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      Identification of disease-promoting stromal components by comparative proteomic and transcriptomic profiling of canine mammary tumors using laser-capture microdissected FFPE tissue

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          Abstract

          Cancer-associated stroma (CAS) profoundly influences progression of tumors including mammary carcinoma (mCA). Canine simple mCA represent relevant models of human mCA, notably also with respect to CAS. While transcriptomic changes in CAS of mCA are well described, it remains unclear to what extent these translate to the protein level. Therefore, we sought to gain insight into the proteomic changes in CAS and compare them with transcriptomic changes in the same tissue. To this end, we analyzed CAS and matched normal stroma using laser-capture microdissection (LCM) and LC-MS/MS in a cohort of 14 formalin-fixed paraffin embedded (FFPE) canine mCAs that we had previously characterized using LCM-RNAseq. Our results reveal clear differences in protein abundance between CAS and normal stroma, which are characterized by changes in the extracellular matrix, the cytoskeleton, and cytokines such as TNF. The proteomics- and RNAseq-based analyses of LCM-FFPE show a substantial degree of correlation, especially for the most deregulated targets and a comparable activation of pathways. Finally, we validate transcriptomic upregulation of LTBP2, IGFBP2, COL6A5, POSTN, FN1, COL4A1, COL12A1, PLOD2, COL4A2, and IGFBP7 in CAS on the protein level and demonstrate their adverse prognostic value for human breast cancer. Given the relevance of canine mCA as a model for the human disease, our analysis substantiates these targets as disease-promoting stromal components with implications for breast cancer in both species.

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          Most cited references64

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

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            MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

            Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
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              Accessories to the crime: functions of cells recruited to the tumor microenvironment.

              Mutationally corrupted cancer (stem) cells are the driving force of tumor development and progression. Yet, these transformed cells cannot do it alone. Assemblages of ostensibly normal tissue and bone marrow-derived (stromal) cells are recruited to constitute tumorigenic microenvironments. Most of the hallmarks of cancer are enabled and sustained to varying degrees through contributions from repertoires of stromal cell types and distinctive subcell types. Their contributory functions to hallmark capabilities are increasingly well understood, as are the reciprocal communications with neoplastic cancer cells that mediate their recruitment, activation, programming, and persistence. This enhanced understanding presents interesting new targets for anticancer therapy. Copyright © 2012 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Journal
                Neoplasia
                Neoplasia
                Neoplasia (New York, N.Y.)
                Neoplasia Press
                1522-8002
                1476-5586
                29 March 2021
                April 2021
                29 March 2021
                : 23
                : 4
                : 400-412
                Affiliations
                [a ]Institute of Veterinary Pharmacology and Toxicology, Vetsuisse Faculty, University of Zurich, Zurich, Switzerland
                [b ]Functional Genomics Center Zürich, ETH Zürich/University of Zurich, Zurich, Switzerland
                [c ]Institute of Veterinary Pathology Vetsuisse Faculty, University of Zurich, Zürich, Switzerland
                [d ]The Royal (Dick) School of Veterinary Studies and The Roslin Institute Easter Bush Campus, Midlothian, Scotland
                Author notes
                [* ]Corresponding author. enni.markkanen@ 123456vetpharm.uzh.ch
                Article
                S1476-5586(21)00011-7
                10.1016/j.neo.2021.03.001
                8042244
                33794398
                dc3585bb-409f-4aff-84ef-213218790b60
                © 2021 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 12 January 2021
                : 1 March 2021
                : 2 March 2021
                Categories
                Original article

                tumor microenvironment,cancer-associated fibroblasts,comparative oncology,breast cancer,tumor stroma,canine mammary carcinoma

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