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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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’).
The authors declare no competing interests.
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Integrated supplementary information
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.
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).
a, Schema showing the pipeline and number of DEG obtained from each indicated APC from tumor TNBC versus tumor LBC.
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.
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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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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