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Adjustment of dendritic cells to the breast-cancer microenvironment is subset specific

Nature Immunology volume 19, pages 885–897 (2018)Cite this article

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Abstract

The functions and transcriptional profiles of dendritic cells (DCs) result from the interplay between ontogeny and tissue imprinting. How tumors shape human DCs is unknown. Here we used RNA-based next-generation sequencing to systematically analyze the transcriptomes of plasmacytoid pre-DCs (pDCs), cell populations enriched for type 1 conventional DCs (cDC1s), type 2 conventional DCs (cDC2s), CD14+ DCs and monocytes-macrophages from human primary luminal breast cancer (LBC) and triple-negative breast cancer (TNBC). By comparing tumor tissue with non-invaded tissue from the same patient, we found that 85% of the genes upregulated in DCs in LBC were specific to each DC subset. However, all DC subsets in TNBC commonly showed enrichment for the interferon pathway, but those in LBC did not. Finally, we defined transcriptional signatures specific for tumor DC subsets with a prognostic effect on their respective breast-cancer subtype. We conclude that the adjustment of DCs to the tumor microenvironment is subset specific and can be used to predict disease outcome. Our work also provides a resource for the identification of potential targets and biomarkers that might improve antitumor therapies.

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Fig. 1: Phenotypic and molecular characterization of innate APCs that infiltrate breast cancer tissue.
Fig. 2: Subset-specific signatures that define tumor APCs.
Fig. 3: 'Tumor-emergent genes' from innate APC are subset specific.
Fig. 4: Absence of immunological function enrichment in tumor-upregulated genes.
Fig. 5: Transcriptional profile of innate APC subset is dependent on breast-cancer subtype.
Fig. 6: The type I interferon pathway is upregulated in all APC subsets in TNBC.
Fig. 7: Subset-specific signatures are linked to distinct disease-free survival depending on the subset and breast cancer type.

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Acknowledgements

We thank the Institut Curie Cytometry Core facility for cell sorting; INSERM U932, particularly C. Laurent and A.S. Hamy-Petit, for bioinformatics advice; and S. Alculumbre and P. Vargas for discussions. F. Noël was supported by a fellowship from the French Ministry of Research. This work was supported by funding from INSERM (BIO2012-02, BIO2014-08, HTE2016), Fondation pour la Recherche Médicale, ANR-10-IDEX-0001-02 PSL* and ANR-11-LABX-0043, European Research Council (IT-DC 281987) and CIC IGR-Curie 1428, INCA EMERG-15-ICR-1, la Ligue contre le cancer (labellisation EL2016.LNCC/VaS). High-throughput sequencing wasperformed by the ICGex NGS platform of the Institut Curie supported by grants ANR-10-EQPX-03 (Equipex) and ANR-10-INBS-09-08 (France Génomique Consortium), InCA from ANR (‘Investissements d’Avenir’ program), by the Canceropole Ile-de-France and by the SiRIC-Curie program (SiRIC Grant ‘INCa-DGOS- 4654’).

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  1. These authors contributed equally: Paula Michea, Floriane Noël.

Authors and Affiliations

  1. Institut Curie, PSL Research University, Paris, France

    Paula Michea, Floriane Noël, Eve Zakine, Urszula Czerwinska, Philémon Sirven, Omar Abouzid, Christel Goudot, Alix Scholer-Dahirel, Sebastian Amigorena, Maude Guillot-Delost, Elodie Segura & Vassili Soumelis

  2. INSERM, UMR 932, F-75005, Paris, France

    Paula Michea, Floriane Noël, Eve Zakine, Urszula Czerwinska, Philémon Sirven, Omar Abouzid, Christel Goudot, Alix Scholer-Dahirel, Sebastian Amigorena, Maude Guillot-Delost, Elodie Segura & Vassili Soumelis

  3. Université Paris Sud, Université Paris-Saclay, Orsay, France

    Floriane Noël

  4. INSERM, U900, F-75005, Paris, France

    Urszula Czerwinska

  5. Mines ParisTech, Paris, France

    Urszula Czerwinska

  6. Centre de Recherches Interdisciplinaires, Paris Descartes, Paris, France

    Urszula Czerwinska

  7. Institut Curie, Service de Pathologie, Paris, France

    Anne Vincent-Salomon

  8. INSERM, U934, F-75005, Paris, France

    Anne Vincent-Salomon

  9. INSERM, UMR 932, Département de Recherche Translationnelle, Residual Tumor & Response to Treatment Laboratory (RT2Lab), Paris, France

    Fabien Reyal

  10. Institut Curie, Département de Chirurgie, Paris, France

    Fabien Reyal

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Contributions

P.M. designed and performed experiments, analyzed results and wrote the manuscript; F.N. performed bioinformatics analyses and wrote the manuscript; E.Z., U.C. and C.G. analyzed results; P.S. and O.A. performed experiments; A.S.-D. and M.G-D. contributed to project management; A.V.-S. contributed to clinical project management and pathology review and provided clinical samples; F.R. contributed to clinical project management; S.A. and E.S. provided strategic advice and revised the manuscript; and V.S. designed experiments, supervised the research and wrote the manuscript.

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Correspondence to Vassili Soumelis.

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Supplementary Figure 1 Phenotypic characterization of innate APC infiltrating breast cancer tissue.

a, Flow cytometry contour plots showing the entire gate strategy utilized to distinguish tumor-infiltrating APC in LBC. b, Histograms of mean fluorescent intensity of FcεR1, CD64 and CD206 expression by the indicated APC subsets in LBC samples. Isotype control is shown in grey. c, Representative flow cytometry contour plots from DAPI-CD45+cells comparing APC subset gates from CD3-, CD19-, CD56+ (upper row), CD3-, CD19-, CD56- (middle row), and directly from CD3-, CD19- (lower row) in LBC samples. Middle row corresponds to the strategy use in this study. d, Representative flow cytometry contour plots showing the frequency of cDC1 expressing CD141 markers in digested or undigested PBMC from healthy donors. Histograms shows the mean fluorescent intensity of CLEC9A expression at the surface of undigested (solid line) or digested (dashed line) blood cDC1. Specific staining is in red and the isotype control in black. e, Scheme showing the pipeline used to generate tumor-infiltrating APC transcriptome from breast cancer samples. a one representative donor out of 22 with similar results, b one representative donor out of 15 with similar results, c,d one representative donor out of 3 with similar results.

Supplementary Figure 2 Comparison of tumor versus juxta-tumor APC infiltrating LBC.

a, Representative flow cytometry contour plots from DAPI-CD45+Lin- cells showing the indicated APC subsets in tumor (upper panel), and juxta-tumor (lower panel) samples from LBC patients. b, Schema showing the pipeline and number of DEG obtained from each indicated APC tumor versus juxta-tumor LBC. c, Box plots showing the RNA expression of IL3RA, HLA-DRA, EPCAM, and SCGB2A2 by tumor and juxta-tumor pDC transcriptome from this study (upper panels), breast cancer cell line database from Broad Institute (lower left), and pDC dataset from healthy donor blood (Novershtern, et al 2011).

Supplementary Figure 3 Comparison of tumor-infiltrating APC from TNBC versus LBC.

a, Schema showing the pipeline and number of DEG obtained from each indicated APC from tumor TNBC versus tumor LBC.

Supplementary Figure 4

a, Extended list of enriched pathways and corresponding GO term from genes upregulated in TNBC versus LBC that were shared with 2 or 3 subsets, as indicated. b, Genes included in the IFN pathway metagene separated in IFN production and IFN response that were used for the analyses in Fig. 6c-f. c, Genes included in the costimulatory metagene used for the analysis in Fig. 6d,e. d, Heat map indicating the correlation coefficient between the indicated costimulatory gene, and the IFN pathway metagene for each APC subset from LBC and TNBC, as indicated. e, GO term associated to the ECM organization metagene used for the analyses in Fig. 6f.

Supplementary Figure 5

a, Schema showing the pipeline used to analyze disease-free survival of the indicated subset-specific signature in the METABRIC public dataset.

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Supplementary Figures 1-5, Supplementary Tables 1-3

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Michea, P., Noël, F., Zakine, E. et al. Adjustment of dendritic cells to the breast-cancer microenvironment is subset specific. Nat Immunol 19, 885–897 (2018). https://doi.org/10.1038/s41590-018-0145-8

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