Abstract

Diversity within or between tumours and metastases (known as intra-patient tumour heterogeneity) that develops during disease progression is a serious hurdle for therapy1,2,3. Metastasis is the fatal hallmark of cancer and the mechanisms of colonization, the most complex step in the metastatic cascade4, remain poorly defined. A clearer understanding of the cellular and molecular processes that underlie both intra-patient tumour heterogeneity and metastasis is crucial for the success of personalized cancer therapy. Here, using transcriptional profiling of tumours and matched metastases in patient-derived xenograft models in mice, we show cancer-site-specific phenotypes and increased glucocorticoid receptor activity in distant metastases. The glucocorticoid receptor mediates the effects of stress hormones, and of synthetic derivatives of these hormones that are used widely in the clinic as anti-inflammatory and immunosuppressive agents. We show that the increase in stress hormones during breast cancer progression results in the activation of the glucocorticoid receptor at distant metastatic sites, increased colonization and reduced survival. Our transcriptomics, proteomics and phospho-proteomics studies implicate the glucocorticoid receptor in the activation of multiple processes in metastasis and in the increased expression of kinase ROR1, both of which correlate with reduced survival. The ablation of ROR1 reduced metastatic outgrowth and prolonged survival in preclinical models. Our results indicate that the activation of the glucocorticoid receptor increases heterogeneity and metastasis, which suggests that caution is needed when using glucocorticoids to treat patients with breast cancer who have developed cancer-related complications.

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Data availability

All mass spectrometry raw data files have been deposited to the ProteomeXchange Consortium- accession code PXD009102, http://proteomecentral.proteomexchange.org. The mRNA sequencing data are deposited in the Gene Expression Omnibus (GEO) database under accession code GSE124817. Processed transcriptomic data that support the findings of this study are available on reasonable request from the corresponding author.

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

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Acknowledgements

We thank members of the Bentires-Alj laboratory for advice and discussion. Tissue samples that correspond to PDX1, PDX2 and PDX4–PDX11 were provided by the Cooperative Human Tissue Network, which is funded by the National Cancer Institute. Other investigators may have received specimens from the same subjects. We thank A. L. Welm (University of Utah) for the PDX3 and PDX12–PDX16 models; H.-R. Hotz for offering the QuasR and edgeR tools in the FMI Galaxy server; and S. Bichet and P. Hirschmann for help with immunohistochemistry. We are grateful for the support of the FMI, DBM and Biozentrum core facilities. Research in the Bentires-Alj laboratory is supported by the Swiss Initiative for Systems Biology- SystemsX, the European Research Council, the Swiss National Science Foundation, Novartis, the Krebsliga Beider Basel, the Swiss Cancer League, the Swiss Personalized Health Network (Swiss Personalized Oncology driver project) and the Department of Surgery of the University Hospital Basel.

Reviewer information

Nature thanks Melanie Flint and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

    • Milan M. S. Obradović

    Present address: Wellmera AG, Basel, Switzerland

    • Nenad Manevski

    Present address: UCB Celltech, Development Sciences, Slough, UK

    • Joana Pinto Couto

    Present address: Novartis Institutes for BioMedical Research, Basel, Switzerland

Affiliations

  1. Department of Biomedicine, Department of Surgery, University Hospital Basel, University of Basel, Basel, Switzerland

    • Milan M. S. Obradović
    • , Baptiste Hamelin
    • , Joana Pinto Couto
    • , Atul Sethi
    • , Marie-May Coissieux
    • , Ryoko Okamoto
    •  & Mohamed Bentires-Alj
  2. Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland

    • Milan M. S. Obradović
    • , Joana Pinto Couto
    • , Atul Sethi
    • , Marie-May Coissieux
    • , Ryoko Okamoto
    • , Hubertus Kohler
    •  & Mohamed Bentires-Alj
  3. Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland

    • Nenad Manevski
  4. Swiss Institute of Bioinformatics, Basel, Switzerland

    • Atul Sethi
  5. Institute of Pathology, University Hospital Basel, University of Basel, Basel, Switzerland

    • Simone Münst
  6. Proteomics Core Facility, Biozentrum, University of Basel, Basel, Switzerland

    • Alexander Schmidt

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Contributions

M.M.S.O. conceived the study, designed and performed the experiments, analysed the data, interpreted the results and wrote the manuscript. B.H. performed experiments on gene expression and helped with ROR1 shRNA and mouse experiments, analysed the data and interpreted the results. N.M. established methods and measured stress hormones levels in plasma, analysed the data and interpreted the results. J.P.C. designed experiments, analysed the data and interpreted the results. S.M. performed histopathological analysis of the PDX models, analysed the data and interpreted the results. R.O. characterized the metastatic potential of PDX models, analysed the data and interpreted the results. A. Sethi performed computational analysis of metastatic breast cancer samples, analysed the data and interpreted the results. H.K. performed fluorescence-activated cell sorting experiments, analysed the data and interpreted the results. M.-M.C. performed intravital imaging, analysed the data and interpreted the results. A. Schmidt performed proteomics and phosphoproteomics experiments, analysed the data and interpreted the results. M.B.-A. conceived the study, designed the experiments and interpreted the results. All authors read and approved the final manuscript.

Competing interests

B.H., R.O., S.M., M.-M.C., A. Sethi, A. Schmidt and M.B.-A. declare no competing interests. N.M. is an employee of UCB Pharma. H.K. and J.P.C. are employees of Novartis. M.M.S.O. is an employee of Wellmera AG.

Corresponding author

Correspondence to Mohamed Bentires-Alj.

Extended data figures and tables

  1. Extended Data Fig. 1 Increase in GR activation in breast cancer metastases.

    a, Tumours and matched lung, liver, ovary and spleen metastases in the MDA-MB 231 model (haematoxylin and eosin staining). Scale bar, 200 μm. Right, frequency of metastases detected in distant organs upon tumour resection. n = 10 from 5 independent experiments. b, FACS analysis of organs affected with distant metastases in the MDA-MB 231 model. n = 10. c, Tumour growth kinetics after orthotopic transplantation in the MDA-MB 231 model (n = 9) and PDX1, PDX2 and PDX3 models (n = 5, 4 and 9 respectively). Mean ± s.e.m. Right, tumour and matched lung metastases in the PDX1 and PDX2 models (haematoxylin and eosin staining). Scale bar, 200 μm. d, Tumour and matched lung, liver or ovary metastases in the PDX3 model (haematoxylin and eosin staining). Scale bar, 200 μm. Right, frequency of metastases detected in distant organs upon tumour resection. n = 10. e, FACS analysis of PDX3 tumour and organs with matched metastases. n = 5. f, Principal component analysis of PDX3 tumours (n = 4) and matched liver (n = 3) and lung (n = 3) metastases. gi, Heat maps of genes that are differentially expressed and upstream regulator analysis, for the MDA-MB 231 model. n = 3, Fisher’s exact test. g, Tumours and liver metastases. h, Tumours and circulating tumour cells. i, Tumour and spleen. jm, Heat maps of genes that are differentially expressed for PDX models. j, PDX1 tumour (n = 4) and lung metastases (n = 3). k, PDX2 tumour and lung metastases (n = 4). l, PDX3 tumour and lung metastases (n = 4). m, PDX3 tumour and liver metastases (n = 4). n represents biological replicates (mice) in all panels. Threshold criteria for all differential-expression heat map analyses are a fold-change ≥ 2 and FDR < 0.05. The statistical approach for differential-expression analysis is provided in Methods. Source Data

  2. Extended Data Fig. 2 GR activation in distant metastases and circulating tumour cells.

    a, ISMARA transcription-factor-activity plot of the tumour, lung, liver metastases and circulating tumour cells in the MDA-MB 231 model. n = 3 biological replicates. b, ISMARA transcription-factor activity of PDX models. n = 4 (apart from PDX1 lung metastases, n = 3) biological replicates (mice). c, GR transcription-factor binding sites in lung metastases of PDX1, PDX2, PDX3, MDA-MB 231 and BALB/c–NeuT models14. n = 4 (apart from PDX1 lung metastases, n = 3) biological replicates (mice). d, GR expression in tumours and matched lung metastases in MDA-MB 231, PDX1, PDX2 and PDX3 models. n = 3 biological replicates (mice). Scale bar,100 μm. eh, Expression of genes involved in glucocorticoid synthesis, with HPRT1 as an internal control. e, MDA-MB 231, n = 3. f, PDX1, n = 4 tumours and n = 3 lung metastases. g, PDX2, n = 4. h, PDX3 models, n = 4. Mean ± s.d., n indicates biological replicates (mice). ik, Subgroup analysis of plasma hormone levels in mice of the MDA-MB 231 model before tumour resection (M0 mice). The M0 group has been split at the median into two groups, one with smaller tumours (mean volume, 446 mm3) and the other with larger tumours (mean volume, 692 mm3). i, Cortisol levels. j, Corticosterone levels. k, Adrenocorticotropic hormone levels. Means and single data points are represented. n = 5 biological replicates (mice), two-tailed Student’s t-test. Source Data

  3. Extended Data Fig. 3 Glucocorticoids promote colonization via GR.

    a, Expression of GR targets (qPCR). Mean ± s.d., n = 6 biological replicates, in technical duplicates; two-tailed Student’s t-test. b, Expression of GR targets three weeks after discontinuation of GR activation by dexamethasone. Mean ± s.d., n = 3 biological replicates in technical duplicates, two-tailed Student’s t-test. c, Number of metastatic foci in lungs of mice injected with 4T1 dexamethasone or vehicle-treated cells. n = 9 mice, two-tailed Student’s t-test. d, GR downregulation in MDA-MB 231 cells (left, qPCR; right, immunoblotting). Mean ± s.d., n = 3 biological replicates, two-tailed Student’s t-test. e, MDA-MB 231 cells were propagated in the presence of dexamethasone or vehicle for seven days. n = 7 biological replicates. f, GR-downregulated MDA-MB 231 cells did not express the GR-activation marker gene set upon dexamethasone treatment. Mean ± s.d., n = 3 biological replicates in technical duplicates, two-tailed Student’s t-test. g, Bioluminescence imaging 12 h, 24 h and 48 h after intravenous injection of control and dexamethasone-treated MDA-MB 231 cells transduced with control or GR shRNA. n represents biological replicates (mice), mean ± s.d., two-tailed Student’s t-test. h, Bioluminescence imaging of mice two weeks after intravenous injection. n = 15 for vehicle + control shRNA, dexamethasone + control shRNA, and dexamethasone + GR shRNA 2 (shGR2); n = 14 for dexamethasone + GR shRNA 1 (shGR1); n = 13 for vehicle + GR shRNA 2; n = 12 for vehicle + GR shRNA 1. Three independent experiments; n represents biological replicates (mice), two-tailed Student’s t-test. i, Bioluminescence imaging two weeks after intravenous injection of GR-activated, mifepristone- or vehicle-treated MDA-MB 231 cells. n = 10 mice, 2 independent experiments, two-tailed Student’s t-test. j, Kaplan–Meier survival analysis of mice upon intravenous injection of GR-activated, mifepristone- or vehicle-treated MDA-MB 231 cells. n = 10 mice for vehicle and dexamethasone groups; n = 9 for mifepristone group. Two independent experiments, two-tailed log-rank test, vehicle-treated versus mifepristone-treated groups, P = 0.054. In box plots, the centre line indicates the median, the box extends from the 25th to 75th percentiles and whiskers extend to the most extreme data points. Source Data

  4. Extended Data Fig. 4 Dexamethasone offsets the response to paclitaxel.

    a, Kaplan–Meier survival analysis of mice intravenously injected with 4T1 GR-activated or control cells. Dexamethasone decreases 4T1 response to paclitaxel in vivo. Paclitaxel was administrated five days after 4T1 cell inoculation. n = 9 control; n = 10 dexamethasone-treated biological replicates (mice) per group; 2 mice were censored; two-tailed log-rank test. b, Dexamethasone offsets paclitaxel effect in the MDA-MB 231 model. Analysis of colonization potential under paclitaxel treatment of MDA-MB 231 cells transduced with one of the two GR shRNAs or control shRNA, and treated with dexamethasone or vehicle, intravenously injected into NSG mice. Two paclitaxel injections (15 and 22 days after cell injection). Mean ± s.d., n = 5 mice per group, two-tailed Student’s t-test. c, Bioluminescence imaging corresponding to day 21 of b. d, Kaplan–Meier survival analysis of mice shown in b. n = 5 biological replicates (mice) per group, two-tailed log-rank test. Source Data

  5. Extended Data Fig. 5 Dexamethasone reduces overall survival and GR downregulation increases cancer cell dissemination from the primary site.

    a, b, Upon tumour removal from the 4th mammary gland, randomized mice were treated with dexamethasone or vehicle on 5 consecutive days (intraperitoneal injection of 0.1 mg kg−1 dexamethasone once daily). Kaplan–Meier survival analysis in PDX1 model (n = 8 control; n = 7 dexamethasone-treated biological replicates (mice)) (a) or 4T1 model (n = 8 control; n = 9 dexamethasone-treated biological replicates (mice)) (b). Two 4T1 mice were censored. Two-tailed log-rank test (a, b). c, GR downregulation in MDA-MB 231 cells does not affect tumour volume, relative to tumour cells transduced with control shRNA, at resection. Mean ± s.d., n = 14 biological replicates (mice), pooled data from 3 independent experiments, two-tailed Student’s t-test. d, Circulating tumour cell count measured by the number of in vitro propagated colonies upon circulating tumour cell isolation from peripheral blood of tumour-bearing mice at tumour resection time point. Mean ± s.d., n = 5 biological replicates (mice), two-tailed Student’s t-test. e, In vivo bioluminescence imaging upon tumour removal. In the box plot, the centre line is the median, the box extends from the 25th to 75th percentiles, and whiskers extend to the most extreme data points. n = 17 control shRNA; n = 10 GR shRNA 1 and 2 biological replicates (mice), pooled data from 3 independent experiments, two-tailed Student’s t-test. f, Kaplan–Meier survival analysis of mice upon of removal of MDA-MB 231 tumours transduced with control shRNA or one of the two GR shRNAs, and treatment with dexamethasone or vehicle. n = 13 biological replicates (mice) per group, pooled data from 3 independent experiments, two-tailed log-rank test. g, Tumour volumes at resection. In vitro dexamethasone- or vehicle-treated MDA-MB 231 cells transduced with control shRNA, GR shRNA 1 or GR shRNA 2 inoculated into the mammary fat pad of NSG mice. n = 13 control shRNA; n = 8 GR shRNA 1 and 2 biological replicates (mice), pooled data from 2 independent experiments, two-tailed Student’s t-test. All tumours in all experiments were resected at the same time point. Source Data

  6. Extended Data Fig. 6 Differential expression of protein kinases in tumours and matched metastases.

    ad, Expression of protein kinases in MDA-MB 231 model, n = 3 biological replicates (mice) (a); PDX1 model, n = 4 tumour and n = 3 matched lung metastases; biological replicates (mice) (b); PDX2 model, n = 4 biological replicates (mice) (c); and PDX3 model, n = 4 biological replicates (mice) (d). The threshold criteria used for the analysis are fold-change ≥ 2 and P < 0.05. Further details of the statistical analysis are provided in Methods.

  7. Extended Data Fig. 7 Differential protein abundance upon GR activation.

    a, Volcano plot of protein abundance after GR activation in MDA-MB 231 cells. n = 3 control; n = 4 dexamethasone-treated biological replicates, Bayes-moderated t-statistics, P values corrected for multiple testing using the Benjamini–Hochberg method, calculations performed in R using the LIMMA package, Bioconductor. b, Heat map of differentially abundant proteins in dexamethasone-treated and vehicle-treated (control) cells. n = 3 control; n = 4 dexamethasone-treated biological replicates; FDR < 0.05, Bayes-moderated t-statistics; P values were corrected for multiple testing using the Benjamini–Hochberg method, calculations performed in R using the LIMMA package, Bioconductor. c, Abundance of proteins used for generation of the GR activation signature. n = 3 control; n = 4 dexamethasone-treated biological replicates, mean ± s.d., two-tailed Student’s t-test. d, Pathway enrichment analysis of all phospho-proteins with significant abundance changes against all phospho-proteins quantified as a background using MetaCore (Clarivate Analytics). Enrichment P values and FDRs were determined by the software-specific algorithms using default parameters. Source Data

  8. Extended Data Fig. 8 GR activation increases the expression of kinases that are predictive of survival in breast cancer.

    Survival based on the expression of the protein kinase signature that is upregulated in the metastases. a, Relapse-free survival, two-tailed log-rank test. b, Distant-metastasis-free survival, two-tailed log-rank test. c, Postprogression survival, two-tailed log-rank test. Number of patients (n) and P values are presented in the panels. d, Individual protein kinases, relapse-free survival, n = 1,764, two-tailed log-rank test. e, Co-occurrence of GR and protein kinases in publically available breast cancer datasets, Fisher’s exact test, n = 2,509 (refs. 27,28).

  9. Extended Data Fig. 9 ROR1 expression in breast cancer and metastases.

    a, Relapse-free survival analysis of patients with the ROR1 signature (G-2-0, Kaplan–Meier), n = 4,029, two-tailed log-rank test. b, GR-activation signature correlates with increased levels of ROR1 in breast cancer metastases. n = 21 lymph node; n = 34 liver metastases. Pearson correlation. c, Co-occurrence of GR-activation gene signature with GR and ROR1, n = 2,509 (refs. 27,28), Fisher’s exact test. d, Breast cancers that express high levels of GR mRNA were enriched in the claudinlow profile, n = 299 (refs. 27,28).

  10. Extended Data Fig. 10 Dexamethasone increases metastases and precipitates death, via ROR1.

    a, ROR1 expression in in vitro-propagated control and GR-downregulated cells. Mean ± s.d., n = 6 biological replicates, two-tailed Student’s t-test. b, c, ROR1 qPCR. RNA from tissues of mice injected with control or GR-downregulated cells in tumours (n = 4 biological replicates (mice)) (b) and lung metastases (n = 4, vehicle + control shRNA, dexamethasone + control shRNA and dexamethasone +GR shRNA 1 or 2; n = 3 vehicle + GR shRNA 1 or 2, biological replicates (mice) in technical duplicates or triplicates) (c). Mean ± s.d., two-tailed Student’s t-test. d, Levels of WNT5A protein in supernatant of dexamethasone-treated or vehicle-treated MDA-MB 231 cells transduced with control or one of the two GR shRNAs. Mean ± s.d., n = 3 biological replicates, two-tailed Student’s t-test. e, WNT5A qPCR in dexamethasone-treated or vehicle-treated MDA-MB 231 cells transduced with control or one of the two GR shRNAs. Mean ± s.d., n = 4 biological replicates in technical triplicates, two-tailed Student’s t-test. f, Levels of WNT5A protein in tumours transduced with control or one of the two GR shRNAs, and their matched metastases. Mean ± s.d., n = 3 biological replicates (mice) in technical duplicates, two-tailed Student’s t-test. g, Pearson correlation of GR activation, ROR1, WNT5A and Wnt signalling pathway members in breast cancer metastases. n = 88 breast cancer metastases; n = 21 lymph node; n = 34 liver; n = 7 bone metastases. h, ROR1 downregulation in MDA-MB 231 cells. Mean ± s.d., n = 3 biological replicates in technical duplicates, two-tailed Student’s t-test, qPCR (top) and flow cytometry (bottom). ik, Kaplan–Meier survival analysis of mice intravenously inoculated with vehicle-treated or dexamethasone-treated MDA-MB 231cells transduced with control shRNA (i), ROR1 shRNA 1 (shROR1-1) (j) or ROR1 shRNA 2 (shROR1-2) (k). n = 5, two-tailed log-rank test. l, Kaplan–Meier survival analysis of mice injected in the mammary fat pad with MDA-MB 231 cells transduced with control or one of the two ROR1 shRNAs. n = 21 control shRNA; n = 23 ROR1 shRNA 1 or 2, two-tailed log-rank test. mo, Kaplan–Meier survival analysis of mice inoculated in the mammary fat pad with MDA-MB 231 cells transduced with control shRNA (m), ROR1 shRNA 1 (n) or ROR1 shRNA 2 (o), propagated in the presence of dexamethasone or vehicle. n = 8 control shRNA; n = 9 ROR1 shRNA 1 or 2, two-tailed log-rank test. Source Data

Supplementary information

  1. Supplementary Figure 1

    This file contains the uncropped western blots.

  2. Reporting Summary

  3. Supplementary Table 1

    Characterization of the PDX models. Characterization of 17 primary-derived xenografts (PDX) or cell lines implanted into the 4th mammary gland of NOD-scid IL2rγnull (NSG) immunodeficient mice. Upon primary tumour resection, metastatic potential (metastatic onset and pattern) was analysed.

  4. Supplementary Table 2

    Gene Set Enrichment Analysis (GSEA). a, MDA-MB 231, n=3; b, PDX3 n=4; c, PDX2 n=4; d, PDX1 n=4 tumours and n=3 lung metastases; n represents biological replicates (mice). GSEA was performed using the JAVA application from the Broad Institute v2.0. The GSEA use the Kolmogorov-Smirnov statistic.

  5. Supplementary Table 3

    Proteomic analysis. Protein abundance analysis of DEX GR-activated and vehicle-treated MDA-MB 231, n=3 “vehicle” biological replicates, n=4 “DEX” biological replicates, Bayes moderated t-statistics, P-values were corrected for multiple testing using the Benjamini−Hochberg method.

  6. Supplementary Table 4

    Phosphoproteomic analysis. Phosphoproteomic analysis of DEX GR-activated and vehicle-treated MDA-MB 231, n=3 “vehicle” biological replicates, n=4 “DEX” biological replicates. Used statistical analysis described in the methods section.

  7. Supplementary Table 5

    Proteomic and Phosphoproteomic analysis. a, YAP targets protein abundance- DEX GR-activated and vehicle-treated MDA-MB 231. b-c, Phosphoproteomic analysis of DEX GR-activated and vehicle-treated MDA-MB 231: b, HIPPO pathway and c, MAPK-ERK pathway. d, Proteomic and Phosphoproteomic analysis of Wnt signalling members. Bayes moderated t-statistics, P-values were corrected for multiple testing using the Benjamini−Hochberg method. n=3 “vehicle” biological replicates, n=4 “DEX” biological replicates for all provided analysis. Further details on statistical analysis are provided in the methods section.

  8. Video 1: Intravital tumour imaging.

    Time-lapse sequence (30-min; 2D) of GFP-labelled shCTRL MDA-MB-231 tumour cells (green) in the 4th mammary gland of a living mouse, n=4. Scale bar 100 μm.

  9. Video 2: Intravital tumour imaging.

    Time-lapse sequence (30-min; 2D) of GFP-labelled shGR1 MDA-MB-231 tumour cells (green) in the 4th mammary gland of a living mouse, n=3. Scale bar 100 μm.

  10. Video 3: Intravital tumour imaging.

    Time-lapse sequence (30-min; 2D) of GFP-labelled shGR2 MDA-MB-231 tumour cells (green) in the 4th mammary gland of a living mouse, n=4. Scale bar 100 μm.

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https://doi.org/10.1038/s41586-019-1019-4

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