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A tumor-derived type III collagen-rich ECM niche regulates tumor cell dormancy

Nature Cancer volume 3, pages 90–107 (2022)Cite this article

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Abstract

Cancer cells disseminate and seed in distant organs, where they can remain dormant for many years before forming clinically detectable metastases. Here we studied how disseminated tumor cells sense and remodel the extracellular matrix (ECM) to sustain dormancy. ECM proteomics revealed that dormant cancer cells assemble a type III collagen-enriched ECM niche. Tumor-derived type III collagen is required to sustain tumor dormancy, as its disruption restores tumor cell proliferation through DDR1-mediated STAT1 signaling. Second-harmonic generation two-photon microscopy further revealed that the dormancy-to-reactivation transition is accompanied by changes in type III collagen architecture and abundance. Analysis of clinical samples revealed that type III collagen levels were increased in tumors from patients with lymph node-negative head and neck squamous cell carcinoma compared to patients who were positive for lymph node colonization. Our data support the idea that the manipulation of these mechanisms could serve as a barrier to metastasis through disseminated tumor cell dormancy induction.

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Fig. 1: Characterization of the ECM architecture around dormant cells.
Fig. 2: Proteomic analysis of the ECM of dormant and proliferative tumors.
Fig. 3: Type III collagen-enriched microenvironments induce dormancy.
Fig. 4: Tumor cell-derived type III collagen regulates dormancy.
Fig. 5: DDR1 is required to sustain dormancy.
Fig. 6: A DDR1–STAT1 pathway regulates dormancy and COL3A1 expression.

J. Gregory. Used with permission from Mount Sinai Health System.

Data availability

The raw mass spectrometry proteomic data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository66 with the dataset identifiers PXD019185 (T-HEp3 and D-HEp3 tumors) and PXD018883 (shCTRL and shDDR1 D-HEp3 tumors).

RNA-seq data that support the findings of this study have been deposited in the Gene Expression Omnibus under accession code GSE182890. Source data is provided with this paper.

References

  1. Steeg, P. S. Targeting metastasis. Nat. Rev. Cancer https://doi.org/10.1038/nrc.2016.25 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Sosa, M. S., Bragado, P. & Aguirre-Ghiso, J. A. Mechanisms of disseminated cancer cell dormancy: an awakening field. Nat. Rev. Cancer 14, 611–622 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Linde, N., Fluegen, G. & Aguirre-Ghiso, J. A. The relationship between dormant cancer cells and their microenvironment. Adv. Cancer Res. 132, 45–71 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Giancotti, F. G. Review mechanisms governing metastatic dormancy and reactivation. Cell 155, 750–764 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Holmgren, L., O’Reilly, M. S. & Folkman, J. Dormancy of micrometastases: balanced proliferation and apoptosis in the presence of angiogenesis suppression. Nat. Med. 1, 149–153 (1995).

    CAS  PubMed  Google Scholar 

  6. Townson, J. L. & Chambers, A. F. Dormancy of solitary metastatic cells. Cell Cycle 5, 1744–1750 (2006).

    CAS  PubMed  Google Scholar 

  7. Yeh, A. C. & Ramaswamy, S. Mechanisms of cancer cell dormancy—another hallmark of cancer? Cancer Res. 75, 5014–5022 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Phan, T. G. & Croucher, P. I. The dormant cancer cell life cycle. Nat. Rev. Cancer 20, 398–411 (2020).

    CAS  PubMed  Google Scholar 

  9. Fluegen, G. et al. Phenotypic heterogeneity of disseminated tumour cells is preset by primary tumour hypoxic microenvironments. Nat. Cell Biol. https://doi.org/10.1038/ncb3465 (2017).

  10. Bragado, P. et al. TGF-β2 dictates disseminated tumour cell fate in target organs through TGF-β-RIII and p38α/β signalling. Nat. Cell Biol. 15, 1351–1361 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Gao, H. et al. The BMP inhibitor Coco reactivates breast cancer cells at lung metastatic sites. Cell 150, 764–779 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Kobayashi, A. et al. Bone morphogenetic protein 7 in dormancy and metastasis of prostate cancer stem-like cells in bone. J. Exp. Med. 208, 2641–2655 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Liu, Y. et al. Blockade of IDO-kynurenine-AhR metabolic circuitry abrogates IFN-γ-induced immunologic dormancy of tumor-repopulating cells. Nat. Commun. 8, 15207 (2017).

    PubMed  PubMed Central  Google Scholar 

  14. Kai, F. B., Drain, A. P. & Weaver, V. M. The extracellular matrix modulates the metastatic journey. Dev. Cell 49, 332–346 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Bissell, M. J., Hall, H. G. & Parry, G. How does the extracellular matrix direct gene expression? J. Theor. Biol. https://doi.org/10.1016/0022-5193(82)90388-5 (1982).

    Article  PubMed  Google Scholar 

  16. Bissell, M. J. & Aggeler, J. Dynamic reciprocity: how do extracellular matrix and hormones direct gene expression?. Prog. Clin. Biol. Res. 249, 251–262 (1987).

    CAS  PubMed  Google Scholar 

  17. Eliceiri, K. et al. Automated quantification of aligned collagen for human breast carcinoma prognosis. J. Pathol. Inform. https://doi.org/10.4103/2153-3539.139707 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Provenzano, P. P. et al. Collagen reorganization at the tumor-stromal interface facilitates local invasion. BMC Med. 4, 38 (2006).

    PubMed  PubMed Central  Google Scholar 

  19. Conklin, M. W. et al. Aligned collagen is a prognostic signature for survival in human breast carcinoma. Am. J. Pathol. https://doi.org/10.1016/j.ajpath.2010.11.076 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Naba, A. et al. The matrisome: in silico definition and in vivo characterization by proteomics of normal and tumor extracellular matrices. Mol. Cell. Proteomics 11, M111.014647 (2012).

    PubMed  Google Scholar 

  21. Naba, A., Clauser, K. R., Lamar, J. M., Carr, S. A. & Hynes, R. O. Extracellular matrix signatures of human mammary carcinoma identify novel metastasis promoters. eLife 3, e01308 (2014).

    PubMed  PubMed Central  Google Scholar 

  22. Socovich, A. M. & Naba, A. The cancer matrisome: from comprehensive characterization to biomarker discovery. Semin. Cell Dev. Biol. https://doi.org/10.1016/j.semcdb.2018.06.005 (2019).

    Article  PubMed  Google Scholar 

  23. Taha, I. N. & Naba, A. Exploring the extracellular matrix in health and disease using proteomics. Essays Biochem. 63, 417–432 (2019).

    CAS  PubMed  Google Scholar 

  24. Hebert, J. D. et al. Proteomic profiling of the ECM of xenograft breast cancer metastases in different organs reveals distinct metastatic niches. Cancer Res. https://doi.org/10.1158/0008-5472.can-19-2961 (2020).

  25. Tian, C. et al. Proteomic analyses of ECM during pancreatic ductal adenocarcinoma progression reveal different contributions by tumor and stromal cells. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1908626116 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Tian, C. et al. Cancer-cell-derived matrisome proteins promote metastasis in pancreatic ductal adenocarcinoma. Cancer Res. https://doi.org/10.1158/0008-5472.can-19-2578 (2020).

  27. Oskarsson, T. et al. Breast cancer cells produce tenascin C as a metastatic niche component to colonize the lungs. Nat. Med. 17, 867–874 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Barkan, D. et al. Metastatic growth from dormant cells induced by a Col-I-enriched fibrotic environment. Cancer Res. 70, 5706–5716 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Aguirre-Ghiso, J. A., Liu, D., Mignatti, A., Kovalski, K. & Ossowski, L. Urokinase receptor and fibronectin regulate the ERK(MAPK) to p38(MAPK) activity ratios that determine carcinoma cell proliferation or dormancy in vivo. Mol. Biol. Cell 12, 863–879 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Ghajar, C. M. et al. The perivascular niche regulates breast tumour dormancy. Nat. Cell Biol. 15, 807–817 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Albrengues, J. et al. Neutrophil extracellular traps produced during inflammation awaken dormant cancer cells in mice. Science https://doi.org/10.1126/science.aao4227 (2018).

  32. Hamidi, H. & Ivaska, J. Every step of the way: integrins in cancer progression and metastasis. Nat. Rev. Cancer https://doi.org/10.1038/s41568-018-0038-z (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Takai, K. et al. Discoidin domain receptor 1 (DDR1) ablation promotes tissue fibrosis and hypoxia to induce aggressive basal-like breast cancers. Genes Dev. 32, 244–257 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Gao, H. et al. Multi-organ site metastatic reactivation mediated by non-canonical discoidin domain receptor 1 signaling. Cell 166, 47–62 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Adam, A. P. et al. Computational identification of a p38SAPK-regulated transcription factor network required for tumor cell quiescence. Cancer Res. https://doi.org/10.1158/0008-5472.CAN-08-3820 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Sosa, M. S. et al. NR2F1 controls tumour cell dormancy via SOX9- and RARβ-driven quiescence programmes. Nat. Commun. 6, 6170 (2015).

    CAS  PubMed  Google Scholar 

  37. Kim, R. S. et al. Dormancy signatures and metastasis in estrogen receptor-positive and negative breast cancer. PLoS ONE 7, e35569 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Aguirre-Ghiso, J. A., Estrada, Y., Liu, D. & Ossowski, L. ERKMAPK activity as a determinant of tumor growth and dormancy; regulation by p38SAPK. Cancer Res. 63, 1684–1695 (2003).

    CAS  PubMed  Google Scholar 

  39. Malladi, S. et al. Metastatic latency and immune evasion through autocrine inhibition of WNT. Cell 165, 45–60 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Aguirre-Ghiso, J. A., Ossowski, L. & Rosenbaum, S. K. Green fluorescent protein tagging of extracellular signal-regulated kinase and p38 pathways reveals novel dynamics of pathway activation during primary and metastatic growth. Cancer Res. 64, 7336–7345 (2004).

    CAS  PubMed  Google Scholar 

  41. Aslakson, C. J. & Miller, F. R. Selective events in the metastatic process defined by analysis of the sequential dissemination of subpopulations of a mouse mammary tumor. Cancer Res. 52, 1399–1405 (1992).

    CAS  PubMed  Google Scholar 

  42. Ossowski, L. Plasminogen activator dependent pathways in the dissemination of human tumor cells in the chick embryo. Cell 52, 321–328 (1988).

    CAS  PubMed  Google Scholar 

  43. Aguirre-Ghiso, J. A., Kovalski, K. & Ossowski, L. Tumor dormancy induced by downregulation of urokinase receptor in human carcinoma involves integrin and MAPK signaling. J. Cell Biol. 147, 89–104 (1999).

    CAS  PubMed  Google Scholar 

  44. Montagner, M. et al. Crosstalk with lung epithelial cells regulates Sfrp2-mediated latency in breast cancer dissemination. Nat. Cell Biol. 22, 289–296 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Spencer, S. L. et al. The proliferation-quiescence decision is controlled by a bifurcation in CDK2 activity at mitotic exit. Cell 155, 369–383 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Naba, A. et al. The extracellular matrix: tools and insights for the ‘omics’ era. Matrix Biol. https://doi.org/10.1016/j.matbio.2015.06.003 (2016).

    Article  PubMed  Google Scholar 

  47. Vogel, B., Siebert, H., Hofmann, U. & Frantz, S. Determination of collagen content within picrosirius red stained paraffin-embedded tissue sections using fluorescence microscopy. MethodsX 2, 124–134 (2015).

    PubMed  PubMed Central  Google Scholar 

  48. Rittié, L. Method for picrosirius red-polarization detection of collagen fibers in tissue sections. Methods Mol. Biol. 1627, 395–407 (2017).

    PubMed  Google Scholar 

  49. Wegner, K. A., Keikhosravi, A., Eliceiri, K. W. & Vezina, C. M. Fluorescence of picrosirius red multiplexed with immunohistochemistry for the quantitative assessment of collagen in tissue sections. J. Histochem. Cytochem. 65, 479–490 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Coelho, P. G. B., Souza, M. V., de, Conceição, L. G., Viloria, M. I. V. & Bedoya, S. A. O. Evaluation of dermal collagen stained with picrosirius red and examined under polarized light microscopy. An. Bras. Dermatol. 93, 415–418 (2018).

    PubMed  PubMed Central  Google Scholar 

  51. Brisson, B. K. et al. Type III collagen directs stromal organization and limits metastasis in a murine model of breast cancer. Am. J. Pathol. 185, 1471–1486 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Coelho, N. M. et al. Discoidin domain receptor 1 mediates myosin-dependent collagen contraction. Cell Rep. https://doi.org/10.1016/j.celrep.2017.01.061 (2017).

    Article  PubMed  Google Scholar 

  53. Coelho, N. M. & McCulloch, C. A. Mechanical signaling through the discoidin domain receptor 1 plays a central role in tissue fibrosis. Cell Adh. Migr. 12, 348–362 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Faraci, E., Eck, M., Gerstmayer, B., Bosio, A. & Vogel, W. F. An extracellular matrix-specific microarray allowed the identification of target genes downstream of discoidin domain receptors. Matrix Biol. 22, 373–381 (2003).

    CAS  PubMed  Google Scholar 

  55. Gearing, L. J. et al. CiiiDER: a tool for predicting and analysing transcription factor binding sites. PLoS ONE 14, e0215495 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Beck, A. H., Espinosa, I., Gilks, C. B., van de Rijn, M. & West, R. B. The fibromatosis signature defines a robust stromal response in breast carcinoma. Lab. Invest. 88, 591–601 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Aguirre-Ghiso, J. et al. A mesenchymal-like program of dormancy controlled by ZFP281 serves as a barrier to metastatic progression of early disseminated cancer cells. Preprint at Research Square https://doi.org/10.21203/rs.3.rs-145308/v1 (2021).

  58. Chiusa, M. et al. The extracellular matrix receptor discoidin domain receptor 1 regulates collagen transcription by translocating to the nucleus. J. Am. Soc. Nephrol. 30, 1605–1624 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Gould, L. J. Topical collagen-based biomaterials for chronic wounds: rationale and clinical application. Adv. Wound Care 5, 19–31 (2016).

    Google Scholar 

  60. Aguirre-Ghiso, J. A., Bragado, P. & Sosa, M. S. Metastasis awakening: targeting dormant cancer. Nat. Med. 19, 276–277 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Ghajar, C. M. Metastasis prevention by targeting the dormant niche. Nat. Rev. Cancer 15, 238–247 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Juin, A. et al. Discoidin domain receptor 1 controls linear invadosome formation via a Cdc42-Tuba pathway. J. Cell Biol. 207, 517–533 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Naba, A., Clauser, K. R. & Hynes, R. O. Enrichment of extracellular matrix proteins from tissues and digestion into peptides for mass spectrometry analysis. J. Vis. Exp. https://doi.org/10.3791/53057 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Di Martino, J. et al. 2D and 3D matrices to study linear invadosome formation and activity. J. Vis. Exp. 350, 78–89 (2017).

    Google Scholar 

  65. Franco-Barraza, J., Beacham, D. A., Amatangelo, M. D. & Cukierman, E. Preparation of extracellular matrices produced by cultured and primary fibroblasts. Curr. Protoc. Cell Biol. https://doi.org/10.1002/cpcb.2 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).

    CAS  PubMed  Google Scholar 

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Acknowledgements

J.J.B.-C., E.J.F. and A.N. thank the National Cancer Institute (NCI) and Sage Bionetwork’s Interdisciplinary Approaches to Cancer Metastasis workshop for inspiring this project. We thank S. Spencer for providing the DHB-Venus plasmid, B. Leitinger for sharing DDR1 plasmids, M. Soengas for the FG12-GFP plasmid, L. Hodgson for providing HEK cells, non-targeting CRISPR controls and lentiviral packaging plasmids, E. Farias for guidance on mouse surgery, M. Djedaini and S. Bekri for guidance on FACS, B. Wu for building the plastic imaging window and J. Cheung for guidance on the CAM model. We acknowledge the Microscopy and Advanced Bioimaging Core and the Flow Cytometry Core at Mount Sinai. We thank J. Gregory for her illustration of the graphical abstract. We thank the Aguirre-Ghiso and Sosa laboratories for helpful discussions. We also thank T. Martin for revising the statistical analysis throughout the paper. We thank H. Chen from the Mass Spectrometry Core facility at the University of Illinois at Chicago and G. Chlipala from the Research Informatics Core facility at the University of Illinois at Chicago for their technical assistance with the analysis of D-HEp3 shCTRL versus D-HEp3 shDDR1 tumors and R. Schiavoni from the Proteomics Core Facility at the Koch Institute for Integrative Cancer Research at MIT and K. Clauser from the Broad Institute for their assistance with the analysis of T-HEp3 and D-HEp3 tumors. This work was supported by a Susan G. Komen Career Catalyst Research award (CCR18547848 to J.J.B.-C.), an NCI Career Transition Award (K22CA196750 to J.J.B.-C.), an NCI R01 grant (R01CA244780 to J.J.B.-C.), the Tisch Cancer Institute National Institutes of Health (NIH) Cancer Center grant (P30-CA196521), the Schneider-Lesser Foundation Award (to J.J.B.-C.) and a Stony Brook-Mount Sinai pilot award (to J.J.B.-C.). C.M. received support from the NIH T32 CA078207 Training Program in Cancer Biology. This work was partially supported by a start-up fund from the Department of Physiology and Biophysics at the University of Illinois at Chicago to A.N. I.T. is the recipient of a Research Grant from the Honors College at the University of Illinois at Chicago and a LASURI award from the College of Liberal Arts and Sciences at the University of Illinois at Chicago. Proteomics services were provided by the UIC Research Resources Center Mass Spectrometry Core, which was established in part by a grant from the Searle Funds at the Chicago Community Trust to the Chicago Biomedical Consortium, and by the Proteomics Core Facility of the Koch Institute for Integrative Cancer Research at MIT, supported in part by a Cancer Center Support Grant from the NCI. Bioinformatic analyses of the proteomics data were performed by the UIC Research Informatics Core, supported in part by the National Center for Advancing Translational Sciences (grant UL1TR002003). J.A.A.-G. and A.R.N. were supported by grants from NIH/NCI (CA109182 and CA196521). J.A.A.-G. is a Samuel Waxman Cancer Research Foundation Investigator. E.J.F. was supported by NCI (P30 CA006973).

Author information

Author notes

  1. Ana Rita Nobre

    Present address: Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

  2. Julio A. Aguirre-Ghiso

    Present address: Department of Cell Biology, Cancer Dormancy and Tumor Microenvironment Institute, Gruss Lipper Biophotonics Center, Albert Einstein Cancer Center, Albert Einstein College of Medicine, Bronx, NY, USA

Authors and Affiliations

  1. Division of Hematology and Oncology, Department of Medicine, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Julie S. Di Martino, Chandrani Mondal, Eduardo F. Farias & Jose Javier Bravo-Cordero

  2. Division of Hematology and Oncology, Department of Medicine and Department of Otolaryngology, Department of Oncological Sciences, Black Family Stem Cell Institute, Precision Immunology Institute, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Ana Rita Nobre & Julio A. Aguirre-Ghiso

  3. Abel Salazar Biomedical Sciences Institute, University of Porto, Porto, Portugal

    Ana Rita Nobre

  4. Department of Physiology and Biophysics, University of Illinois at Chicago, Chicago, IL, USA

    Isra Taha & Alexandra Naba

  5. University of Illinois Cancer Center, University of Illinois at Chicago, Chicago, IL, USA

    Isra Taha & Alexandra Naba

  6. Departments of Oncology, Applied Mathematics and Statistics and Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Elana J. Fertig

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Contributions

J.D.M. designed and performed experiments, analyzed and interpreted the data, assembled the figures and contributed to writing and editing of the manuscript. A.R.N. performed cell-sorting experiments. C.M. performed lung metastasis experiments in MDA-MB-231 xenografts. E.F. performed the first mouse tumor surgery experiments. E.J.F. performed the RNA-seq analysis. A.N. and I.T. performed the decellularization and mass spectrometry analysis of the tumor samples and contributed to the data interpretation. J.A.A.-G. contributed to designing and interpreting experiments and provided HEp3 cellular models. J.J.B.-C. coordinated the study and contributed to design and interpretation of the experiments and to the writing of the manuscript.

Corresponding author

Correspondence to Jose Javier Bravo-Cordero.

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Competing interests

E.J.F. is a member of the scientific advisory board of Viosera Therapeutics. J.A.A.-G. is a scientific co-founder of, scientific advisory board member of and equity owner in HiberCell and receives financial compensation as a consultant for HiberCell, a Mount Sinai spin-off company focused on therapeutics that prevent or delay cancer recurrence. The other authors declare no competing interests.

Additional information

Peer review information Nature Cancer thanks Edna Cukierman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Supportive Data to Main Fig. 1.

All numerical data are presented as mean +/−SEM. (a) Experimental design of CAMs experiments. d refers to days on timeline scheme. Left panel: Representative images of day 6 collected tumors. Right panel: Top graph: T-HEp3 and D-HEp3 (n = 6 independent CAMs). Bottom graph: D2.A1 and D2.OR (n = 5 independent CAMs). Number of cells per tumor compared with an unpaired two-tailed Mann-Whitney test with 95% confidence level. (b) Representative multiphoton images of T-HEp3 and D-HEp3 CAM tumors. Scale bar, 50μm. d refers to days on timeline scheme. (c) Tissue microarray SHG analysis. Left panel: representative images of normal tissue versus stage IV HNSCC ECM architecture. Right images are a zoom of white squares on left image. Scale bars, 200μm. Scale bar zoom, 50 μm. Right panel: Collagen orientation between normal tissues (n =43 samples) and malignant HNSCC (n =289 samples) and between stage I to III (n =130 samples) and stage IV and IVa (n =53 samples). Data were compared using an unpaired two-tailed Mann-Whitney test with 95% confidence level. (d) Imaging window design and implantation site in mice (n =5). Representative images of T-HEp3-GFP in primary site. Scale bar, 100μm. Zoom Scale bar, 50μm. d refers to days on timeline scheme. (e) Left panel: Nude mice lung representative images with or without T-HEp3 GFP spontaneously disseminated cells. Scale bar, 50μm. Right panel: NCG mice lungs representative images with MDA-MB-231 GFP spontaneously disseminated cells. Scale bar, 50μm.

Source data

Extended Data Fig. 2 Supportive Data to Main Fig. 2.

(a) ECM enrichment pipeline for mass spectrometry. (b) ECM-enrichment validation by western blot before mass spectrometry analysis. Removal of intracellular components and ECM enrichment via sequential decellularization (lanes 2-4) from the total tissue lysate (1) was monitored by immunoblotting for actin (cytoskeleton protein) and histones (nuclear proteins). The remaining insoluble fraction (5) was highly enriched for ECM proteins (collagen I) and largely depleted for intracellular components. (c) Proportion of the mass-spectrometric signal intensity from matrisome (blue) and non-matrisome (grey) peptides for each sample, related to Supplementary Table 1b. (d) Masson’s trichrome staining of proliferative and dormant mice tumors. Scale bars, 50μm. (e) Percentage of tumor-derived and stroma-derived ECM d in D-HEp3 and T-HEp3 mice tumors, related to Supplementary Tables 1h, i. (f) Collagen III staining specificity tested in immunohistochemistry staining on human skin tissues (Scale bar, 100μm) and by western blot using purified native human collagen I and III.

Source data

Extended Data Fig. 3 Supportive Data to Main Fig. 3.

All numerical data are presented as mean +/−SEM. (a) Representative images of Masson’s Trichrome from T-HEp3 mice tumors with or without type III collagen co-injection. Scale bar, 50μm. (b) Normalized distribution of collagen fiber orientation from tumors presented in A (n = 5 independent tumors per group, 2 images analyzed per tumors). Cumulative distributions were compared using an unpaired two-tailed Kolmogorov Smirnov test with 95% confidence level. (c) Tumor growth of D2.A1 +/− type III collagen co-injection (n = 5 mice per group). Curves were compared using a two-way ANOVA with mixed model effects analysis and a Bonferroni correction and a 95% confidence interval. (d) Tumor growth of 4T1 +/− type III collagen co-injection (n = 5 mice per group). Curves were compared using a two-way ANOVA with mixed model effects analysis and a Bonferroni correction and a 95% confidence interval. (e) FACS analysis for percentage of T-HEp3 live cells (green), dead cells (red), apoptotic cells(yellow) and necrotic cells (orange) plated on plastic, type I collagen, or type III collagen matrix (n = 3 independent experiments). Distributions were compared using a Chi-squared test with 95% confidence interval. (f) Time points from an 18hrs time lapse movie of D-HEp3 plated on type I or III collagen. (t=hours). Scale bar, 10 μm. Related to Supplemental Movies 3 and 4. (g) APOTOX assay of T-HEp3 plated on different concentrations of type III collagen for 24hrs (n = 3 independent experiments).

Source data

Extended Data Fig. 4 Supportive Data to Main Fig. 4.

(a) Representative multiphoton images of MRC5 fibroblasts shRNA CTRL or expressing 2 independent shRNA targeting COL3A1 seed in CAMs for 24hrs. Scale bar, 50μm. (b) Top panel: representative brightfield images of D2.OR shRNA CTRL or expressing 2 independent shRNAs targeting col3a1 in vitro. Bottom panel: immunofluorescence of D2.OR shRNA CTRL or expressing 2 independent shRNAs targeting COL3A1 in vitro for E-cadherin. Scale bar, 50μm. (c) Number of cells per tumor for D-HEp3 expressing a control siRNA or siRNA targeting COL1A1, COL1A2, COL5A1, COL5A2, COL5A3, COL6A1, COL6A2, COL6A3, COL16A1 or COL18A1 in CAMs. (n = number of CAMs per group are described in the graphs). Data were compared using unpaired two-tailed Mann-Whitney test with 95% confidence level. All numerical data are presented as mean +/−SEM.

Source data

Extended Data Fig. 5 Supportive Data to Main Fig. 5.

All numerical data are presented as mean +/−SEM. (a) Adhesion assay for T-HEp3 and D-HEp3 to fibronectin. (n = 3 independent experiment with triplicates). Data were compared using unpaired two-tailed Mann-Whitney test with 95% confidence level. (b) Adhesion assay for D2.OR expressing an shRNA control or targeting COL3A1 to type III collagen. (n = 3 independent experiment with triplicates). Data were compared using unpaired two-tailed Mann-Whitney test with 95% confidence level. (c) Upper panel: Number of cells per CAM tumors of D2.OR shCTRL or sh DDR1 (n =8 independent CAMs shCTRL, n =4 shDDR1#1, n =5 sh DDR1#2, n =7 shDDR1#3). Data were compared using unpaired two-tailed Mann-Whitney test with 95% confidence level. Note that shRNA 1 and 3 only deplete DDR1. Lower panel: Western blot showing DDR1, DDR2 and tubulin levels upon DDR1 depletion. (d)Upper panel: Number of cells per CAMs tumors of BM-HEp3 (dormant) expressing a control siRNA or siRNA targeting DDR1. (n =5 independent CAMs per condition). Data were compared using unpaired two-tailed Mann-Whitney test with 95% confidence level. Lower panel: Western blot showing DDR1 and tubulin levels upon DDR1 depletion. (e) Percentage of G0 cells from D-HEp3 cells expressing a control siRNA or siRNA targeting DDR1. (n =3 independent experiments). Data were compared using unpaired two-tailed Mann-Whitney test with 95% confidence level. (f) Western blot for DDR1 and tubulin in D-HEp3 NT sgRNA control and expressing an sgRNA against DDR1. (g) Number of cells per CAM tumors in D-HEp3 shCTRL or shDDR1, or shDDR1 rescued with overexpression of either an empty vector (EV), a DDR1b full length, a binding deficient mutant (W53A) or a kinase dead mutant (K655A) (n = 5 independent CAMs). Data were compared using an ordinary one-way ANOVA test with multiple comparison to shCTRL condition with 95% confidence level. (h) Number of cells per CAM tumors of D-HEp3 +/− Nilotinib treatment (n =12 control CAMs and n =17 treated CAMs) Data were compared using unpaired two-tailed Mann-Whitney test with 95% confidence level. Representative tumors and Western blot showing phospho-Tyrosin (pTYR), total DDR1 and tubulin levels upon nilotinib treatment are displayed below. (i) FACS analysis for percentage of T-HEp3 live cells (green), dead cells (red), apoptotic cells(yellow), and necrotic cells (orange), treated with jetPRIME only or expressing a control empty plasmid (EV) or DDR1b full length (n = 3 independent experiments). Distributions were compared using a one-tailed Chi-squared test with 95% confidence interval. (j) Number of T-HEp3 cells per CAM tumors expressing a control shRNA or an shRNA targeting DDR1 (n = 8 control CAMs and n =12 DDR1-depleted CAMs). Data were compared using unpaired two-tailed Mann-Whitney test with 95% confidence level. Representative tumors and Western blot showing DDR1, DDR2 and tubulin levels upon DDR1 depletion are displayed below. (k) FACS analysis for percentage of T-HEp3 live cells (green), dead cells (red), apoptotic cells(yellow), and necrotic cells (orange), expressing either a control shRNA or an shRNA targeting DDR1 (n = 3 independent experiments). Distributions were compared using a one-tailed Chi-squared test with 95% confidence interval. (l) Number of cells per CAM tumors of T-HEp3 +/− Nilotinib treatment (n =10 control CAMs and n =7 treated CAMs). Data were compared using unpaired two-tailed Mann-Whitney test with 95% confidence level. Representative tumors and Western blot showing phospho-Tyrosin (pTYR), total DDR1 and tubulin levels upon nilotinib treatment are displayed below. (m) Number of R-HEp3 cells per tumors in CAM expressing a control shRNA or an shRNA targeting DDR1. (n = 13 control CAMs, n =13 shDDR1#1, n =14 shDDR1#2). Data were compared using unpaired two-tailed Mann-Whitney test with 95% confidence level. Representative tumors and Western blot showing DDR1 and tubulin levels upon DDR1 depletion are displayed below. (n) Enrichment plot for matrisome signature from RNA sequencing performed in D-HEp3 shRNA CTRL and D-HEp3 shDDR1 mice tumors (p=7.68e-10). X-axis shows log2FC for D-HEP3 shRNA DDR1 vs D-HEp3 shRNA CTRL. Black bars represent matrisome genes. Related to Supplemental Table 4. (o) Heat map related to Tables 2 and 4 where the entire transcriptome is displayed and organized by alphabetical order of genes. T-HEp3 and Reactivated D-shDDR1 cells show similar profiles compared with D-HEp3 and D-shCTRL conditions. Heat maps were generated using the Biojupie tool (https://maayanlab.cloud/biojupies/)66.

Source data

Extended Data Fig. 6 Supportive Data to Main Fig. 6.

All numerical data are presented as mean +/−SEM. (a) Map of predicted sites for STAT1 in DDR1 promoter region using the CiiiDER tool. (b) Number of mice presenting single cells, clusters of less than 20 cells or micromets in their lungs after tail vein injection of D-HEp3 +/− si STAT1. (c)RT-qPCR for STAT1 from RNA extracted from D-HEp3 shRNA CTRL or shDDR1 tumors in vivo (n = 3 independent RNA extraction from 3 different tumors, in duplicate). Data were compared using unpaired two-tailed Mann-Whitney test with 95% confidence level. (d) RT-qPCR for STAT1 from RNA extracted from D-HEp3 cells in vitro expressing a control siRNA or siRNA targeting COL3A1 (n = 3 independent RNA extractions in duplicate). Data were compared using unpaired two-tailed Mann-Whitney test with 95% confidence level.

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Supplementary information

Supplementary Information

Gating strategies for FACS experiments related to Extended Data Figs. 3e and 5e,i,k.

Reporting Summary

Supplementary Video 1

T-HEp3 plated on type I collagen for 18 h. Images were acquired every 30 min. Cells express a CDK2 sensor (green) and collagen was labeled in red. Scale bar, 10 μm.

Supplementary Video 2

T-HEp3 plated on type III collagen for 18 h. Images were acquired every 30 min. Cells express a CDK2 sensor (green) and collagen was labeled in red. Scale bar, 10 μm.

Supplementary Video 3

D-HEp3 plated on type III collagen for 18 h. Images were acquired every 30 min. Cells express a CDK2 sensor (green) and collagen was labeled in red. Scale bar, 10 μm.

Supplementary Video 4

D-HEp3 plated on type I collagen for 18 h. Images were acquired every 30 min. Cells express a CDK2 sensor (green) and collagen was labeled in red. Scale bar, 10 μm.

Supplementary Tables 1–8

Supplementary Table 1: D-HEp3 and T-HEp3 proteomic data. a, Samples. b, Complete MS output. c, Complete matrisome. d, Normalization and enrichment. e, T-HEp3 matrisome. f, D-HEp3 matrisome. g, T-HEp3 versus D-HEp3 comparison. h, All collagens. i, Tumor cell-derived collagens. Supplementary Table 2: D-HEp3 and T-HEp3 RNA-seq dataset. Supplementary Table 3: D-HEp3 shRNA CTRL versus D-HEp3 shDDR1 proteomic data. a, Samples. b, Complete MS output. c, Complete matrisome. d, Normalization and enrichment. e, All collagens. f, Collagens normal to all. Supplementary Table 4: D-HEp3 shCTRL versus D-HEp3 shDDR1 RNA-seq dataset. Supplementary Table 5: T-HEp3 versus D-HEp3 TFs. Supplementary Table 6: D-HEp3 shCTRL versus D-HEp3 shDDR1 TFs. Supplementary Table 7: Primers, siRNA and shRNA sequences, antibodies, cell lines and plasmids used. Supplementary Table 8: Exact P values for P < 0.0001.

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Di Martino, J.S., Nobre, A.R., Mondal, C. et al. A tumor-derived type III collagen-rich ECM niche regulates tumor cell dormancy. Nat Cancer 3, 90–107 (2022). https://doi.org/10.1038/s43018-021-00291-9

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