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


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.


  1. 1.

    Banchereau, J. & Steinman, R. M. Dendritic cells and the control of immunity. Nature 392, 245–252 (1998).

    CAS  PubMed  Google Scholar 

  2. 2.

    Collin, M., McGovern, N. & Haniffa, M. Human dendritic cell subsets. Immunology 140, 22–30 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Mildner, A. & Jung, S. Development and function of dendritic cell subsets. Immunity 40, 642–656 (2014).

    CAS  PubMed  Google Scholar 

  4. 4.

    Shay, T. & Kang, J. Immunological Genome Project and systems immunology. Trends Immunol. 34, 602–609 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Miller, J. C. et al. Deciphering the transcriptional network of the dendritic cell lineage. Nat. Immunol. 13, 888–899 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Heidkamp, G. F. et al. Human lymphoid organ dendritic cell identity is predominantly dictated by ontogeny, not tissue microenvironment. Sci. Immunol. 1, eaai7677 (2016).

    PubMed  Google Scholar 

  7. 7.

    Haniffa, M. et al. Human tissues contain CD141hi cross-presenting dendritic cells with functional homology to mouse CD103+ nonlymphoid dendritic cells. Immunity 37, 60–73 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Watchmaker, P. B. et al. Comparative transcriptional and functional profiling defines conserved programs of intestinal DC differentiation in humans and mice. Nat. Immunol. 15, 98–108 (2014).

    CAS  PubMed  Google Scholar 

  9. 9.

    Liu, Y. J. IPC: professional type 1 interferon-producing cells and plasmacytoid dendritic cell precursors. Annu. Rev. Immunol. 23, 275–306 (2005).

    CAS  PubMed  Google Scholar 

  10. 10.

    Lindstedt, M., Lundberg, K. & Borrebaeck, C. A. Gene family clustering identifies functionally associated subsets of human in vivo blood and tonsillar dendritic cells. J. Immunol. 175, 4839–4846 (2005).

    CAS  PubMed  Google Scholar 

  11. 11.

    Mora, J. R. et al. Selective imprinting of gut-homing T cells by Peyer’s patch dendritic cells. Nature 424, 88–93 (2003).

    CAS  PubMed  Google Scholar 

  12. 12.

    Huang, Q. et al. The plasticity of dendritic cell responses to pathogens and their components. Science 294, 870–875 (2001).

    CAS  PubMed  Google Scholar 

  13. 13.

    Pulendran, B., Palucka, K. & Banchereau, J. Sensing pathogens and tuning immune responses. Science 293, 253–256 (2001).

    CAS  PubMed  Google Scholar 

  14. 14.

    Stagg, J. & Allard, B. Immunotherapeutic approaches in triple-negative breast cancer: latest research and clinical prospects. Ther. Adv. Med. Oncol. 5, 169–181 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Liu, Y. J. Dendritic cell subsets and lineages, and their functions in innate and adaptive immunity. Cell 106, 259–262 (2001).

    CAS  PubMed  Google Scholar 

  16. 16.

    Dalod, M., Chelbi, R., Malissen, B. & Lawrence, T. Dendritic cell maturation: functional specialization through signaling specificity and transcriptional programming. EMBO J. 33, 1104–1116 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Soumelis, V., Pattarini, L., Michea, P. & Cappuccio, A. Systems approaches to unravel innate immune cell diversity, environmental plasticity and functional specialization. Curr. Opin. Immunol. 32, 42–47 (2015).

    CAS  PubMed  Google Scholar 

  18. 18.

    Segura, E. & Amigorena, S. Inflammatory dendritic cells in mice and humans. Trends Immunol. 34, 440–445 (2013).

    CAS  PubMed  Google Scholar 

  19. 19.

    Wollenberg, A., Haberstok, J., Teichmann, B., Wen, S. P. & Bieber, T. Demonstration of the low-affinity IgE receptor FcεRII/CD23 in psoriatic epidermis: inflammatory dendritic epidermal cells (IDEC) but not Langerhans cells are the relevant CD1a-positive cell population. Arch. Dermatol. Res. 290, 517–521 (1998).

    CAS  PubMed  Google Scholar 

  20. 20.

    Zaba, L. C., Krueger, J. G. & Lowes, M. A. Resident and “inflammatory” dendritic cells in human skin. J. Invest. Dermatol. 129, 302–308 (2009).

    CAS  PubMed  Google Scholar 

  21. 21.

    Segura, E. et al. Human inflammatory dendritic cells induce Th17 cell differentiation. Immunity 38, 336–348 (2013).

    CAS  PubMed  Google Scholar 

  22. 22.

    Dunn, G. P., Bruce, A. T., Ikeda, H., Old, L. J. & Schreiber, R. D. Cancer immunoediting: from immunosurveillance to tumor escape. Nat. Immunol. 3, 991–998 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Bell, D. et al. In breast carcinoma tissue, immature dendritic cells reside within the tumor, whereas mature dendritic cells are located in peritumoral areas. J. Exp. Med. 190, 1417–1426 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    DeNardo, D. G. & Coussens, L. M. Inflammation and breast cancer. Balancing immune response: crosstalk between adaptive and innate immune cells during breast cancer progression. Breast Cancer Res. 9, 212 (2007).

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Ghiringhelli, F. et al. CD4+CD25+ regulatory T cells inhibit natural killer cell functions in a transforming growth factor-β-dependent manner. J. Exp. Med. 202, 1075–1085 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Faget, J. et al. ICOS-ligand expression on plasmacytoid dendritic cells supports breast cancer progression by promoting the accumulation of immunosuppressive CD4+ T cells. Cancer Res. 72, 6130–6141 (2012).

    CAS  PubMed  Google Scholar 

  27. 27.

    Ghirelli, C. et al. Breast cancer cell-derived GM-CSF licenses regulatory Th2 induction by plasmacytoid predendritic cells in aggressive disease subtypes. Cancer Res. 75, 2775–2787 (2015).

    CAS  PubMed  Google Scholar 

  28. 28.

    Topalian, S. L., Drake, C. G. & Pardoll, D. M. Immune checkpoint blockade: a common denominator approach to cancer therapy. Cancer Cell 27, 450–461 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Chen, D. S. & Mellman, I. Oncology meets immunology: the cancer-immunity cycle. Immunity 39, 1–10 (2013).

    PubMed  Google Scholar 

  30. 30.

    Angel, C. E. et al. Cutting edge: CD1a+ antigen-presenting cells in human dermis respond rapidly to CCR7 ligands. J. Immunol. 176, 5730–5734 (2006).

    CAS  PubMed  Google Scholar 

  31. 31.

    Bakdash, G. et al. Expansion of a BDCA1+CD14+ myeloid cell population in melanoma patients may attenuate the efficacy of dendritic cell vaccines. Cancer Res. 76, 4332–4346 (2016).

    CAS  PubMed  Google Scholar 

  32. 32.

    McGovern, N. et al. Human dermal CD14+ cells are a transient population of monocyte-derived macrophages. Immunity 41, 465–477 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Bronte, V. et al. Recommendations for myeloid-derived suppressor cell nomenclature and characterization standards. Nat. Commun. 7, 12150 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Guilliams, M. et al. Unsupervised high-dimensional analysis aligns dendritic cells across tissues and species. Immunity 45, 669–684 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Taieb, J. et al. A novel dendritic cell subset involved in tumor immunosurveillance. Nat. Med. 12, 214–219 (2006).

    CAS  PubMed  Google Scholar 

  36. 36.

    Caminschi, I. et al. Putative IKDCs are functionally and developmentally similar to natural killer cells, but not to dendritic cells. J. Exp. Med. 204, 2579–2590 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Krasselt, M., Baerwald, C., Wagner, U. & Rossol, M. CD56 + monocytes have a dysregulated cytokine response to lipopolysaccharide and accumulate in rheumatoid arthritis and immunosenescence. Arthritis Res. Ther. 15, R139 (2013).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Villani, A. C. et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356, eaah4573 (2017).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Grünewald, K. et al. Mammaglobin gene expression: a superior marker of breast cancer cells in peripheral blood in comparison to epidermal-growth-factor receptor and cytokeratin-19. Lab. Invest. 80, 1071–1077 (2000).

    PubMed  Google Scholar 

  40. 40.

    Han, J. H. et al. Mammaglobin expression in lymph nodes is an important marker of metastatic breast carcinoma. Arch. Pathol. Lab. Med. 127, 1330–1334 (2003).

    CAS  PubMed  Google Scholar 

  41. 41.

    Kowalewska, M., Chechlińska, M., Markowicz, S., Kober, P. & Nowak, R. The relevance of RT-PCR detection of disseminated tumour cells is hampered by the expression of markers regarded as tumour-specific in activated lymphocytes. Eur. J. Cancer 42, 2671–2674 (2006).

    CAS  PubMed  Google Scholar 

  42. 42.

    Novershtern, N., Regev, A. & Friedman, N. Physical module networks: an integrative approach for reconstructing transcription regulation. Bioinformatics 27, i177–i185 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–352 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Broz, M. L. et al. Dissecting the tumor myeloid compartment reveals rare activating antigen-presenting cells critical for T cell immunity. Cancer Cell 26, 638–652 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Galea, M. H., Blamey, R. W., Elston, C. E. & Ellis, I. O. The Nottingham Prognostic Index in primary breast cancer. Breast Cancer Res. Treat. 22, 207–219 (1992).

    CAS  PubMed  Google Scholar 

  46. 46.

    Gautier, E. L. et al. Gene-expression profiles and transcriptional regulatory pathways that underlie the identity and diversity of mouse tissue macrophages. Nat. Immunol. 13, 1118–1128 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Robbins, S. H. et al. Novel insights into the relationships between dendritic cell subsets in human and mouse revealed by genome-wide expression profiling. Genome Biol. 9, R17 (2008).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Franklin, R. A. et al. The cellular and molecular origin of tumor-associated macrophages. Science 344, 921–925 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Ojalvo, L. S., Whittaker, C. A., Condeelis, J. S. & Pollard, J. W. Gene expression analysis of macrophages that facilitate tumor invasion supports a role for Wnt-signaling in mediating their activity in primary mammary tumors. J. Immunol. 184, 702–712 (2010).

    CAS  PubMed  Google Scholar 

  50. 50.

    Pyfferoen, L. et al. The transcriptome of lung tumor-infiltrating dendritic cells reveals a tumor-supporting phenotype and a microRNA signature with negative impact on clinical outcome. OncoImmunology 6, e1253655 (2016).

    PubMed  PubMed Central  Google Scholar 

  51. 51.

    Wargo, J. A., Reddy, S. M., Reuben, A. & Sharma, P. Monitoring immune responses in the tumor microenvironment. Curr. Opin. Immunol. 41, 23–31 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Fuertes, M. B. et al. Host type I IFN signals are required for antitumor CD8+ T cell responses through CD8à+ dendritic cells. J. Exp. Med. 208, 2005–2016 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Salmon, H. et al. Expansion and activation of CD103+ dendritic cell progenitors at the tumor site enhances tumor responses to therapeutic PD-L1 and BRAF inhibition. Immunity 44, 924–938 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Gajewski, T. F., Schreiber, H. & Fu, Y. X. Innate and adaptive immune cells in the tumor microenvironment. Nat. Immunol. 14, 1014–1022 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Spranger, S. & Gajewski, T. F. Tumor-intrinsic oncogene pathways mediating immune avoidance. OncoImmunology 5, e1086862 (2015).

    PubMed  PubMed Central  Google Scholar 

  56. 56.

    Stanton, S. E., Adams, S. & Disis, M. L. Variation in the incidence and magnitude of tumor-infiltrating lymphocytes in breast cancer subtypes: a systematic review. JAMA Oncol. 2, 1354–1360 (2016).

    PubMed  Google Scholar 

  57. 57.

    Foulkes, W. D., Smith, I. E. & Reis-Filho, J. S. Triple-negative breast cancer. N. Engl. J. Med. 363, 1938–1948 (2010).

    CAS  PubMed  Google Scholar 

  58. 58.

    Bianchini, G., Balko, J. M., Mayer, I. A., Sanders, M. E. & Gianni, L. Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease. Nat. Rev. Clin. Oncol. 13, 674–690 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Carpentier, S. et al. Comparative genomics analysis of mononuclear phagocyte subsets confirms homology between lymphoid tissue-resident and dermal XCR1+ DCs in mouse and human and distinguishes them from Langerhans cells. J. Immunol. Methods 432, 35–49 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

    PubMed  PubMed Central  Google Scholar 

  61. 61.

    Anders, S., Pyl, P. T. & Huber, W. HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    CAS  Google Scholar 

  62. 62.

    Risso, D., Ngai, J., Speed, T. P. & Dudoit, S. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol. 32, 896–902 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Servant, N. et al. EMA - A R package for Easy Microarray data analysis. BMC Res. Notes 3, 277 (2010).

    PubMed  PubMed Central  Google Scholar 

  64. 64.

    Spinelli, L., Carpentier, S., Montañana Sanchis, F., Dalod, M. & Vu Manh, T. P. BubbleGUM: automatic extraction of phenotype molecular signatures and comprehensive visualization of multiple Gene Set Enrichment Analyses. BMC Genom. 16, 814 (2015).

    Google Scholar 

  65. 65.

    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Margolin, A. A. et al. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinforma. 7, S7 (2006).

    Google Scholar 

  67. 67.

    Basso, K. et al. Reverse engineering of regulatory networks in human B cells. Nat. Genet. 37, 382–390 (2005).

    CAS  PubMed  Google Scholar 

  68. 68.

    Joshi, A. et al. Technical Advance: Transcription factor, promoter, and enhancer utilization in human myeloid cells. J. Leukoc. Biol. 97, 985–995 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Bindea, G. et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25, 1091–1093 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

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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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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.

Corresponding author

Correspondence to Vassili Soumelis.

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Integrated supplementary information

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).

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