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

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.

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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

Authors and Affiliations

Authors

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.

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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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