Differential stromal reprogramming in benign and malignant naturally occurring canine mammary tumours identifies disease-modulating stromal components.
Adenoma
/ genetics
Animals
Biomarkers, Tumor
/ genetics
Breast Neoplasms
/ genetics
Cellular Reprogramming
/ genetics
Dog Diseases
/ genetics
Dogs
Female
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Humans
Laser Capture Microdissection
Mammary Neoplasms, Animal
/ genetics
Prognosis
Stromal Cells
/ pathology
Tumor Microenvironment
/ genetics
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
26 03 2020
26 03 2020
Historique:
received:
05
12
2019
accepted:
12
03
2020
entrez:
29
3
2020
pubmed:
29
3
2020
medline:
1
12
2020
Statut:
epublish
Résumé
While cancer-associated stroma (CAS) in malignant tumours is well described, stromal changes in benign forms of naturally occurring tumours remain poorly characterized. Spontaneous canine mammary carcinomas (mCA) are viewed as excellent models of human mCA. We have recently reported highly conserved stromal reprogramming between canine and human mCA based on transcriptome analysis of laser-capture-microdissected FFPE specimen. To identify stromal changes between benign and malignant mammary tumours, we have analysed matched normal and adenoma-associated stroma (AAS) from 13 canine mammary adenomas and compared them to previous data from 15 canine mCA. Our analyses reveal distinct stromal reprogramming even in small benign tumours. While similarities between AAS and CAS exist, the stromal signature clearly distinguished adenomas from mCA. The distinction between AAS and CAS is further substantiated by differential enrichment in several hallmark signalling pathways as well as differential abundance in cellular composition. Finally, we identify COL11A1, VIT, CD74, HLA-DRA, STRA6, IGFBP4, PIGR, and TNIP1 as strongly discriminatory stromal genes between adenoma and mCA, and demonstrate their prognostic value for human breast cancer. Given the relevance of canine CAS as a model for the human disease, our approach identifies disease-modulating stromal components with implications for both human and canine breast cancer.
Identifiants
pubmed: 32218455
doi: 10.1038/s41598-020-62354-8
pii: 10.1038/s41598-020-62354-8
pmc: PMC7099087
doi:
Substances chimiques
Biomarkers, Tumor
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
5506Références
Hanahan, D. & Coussens, L. M. Accessories to the Crime: Functionsof Cells Recruited to the Tumor Microenvironment. Cancer Cell 21, 309–322 (2012).
doi: 10.1016/j.ccr.2012.02.022
pubmed: 22439926
pmcid: 22439926
Bissell, M. J. & Hines, W. C. Why don’t we get more cancer? A proposed role of the microenvironment in restraining cancer progression. Nat. Med 17, 320–329 (2011).
doi: 10.1038/nm.2328
pubmed: 21383745
pmcid: 21383745
Gardner, H. L., Fenger, J. M. & London, C. A. Dogs as a Model for Cancer. Annu. Rev. Anim. Biosci. 4, annurev–animal–022114–110911 (2015).
Karlsson, E. K. & Lindblad-Toh, K. Leader of the pack: gene mapping in dogs and other model organisms. Nat. Rev. Genet. 9, 713–725 (2008).
doi: 10.1038/nrg2382
pubmed: 18714291
pmcid: 18714291
Rogers, N. Canine clues: Dog genomes explored in effort to bring human cancer to heel. Nat. Med 21, 1374–1375 (2015).
doi: 10.1038/nm1215-1374
pubmed: 26646483
pmcid: 26646483
Queiroga, F. L., Raposo, T., Carvalho, M. I., Prada, J. & Pires, I. Canine mammary tumours as a model to study human breast cancer: most recent findings. In Vivo 25, 455–465 (2011).
pubmed: 21576423
pmcid: 21576423
Liu, D. et al. Molecular homology and difference between spontaneous canine mammary cancer and human breast cancer. Cancer Res. 74, 5045–5056 (2014).
doi: 10.1158/0008-5472.CAN-14-0392
pubmed: 4167563
pmcid: 4167563
Schiffman, J. D. & Breen, M. Comparative oncology: what dogs and other species can teach us about humans with cancer. Philos. Trans. R. Soc. Lond., B, Biol. Sci. 370, (2015).
Goldschmidt, M., Pena, L., Rasotto, R. & Zappulli, V. Classification and Grading of Canine Mammary Tumors. Vet. Pathol. 48, 117–131 (2011).
doi: 10.1177/0300985810393258
pubmed: 21266722
pmcid: 21266722
Ettlin, J., Clementi, E., Amini, P., Malbon, A. & Markkanen, E. Analysis of Gene Expression Signatures in Cancer-Associated Stroma from Canine Mammary Tumours Reveals Molecular Homology to Human Breast Carcinomas. Int. J. Mol. Sci. 1–19 https://doi.org/10.3390/ijms18051101 (2017)
Amini, P. et al. An optimised protocol for isolation of RNA from small sections of laser-capture microdissected FFPE tissue amenable for next-generation sequencing. BMC Mol. Biol. 18, 22 (2017).
doi: 10.1186/s12867-017-0099-7
pubmed: 28835206
pmcid: 28835206
Amini, P., Nassiri, S., Ettlin, J., Malbon, A. & Markkanen, E. Next-generation RNA sequencing of FFPE subsections reveals highly conserved stromal reprogramming between canine and human mammary carcinoma. Dis. Model Mech. https://doi.org/10.1242/dmm.040444 (2019)
Conklin, M. W. & Keely, P. J. Why the stroma matters in breast cancer: insights into breast cancer patient outcomes through the examination of stromal biomarkers. Cell Adh. Migr. 6, 249–260 (2012).
doi: 10.4161/cam.20567
pubmed: 22568982
pmcid: 22568982
Yaari, G., Bolen, C. R., Thakar, J. & Kleinstein, S. H. Quantitative set analysis for gene expression: a method to quantify gene set differential expression including gene-gene correlations. Nucleic Acids Research 41, e170 (2013).
doi: 10.1093/nar/gkt660
pubmed: 23921631
pmcid: 23921631
Zhao, W. et al. Weighted Gene Coexpression Network Analysis: State of the Art. Journal of Biopharmaceutical Statistics 20, 281–300 (2010).
doi: 10.1080/10543400903572753
pubmed: 20309759
pmcid: 20309759
van Dam, S., Võsa, U., van der Graaf, A., Franke, L. & de Magalhães, J. P. Gene co-expression analysis for functional classification and gene–disease predictions. Brief Bioinform 16(Suppl 4), bbw139 (2017).
Racle, J., de Jonge, K., Baumgaertner, P., Speiser, D. E. & Gfeller, D. Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data. eLife 6, (2017).
Li, T. et al. TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res. 77, e108–e110 (2017).
doi: 10.1158/0008-5472.CAN-17-0307
pubmed: 29092952
pmcid: 29092952
Calvo, F. et al. Mechanotransduction and YAP-dependent matrixremodelling is required for the generation andmaintenance of cancer-associated fibroblasts. Nat. Cell Biol. 15, 637–646 (2013).
doi: 10.1038/ncb2756
pubmed: 23708000
pmcid: 23708000
Finak, G. et al. Stromal gene expression predicts clinical outcome in breast cancer. Nat. Med. 14, 518–527 (2008).
doi: 10.1038/nm1764
pubmed: 18438415
pmcid: 18438415
Ma, X.-J., Dahiya, S., Richardson, E., Erlander, M. & Sgroi, D. C. Gene expression profiling of the tumor microenvironment during breast cancer progression. Breast Cancer Res. 11, 46 (2009).
doi: 10.1186/bcr2222
Yoshimura, H., Michishita, M., Ohkusu-Tsukada, K. & Takahashi, K. Increased presence of stromal myofibroblasts and tenascin-C with malignant progression in canine mammary tumors. Vet. Pathol. 48, 313–321 (2011).
doi: 10.1177/0300985810369901
pubmed: 20571146
pmcid: 20571146
Barth, P. J., Ebrahimsade, S., Ramaswamy, A. & Moll, R. CD34+ fibrocytes in invasive ductal carcinoma, ductal carcinoma in situ, and benign breast lesions. Virchows Arch. 440, 298–303 (2001).
doi: 10.1007/s004280100530
pubmed: 11889601
pmcid: 11889601
Brummer, O., Athar, S., Riethdorf, L., Löning, T. & Herbst, H. Matrix-metalloproteinases 1, 2, and 3 and their tissue inhibitors 1 and 2 in benign and malignant breast lesions: an in situ hybridization study. Virchows Arch. 435, 566–573 (1999).
doi: 10.1007/s004280050442
pubmed: 10628798
pmcid: 10628798
Elenbaas, B. & Weinberg, R. A. Heterotypic signaling between epithelial tumor cells and fibroblasts in carcinoma formation. Exp. Cell Res. 264, 169–184 (2001).
doi: 10.1006/excr.2000.5133
pubmed: 11237532
pmcid: 11237532
Sappino, A. P., Skalli, O., Jackson, B., Schürch, W. & Gabbiani, G. Smooth-muscle differentiation in stromal cells of malignant and non-malignant breast tissues. Int. J. Cancer 41, 707–712 (1988).
doi: 10.1002/ijc.2910410512
pubmed: 2835323
pmcid: 2835323
Surowiak, P. et al. Occurence of stromal myofibroblasts in the invasive ductal breast cancer tissue is an unfavourable prognostic factor. Anticancer Res. 27, 2917–2924 (2007).
pubmed: 17695471
pmcid: 17695471
Yamashita, M. et al. Role of stromal myofibroblasts in invasive breast cancer: stromal expression of alpha-smooth muscle actin correlates with worse clinical outcome. Breast Cancer 19, 170–176 (2012).
doi: 10.1007/s12282-010-0234-5
pubmed: 20978953
pmcid: 20978953
Chen, X. & Song, E. Turning foes to friends: targeting cancer-associated fibroblasts. Nat. Rev. Drug. Discov. 1–17 https://doi.org/10.1038/s41573-018-0004-1 (2019)
Costa, A. et al. Fibroblast Heterogeneity and Immunosuppressive Environment in Human Breast Cancer. Cancer Cell 33, 463–479.e10 (2018).
doi: 10.1016/j.ccell.2018.01.011
Sorenmo, K. U. et al. Canine mammary gland tumours; a histological continuum from benign to malignant; clinical and histopathological evidence. Vet. Comp. Oncol. 7, 162–172 (2009).
doi: 10.1111/j.1476-5829.2009.00184.x
pubmed: 19691645
pmcid: 19691645
Petrova, V., Annicchiarico-Petruzzelli, M., Melino, G. & Amelio, I. The hypoxic tumour microenvironment. Oncogenesis 1–13 https://doi.org/10.1038/s41389-017-0011-9 (2018).
Deraison, C. et al. LEKTI fragments specifically inhibit KLK5, KLK7, and KLK14 and control desquamation through a pH-dependent interaction. Mol. Biol. Cell 18, 3607–3619 (2007).
doi: 10.1091/mbc.e07-02-0124
pubmed: 17596512
pmcid: 17596512
Mohamad, J. et al. Filaggrin 2 Deficiency Results in Abnormal Cell-Cell Adhesion in the Cornified Cell Layers and Causes Peeling Skin Syndrome Type A. Journal of Investigative Dermatology 138, 1736–1743 (2018).
doi: 10.1016/j.jid.2018.04.032
pubmed: 29758285
pmcid: 29758285
Leclerc, E. A., Huchenq, A., Kezic, S., Serre, G. & Jonca, N. Mice deficient for the epidermal dermokine and isoforms display transient cornification defects. J. Cell. Sci. 127, 2862–2872 (2014).
doi: 10.1242/jcs.144808
pubmed: 24794495
pmcid: 24794495
Hammers, C. M. & Stanley, J. R. Desmoglein-1, differentiation, and disease. J. Clin. Invest. 123, 1419–1422 (2013).
doi: 10.1172/JCI69071
pubmed: 23524961
pmcid: 23524961
Roth, W. et al. Keratin 1 maintains skin integrity and participates in an inflammatory network in skin through interleukin-18. J. Cell. Sci. 125, 5269–5279 (2013).
doi: 10.1242/jcs.116574
Moll, R., Divo, M. & Langbein, L. The human keratins: biology and pathology. Histochem. Cell Biol 129, 705–733 (2008).
doi: 10.1007/s00418-008-0435-6
pubmed: 18461349
pmcid: 18461349
Lee, M.-S. et al. Identification of a novel partner gene, KIAA1217, fused to RET: Functional characterization and inhibitor sensitivity of two isoforms in lung adenocarcinoma. Oncotarget 7, 36101–36114 (2016).
pubmed: 27150058
pmcid: 27150058
Yamazaki, S. et al. The Transcription Factor Ehf Is Involved in TGF-β–Induced Suppression of FcεRI and c-Kit Expression and FcεRI-Mediated Activation in Mast Cells. The Journal of Immunology 195, 3427–3435 (2015).
doi: 10.4049/jimmunol.1402856
pubmed: 26297757
pmcid: 26297757
Uhland, K. Matriptase and its putative role in cancer. Cell Mol. Life. Sci. 63, 2968–2978 (2006).
doi: 10.1007/s00018-006-6298-x
pubmed: 17131055
pmcid: 17131055
Schaefer, L., Tredup, C., Gubbiotti, M. A. & Iozzo, R. V. Proteoglycan neofunctions: regulation of inflammation and autophagy in cancer biology. FEBS J. 284, 10–26 (2016).
doi: 10.1111/febs.13963
pubmed: 27860287
pmcid: 27860287
Yau, S. W., Azar, W. J., Sabin, M. A., Werther, G. A. & Russo, V. C. IGFBP-2 - taking the lead in growth, metabolism and cancer. J. Cell Commun. Signal. 9, 125–142 (2015).
doi: 10.1007/s12079-015-0261-2
pubmed: 25617050
pmcid: 25617050
Scheitz, C. J. F. & Tumbar, T. New insights into the role of Runx1 in epithelial stem cell biology and pathology. J. Cell. Biochem. 114, 985–993 (2013).
doi: 10.1002/jcb.24453
pubmed: 23150456
pmcid: 23150456
VanOudenhove, J. J. et al. Stem Cell Reports. Stem Cell Reports 7, 884–896 (2016).
doi: 10.1016/j.stemcr.2016.09.006
pubmed: 27720906
pmcid: 27720906
Wang, Z.-Q., Milne, K., Webb, J. R. & Watson, P. H. CD74 and intratumoral immune response in breast cancer. Oncotarget 8, 12664–12674 (2017).
pubmed: 27058619
pmcid: 27058619
Afshar-Kharghan, V. The role of the complement system in cancer. Journal of Clinical Investigation 127, 780–789 (2017).
doi: 10.1172/JCI90962
pubmed: 28248200
pmcid: 28248200
Saraiva, D. P., Jacinto, A., Borralho, P., Braga, S. & Cabral, M. G. HLA-DR in Cytotoxic T Lymphocytes Predicts Breast Cancer Patients’ Response to Neoadjuvant Chemotherapy. Front Immunol 9, 2605 (2018).
doi: 10.3389/fimmu.2018.02605
pubmed: 30555458
pmcid: 30555458
Terra, R. et al. To Investigate the Necessity of STRA6 Upregulation in T Cells during T Cell Immune Responses. PLoS ONE 8, e82808 (2013).
doi: 10.1371/journal.pone.0082808
pubmed: 24391722
pmcid: 24391722
Miyagawa, I. et al. Induction of Regulatory T Cells and Its Regulation with Insulin-like Growth Factor/Insulin-like Growth Factor Binding Protein-4 by Human Mesenchymal Stem Cells. The Journal of Immunology 199, 1616–1625 (2017).
doi: 10.4049/jimmunol.1600230
pubmed: 28724578
pmcid: 28724578
Freire, J. et al. Collagen Type XI Alpha 1 Expression in Intraductal Papillomas Predicts Malignant Recurrence. Biomed Res. Int. 2015, 1–5 (2015).
Kleinert, R. et al. Gene Expression of Col11A1 Is a Marker Not only for Pancreas Carcinoma But also for Adenocarcinoma of the Papilla of Vater, Discriminating Between Carcinoma and Chronic Pancreatitis. Anticancer Res. 35, 6153–6158 (2015).
pubmed: 26504042
pmcid: 26504042
Ma, B. et al. ADAM12 expression predicts clinical outcome in estrogen receptor-positive breast cancer. Int. J. Clin. Exp. Pathol. 8, 13279–13283 (2015).
pubmed: 26722530
pmcid: 26722530
Gilkes, D. M. et al. Procollagen Lysyl Hydroxylase 2 Is Essential for Hypoxia-Induced Breast Cancer Metastasis. Molecular Cancer Research 11, 456–466 (2013).
doi: 10.1158/1541-7786.MCR-12-0629
pubmed: 23378577
pmcid: 23378577
Tu, C.-F., Wu, M.-Y., Lin, Y.-C., Kannagi, R. & Yang, R.-B. FUT8 promotes breast cancer cell invasiveness by remodeling TGF-β receptorcore fucosylation. 1–15 https://doi.org/10.1186/s13058-017-0904-8 (2017).
Kim, S., Kon, M. & DeLisi, C. Pathway-based classification of cancer subtypes. 1–22 https://doi.org/10.1186/1745-6150-7-21 (2012).
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nature Biotechnology 34, 525–527 (2016).
doi: 10.1038/nbt.3519
pubmed: 27043002
pmcid: 27043002
Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res 4, 1521 (2015).
doi: 10.12688/f1000research.7563.1
pubmed: 26925227
pmcid: 26925227
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 15, 31 (2014).
doi: 10.1186/s13059-014-0550-8
Kolde, R. pheatmap: Pretty Heatmaps. R package version 1.0.10.
Leek, J. T. et al. SVA: Surrogate Variable Analysis. R package version 3.30.1.
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43, e47 (2015).
doi: 10.1093/nar/gkv007
pubmed: 25605792
pmcid: 25605792
Rohart, F., Gautier, B., Singh, A. & Lê Cao, K.-A. mixOmics: An R package for ‘omics feature selection and multiple data integration. PLoS Comput. Biol. 13, e1005752 (2017).
doi: 10.1371/journal.pcbi.1005752
pubmed: 29099853
pmcid: 29099853
Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013).
doi: 10.1186/1471-2105-14-7
pubmed: 23323831
pmcid: 23323831
Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).
doi: 10.1186/1471-2105-9-559
pubmed: 2631488
pmcid: 2631488
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
doi: 10.1101/gr.1239303
pubmed: 14597658
pmcid: 14597658