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The single-cell pathology landscape of breast cancer

Abstract

Single-cell analyses have revealed extensive heterogeneity between and within human tumours1,2,3,4, but complex single-cell phenotypes and their spatial context are not at present reflected in the histological stratification that is the foundation of many clinical decisions. Here we use imaging mass cytometry5 to simultaneously quantify 35 biomarkers, resulting in 720 high-dimensional pathology images of tumour tissue from 352 patients with breast cancer, with long-term survival data available for 281 patients. Spatially resolved, single-cell analysis identified the phenotypes of tumour and stromal single cells, their organization and their heterogeneity, and enabled the cellular architecture of breast cancer tissue to be characterized on the basis of cellular composition and tissue organization. Our analysis reveals multicellular features of the tumour microenvironment and novel subgroups of breast cancer that are associated with distinct clinical outcomes. Thus, spatially resolved, single-cell analysis can characterize intratumour phenotypic heterogeneity in a disease-relevant manner, with the potential to inform patient-specific diagnosis.

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Fig. 1: Single-cell phenotypes in high-dimensional histopathology of breast cancer.
Fig. 2: A global map of the cellular neighbourhoods and interaction networks of breast cancer.
Fig. 3: Single-cell pathology identifies subgroups of patients with breast cancer.
Fig. 4: Single-cell pathology subgroups have distinct clinical outcomes.

Data availability

The data supporting the findings of this study (including high-dimensional TIFF images, single-cell and tumour and stroma masks, single-cell and patient data) are available online at Zenodo (https://doi.org/10.5281/zenodo.3518284).

Code availability

All of the code used to produce the results of this study is available at https://github.com/BodenmillerGroup/SCPathology_publication.

References

  1. 1.

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

    CAS  Article  Google Scholar 

  2. 2.

    Bedard, P. L., Hansen, A. R., Ratain, M. J. & Siu, L. L. Tumour heterogeneity in the clinic. Nature 501, 355–364 (2013).

    CAS  ADS  Article  Google Scholar 

  3. 3.

    Dagogo-Jack, I. & Shaw, A. T. Tumour heterogeneity and resistance to cancer therapies. Nat. Rev. Clin. Oncol. 15, 81–94 (2018).

    CAS  Article  Google Scholar 

  4. 4.

    McGranahan, N. & Swanton, C. Biological and therapeutic impact of intratumor heterogeneity in cancer evolution. Cancer Cell 27, 15–26 (2015).

    CAS  Article  Google Scholar 

  5. 5.

    Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417–422 (2014).

    CAS  Article  Google Scholar 

  6. 6.

    Coates, A. S. et al. Tailoring therapies—improving the management of early breast cancer: St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2015. Ann. Oncol. 26, 1533–1546 (2015).

    CAS  Article  Google Scholar 

  7. 7.

    Hammond, M. E. H. et al. American Society of Clinical Oncology/College Of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. J. Clin. Oncol. 28, 2784–2795 (2010).

    Article  Google Scholar 

  8. 8.

    Wolff, A. C. et al. Human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline focused update. J. Clin. Oncol. 36, 2105–2122 (2018).

    CAS  Article  Google Scholar 

  9. 9.

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

    CAS  Article  Google Scholar 

  10. 10.

    Sørlie, T. et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl Acad. Sci. USA 98, 10869–10874 (2001).

    ADS  Article  Google Scholar 

  11. 11.

    Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).

    ADS  Article  Google Scholar 

  12. 12.

    Beck, A. H. et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci. Transl. Med. 3, 108ra113 (2011).

    Article  Google Scholar 

  13. 13.

    Focke, C. M., Decker, T. & van Diest, P. J. Intratumoral heterogeneity of Ki67 expression in early breast cancers exceeds variability between individual tumours. Histopathology 69, 849–861 (2016).

    Article  Google Scholar 

  14. 14.

    Rye, I. H. et al. Intratumor heterogeneity defines treatment-resistant HER2+ breast tumors. Mol. Oncol. 12, 1838–1855 (2018).

    CAS  Article  Google Scholar 

  15. 15.

    Angelo, M. et al. Multiplexed ion beam imaging of human breast tumors. Nat. Med. 20, 436–442 (2014).

    CAS  Article  Google Scholar 

  16. 16.

    Gerdes, M. J. et al. Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue. Proc. Natl Acad. Sci. USA 110, 11982–11987 (2013).

    CAS  ADS  Article  Google Scholar 

  17. 17.

    Schapiro, D. et al. histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data. Nat. Methods 14, 873–876 (2017).

    CAS  Article  Google Scholar 

  18. 18.

    Keren, L. et al. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 174, 1373–1387 (2018).

    CAS  Article  Google Scholar 

  19. 19.

    Carvajal-Hausdorf, D. E. et al. Multiplexed (18-plex) measurement of signaling targets and cytotoxic T cells in trastuzumab-treated patients using imaging mass cytometry. Clin. Cancer Res. 25, 3054–3062 (2019).

    CAS  Article  Google Scholar 

  20. 20.

    Damond, N. et al. A map of human type 1 diabetes progression by imaging mass cytometry. Cell Metab. 29, 755–768 (2019).

    CAS  Article  Google Scholar 

  21. 21.

    Bodenmiller, B. Multiplexed epitope-based tissue imaging for discovery and healthcare applications. Cell Syst. 2, 225–238 (2016).

    CAS  Article  Google Scholar 

  22. 22.

    Lin, J.-R. et al. Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes. eLife7, e31657 (2018).

  23. 23.

    Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).

    CAS  Article  Google Scholar 

  24. 24.

    Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. 2008, P10008 (2008).

    Article  Google Scholar 

  25. 25.

    Kalluri, R. The biology and function of fibroblasts in cancer. Nat. Rev. Cancer 16, 582–598 (2016).

    CAS  Article  Google Scholar 

  26. 26.

    Kononen, J. et al. Tissue microarrays for high-throughput molecular profiling of tumor specimens. Nat. Med. 4, 844–847 (1998).

    CAS  Article  Google Scholar 

  27. 27.

    Kündig, P. et al. Limited utility of tissue micro-arrays in detecting intra-tumoral heterogeneity in stem cell characteristics and tumor progression markers in breast cancer. J. Transl. Med. 16, 118 (2018).

    Article  Google Scholar 

  28. 28.

    Chevrier, S. et al. Compensation of signal spillover in suspension and imaging mass cytometry. Cell Syst. 6, 612–620 (2018).

    CAS  Article  Google Scholar 

  29. 29.

    Sommer, C., Straehle, C., Köthe, U. & Hamprecht, F. A. Ilastic: interactive learning and segmentation toolkit. In Proc. 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro 230–233 (IEEE, 2011).

  30. 30.

    Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).

    Article  Google Scholar 

  31. 31.

    Amir, A. D. et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31, 545–552 (2013).

    CAS  Article  Google Scholar 

  32. 32.

    Angel Martín, M., & Rey, J.-M. (2000). On the role of Shannon’s entropy as a measure of heterogeneity. Geoderma 98, 1–3 (2000).

    ADS  Article  Google Scholar 

  33. 33.

    Kullback, S. & Leibler, R. A. On information and sufficiency. Ann. Math. Stat. 22, 79–86 (1951).

    MathSciNet  Article  Google Scholar 

  34. 34.

    Expert, P., Evans, T. S., Blondel, V. D. & Lambiotte, R. Uncovering space-independent communities in spatial networks. Proc. Natl Acad. Sci. USA, 108, 7663–7668 (2011).

    CAS  ADS  Article  Google Scholar 

Download references

Acknowledgements

We thank patients who donated tumour samples; S. Eppenberger and S. Dettwiler for the coordination of tissue collection and for construction of TMAs; the B.B. laboratory, in particular N. de Souza for help with writing the manuscript; and D. Schulz for fruitful discussions. B.B. was funded by a SNSF R’Equip grant, a SNSF Assistant Professorship grant, the SystemsX Transfer Project ‘Friends and Foes’, the SystemX grants Metastasix and PhosphoNEtX, a NIH grant (UC4 DK108132), the CRUK IMAXT Grand Challenge and the European Research Council (ERC) under the European Union's Seventh Framework Program (FP/2007-2013) and the ERC Grant Agreement no. 336921. H.W.J. was funded by the SystemsX Transitional Post-Doctoral Fellowship, the Canadian Institute of Health Research Post-Doctoral Fellowship and the Cancer Research Society Scholarship for the Next Generation of Scientists.

Author information

Affiliations

Authors

Contributions

H.W.J. and B.B. conceived the study. H.W.J. performed all image quantification and IMC experiments. Z.V. performed immunohistochemical staining. J.R.F. performed data analysis. V.R.T.Z. constructed image processing and analysis tools. H.W.J. and J.R.F. performed the biological analysis and interpretation with input from the co-authors. H.R.A. provided input on clinical interpretation and survival analysis. H.M., Z.V., R.M., S.D.S., S.M. and W.P.W. provided patient samples and clinical input throughout the study. H.W.J., J.R.F. and B.B. wrote the manuscript.

Corresponding author

Correspondence to Bernd Bodenmiller.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks David Rimm and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data figures and tables

Extended Data Fig. 1 Antibody panel and example pseudocoloured images of markers.

Antigens targeted by the antibodies in the panel of 35 isotope-conjugated antibodies that was used to stain the breast cancer tissue, and representative marker images from the analysed cohort generated by IMC. Every marker is visualized at least once. Each image represents a different tumour of the analysed cohort. Each marker was individually scaled to enable visualization. RTK, receptor tyrosine kinase; EMT, epithelial–mesenchymal transition; TF, transcription factor. Scale bars,100 μm.

Extended Data Fig. 2 Comparison and reproducibility analysis of immunohistochemistry and IMC.

a, Representative IMC and immunohistochemistry images of the quantified stains in sections of the same tumour core. b, Scatter plot and correlation of total immunohistochemistry (IHC) and IMC signal in sections of the same tumour core (IHC, optical density per μm2, IMC, ion counts per μm2; n = 319 cores). c, Scatter plot and correlation of the number of positively stained cells in sections from the same tumour core (n = 319 cores). d, Bland–Altman plots for reproducibility of the IMC signal in positively stained cells across images from different regions of the same tumour, adapted to visualize the average across four samples on the x axis and the difference of every individual sample to the tumour average on the y axis. Only images that contained positively stained cells and more than 200 cells in total were taken into account for this analysis (ER, n = 280 cores from 72 patients; PR, n = 213 cores from 66 patients; HER2, n = 291 cores from 72 patients; E/P-cadherin, n = 200 cores from 65 patients; Ki67, n = 281 cores from 72 patients). Red line represents the overall average of the differences to the tumour mean; blue lines represent the 95% confidence interval (1.96 × s.d.). The percentage of observations that fall within the confidence interval is indicated at the top of each plot.

Extended Data Fig. 3 Simultaneous immunofluorescence and mass cytometry imaging.

Immunofluorescence (IF) and mass cytometry (IMC) imaging of the same tissue sample using metal-conjugated HER2 and pan-CK primary antibodies and both fluorescent and metal-conjugated secondary stains. Pseudocolour images of individual channels (a), three-marker images produced from each label type (b) (white, overlap; red, HER2; green, pan-CK; blue, DNA intercalator), as well an overlay of the same marker from all three label types (c) (white, overlap; red, secondary immunofluorescence; green, secondary IMC; blue, primary IMC). d, High-magnification images of the regions labelled with white squares in b, comparing the resolution, expression and similarity of immunofluorescence and IMC.

Extended Data Fig. 4 Single-cell localization relative to the tumour–stroma interface.

a–d, t-SNE maps of 171,288 subsampled single cells from high-dimensional images of breast tumours, coloured by patient (a), localization relative to the tumour–stroma interface (b), single-cell distance to the tumour–stroma interface (c) and number of neighbouring cells (d). eg, Representative images with single-cell mask, labelled by metacluster identifier (e), tumour and stroma masks (f) and heat map that represents the distances of single cells to the tumour–stroma interface from each side (g). Scale bar, 100 μm. h, i, Log-transformed distances to tumour front of stromal cell clusters (h) and tumour cell metaclusters (i). j, Binned distances of all metaclusters to the tumour front. Bin number 0 contains all cells that are directly touching the interface. Negative distances represent the distance to the tumour boundary from inside the tumour and positive values indicate the distance outside the tumour.

Extended Data Fig. 5 Metaclustering and cluster matching across cohorts.

a, Heat map showing z-scored mean marker expression of single-cell phenotypic clusters identified by PhenoGraph (Fig. 1) with colours on the colour bar and hierarchical clustering indicating the corresponding metacluster. Red stars on the hierarchical clustering tree indicate subgroups that robustly reappear as separate groups using multiscale bootstrap resampling (P < 0.05 (R function pvclust)). b, Examples of untransformed distributions of cluster marker expressions that differ between metaclusters. c, Heat maps showing the z-scored mean marker expression or distance to the tumour–stroma interface for each metacluster defined in the cohort of 281 patients from University Hospital Basel and each matched PhenoGraph cluster from the multicore cohort of 71 patients from University Hospital Zurich. PhenoGraph clusters of the Zurich cohort were matched to the metaclusters of the Basel cohort on the basis of the Pearson correlation of the mean marker expression (Methods).

Extended Data Fig. 6 Densities of single-cell phenotypes in different clinical subtypes and SCP patient subgroups.

Box plots of cellular metacluster densities in patients of each clinical subtype (a) (HR+HER2, n = 173; HR+HER2+, n = 29; HRHER2+, n = 23; triple negative (HRHER2), n = 48) (a) and each SCP subgroup (b) (SCP1, n = 17; SCP2, n = 21; SCP3, n = 20; SCP4, n = 12; SCP5, n = 32; SCP6, n = 10; SCP7, n = 13; SCP8, n = 11; SCP9, n = 20; SCP10, n = 24; SCP11, n = 31; SCP12, n = 14; SCP13, n = 15; SCP14, n = 11; SCP15, n = 8; SCP16, n = 10; SCP17, n = 9; SCP18, n = 3). For box plots, centre line represents the median, box limits are the first and third quartiles, whiskers extend to 1.5 × interquartile range and the points beyond the whiskers are outliers.

Extended Data Fig. 7 Stromal environments based on their composition of microenvironment communities and their distinct pairwise cell-type interactions.

a, Hierarchical clustering of tumour cores (n = 281) according to stromal community content and splitting into corresponding stromal environments (n = 11). The stacked bar plot at the top indicates the average number of cells from each cellular metacluster present within each type of microenvironment community. b, The presence of significant (P < 0.01) cell–cell interactions (red) and cell–cell avoidances (blue) identified per image based on a permutation test (1,000 permutations). Black outlined regions indicate significant interactions that are enriched in images from the respective stromal environments (P < 0.05 (one-sided Fisher’s exact test for enrichment, corrected for multiple testing). Colour bars on the right indicate the SCP subgroup, grade and clinical subtype of the tumour. Cell-type interactions along the top are indicated by the labelled cell type of interest and neighbouring cell.

Extended Data Fig. 8 Comparisons and enrichments between classifications.

a, b, Bubble plots visualizing the overlap between SCP subgroups of breast cancer (SCP1, n = 17; SCP2, n = 21; SCP3, n = 20; SCP4, n = 12; SCP5, n = 32; SCP6, n = 10; SCP7, n = 13; SCP8, n = 11; SCP9, n = 20; SCP10, n = 24; SCP11, n = 31; SCP12, n = 14; SCP13, n = 15; SCP14, n = 11; SCP15, n = 8; SCP16, n = 10; SCP17, n = 9 (SCP18, n = 3 excluded)) and clinical subtypes (a) (HR+HER2, n = 173; HR+HER2+, n = 29; HRHER2+, n = 23; triple negative (HRHER2), n = 48) and stromal environments (b) (SE1, n = 49; SE2, n = 88; SE3, n = 9; SE4, n = 24; SE5, n = 25; SE6, n = 24; SE7, n = 8; SE8, n = 14; SE9, n = 14; SE10, n = 18; SE11, n = 2). •P < 0.1, *P < 0.05, **P < 0.01, ***P < 0.001 (one-sided Fisher’s exact test for enrichment.). Exact P values for highlighted pairs: SE1 and SCP7, P = 0.013; SE3 and SCP8, P = 0.021; SE5 and SCP15, P = 0.031; SE6 and SCP3, P = 0.034; SE8 and SCP14, P = 0.008; SE8 and SCP8, P = 0.093; SE9 and SCP12, P = 0.036; HR+HER2 and SCP3, P = 0.079; HR+HER2 and SCP5, P = 3.58 × 10−4; HR+HER2+ and SCP2, P = 0.032; HRHER2+ and SCP11, P = 2.36 × 10−4; HRHER2 and SCP8, P = 0.060; HRHER2 and SCP14, P = 0.008; HRHER2 and SCP15, P = 6.13× 10−6; HRHER2 and SCP16, P = 0.031.

Extended Data Fig. 9 Kaplan–Meier survival curves for overall and disease-free survival.

a, b, Overall survival for stromal environments that are not shown in Fig. 4. cl, Disease-free survival for each patient group on the basis of clinical subtype (c), grade (d), SCP subgroup (eh) and stromal environment (if). *P < 0.05 compared to all other samples (two-sided log-rank test). For exact P values, see Supplementary Tables 5 and 7.

Extended Data Fig. 10 Multicore cohort regional heterogeneity analysis.

Quantification of intratumour regional heterogeneity in the Zurich multicore cohort. a, Hierarchically clustered stacked bar plot of cell-type metacluster densities in each tumour, grouped by patient. Coloured columns (right) indicate patient, clinical subtype, SCP subgroup, location of core in the tumour, Shannon entropy (intracore heterogeneity) and tumour-specific cohesiveness score. b, Dot plot of the Kullback–Leibler divergence from the cell-type distribution of an individual tumour region to the average distribution of the patient, coloured according to the SCP subgroup classification per tumour region (n = 263 tumour cores), grouped by patient (n = 71 patients) and ordered by increasing average Kullback–Leibler divergence per patient. c, Box plots of the same Kullback–Leibler divergence of each region to the average cell-type distribution of the patient, grouped by tumour regions that are individually identified as the same SCP subgroup, independent of the patient. Box plots as in Extended Data Fig. 6. SCP1, n = 12; SCP2, n = 13; SCP3, n = 11; SCP4, n = 10; SCP6, n = 76; SCP7, n = 7; SCP8, n = 3; SCP9, n = 5; SCP10, n = 1; SCP11, n = 26; SCP12, n = 51; SCP13, n = 15; SCP14, n = 18; SCP15, n = 4; SCP16, n = 5; SCP17, n = 5; SCP18, n = 1). d, Bar indicating the percentage of patients (n = 71) with the indicated fraction of individually classified images that match the whole tumour classification. e, Bubble plot visualizing the variation in intratumour regions within patients of each SCP subgroup. Rows represent tumours of each SCP subgroup as identified by the combined analysis of all imaged regions. Columns represent tumour regions individually matched to a SCP subgroup. For each whole-tumour classification on the y axis, the size of the circle indicates the fraction of corresponding images individually classified as a SCP subgroup. For each image classification on the x axis, colour indicates the fraction of images within each tumour type.

Supplementary information

Supplementary Information

The SI file contains example images from the analyzed cohort and Supplementary Tables 1-9. The Supplementary Tables yield an overview over the clinical and patient information and summarize the output of statistical analyses.

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Jackson, H.W., Fischer, J.R., Zanotelli, V.R.T. et al. The single-cell pathology landscape of breast cancer. Nature 578, 615–620 (2020). https://doi.org/10.1038/s41586-019-1876-x

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