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Immune landscape in invasive ductal and lobular breast cancer reveals a divergent macrophage-driven microenvironment

Nature Cancer volume 4, pages 516–534 (2023)Cite this article

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A Publisher Correction to this article was published on 03 April 2023

This article has been updated

Abstract

T cell-centric immunotherapies have shown modest clinical benefit thus far for estrogen receptor-positive (ER+) breast cancer. Despite accounting for 70% of all breast cancers, relatively little is known about the immunobiology of ER+ breast cancer in women with invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC). To investigate this, we performed phenotypic, transcriptional and functional analyses for a cohort of treatment-naive IDC (n = 94) and ILC (n = 87) tumors. We show that macrophages, and not T cells, are the predominant immune cells infiltrating the tumor bed and the most transcriptionally diverse cell subset between IDC and ILC. Analysis of cellular neighborhoods revealed an interplay between macrophages and T cells associated with longer disease-free survival in IDC but not ILC. Our datasets provide a rich resource for further interrogation into immune cell dynamics in ER+ IDC and ILC and highlight macrophages as a potential target for ER+ breast cancer.

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Fig. 1: Phenotypic and functional characterization of immune infiltrate in ER+ IDC and ILC.
Fig. 2: In situ spatial distribution and outcome association in ER+ IDC and ILC.
Fig. 3: Composition and potential impact of cellular neighborhoods on outcome.
Fig. 4: Transcriptional profile of CD45+ immune cells in ER+ IDC and ILC.
Fig. 5: Transcriptomic landscape of tumor-infiltrating monocytes and macrophages.
Fig. 6: Macrophage function and potential interaction with T cells in IDC and ILC.
Fig. 7: Enrichment of M2-like macrophages in ER+ ILC.

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Data availability

Raw FASTQ files for scRNA-seq data and processed feature barcode matrices for all scRNA-seq data have been deposited through the Gene Expression Omnibus (GEO) with accession number GSE193911. The human breast cancer bulk RNA-seq dataset derived from the TCGA Research Network was obtained from GEO (https://www.ncbi.nlm.nih.gov/geo) using accession number GSM1536836. RNA-seq data from METABRIC were obtained from cBioPortal for cancer genomics (https://www.cbioportal.org/study/summary?id=brca_metabric). Bulk RNA-seq data from SCAN-B were obtained from GEO with accession number GSE96058. Gene signatures from the MSigDB can be found on the database website (http://www.gsea-msigdb.org/gsea/msigdb). The following cell–cell interaction databases were used for scRNA-seq receptor–ligand analysis: celltalkDB human_lr_pair (http://tcm.zju.edu.cn/celltalkdb/download.php), CellCellInteractions receptor_ligand_interactions v1.0 (https://baderlab.org/CellCellInteractions) and CellTalker (https://arc85.github.io/celltalker/index.html). The remaining data are available within the article. Source data are provided with this paper.

Code availability

No custom code was used or developed for any of the analyses in our study. Standard workflows and open source software were used (Methods).

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References

  1. Gatti-Mays, M. E. et al. If we build it they will come: targeting the immune response to breast cancer. NPJ Breast Cancer 5, 37 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Emens, L. A. Breast cancer immunotherapy: facts and hopes. Clin. Cancer Res. 24, 511–520 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Barroso-Sousa, R. & Metzger-Filho, O. Differences between invasive lobular and invasive ductal carcinoma of the breast: results and therapeutic implications. Ther. Adv. Med. Oncol. 8, 261–266 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Pestalozzi, B. C. et al. Distinct clinical and prognostic features of infiltrating lobular carcinoma of the breast: combined results of 15 International Breast Cancer Study Group clinical trials. J. Clin. Oncol 26, 3006–3014 (2008).

    Article  PubMed  Google Scholar 

  5. Arpino, G., Bardou, V. J., Clark, G. M. & Elledge, R. M. Infiltrating lobular carcinoma of the breast: tumor characteristics and clinical outcome. Breast Cancer Res. 6, R149–R156 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Yayoi, A. et al. Comparison of clinical outcomes between luminal invasive ductal carcinoma and luminal invasive lobular carcinoma. BMC Cancer 16, 248 (2016).

    Article  Google Scholar 

  7. Bense, R. D. et al. Relevance of tumor-infiltrating immune cell composition and functionality for disease outcome in breast cancer. J. Natl Cancer Inst. 109, djw192 (2017).

    Article  PubMed  Google Scholar 

  8. Denkert, C. et al. Tumor-associated lymphocytes as an independent predictor of response to neoadjuvant chemotherapy in breast cancer. J. Clin. Oncol. 28, 105–113 (2010).

    Article  CAS  PubMed  Google Scholar 

  9. Loi, S. et al. Tumor infiltrating lymphocytes are prognostic in triple negative breast cancer and predictive for trastuzumab benefit in early breast cancer: results from the FinHER trial. Ann. Oncol. 25, 1544–1550 (2014).

    Article  CAS  PubMed  Google Scholar 

  10. Loi, S. et al. Prognostic and predictive value of tumor-infiltrating lymphocytes in a phase III randomized adjuvant breast cancer trial in node-positive breast cancer comparing the addition of docetaxel to doxorubicin with doxorubicin-based chemotherapy: BIG 02-98. J. Clin. Oncol. 31, 860–867 (2013).

    Article  CAS  PubMed  Google Scholar 

  11. Desmedt, C. et al. Immune infiltration in invasive lobular breast cancer. J. Natl Cancer Inst. 110, 768–776 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Wagner, J. et al. A single-cell atlas of the tumor and immune ecosystem of human breast. Cancer Cell 177, 1330–1345 (2019).

    CAS  Google Scholar 

  13. Michaut, M. et al. Integration of genomic, transcriptomic and proteomic data identifies two biologically distinct subtypes of invasive lobular breast cancer. Sci. Rep. 6, 18517 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ciriello, G. et al. Comprehensive molecular portraits of invasive lobular breast cancer. Cell 163, 506–519 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Sobral-Leite, M. et al. Assessment of PD-L1 expression across breast cancer molecular subtypes, in relation to mutation rate, BRCA1-like status, tumor-infiltrating immune cells and survival. Oncoimmunology 7, e1509820 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Rugo, H. S. et al. Safety and antitumor activity of pembrolizumab in patients with estrogen receptor-positive/human epidermal growth factor receptor 2-negative advanced breast cancer. Clin. Cancer Res. 24, 2804–2811 (2018).

    Article  CAS  PubMed  Google Scholar 

  17. Nanda, R. et al. Effect of pembrolizumab plus neoadjuvant chemotherapy on pathologic complete response in women with early-stage breast cancer: an analysis of the ongoing phase 2 adaptively randomized I-SPY2 trial. JAMA Oncol. 6, 676–684 (2020).

    Article  PubMed  Google Scholar 

  18. Dirix, L. Y. et al. Avelumab, an anti-PD-L1 antibody, in patients with locally advanced or metastatic breast cancer: a phase 1b JAVELIN solid tumor study. Breast Cancer Res. Treat. 125, 65–72 (2018).

    Google Scholar 

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

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Hollern, D. P. et al. B cells and T follicular helper cells mediate response to checkpoint inhibitors in high mutation burden mouse models of breast cancer. Cell 179, 1191–1206 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Cassetta, L. & Pollard, J. Targeting macrophages: therapeutic approaches in cancer. Nat. Rev. Drug Discov. 17, 887–904 (2018).

    Article  CAS  PubMed  Google Scholar 

  23. Chung, W. et al. Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer. Nat. Commun. 8, 15081 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Keren, L., Bosse, M., Marquez, D. & Angoshtari, R. A structured tumor–immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 174, 1373–1387 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Gruosso, T., Gigoux, M., Manem, V. S. K. & Bertos, N. Spatially distinct tumor immune microenvironments stratify triple-negative breast cancers. J. Clin. Invest. 129, 1785–1800 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Kuroda, H. et al. Tumor-infiltrating B cells and T cells correlate with postoperative prognosis in triple-negative carcinoma of the breast. BMC Cancer 21, 286 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Goldberg, J. et al. The immunology of hormone receptor positive breast cancer. Front. Immunol. 12, 674192 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Kunert, A. et al. CD45RA+CCR7− CD8 T cells lacking co-stimulatory receptors demonstrate enhanced frequency in peripheral blood of NSCLC patients responding to nivolumab. J. Immunother. Cancer 7, 149 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Bernal-Estévez, D., Sánchez, R., Tejada, R. E. & Parra-López, C. Chemotherapy and radiation therapy elicits tumor specific T cell responses in a breast cancer patient. BMC Cancer 16, 591 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Kuznetsova, M. et al. Cytotoxic activity and memory T cell subset distribution of in vitro-stimulated CD8+ T cells specific for HER2/neu epitopes. Front. Immunol. 10, 1017 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Egelston, C. A. et al. Human breast tumor-infiltrating CD8+ T cells retain polyfunctionality despite PD-1 expression. Nat. Commun. 9, 4297 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Cheng, S. et al. A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells. Cell 184, 792–809 (2021).

    Article  CAS  PubMed  Google Scholar 

  33. Reed, A. E. M., Kutasovic, J. R., Lakhani, S. R. & Simpson, P. T. Invasive lobular carcinoma of the breast: morphology, biomarkers and ‘omics. Breast Cancer Res. 17, 12 (2015).

    Article  PubMed Central  Google Scholar 

  34. Christgen, M. & Derksen, P. Lobular brast cancer: molecular basis, mouse and cellular models. Breast Cancer Res. 17, 16 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Oshi, M. et al. CD8 T cell score as a prognostic biomarker for triple negative breast cancer. Int. J. Mol. Sci. 21, 6968 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Schürch, C. M. et al. Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell 182, 1341–1359 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Richard, F. et al. Characterization of stromal tumor-infiltrating lymphocytes and genomic alterations in metastatic lobular breast cancer. Clin. Cancer Res. 26, 6254–6265 (2020).

    Article  CAS  PubMed  Google Scholar 

  38. Cillo, A. R. et al. Immune landscape of viral- and carcinogen-driven head and neck cancer. Immunity 52, 183–199 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Crowe, L. A. N. et al. S100A8 & S100A9: alarmin mediated inflammation in tendinopathy. Sci. Rep. 9, 1463 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Xia, C., Braunstein, Z., Toomey, A. C., Zhong, J. & Rao, X. S100 proteins as an important regulator of macrophage inflammation. Front. Immunol. 8, 1908 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Dhakal, B. et al. Versican proteolysis predicts immune effector infiltration and post-transplant survival in myeloma. Leuk. Lymphoma 60, 2558–2562 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Wight, T. N. et al. Veriscan—a critical extracellular matrix regulator of immunity and inflammation. Front. Immunol. 11, 512 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Peranzoni, E. et al. Macrophages impede CD8 T cells from reaching tumor cells and limit the efficacy of anti-PD-1 treatment. Proc. Natl Acad. Sci. USA 115, E4041–E4050 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Rahman, M. et al. Alternative preprocessing of RNA-sequencing data in The Cancer Genome Atlas leads to improved analysis results. Bioinformatics 31, 3666–3672 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Brueffer, C. et al. Clinical value of RNA sequencing-based classifiers for prediction of the five conventional breast cancer biomarkers: a report from the population-based multicenter Sweden Cancerome Analysis Network-Breast initiative. JCO Precis. Oncol. 2, PO.17.00135 (2018).

    PubMed  PubMed Central  Google Scholar 

  47. Kurowska-Stolarska, M. et al. IL-33 amplifies the polarization of alternatively activated macrophages that contribute to airway inflammation. J. Immunol. 183, 6469–6477 (2009).

    Article  CAS  PubMed  Google Scholar 

  48. Ruckert, R. et al. Interleukin-15 stimulates macrophages to activate CD4+ T cells: a role in pathgenesis of rheumatoid arthritis? Immunology 126, 63–73 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Garaud, S. et al. Tumor-infiltrating B cells signal functional humoral immune responses in breast cancer. JCI Insight 4, e129641 (2019).

    Article  PubMed Central  Google Scholar 

  50. Chauvin, J. M. & Zarour, H. TIGIT in cancer immunotherapy. J. Immunother. Cancer 8, e000957 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Ruffo, E., Wu, R. C., Bruno, T. C., Workman, C. J. & Vignali, D. A. A. Lymphocyte-activation gene 3 (LAG3): the next immune checkpoint receptor. Semin. Immunol. 42, 101305 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Acharya, N., Sabatos-Peyton, C. & Anderson, A. C. Tim-3 finds its place in the cancer immunotherapy landscape. J. Immunother. Cancer. 8, e000911 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Azizi, E. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 1293–1308 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Deligne, C. & Midwood, K. Macrophages and extracellular matrix in breast cancer: partners in crime or protective allies? Front. Oncol. 11, 620773 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Wu, S. Z. et al. A single-cell and spatially resolved atlas of human breast cancers. Nat. Genet. 53, 1334–1347 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Pal, B. et al. A single-cell RNA expression atlas of normal, preneoplastic and tumorigenic states in the human breast. EMBO J. 40, e107333 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Guerriero, J. L. et al. Class IIa HDAC inhibition reduces breast tumours and metastases through anti-tumour macrophages. Nature 543, 428–432 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Molgora, M. et al. TREM2 modulation remodels the tumor myeloid landscape enhancing anti-PD-1 immunotherapy. Cell 182, 886–900 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Lin, F. et al. A novel blockade CD47 antibody with therapeutic potential for cancer. Front. Oncol. 10, 615534 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  60. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Bergen, V., Lange, M., Peidli, S., Wolf, A. F. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38, 1408–1414 (2020).

    Article  CAS  PubMed  Google Scholar 

  62. Ramilowski, J. et al. A draft network of ligand–receptor-mediated multicellular signalling in human. Nat. Commun. 6, 7866 (2015).

    Article  CAS  PubMed  Google Scholar 

  63. Shao, X. et al. CellTalkDB: a manually curated database of ligand–receptor interactions in humans and mice. Brief. Bioinform. 33, bbaa269 (2021).

    Article  Google Scholar 

  64. Jayasingam, S. D. et al. Evaluating the polarization of tumor-associated macrophages into M1 and M2 phenotypes in human cancer tissue: technicalities and challenges in routine clinical practice. Front. Oncol. 9, 1512 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Wright, P. B. et al. The mannose receptor (CD206) identifies a population of colonic macrophages in health and inflammatory bowel disease. Sci. Rep. 11, 19616 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We would like to thank everyone in the Vignali (vignali-lab.com; @Vignali_Lab), Bruno (@BcellBruno) and Lee-Oesterreich labs (leeoesterreich.org) for all their constructive comments and advice during this project. We thank G. Carter, E. Kalanja, C. Kline, N. Roehrig, H. Havrilla, C. Mongelli and J. Tarr for collection of human samples at the University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center and Tissue and Research Pathology/Pitt Biospecimen Core shared resource, which is supported in part by award P30CA047904. This work used the UPMC Hillman Cancer Center Flow Cytometry facility, a shared resource at the University of Pittsburgh supported by the Cancer Center Support Grant P30CA047904. We thank P. Dascani and M. Meyer for cell sorting support, J. Xavier (Lee-Oesterreich lab), V. Mazzarella and S. Winters (UPMC Cancer Registry Network) for their critical support with clinical data acquisition, Y. Li (Tseng lab, University of Pittsburgh School of Public Health) for extending expertise on statistical analyses, the University of Colorado Human Immune Monitoring Shared Research Facility (A. Minic and K. Jordan) for mIHC (Vectra) imaging and University of Pittsburgh Genomics Core and Center for Research Computing (RRID: SCR_022735) for RNA-seq services and computational resources supported by NIH award number S10OD028483. This work was supported by the National Institutes of Health (R35 CA263850, R01 CA203689 and P01 AI108545 (D.A.A.V.)), the Cancer Immunology Training Program T32 (T32 CA082084 (D.A.A.V.), awarded to A.R.C.), Hillman Postdoctoral Fellowship for Innovative Cancer Research (A.R.C.), NIH R01 CA252378 (S. Oesterreich and A.V.L.)), BCRF (S. Oesterreich and A.V.L.), The Shear Family Foundation and Magee Womens Research Institute and Foundation, NIH (R35GM146989 (H.U.O.) and T15 LM007059-35 (A.S.)). The funders had no role in study design, data collection and analysis, decision to publish or preparation of this paper. The NSABP Foundation and the Pennsylvania Department of Health specifically disclaim responsibility for any analysis, interpretations or conclusions.

Author information

Author notes

  1. These authors jointly supervised this work: Steffi Oesterreich, Dario A. A.Vignali.

Authors and Affiliations

  1. Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    Sayali Onkar, Jian Cui, Carly Cardello, Anthony R. Cillo, Tullia C. Bruno & Dario A. A. Vignali

  2. Tumor Microenvironment Center, UPMC Hillman Cancer Center, Pittsburgh, PA, USA

    Sayali Onkar, Jian Cui, Carly Cardello, Anthony R. Cillo, Tullia C. Bruno & Dario A. A. Vignali

  3. Graduate Program in Microbiology and Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    Sayali Onkar

  4. Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA

    Jian Zou & George C. Tseng

  5. Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA

    Mostofa Rafid Uddin

  6. Joint Carnegie Mellon University–University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA

    Mostofa Rafid Uddin

  7. Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA

    April Sagan & Hatice U. Osmanbeyoglu

  8. UPMC Hillman Cancer Center, Pittsburgh, PA, USA

    April Sagan, Marion Joy & Peter C. Lucas

  9. NSABP Foundation, Pittsburgh, PA, USA

    Marion Joy, Katherine L. Pogue-Geile & Peter C. Lucas

  10. Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA

    Hatice U. Osmanbeyoglu

  11. Section of Breast Surgery, Division of Surgical Oncology, Department of Surgery, University of Pittsburgh College of Medicine, Magee Women’s Hospital of UPMC, Pittsburgh, PA, USA

    Priscilla F. McAuliffe

  12. Women’s Cancer Research Center, UPMC Hillman Cancer Center, Magee-Womens Research Institute, University of Pittsburgh, Pittsburgh, PA, USA

    Priscilla F. McAuliffe, Adrian V. Lee & Steffi Oesterreich

  13. Department of Pathology, University of Pittsburgh, Pittsburgh, PA, USA

    Peter C. Lucas

  14. Cancer Biology Program, UPMC Hillman Cancer Center, Pittsburgh, PA, USA

    Peter C. Lucas, Adrian V. Lee & Steffi Oesterreich

  15. Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA, USA

    Adrian V. Lee & Steffi Oesterreich

  16. Cancer Immunology and Immunotherapy Program, UPMC Hillman Cancer Center, Pittsburgh, PA, USA

    Tullia C. Bruno & Dario A. A. Vignali

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Contributions

S. Oesterreich and D.A.A.V. conceptualized the study and provided funding. S. Oesterreich, D.A.A.V., T.C.B. and S. Onkar developed strategies for experiments. S. Onkar performed human sample processing, experimental methods and data analysis for flow cytometry, mIHC and in vitro cell culture experiments with statistical analysis. C.C. prepared samples for scRNA-seq, and J.C. performed analysis with input from A.R.C. J.Z. performed clinical correlates, survival and associated statistical analyses under guidance from G.C.T. M.J. designed mIHC panels used in the study (under guidance from K.L.P.-G.). M.R.U. and A.S. performed computational neighborhood and predictive modeling analysis of mIHC data under guidance from H.U.O. P.F.M. and P.C.L. facilitated surgical sample selection and provided pathology review/expertise. S. Onkar, T.C.B., S. Oesterreich and D.A.A.V. interpreted data, and A.V.L. provided critical feedback. S. Onkar wrote the original draft. S. Oesterreich, D.A.A.V. and S. Onkar revised and edited the paper. All authors reviewed and approved the paper.

Corresponding authors

Correspondence to Steffi Oesterreich or Dario A. A. Vignali.

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Competing interests

D.A.A.V. discloses the following conflicts: cofounder and stockholder: Novasenta, Potenza, Tizona and Trishula; stockholder: Oncorus and Werewolf; patents licensed and royalties: Astellas, BMS and Novasenta; scientific advisory board member: Tizona, Werewolf, F-Star, Bicara, Apeximmune and T7/Imreg Bio; consultant: Astellas, BMS, Almirall, Incyte, G1 Therapeutics, Inzen Therapeutics, Regeneron and Avidity Partners; research funding: BMS and Novasenta. All authors declare no competing financial or non-financial interests in relation to the work submitted in this paper.

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Nature Cancer thanks Marleen Kok, Elizabeth Mittendorf and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Flow cytometry panel, gating and immune cell distributions.

(a) Representative flow cytometry plots with gating strategy used for lymphoid panel (left) and myeloid panel (right) (b) Marker combinations used to define immune cell populations in lymphoid cell panel (left) and myeloid cell panel (right) (c) Intra-patient lymphoid cell frequencies in ER+ IDC TA (n = 27) and matched tumor (n = 29) (left panel) and ER+ ILC TA (n = 23) and matched tumor (n = 24) (right panel). Paired two-sided Wilcoxon rank-sum test was used for statistical analysis. Exact p values are- B cells (p = 0.000153), CD4+ T cells (p = 0.000551), CD8+ T cells(p = 0.000607) and Tregs (p = 0.000016) (d) Intra- patient myeloid cell frequencies in ER+ IDC TA (n = 19) and matched tumor (n = 23) (left panel) and ER+ ILC TA (n = 21) and matched tumor (n = 23) (right panel). Paired two-sided Wilcoxon rank-sum test was used for statistical analysis (e) Comparison of lymphoid infiltrate in ER+ IDC (n = 29) vs ILC (n = 24) tumor tissues. Non-parametric two-sided Mann-Whitney test was used for statistical analysis (f) Comparison of myeloid infiltrate in ER+ IDC (n = 23) vs ILC (n = 23) tumor tissues. Non-parametric two-sided Mann-Whitney test was used for statistical analysis.

Extended Data Fig. 2 T cell characteristics in tumor and blood.

(a) Stacked bar graph demonstrating patient-wise immune cell composition of ER+ IDC tumors (n = 29) (b)Stacked bar graph demonstrating patient-wise immune cell composition of ER+ ILC tumors (n = 24) (c) Pie chart depicting the median percent of T cell functional phenotype composition for CD4+ Tconv (left) and CD8+ T cells (right) in ER+ TIL (top panel) and ER+ PBL (bottom panel) (d) Profile of inhibitory receptors PD-1, TIGIT, LAG3, TIM3 and CTLA4 expression on CD8+ T cells in ER+ IDC PBL (n = 34 samples) and TIL (n = 25 samples) (left panel) and ER+ ILC PBL (n = 10 samples) and TIL (n = 10 samples) right panel. Dotted line along Y axis marks 10% of all cells. Two-sided paired Wilcoxon Rank Test was used for statistical analysis. (e) Profile of inhibitory receptors PD-1, TIGIT, LAG3, TIM3 and CTLA4 expression on CD4+ T cells in ER+ IDC PBL (n = 34 samples) and TIL (n = 25 samples) (left panel) and ER+ ILC PBL (n = 10 samples) and TIL (n = 10 samples) right panel. Dotted line along Y axis denoted 10% of all cells. Two-sided paired Wilcoxon Rank Test was used for statistical analysis.

Extended Data Fig. 3 Clinical correlate analysis for flow cohort.

(a) Correlation of pathological grade with total percent CD45+ immune infiltrate (top left), CD4 T cells (top right), CD8 T cells (middle left), macrophages (middle right), M1-like macs (bottom left) and M2-like macs (bottom right) for all ER+ tumors (n = 39) and grade-matched ER+ IDC (n = 20) and ILC (n = 19) samples used in the flow cohort. Center of the box plot represents median with 25th and 75th percentile bounds of the box and whiskers representing least and highest value in dataset. Two-sided non-parametric Wilcoxon rank sum or Kruskal-Wallis test were used for statistical analysis without correction. (b) Correlation of ER IHC scores with percent total CD45+ immune infiltrate for all ER+ tumors (n = 41) and for ER+ IDC tumors (n = 22), ILC tumors (n = 19). Spearman correlation was calculated, and asymptomatic t-distribution approximation was used for statistical analysis with p < 0.05 for significance (c) ER IHC score in IDC and ILC for the entire cohort. Center of the box plot represents median with 25th and 75th percentile bounds of the box and whiskers representing least and highest value in dataset. Two-sided non-parametric Mann Whitney test was used for statistical analysis without correction and p < 0.05 considered significant.

Extended Data Fig. 4 Multispectral imaging workflow and spatial distribution comparisons.

(a) Schematic showing workflow for multispectral immunohistochemistry imaging panel and downstream analysis using Biorender.com (b) Composite and single channel images for each marker used in the panel to demonstrate marker specificity and quality of unmixing. Images represent a single region of interest (ROI) with fairly consistent results across each channel observed for all ROIs (>1200) in the mIHC dataset (c) Median cell frequencies for immune cell subsets by mIHC in ER+ tumors (n = 115 patients) compared to TNBC (n = 21 patients) in stroma (left panel) and tumor bed (right panel). Each circle represents the median value across all ROIs for each patient and bar represents group median. Multiple Mann Whitney non-parametric two-sided T test was used for statistical analysis with Holm-Sidak correction for multiple comparison.(d) Median immune cell frequency distributions in ER+ IDC and ILC and TNBC after excluding HER2+ER+ IDC and ILC sample across stromal (left panel) and tumor (right panel) compartments. Each circle represents the median value across all ROIs for each patient and bar represents group median. Multiple Mann Whitney non-parametric two-sided T test was used for statistical analysis with Holm-Sidak correction for multiple comparison. (e) Pie charts demonstrating median composition of tumor cells and immune cells across all ROIs in ER+IDC (n = 50 patients) (top panel), ILC (n = 65 patients) (middle panel) and TNBC (n = 21 patients) (bottom panel).

Extended Data Fig. 5 Concordance between flow, mIHC cohort and association of cell frequencies with outcome.

(a) Trends for frequency of B cells, CD4+ T cell, CD8+ T cell, Treg and macrophages in ER+ IDC and ILC by flow cytometry (left panel) and mIHC (right panel) (b) Median immune cell frequencies for ER+ IDC recurrence (n = 17) compared to non-recurrence(n = 20) in tumor bed (top panel) and stroma (bottom panel). Each circle represents the median value across all regions of interest (ROIs) for each patient and bar represents group median. Multiple Mann Whitney non-parametric two-sided T test was used for statistical analysis with Holm-Sidak correction for multiple comparison (c) Median immune cell frequencies for ER+ ILC recurrence (n = 21) compared to non-recurrences (n = 30) in tumor bed (top panel) and stroma (bottom panel). Each circle represents the median value across all ROIs for each patient and bar represents group median. Multiple Mann Whitney non-parametric two-sided T test was used for statistical analysis with Holm-Sidak correction for multiple comparison. (d) Median M2-like (CD163+CD68+) and M1-like (MHCII+CD68+) frequencies for ER+ IDC recurrence (n = 17) compared to non-recurrences (n = 20) in tumor bed (left panel) and ER+ ILC recurrence (n = 11) compared to non-recurrences (n = 20) (right panel). Each circle represents the median value across all ROIs for each patient and bar represents group median. Mann Whitney non-parametric two-sided T test was used for statistical testing. (e) Tumoral M2: M1 ratio in ER+ IDC ER+ IDC recurrence (n = 17) compared to non-recurrences (n = 20) (left panel) and in ER+ ILC recurrence (n = 11) compared to non-recurrences (n = 20) (right panel). Circles represents M2:M1 ratio for each patient. Mann Whitney non-parametric two-sided T test was used for statistical testing.

Extended Data Fig. 6 Distribution of immune cells across different tumor grades for mIHC cohort.

(a)Table listing p values for correlation of individual immune cell subsets like CD4, CD8 T cells, total macrophages and M1-like and M2-like macrophage with distribution across grade within IDC and ILC. Grade1 (IDC n = 3, ILC n = 3 patients), grade 2 (IDC n = 30, ILC n = 29 patients), grade 3 (IDC n = 16, ILC n = 3 patients). Two-sided Kruskal Wallis test was used for statistical analysis with p < 0.05 considered significant. (b) Correlation of immune cell frequencies in the tumor compartment with pathological grade in all biologically independent ER+ patient samples and across grades 1,2 and 3 in ER+ IDC vs ILC. Center of the box plot represents median with 25th and 75th percentile bounds of the box and whiskers representing least and highest value in dataset. Two-sided Kruskal- Wallis test was used for statistical analysis with p < 0.05 considered significant.(c) Correlation of immune cell frequencies in the stromal compartment with pathological grade in all biologically independent ER+ patient samples and across grades 1,2 and 3 in ER+ IDC vs ILC. Center of the box plot represents median with 25th and 75th percentile bounds of the box and whiskers representing least and highest value in dataset. Kruskal- Wallis test was used for statistical analysis with p < 0.05 considered significant.

Extended Data Fig. 7 Neighborhood analysis, validation and outcome association.

(a) Schematic representing process of identification of cellular neighborhoods with representative images for ER+ IDC, ILC and TNBC (b) False color image example of cellular neighborhood composition (left panel) and cell type distribution (right panel) along the X, Y coordinates for cells within a ROI (c) Types of cellular neighborhoods with a distance threshold of 20 μm instead of 50 μm (Fig. 2c) and relative enrichment above or below mean across neighborhoods for B cells (CD20+), CD8+ T cells, CD4+ T cells, Tregs (Foxp3+), tumor cells (PanCK+) and macrophages (CD68+). Likelihood of enrichment calculated as log odds ratio normalized between 5 and −5 (d) Frequency distribution of cellular neighborhoods at distance threshold 20 μm within ER+ IDC (n = 50) and ER+ ILC (n = 65). Two tailed Mann Whitney non-parametric T test was used for statistical analysis with p < 0.05 considered significant. (e) Types of cellular neighborhoods with a distance threshold of 70 μm instead of 50 μm (Fig. 2C) and relative enrichment above or below mean across neighborhoods for B cells (CD20+), CD8+ T cells, CD4+ T cells, Tregs (Foxp3+), tumor cells (PanCK+) and macrophages (CD68+). Likelihood of enrichment calculated as log odds ratio normalized between 5 and −5 (f) Frequency distribution of cellular neighborhoods at distance threshold 70 μm within ER+ IDC (n = 50 samples) and ER+ ILC (n = 65 samples). Two tailed Mann Whitney non-parametric T test was used for statistical analysis. (g) Boxplot visualizing individual patient CN frequencies across ROIs and associated tables listing correlation values for individual ROIs (ranging between 3–19) with median value of neighborhood frequency for the patient as a measure of intrapatient heterogeneity in ER+ IDC (left panel) and ILC (right panel). Box plots bound the first and third quartile, with the center representing the median and whiskers representing the minimum and maximum values. Chi- square test (H-statistic) was performed using Kruskal Wallis test for ILC (n = 552 ROIs, 64 degrees of freedom) and IDC (n = 479 ROIs, 49 degrees of freedom) (h) Cox-proportional hazards model for overall survival (OS) against log frequency of individual cellular neighborhoods (CNs) as variables adjusting for tumor grade in ER+ IDC (n = 50 samples) (top panel) and ER+ ILC (n = 62 samples) (bottom panel). Log Hazard ratios with 95% confidence interval, error bars show 2.5% (lower) and 97.5% (higher) bounds of the confidence Interval. Wald test was implemented to test whether HR=1 without multiple comparison adjustment, p values for significance listed for each parameter. Red box highlights significant association between OS and given variable.

Extended Data Fig. 8 Logistical regression model data.

(a) Table summarizing results of regression models using different features and their associated AUCROC and accuracy for following comparisons- IDC recurrence (n = 17) vs IDC non-recurrence (n = 20) samples, ILC recurrence (n = 21) vs non-recurrence (n = 30) samples, all ER+ recurrence (n = 38) and non-recurrence (n = 50) samples and IDC (n = 50) and ILC (n = 65) samples. Significant features with predictive value are highlighted in bold in blue color. (b) AUC-ROC curves across 5-folds of cross validation (colors represent folds of cross-validation) for ER+ IDC (n = 50 samples) vs ILC (n = 65 samples) subtype classification using neighborhood type (NT) frequencies as a feature and table of model weights associated with features of importance for classification model using neighborhood type frequencies (c) AUC-ROC curves across 5-folds of cross validation (colors represent folds of cross-validation) for ER+ IDC (n = 50 samples) vs ILC (n = 65 samples) subtype classification using cell type (CT) frequencies as a feature and associated table of model weights associated with features of importance (d) AUC-ROC curves across 5-folds of cross validation (colors represent folds of cross-validation) for ER+ IDC (n = 50 samples) vs ILC (n = 65 samples) subtype classification using neighborhood type (NT) and cell type (CT) frequencies together (NT+CT) as a feature and associated table of model weights associated with features of importance.

Extended Data Fig. 9 scRNAseq cluster characteristics.

(a) Density UMAP representing differential cell densities in ER+ TIL (n = 14 samples, top panel) and ER+ PBL (n = 15 samples, bottom panel) (b) Inter patient heterogeneity in percent contribution of immune cell subsets in ER+ IDC and ILC TIL (n = 14, top panel) and ER+ IDC and ILC PBL (n = 15, bottom panel). Percent contributions for cell subsets normalized to total number of cells recovered from each patient. (c) Density UMAP representing differential cell densities in ER+ IDC (middle panel) vs ER+ ILC (right panel) with original tumor infiltrating macrophages UMAP projection as reference (left panel). (d) Table listing top 20 significant genes driving the latent time trajectory of tumor infiltrating macrophages in their respective clusters.

Extended Data Fig. 10 Cytokine and chemokine analysis of tumor cell lines.

(a)Table containing a curated list of cytokines and chemokines directly relevant to monocyte/macrophage polarization, activation and impact on T cell (b) Expression of monocyte and macrophage- related chemokine and cytokine genes in ER+ IDC (n = 532) and ILC (n = 180) patient samples from The Cancer Genome Atlas (TCGA) cohort. Table lists log 2 TPM normalized counts with standard deviation. (c) Expression of monocyte and macrophage- related chemokine and cytokine genes in ER+ IDC (n = 1098) and ILC (n = 122) patient samples from Metabric cohort. Table lists log 2 TPM quantile normalized counts with standard deviation. (d) Expression of monocyte and macrophage- related chemokine and cytokine genes in ER+ IDC (n = 532) and ILC (n = 180) patient samples from SCAN-B cohort. Table lists log 2 FPKM normalized counts with standard deviation. For (b),(c) and (d) highlighted in yellow are the concordant genes found to have a consistent pattern across TCGA, Metabric and SCAN-B cohorts (e) Analysis of cytokine and chemokine concentrations (pg/ ml) using MSD platform in conditioned media collected from ER+ IDC cell lines (n = 4: T47D, MCF7, BT474, ZR75-1) and ER+ ILC cell lines (n = 4: SUM44, MM134, MM330, BCK4) used in macrophage polarization experiments shown in Fig. 7c,d. Values in red denote group medians for each analyte. Non-parametric Mann-Whitney two-sided T test was used for statistical analysis (f) Histograms showing comparison of median fluorescence intensity (MFI) for HLA-DR expression in monocytes subjected to control (M1 or M2) polarizing conditions or test with IL-15 or IL-33 across 3 healthy donor samples. (g) Histograms showing comparison of median fluorescence intensity (MFI) for CD206 expression in monocytes subjected to control (M1 or M2) polarizing conditions or test with IL-15 or IL-33 across 3 healthy donor samples.

Supplementary information

Reporting Summary

Supplementary Tables 1–5

Tables describing cohort characteristics for flow, mIHC and scRNA-seq datasets and list of antibodies.

Source data

Source Data

Statistical source data as a single file with separate tabs labeled for each panel in the main and extended figures.

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Onkar, S., Cui, J., Zou, J. et al. Immune landscape in invasive ductal and lobular breast cancer reveals a divergent macrophage-driven microenvironment. Nat Cancer 4, 516–534 (2023). https://doi.org/10.1038/s43018-023-00527-w

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