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      A mouse SWATH-mass spectrometry reference spectral library enables deconvolution of species-specific proteomic alterations in human tumour xenografts

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          ABSTRACT

          SWATH-mass spectrometry (MS) enables accurate and reproducible proteomic profiling in multiple model organisms including the mouse. Here, we present a comprehensive mouse reference spectral library (MouseRefSWATH) that permits quantification of up to 10,597 proteins (62.2% of the mouse proteome) by SWATH-MS. We exploit MouseRefSWATH to develop an analytical pipeline for species-specific deconvolution of proteomic alterations in human tumour xenografts (XenoSWATH). This method overcomes the challenge of high sequence similarity between mouse and human proteins, facilitating the study of host microenvironment-tumour interactions from ‘bulk tumour’ measurements. We apply the XenoSWATH pipeline to characterize an intraductal xenograft model of breast ductal carcinoma in situ and uncover complex regulation consistent with stromal reprogramming, where the modulation of cell migration pathways is not restricted to tumour cells but also operates in the mouse stroma upon progression to invasive disease. MouseRefSWATH and XenoSWATH open new opportunities for in-depth and reproducible proteomic assessment to address wide-ranging biological questions involving this important model organism.

          Abstract

          Editor's choice: This paper presents the MouseRefSWATH mouse reference spectral library as a standardized community resource for SWATH-mass spectrometry studies and the XenoSWATH pipeline for species-specific deconvolution of human xenograft proteomic data.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              Cytoscape: a software environment for integrated models of biomolecular interaction networks.

              Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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                Author and article information

                Journal
                Dis Model Mech
                Dis Model Mech
                DMM
                dmm
                Disease Models & Mechanisms
                The Company of Biologists Ltd
                1754-8403
                1754-8411
                1 July 2020
                14 July 2020
                14 July 2020
                : 13
                : 7
                : dmm044586
                Affiliations
                [1 ]Division of Molecular Pathology, The Institute of Cancer Research , London SW3 6JB, UK
                [2 ]The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research , London SW3 6JB, UK
                [3 ]Tumour Profiling Unit, The Institute of Cancer Research , London SW3 6JB, UK
                [4 ]Stromal Immunology Group, MRC Laboratory for Molecular Cell Biology, University College London WC1E 6BT , London, UK
                [5 ]Department of Oncology, University of Lausanne , Lausanne CH-1066, Switzerland
                [6 ]Ludwig Institute for Cancer Research , Lausanne CH-1066, Switzerland
                [7 ]The Tumour Microenvironment Team, Institute of Biomedicine and Biotechnology of Cantabria , Santander 39011, Spain
                Author notes
                [* ]Author for correspondence ( paul.huang@ 123456icr.ac.uk )

                Handling Editor: Elaine R. Mardis

                Author information
                http://orcid.org/0000-0003-3972-5087
                Article
                DMM044586
                10.1242/dmm.044586
                7375474
                32493768
                9dcd5966-979c-4091-820b-e9e2c4531af3
                © 2020. Published by The Company of Biologists Ltd

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

                History
                : 22 February 2020
                : 20 May 2020
                Funding
                Funded by: Institute of Cancer Research, http://dx.doi.org/10.13039/501100000027;
                Funded by: Breast Cancer Now, http://dx.doi.org/10.13039/100009794;
                Award ID: 2014NovPR360
                Award ID: 2013NovPhD185
                Funded by: Cancer Research UK, http://dx.doi.org/10.13039/501100000289;
                Award ID: C36478/A19281
                Award ID: CRUK/A19763
                Funded by: Medical Research Council, http://dx.doi.org/10.13039/501100007155;
                Award ID: MC_U12266B
                Funded by: Ramon y Cajal Research Programme;
                Award ID: RYC-2016-20352
                Funded by: Ministerio de Ciencia, Innovación y Universidades;
                Funded by: Agencia Estatal de Investigación, http://dx.doi.org/10.13039/501100011033;
                Funded by: European Regional Development Fund, http://dx.doi.org/10.13039/501100008530;
                Award ID: RTI2018-096778-A-I00
                Funded by: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, http://dx.doi.org/10.13039/501100001711;
                Award ID: 31003A_182470
                Funded by: European Research Council, http://dx.doi.org/10.13039/100010663;
                Award ID: 802773-MitoGuide
                Categories
                Mouse Models
                304
                Resource Article

                Molecular medicine
                mass spectrometry,swath-ms,proteomics,mouse,xenografts,dcis,breast cancer
                Molecular medicine
                mass spectrometry, swath-ms, proteomics, mouse, xenografts, dcis, breast cancer

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