard
- Institute for Bioinformatics and Systems Biology (IBIS), Helmholtz Zentrum München-German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Barbara Brauner
- Institute for Bioinformatics and Systems Biology (IBIS), Helmholtz Zentrum München-German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Irmtraud Dunger-Kaltenbach
- Institute for Bioinformatics and Systems Biology (IBIS), Helmholtz Zentrum München-German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Gisela Fobo
- Institute for Bioinformatics and Systems Biology (IBIS), Helmholtz Zentrum München-German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Goar Frishman
- Institute for Bioinformatics and Systems Biology (IBIS), Helmholtz Zentrum München-German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Corinna Montrone
- Institute for Bioinformatics and Systems Biology (IBIS), Helmholtz Zentrum München-German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Andreas Ruepp
- Institute for Bioinformatics and Systems Biology (IBIS), Helmholtz Zentrum München-German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
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20
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Al-Harazi O, El Allali A, Colak D. Biomolecular Databases and Subnetwork Identification Approaches of Interest to Big Data Community: An Expert Review. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2020; 23:138-151. [PMID: 30883301 DOI: 10.1089/omi.2018.0205] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Next-generation sequencing approaches and genome-wide studies have become essential for characterizing the mechanisms of human diseases. Consequently, many researchers have applied these approaches to discover the genetic/genomic causes of common complex and rare human diseases, generating multiomics big data that span the continuum of genomics, proteomics, metabolomics, and many other system science fields. Therefore, there is a significant and unmet need for biological databases and tools that enable and empower the researchers to analyze, integrate, and make sense of big data. There are currently large number of databases that offer different types of biological information. In particular, the integration of gene expression profiles and protein-protein interaction networks provides a deeper understanding of the complex multilayered molecular architecture of human diseases. Therefore, there has been a growing interest in developing methodologies that integrate and contextualize big data from molecular interaction networks to identify biomarkers of human diseases at a subnetwork resolution as well. In this expert review, we provide a comprehensive summary of most popular biomolecular databases for molecular interactions (e.g., Biological General Repository for Interaction Datasets, Kyoto Encyclopedia of Genes and Genomes and Search Tool for The Retrieval of Interacting Genes/Proteins), gene-disease associations (e.g., Online Mendelian Inheritance in Man, Disease-Gene Network, MalaCards), and population-specific databases (e.g., Human Genetic Variation Database), and describe some examples of their usage and potential applications. We also present the most recent subnetwork identification approaches and discuss their main advantages and limitations. As the field of data science continues to emerge, the present analysis offers a deeper and contextualized understanding of the available databases in molecular biomedicine.
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Affiliation(s)
- Olfat Al-Harazi
- 1 Department of Biostatistics, Epidemiology, and Scientific Computing, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.,2 Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Achraf El Allali
- 2 Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Dilek Colak
- 1 Department of Biostatistics, Epidemiology, and Scientific Computing, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
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21
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Gauthier L, Stynen B, Serohijos AWR, Michnick SW. Genetics' Piece of the PI: Inferring the Origin of Complex Traits and Diseases from Proteome-Wide Protein-Protein Interaction Dynamics. Bioessays 2019; 42:e1900169. [PMID: 31854021 DOI: 10.1002/bies.201900169] [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: 09/16/2019] [Revised: 11/15/2019] [Indexed: 11/07/2022]
Abstract
How do common and rare genetic polymorphisms contribute to quantitative traits or disease risk and progression? Multiple human traits have been extensively characterized at the genomic level, revealing their complex genetic architecture. However, it is difficult to resolve the mechanisms by which specific variants contribute to a phenotype. Recently, analyses of variant effects on molecular traits have uncovered intermediate mechanisms that link sequence variation to phenotypic changes. Yet, these methods only capture a fraction of genetic contributions to phenotype. Here, in reviewing the field, it is proposed that complex traits can be understood by characterizing the dynamics of biochemical networks within living cells, and that the effects of genetic variation can be captured on these networks by using protein-protein interaction (PPI) methodologies. This synergy between PPI methodologies and the genetics of complex traits opens new avenues to investigate the molecular etiology of human diseases and to facilitate their prevention or treatment.
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Affiliation(s)
- Louis Gauthier
- Departement de Biochimie, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada.,Centre Robert-Cedergren en Bioinformatique et Génomique, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada
| | - Bram Stynen
- Departement de Biochimie, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada.,Centre Robert-Cedergren en Bioinformatique et Génomique, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada
| | - Adrian W R Serohijos
- Departement de Biochimie, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada.,Centre Robert-Cedergren en Bioinformatique et Génomique, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada
| | - Stephen W Michnick
- Departement de Biochimie, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada.,Centre Robert-Cedergren en Bioinformatique et Génomique, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada
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22
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Schikora-Tamarit MÀ, Lopez-Grado I Salinas G, Gonzalez-Navasa C, Calderón I, Marcos-Fa X, Sas M, Carey LB. Promoter Activity Buffering Reduces the Fitness Cost of Misregulation. Cell Rep 2019; 24:755-765. [PMID: 30021171 DOI: 10.1016/j.celrep.2018.06.059] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Revised: 05/04/2018] [Accepted: 06/14/2018] [Indexed: 01/21/2023] Open
Abstract
Organisms regulate gene expression through changes in the activity of transcription factors (TFs). In yeast, the response of genes to changes in TF activity is generally assumed to be encoded in the promoter. To directly test this assumption, we chose 42 genes and, for each, replaced the promoter with a synthetic inducible promoter and measured how protein expression changes as a function of TF activity. Most genes exhibited gene-specific TF dose-response curves not due to differences in mRNA stability, translation, or protein stability. Instead, most genes have an intrinsic ability to buffer the effects of promoter activity. This can be encoded in the open reading frame and the 3' end of genes and can be implemented by both autoregulatory feedback and by titration of limiting trans regulators. We show experimentally and computationally that, when misexpression of a gene is deleterious, this buffering insulates cells from fitness defects due to misregulation.
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Affiliation(s)
- Miquel Àngel Schikora-Tamarit
- Systems Bioengineering Program, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Carrer Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Guillem Lopez-Grado I Salinas
- Systems Bioengineering Program, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Carrer Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Carolina Gonzalez-Navasa
- Systems Bioengineering Program, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Carrer Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Irene Calderón
- Systems Bioengineering Program, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Carrer Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Xavi Marcos-Fa
- Systems Bioengineering Program, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Carrer Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Miquel Sas
- Systems Bioengineering Program, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Carrer Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Lucas B Carey
- Systems Bioengineering Program, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Carrer Dr. Aiguader 88, 08003 Barcelona, Spain.
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23
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Sousa A, Gonçalves E, Mirauta B, Ochoa D, Stegle O, Beltrao P. Multi-omics Characterization of Interaction-mediated Control of Human Protein Abundance levels. Mol Cell Proteomics 2019; 18:S114-S125. [PMID: 31239291 PMCID: PMC6692786 DOI: 10.1074/mcp.ra118.001280] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 06/07/2019] [Indexed: 11/13/2022] Open
Abstract
Proteogenomic studies of cancer samples have shown that copy-number variation can be attenuated at the protein level for a large fraction of the proteome, likely due to the degradation of unassembled protein complex subunits. Such interaction-mediated control of protein abundance remains poorly characterized. To study this, we compiled genomic, (phospho)proteomic and structural data for hundreds of cancer samples and find that up to 42% of 8,124 analyzed proteins show signs of post-transcriptional control. We find evidence of interaction-dependent control of protein abundance, correlated with interface size, for 516 protein pairs, with some interactions further controlled by phosphorylation. Finally, these findings in cancer were reflected in variation in protein levels in normal tissues. Importantly, expression differences due to natural genetic variation were increasingly buffered from phenotype differences for highly attenuated proteins. Altogether, this study further highlights the importance of posttranscriptional control of protein abundance in cancer and healthy cells.
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Affiliation(s)
- Abel Sousa
- Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3s), Rua Alfredo Allen 208, 4200-135, Porto, Portugal; Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), Rua Júlio Amaral de Carvalho 45, 4200-135, Porto, Portugal; Graduate Program in Areas of Basic and Applied Biology (GABBA), Abel Salazar Biomedical Sciences Institute, University of Porto, Rua de Jorge Viterbo Ferreira 228, 4050-313, Porto, Portugal; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, Cambridge, UK
| | | | - Bogdan Mirauta
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, Cambridge, UK
| | - David Ochoa
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, Cambridge, UK
| | - Oliver Stegle
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, Cambridge, UK; ‡European Molecular Biology Laboratory, Genome Biology Unit, 69117 Heidelberg, Germany; §Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Pedro Beltrao
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, Cambridge, UK.
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24
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Chen Y, Chen S, Li K, Zhang Y, Huang X, Li T, Wu S, Wang Y, Carey LB, Qian W. Overdosage of Balanced Protein Complexes Reduces Proliferation Rate in Aneuploid Cells. Cell Syst 2019; 9:129-142.e5. [PMID: 31351919 DOI: 10.1016/j.cels.2019.06.007] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 02/27/2019] [Accepted: 06/17/2019] [Indexed: 11/26/2022]
Abstract
Cells with complex aneuploidies display a wide range of phenotypic abnormalities. However, the molecular basis for this has been mainly studied in trisomic (2n + 1) and disomic (n + 1) cells. To determine how karyotype affects proliferation in cells with complex aneuploidies, we generated 92 2n + x yeast strains in which each diploid cell has between 3 and 12 extra chromosomes. Genome-wide and, for individual protein complexes, proliferation defects are caused by the presence of protein complexes in which all subunits are balanced at the 3-copy level. Proteomics revealed that over 50% of 3-copy members of imbalanced complexes were expressed at only 2n protein levels, whereas members of complexes in which all subunits are stoichiometrically balanced at 3 copies per cell had 3n protein levels. We validated this finding using orthogonal datasets from yeast and from human cancers. Taken together, our study provides an explanation of how aneuploidy affects phenotype.
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Affiliation(s)
- Ying Chen
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Genetic Network Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Siyu Chen
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Genetic Network Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ke Li
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Genetic Network Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Yuliang Zhang
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Genetic Network Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiahe Huang
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Genetic Network Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Ting Li
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Shaohuan Wu
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Genetic Network Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yingchun Wang
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Genetic Network Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Lucas B Carey
- Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona 08003, Spain; Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.
| | - Wenfeng Qian
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Genetic Network Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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25
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Sharifi-Noghabi H, Zolotareva O, Collins CC, Ester M. MOLI: multi-omics late integration with deep neural networks for drug response prediction. Bioinformatics 2019; 35:i501-i509. [PMID: 31510700 PMCID: PMC6612815 DOI: 10.1093/bioinformatics/btz318] [Citation(s) in RCA: 157] [Impact Index Per Article: 31.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
MOTIVATION Historically, gene expression has been shown to be the most informative data for drug response prediction. Recent evidence suggests that integrating additional omics can improve the prediction accuracy which raises the question of how to integrate the additional omics. Regardless of the integration strategy, clinical utility and translatability are crucial. Thus, we reasoned a multi-omics approach combined with clinical datasets would improve drug response prediction and clinical relevance. RESULTS We propose MOLI, a multi-omics late integration method based on deep neural networks. MOLI takes somatic mutation, copy number aberration and gene expression data as input, and integrates them for drug response prediction. MOLI uses type-specific encoding sub-networks to learn features for each omics type, concatenates them into one representation and optimizes this representation via a combined cost function consisting of a triplet loss and a binary cross-entropy loss. The former makes the representations of responder samples more similar to each other and different from the non-responders, and the latter makes this representation predictive of the response values. We validate MOLI on in vitro and in vivo datasets for five chemotherapy agents and two targeted therapeutics. Compared to state-of-the-art single-omics and early integration multi-omics methods, MOLI achieves higher prediction accuracy in external validations. Moreover, a significant improvement in MOLI's performance is observed for targeted drugs when training on a pan-drug input, i.e. using all the drugs with the same target compared to training only on drug-specific inputs. MOLI's high predictive power suggests it may have utility in precision oncology. AVAILABILITY AND IMPLEMENTATION https://github.com/hosseinshn/MOLI. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hossein Sharifi-Noghabi
- School of Computing Science, Simon Fraser University, Burnaby, BC, Canada
- Vancouver Prostate Centre, Vancouver, BC, Canada
| | - Olga Zolotareva
- International Research Training Group Computational Methods for the Analysis of the Diversity and Dynamics of Genomes and Genome Informatics, Faculty of Technology and Center for Biotechnology, Bielefeld University, Germany
| | - Colin C Collins
- Vancouver Prostate Centre, Vancouver, BC, Canada
- Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada
| | - Martin Ester
- School of Computing Science, Simon Fraser University, Burnaby, BC, Canada
- Vancouver Prostate Centre, Vancouver, BC, Canada
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26
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Woodsmith J, Stelzl U. Understanding Disease Variants through the Lens of Protein Interactions. Cell Syst 2019; 5:544-546. [PMID: 29284128 DOI: 10.1016/j.cels.2017.12.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
High-density interaction mapping of mitochondrial proteins provides clues to molecular mechanisms implicated in the progression of neurological disorders.
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Affiliation(s)
- Jonathan Woodsmith
- Institute of Pharmaceutical Sciences, University of Graz and BioTechMed-Graz, Graz, Austria.
| | - Ulrich Stelzl
- Institute of Pharmaceutical Sciences, University of Graz and BioTechMed-Graz, Graz, Austria.
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27
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Romanov N, Kuhn M, Aebersold R, Ori A, Beck M, Bork P. Disentangling Genetic and Environmental Effects on the Proteotypes of Individuals. Cell 2019; 177:1308-1318.e10. [PMID: 31031010 PMCID: PMC6988111 DOI: 10.1016/j.cell.2019.03.015] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 01/18/2019] [Accepted: 03/05/2019] [Indexed: 02/07/2023]
Abstract
Proteotypes, like genotypes, have been found to vary between individuals in several studies, but consistent molecular functional traits across studies remain to be quantified. In a meta-analysis of 11 proteomics datasets from humans and mice, we use co-variation of proteins in known functional modules across datasets and individuals to obtain a consensus landscape of proteotype variation. We find that individuals differ considerably in both protein complex abundances and stoichiometry. We disentangle genetic and environmental factors impacting these metrics, with genetic sex and specific diets together explaining 13.5% and 11.6% of the observed variation of complex abundance and stoichiometry, respectively. Sex-specific differences, for example, include various proteins and complexes, where the respective genes are not located on sex-specific chromosomes. Diet-specific differences, added to the individual genetic backgrounds, might become a starting point for personalized proteotype modulation toward desired features. Benchmarking of datasets on human and mouse proteotypes Consistent co-variation landscape of functional modules across individuals Protein complexes vary in their stoichiometry across individuals Quantifying effects of genetic sex and specific diets on complexes
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Affiliation(s)
- Natalie Romanov
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Michael Kuhn
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Switzerland; Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Alessandro Ori
- Leibniz Institute on Aging - Fritz Lipmann Institute, Jena, Germany
| | - Martin Beck
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; Max Delbrück Center for Molecular Medicine, Berlin, Germany.
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28
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Zhou B, Yan Y, Wang Y, You S, Freeman MR, Yang W. Quantitative proteomic analysis of prostate tissue specimens identifies deregulated protein complexes in primary prostate cancer. Clin Proteomics 2019; 16:15. [PMID: 31011308 PMCID: PMC6461817 DOI: 10.1186/s12014-019-9236-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Accepted: 04/09/2019] [Indexed: 12/18/2022] Open
Abstract
Background Prostate cancer (PCa) is the most frequently diagnosed non-skin cancer and a leading cause of mortality among males in developed countries. However, our understanding of the global changes of protein complexes within PCa tissue specimens remains very limited, although it has been well recognized that protein complexes carry out essentially all major processes in living organisms and that their deregulation drives the pathogenesis and progression of various diseases. Methods By coupling tandem mass tagging-synchronous precursor selection-mass spectrometry/mass spectrometry/mass spectrometry with differential expression and co-regulation analyses, the present study compared the differences between protein complexes in normal prostate, low-grade PCa, and high-grade PCa tissue specimens. Results Globally, a large downregulated putative protein–protein interaction (PPI) network was detected in both low-grade and high-grade PCa, yet a large upregulated putative PPI network was only detected in high-grade but not low-grade PCa, compared with normal controls. To identify specific protein complexes that are deregulated in PCa, quantified proteins were mapped to protein complexes in CORUM (v3.0), a high-quality collection of 4274 experimentally verified mammalian protein complexes. Differential expression and gene ontology (GO) enrichment analyses suggested that 13 integrin complexes involved in cell adhesion were significantly downregulated in both low- and high-grade PCa compared with normal prostate, and that four Prothymosin alpha (ProTα) complexes were significantly upregulated in high-grade PCa compared with normal prostate. Moreover, differential co-regulation and GO enrichment analyses indicated that the assembly levels of six protein complexes involved in RNA splicing were significantly increased in low-grade PCa, and those of four subcomplexes of mitochondrial complex I were significantly increased in high-grade PCa, compared with normal prostate. Conclusions In summary, to the best of our knowledge, the study represents the first large-scale and quantitative, albeit indirect, comparison of individual protein complexes in human PCa tissue specimens. It may serve as a useful resource for better understanding the deregulation of protein complexes in primary PCa. Electronic supplementary material The online version of this article (10.1186/s12014-019-9236-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Bo Zhou
- Division of Cancer Biology and Therapeutics, Departments of Surgery and Biomedical Sciences, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Rm. 4009, Davis Research Bldg 8700 Beverly Blvd, Los Angeles, CA 90048 USA
| | - Yiwu Yan
- Division of Cancer Biology and Therapeutics, Departments of Surgery and Biomedical Sciences, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Rm. 4009, Davis Research Bldg 8700 Beverly Blvd, Los Angeles, CA 90048 USA
| | - Yang Wang
- Division of Cancer Biology and Therapeutics, Departments of Surgery and Biomedical Sciences, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Rm. 4009, Davis Research Bldg 8700 Beverly Blvd, Los Angeles, CA 90048 USA
| | - Sungyong You
- Division of Cancer Biology and Therapeutics, Departments of Surgery and Biomedical Sciences, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Rm. 4009, Davis Research Bldg 8700 Beverly Blvd, Los Angeles, CA 90048 USA
| | - Michael R Freeman
- Division of Cancer Biology and Therapeutics, Departments of Surgery and Biomedical Sciences, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Rm. 4009, Davis Research Bldg 8700 Beverly Blvd, Los Angeles, CA 90048 USA
| | - Wei Yang
- Division of Cancer Biology and Therapeutics, Departments of Surgery and Biomedical Sciences, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Rm. 4009, Davis Research Bldg 8700 Beverly Blvd, Los Angeles, CA 90048 USA
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29
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Sonawane AR, Weiss ST, Glass K, Sharma A. Network Medicine in the Age of Biomedical Big Data. Front Genet 2019; 10:294. [PMID: 31031797 PMCID: PMC6470635 DOI: 10.3389/fgene.2019.00294] [Citation(s) in RCA: 110] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Accepted: 03/19/2019] [Indexed: 12/13/2022] Open
Abstract
Network medicine is an emerging area of research dealing with molecular and genetic interactions, network biomarkers of disease, and therapeutic target discovery. Large-scale biomedical data generation offers a unique opportunity to assess the effect and impact of cellular heterogeneity and environmental perturbations on the observed phenotype. Marrying the two, network medicine with biomedical data provides a framework to build meaningful models and extract impactful results at a network level. In this review, we survey existing network types and biomedical data sources. More importantly, we delve into ways in which the network medicine approach, aided by phenotype-specific biomedical data, can be gainfully applied. We provide three paradigms, mainly dealing with three major biological network archetypes: protein-protein interaction, expression-based, and gene regulatory networks. For each of these paradigms, we discuss a broad overview of philosophies under which various network methods work. We also provide a few examples in each paradigm as a test case of its successful application. Finally, we delineate several opportunities and challenges in the field of network medicine. We hope this review provides a lexicon for researchers from biological sciences and network theory to come on the same page to work on research areas that require interdisciplinary expertise. Taken together, the understanding gained from combining biomedical data with networks can be useful for characterizing disease etiologies and identifying therapeutic targets, which, in turn, will lead to better preventive medicine with translational impact on personalized healthcare.
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Affiliation(s)
- Abhijeet R. Sonawane
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Scott T. Weiss
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Kimberly Glass
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Amitabh Sharma
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Brigham and Women’s Hospital, Boston, MA, United States
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30
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Parker BL, Calkin AC, Seldin MM, Keating MF, Tarling EJ, Yang P, Moody SC, Liu Y, Zerenturk EJ, Needham EJ, Miller ML, Clifford BL, Morand P, Watt MJ, Meex RCR, Peng KY, Lee R, Jayawardana K, Pan C, Mellett NA, Weir JM, Lazarus R, Lusis AJ, Meikle PJ, James DE, de Aguiar Vallim TQ, Drew BG. An integrative systems genetic analysis of mammalian lipid metabolism. Nature 2019; 567:187-193. [PMID: 30814737 PMCID: PMC6656374 DOI: 10.1038/s41586-019-0984-y] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 01/23/2019] [Indexed: 12/16/2022]
Abstract
Dysregulation of lipid homeostasis is a precipitating event in the pathogenesis and progression of hepatosteatosis and metabolic syndrome. These conditions are highly prevalent in developed societies and currently have limited options for diagnostic and therapeutic intervention. Here, using a proteomic and lipidomic-wide systems genetic approach, we interrogated lipid regulatory networks in 107 genetically distinct mouse strains to reveal key insights into the control and network structure of mammalian lipid metabolism. These include the identification of plasma lipid signatures that predict pathological lipid abundance in the liver of mice and humans, defining subcellular localization and functionality of lipid-related proteins, and revealing functional protein and genetic variants that are predicted to modulate lipid abundance. Trans-omic analyses using these datasets facilitated the identification and validation of PSMD9 as a previously unknown lipid regulatory protein. Collectively, our study serves as a rich resource for probing mammalian lipid metabolism and provides opportunities for the discovery of therapeutic agents and biomarkers in the setting of hepatic lipotoxicity.
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Affiliation(s)
- Benjamin L Parker
- Metabolic Systems Biology Laboratory, Charles Perkins Centre, School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia
| | - Anna C Calkin
- Lipid Metabolism & Cardiometabolic Disease Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia.
- Central Clinical School, Department of Medicine, Monash University, Melbourne, Victoria, Australia.
| | - Marcus M Seldin
- Department of Human Genetics/Medicine, David Geffen School of Medicine, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Michael F Keating
- Lipid Metabolism & Cardiometabolic Disease Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- Central Clinical School, Department of Medicine, Monash University, Melbourne, Victoria, Australia
- Molecular Metabolism & Ageing Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
| | - Elizabeth J Tarling
- Department of Medicine, Division of Cardiology, University of California Los Angeles (UCLA), Los Angeles, CA, USA
- Molecular Biology Institute, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Pengyi Yang
- Charles Perkins Centre, School of Mathematics and Statistics, University of Sydney, Sydney, New South Wales, Australia
| | - Sarah C Moody
- Lipid Metabolism & Cardiometabolic Disease Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- Molecular Metabolism & Ageing Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
| | - Yingying Liu
- Lipid Metabolism & Cardiometabolic Disease Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- Molecular Metabolism & Ageing Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
| | - Eser J Zerenturk
- Lipid Metabolism & Cardiometabolic Disease Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- Molecular Metabolism & Ageing Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
| | - Elise J Needham
- Metabolic Systems Biology Laboratory, Charles Perkins Centre, School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia
| | - Matthew L Miller
- Molecular Biology Institute, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Bethan L Clifford
- Department of Medicine, Division of Cardiology, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Pauline Morand
- Department of Biological Chemistry, David Geffen School of Medicine, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Matthew J Watt
- Department of Physiology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Victoria, Australia
| | - Ruth C R Meex
- Department of Physiology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Victoria, Australia
| | - Kang-Yu Peng
- Metabolomics Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
| | | | - Kaushala Jayawardana
- Metabolomics Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
| | - Calvin Pan
- Department of Human Genetics/Medicine, David Geffen School of Medicine, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Natalie A Mellett
- Metabolomics Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
| | - Jacquelyn M Weir
- Metabolomics Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
| | - Ross Lazarus
- Metabolomics Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
| | - Aldons J Lusis
- Department of Human Genetics/Medicine, David Geffen School of Medicine, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Peter J Meikle
- Metabolomics Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
| | - David E James
- Metabolic Systems Biology Laboratory, Charles Perkins Centre, School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia
| | - Thomas Q de Aguiar Vallim
- Department of Medicine, Division of Cardiology, University of California Los Angeles (UCLA), Los Angeles, CA, USA.
- Molecular Biology Institute, University of California Los Angeles (UCLA), Los Angeles, CA, USA.
- Department of Biological Chemistry, David Geffen School of Medicine, University of California Los Angeles (UCLA), Los Angeles, CA, USA.
| | - Brian G Drew
- Central Clinical School, Department of Medicine, Monash University, Melbourne, Victoria, Australia.
- Molecular Metabolism & Ageing Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia.
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Chalabi MH, Tsiamis V, Käll L, Vandin F, Schwämmle V. CoExpresso: assess the quantitative behavior of protein complexes in human cells. BMC Bioinformatics 2019; 20:17. [PMID: 30626316 PMCID: PMC6327379 DOI: 10.1186/s12859-018-2573-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Accepted: 12/10/2018] [Indexed: 02/08/2023] Open
Abstract
Background Translational and post-translational control mechanisms in the cell result in widely observable differences between measured gene transcription and protein abundances. Herein, protein complexes are among the most tightly controlled entities by selective degradation of their individual proteins. They furthermore act as control hubs that regulate highly important processes in the cell and exhibit a high functional diversity due to their ability to change their composition and their structure. Better understanding and prediction of these functional states demands methods for the characterization of complex composition, behavior, and abundance across multiple cell states. Mass spectrometry provides an unbiased approach to directly determine protein abundances across different cell populations and thus to profile a comprehensive abundance map of proteins. Results We provide a tool to investigate the behavior of protein subunits in known complexes by comparing their abundance profiles across up to 140 cell types available in ProteomicsDB. Thorough assessment of different randomization methods and statistical scoring algorithms allows determining the significance of concurrent profiles within a complex, therefore providing insights into the conservation of their composition across human cell types as well as the identification of intrinsic structures in complex behavior to determine which proteins orchestrate complex function. This analysis can be extended to investigate common profiles within arbitrary protein groups. CoExpresso can be accessed through http://computproteomics.bmb.sdu.dk/Apps/CoExpresso. Conclusions With the CoExpresso web service, we offer a potent scoring scheme to assess proteins for their co-regulation and thereby offer insight into their potential for forming functional groups like protein complexes. Electronic supplementary material The online version of this article (10.1186/s12859-018-2573-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Morteza H Chalabi
- Department of Biochemistry and Molecular Biology and VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, Campusvej 55, Odense M, 5230, Denmark
| | - Vasileios Tsiamis
- Department of Biochemistry and Molecular Biology and VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, Campusvej 55, Odense M, 5230, Denmark
| | - Lukas Käll
- KTH - Science for Life Laboratory, School of Biotechnology, Royal Institute of Technology, Solna, Sweden
| | - Fabio Vandin
- Department of Information Engineering, University of Padua, Padua, Italy
| | - Veit Schwämmle
- Department of Biochemistry and Molecular Biology and VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, Campusvej 55, Odense M, 5230, Denmark.
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Abstract
The genetic concept of synthetic lethality has now been validated clinically through the demonstrated efficacy of poly(ADP-ribose) polymerase (PARP) inhibitors for the treatment of cancers in individuals with germline loss-of-function mutations in either BRCA1 or BRCA2. Three different PARP inhibitors have now been approved for the treatment of patients with BRCA-mutant ovarian cancer and one for those with BRCA-mutant breast cancer; these agents have also shown promising results in patients with BRCA-mutant prostate cancer. Here, we describe a number of other synthetic lethal interactions that have been discovered in cancer. We discuss some of the underlying principles that might increase the likelihood of clinical efficacy and how new computational and experimental approaches are now facilitating the discovery and validation of synthetic lethal interactions. Finally, we make suggestions on possible future directions and challenges facing researchers in this field.
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Affiliation(s)
- Alan Ashworth
- UCSF Helen Diller Family Comprehensive Cancer Center, San Francisco, CA, USA.
| | - Christopher J Lord
- The CRUK Gene Function Laboratory and Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK.
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Bajrami I, Marlow R, van de Ven M, Brough R, Pemberton HN, Frankum J, Song F, Rafiq R, Konde A, Krastev DB, Menon M, Campbell J, Gulati A, Kumar R, Pettitt SJ, Gurden MD, Cardenosa ML, Chong I, Gazinska P, Wallberg F, Sawyer EJ, Martin LA, Dowsett M, Linardopoulos S, Natrajan R, Ryan CJ, Derksen PWB, Jonkers J, Tutt ANJ, Ashworth A, Lord CJ. E-Cadherin/ROS1 Inhibitor Synthetic Lethality in Breast Cancer. Cancer Discov 2018; 8:498-515. [PMID: 29610289 PMCID: PMC6296442 DOI: 10.1158/2159-8290.cd-17-0603] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 12/12/2017] [Accepted: 01/23/2018] [Indexed: 12/22/2022]
Abstract
The cell adhesion glycoprotein E-cadherin (CDH1) is commonly inactivated in breast tumors. Precision medicine approaches that exploit this characteristic are not available. Using perturbation screens in breast tumor cells with CRISPR/Cas9-engineered CDH1 mutations, we identified synthetic lethality between E-cadherin deficiency and inhibition of the tyrosine kinase ROS1. Data from large-scale genetic screens in molecularly diverse breast tumor cell lines established that the E-cadherin/ROS1 synthetic lethality was not only robust in the face of considerable molecular heterogeneity but was also elicited with clinical ROS1 inhibitors, including foretinib and crizotinib. ROS1 inhibitors induced mitotic abnormalities and multinucleation in E-cadherin-defective cells, phenotypes associated with a defect in cytokinesis and aberrant p120 catenin phosphorylation and localization. In vivo, ROS1 inhibitors produced profound antitumor effects in multiple models of E-cadherin-defective breast cancer. These data therefore provide the preclinical rationale for assessing ROS1 inhibitors, such as the licensed drug crizotinib, in appropriately stratified patients.Significance: E-cadherin defects are common in breast cancer but are currently not targeted with a precision medicine approach. Our preclinical data indicate that licensed ROS1 inhibitors, including crizotinib, should be repurposed to target E-cadherin-defective breast cancers, thus providing the rationale for the assessment of these agents in molecularly stratified phase II clinical trials. Cancer Discov; 8(4); 498-515. ©2018 AACR.This article is highlighted in the In This Issue feature, p. 371.
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Affiliation(s)
- Ilirjana Bajrami
- The Breast Cancer Now Toby Robins Breast Cancer Research Centre, The Institute of Cancer Research, London, United Kingdom
- Cancer Research UK Gene Function Laboratory, The Institute of Cancer Research, London, United Kingdom
| | - Rebecca Marlow
- The Breast Cancer Now Research Unit, King's College London, London, United Kingdom
| | - Marieke van de Ven
- Mouse Clinic for Cancer and Aging (MCCA) Preclinical Intervention Unit, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Rachel Brough
- The Breast Cancer Now Toby Robins Breast Cancer Research Centre, The Institute of Cancer Research, London, United Kingdom
- Cancer Research UK Gene Function Laboratory, The Institute of Cancer Research, London, United Kingdom
| | - Helen N Pemberton
- The Breast Cancer Now Toby Robins Breast Cancer Research Centre, The Institute of Cancer Research, London, United Kingdom
- Cancer Research UK Gene Function Laboratory, The Institute of Cancer Research, London, United Kingdom
| | - Jessica Frankum
- The Breast Cancer Now Toby Robins Breast Cancer Research Centre, The Institute of Cancer Research, London, United Kingdom
- Cancer Research UK Gene Function Laboratory, The Institute of Cancer Research, London, United Kingdom
| | - Feifei Song
- The Breast Cancer Now Toby Robins Breast Cancer Research Centre, The Institute of Cancer Research, London, United Kingdom
- Cancer Research UK Gene Function Laboratory, The Institute of Cancer Research, London, United Kingdom
| | - Rumana Rafiq
- The Breast Cancer Now Toby Robins Breast Cancer Research Centre, The Institute of Cancer Research, London, United Kingdom
- Cancer Research UK Gene Function Laboratory, The Institute of Cancer Research, London, United Kingdom
| | - Asha Konde
- The Breast Cancer Now Toby Robins Breast Cancer Research Centre, The Institute of Cancer Research, London, United Kingdom
- Cancer Research UK Gene Function Laboratory, The Institute of Cancer Research, London, United Kingdom
| | - Dragomir B Krastev
- The Breast Cancer Now Toby Robins Breast Cancer Research Centre, The Institute of Cancer Research, London, United Kingdom
- Cancer Research UK Gene Function Laboratory, The Institute of Cancer Research, London, United Kingdom
| | - Malini Menon
- The Breast Cancer Now Toby Robins Breast Cancer Research Centre, The Institute of Cancer Research, London, United Kingdom
- Cancer Research UK Gene Function Laboratory, The Institute of Cancer Research, London, United Kingdom
| | - James Campbell
- The Breast Cancer Now Toby Robins Breast Cancer Research Centre, The Institute of Cancer Research, London, United Kingdom
- Cancer Research UK Gene Function Laboratory, The Institute of Cancer Research, London, United Kingdom
| | - Aditi Gulati
- The Breast Cancer Now Toby Robins Breast Cancer Research Centre, The Institute of Cancer Research, London, United Kingdom
- Cancer Research UK Gene Function Laboratory, The Institute of Cancer Research, London, United Kingdom
| | - Rahul Kumar
- The Breast Cancer Now Toby Robins Breast Cancer Research Centre, The Institute of Cancer Research, London, United Kingdom
- Cancer Research UK Gene Function Laboratory, The Institute of Cancer Research, London, United Kingdom
| | - Stephen J Pettitt
- The Breast Cancer Now Toby Robins Breast Cancer Research Centre, The Institute of Cancer Research, London, United Kingdom
- Cancer Research UK Gene Function Laboratory, The Institute of Cancer Research, London, United Kingdom
| | - Mark D Gurden
- The Breast Cancer Now Toby Robins Breast Cancer Research Centre, The Institute of Cancer Research, London, United Kingdom
| | - Marta Llorca Cardenosa
- The Breast Cancer Now Toby Robins Breast Cancer Research Centre, The Institute of Cancer Research, London, United Kingdom
- Biomedical Research Institute INCLIVA, Hospital Clinico Universitario Valencia, University of Valencia, Spain
| | - Irene Chong
- The Breast Cancer Now Toby Robins Breast Cancer Research Centre, The Institute of Cancer Research, London, United Kingdom
| | - Patrycja Gazinska
- The Breast Cancer Now Research Unit, King's College London, London, United Kingdom
| | - Fredrik Wallberg
- FACS Core Facility, The Institute of Cancer Research, London, United Kingdom
| | - Elinor J Sawyer
- Division of Cancer Studies, Guy's Hospital, King's College London, London, United Kingdom
| | - Lesley-Ann Martin
- The Breast Cancer Now Toby Robins Breast Cancer Research Centre, The Institute of Cancer Research, London, United Kingdom
| | - Mitch Dowsett
- The Breast Cancer Now Toby Robins Breast Cancer Research Centre, The Institute of Cancer Research, London, United Kingdom
| | - Spiros Linardopoulos
- The Breast Cancer Now Toby Robins Breast Cancer Research Centre, The Institute of Cancer Research, London, United Kingdom
- Cancer Research UK Cancer Therapeutics Unit, Division of Cancer Therapeutics, The Institute of Cancer Research, London, United Kingdom
| | - Rachael Natrajan
- The Breast Cancer Now Toby Robins Breast Cancer Research Centre, The Institute of Cancer Research, London, United Kingdom
| | - Colm J Ryan
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
| | - Patrick W B Derksen
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Jos Jonkers
- Division of Molecular Pathology and Cancer Genomics Netherlands, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Andrew N J Tutt
- The Breast Cancer Now Toby Robins Breast Cancer Research Centre, The Institute of Cancer Research, London, United Kingdom
- The Breast Cancer Now Research Unit, King's College London, London, United Kingdom
| | - Alan Ashworth
- UCSF Helen Diller Family Comprehensive Cancer Center, San Francisco, California.
| | - Christopher J Lord
- The Breast Cancer Now Toby Robins Breast Cancer Research Centre, The Institute of Cancer Research, London, United Kingdom.
- Cancer Research UK Gene Function Laboratory, The Institute of Cancer Research, London, United Kingdom
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