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The single-cell pathology landscape of breast cancer

Author

Listed:
  • Hartland W. Jackson

    (University of Zurich
    University of Zurich)

  • Jana R. Fischer

    (University of Zurich
    University of Zurich
    ETH Zurich and University of Zurich)

  • Vito R. T. Zanotelli

    (University of Zurich
    University of Zurich
    ETH Zurich and University of Zurich)

  • H. Raza Ali

    (University of Zurich
    University of Zurich
    University of Cambridge)

  • Robert Mechera

    (University of Basel and University Hospital Basel)

  • Savas D. Soysal

    (University of Basel
    Clarunis University Center for Gastrointestinal and Liver Diseases Basel)

  • Holger Moch

    (University Hospital Zurich)

  • Simone Muenst

    (University Hospital Basel)

  • Zsuzsanna Varga

    (University Hospital Zurich)

  • Walter P. Weber

    (University of Basel and University Hospital Basel)

  • Bernd Bodenmiller

    (University of Zurich
    University of Zurich)

Abstract

Single-cell analyses have revealed extensive heterogeneity between and within human tumours1–4, 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.

Suggested Citation

  • Hartland W. Jackson & Jana R. Fischer & Vito R. T. Zanotelli & H. Raza Ali & Robert Mechera & Savas D. Soysal & Holger Moch & Simone Muenst & Zsuzsanna Varga & Walter P. Weber & Bernd Bodenmiller, 2020. "The single-cell pathology landscape of breast cancer," Nature, Nature, vol. 578(7796), pages 615-620, February.
    • Handle: RePEc:nat:nature:v:578:y:2020:i:7796:d:10.1038_s41586-019-1876-x
    DOI: 10.1038/s41586-019-1876-x
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    1. Alastair Magness & Emma Colliver & Katey S. S. Enfield & Claudia Lee & Masako Shimato & Emer Daly & David A. Moore & Monica Sivakumar & Karishma Valand & Dina Levi & Crispin T. Hiley & Philip S. Hobso, 2024. "Deep cell phenotyping and spatial analysis of multiplexed imaging with TRACERx-PHLEX," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
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    3. Stefanie Hiltbrunner & Lena Cords & Sabrina Kasser & Sandra N. Freiberger & Susanne Kreutzer & Nora C. Toussaint & Linda Grob & Isabelle Opitz & Michael Messerli & Martin Zoche & Alex Soltermann & Mar, 2023. "Acquired resistance to anti-PD1 therapy in patients with NSCLC associates with immunosuppressive T cell phenotype," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    4. Anissa El Marrahi & Fabio Lipreri & Ziqi Kang & Louise Gsell & Alper Eroglu & David Alber & Jean Hausser, 2023. "NIPMAP: niche-phenotype mapping of multiplex histology data by community ecology," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
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    7. Yael Amitay & Yuval Bussi & Ben Feinstein & Shai Bagon & Idan Milo & Leeat Keren, 2023. "CellSighter: a neural network to classify cells in highly multiplexed images," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    8. Peng Lu & Karolyn A. Oetjen & Diane E. Bender & Marianna B. Ruzinova & Daniel A. C. Fisher & Kevin G. Shim & Russell K. Pachynski & W. Nathaniel Brennen & Stephen T. Oh & Daniel C. Link & Daniel L. J., 2023. "IMC-Denoise: a content aware denoising pipeline to enhance Imaging Mass Cytometry," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    9. Kaiwen Wang & Yuqiu Yang & Fangjiang Wu & Bing Song & Xinlei Wang & Tao Wang, 2023. "Comparative analysis of dimension reduction methods for cytometry by time-of-flight data," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    10. Hugo Croizer & Rana Mhaidly & Yann Kieffer & Geraldine Gentric & Lounes Djerroudi & Renaud Leclere & Floriane Pelon & Catherine Robley & Mylene Bohec & Arnaud Meng & Didier Meseure & Emanuela Romano &, 2024. "Deciphering the spatial landscape and plasticity of immunosuppressive fibroblasts in breast cancer," Nature Communications, Nature, vol. 15(1), pages 1-28, December.
    11. Nastaran Mohammadi Ghahhari & Magdalena K. Sznurkowska & Nicolas Hulo & Lilia Bernasconi & Nicola Aceto & Didier Picard, 2022. "Cooperative interaction between ERα and the EMT-inducer ZEB1 reprograms breast cancer cells for bone metastasis," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    12. Thomas Hu & Mayar Allam & Shuangyi Cai & Walter Henderson & Brian Yueh & Aybuke Garipcan & Anton V. Ievlev & Maryam Afkarian & Semir Beyaz & Ahmet F. Coskun, 2023. "Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    13. Michael J. Geuenich & Dae-won Gong & Kieran R. Campbell, 2024. "The impacts of active and self-supervised learning on efficient annotation of single-cell expression data," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    14. Candace C. Liu & Noah F. Greenwald & Alex Kong & Erin F. McCaffrey & Ke Xuan Leow & Dunja Mrdjen & Bryan J. Cannon & Josef Lorenz Rumberger & Sricharan Reddy Varra & Michael Angelo, 2023. "Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    15. Michele Bortolomeazzi & Lucia Montorsi & Damjan Temelkovski & Mohamed Reda Keddar & Amelia Acha-Sagredo & Michael J. Pitcher & Gianluca Basso & Luigi Laghi & Manuel Rodriguez-Justo & Jo Spencer & Fran, 2022. "A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially-resolved tissue phenotyping at single-cell resolution," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    16. Yunhao Bai & Bokai Zhu & John-Paul Oliveria & Bryan J. Cannon & Dorien Feyaerts & Marc Bosse & Kausalia Vijayaragavan & Noah F. Greenwald & Darci Phillips & Christian M. Schürch & Samuel M. Naik & Edw, 2023. "Expanded vacuum-stable gels for multiplexed high-resolution spatial histopathology," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    17. Vincent Geldhof & Laura P. M. H. Rooij & Liliana Sokol & Jacob Amersfoort & Maxim Schepper & Katerina Rohlenova & Griet Hoste & Adriaan Vanderstichele & Anne-Marie Delsupehe & Edoardo Isnaldi & Naima , 2022. "Single cell atlas identifies lipid-processing and immunomodulatory endothelial cells in healthy and malignant breast," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    18. Vázquez López, Raul, 2023. "La competitividad de las exportaciones del sector químico mexicano en los Estados Unidos de América y la Unión Europea, 1994-2015," Apuntes del Cenes, Universidad Pedagógica y Tecnológica de Colombia, vol. 42(75), pages 83-109, January.
    19. Junyoung Seo & Yeonbo Sim & Jeewon Kim & Hyunwoo Kim & In Cho & Hoyeon Nam & Young-Gyu Yoon & Jae-Byum Chang, 2022. "PICASSO allows ultra-multiplexed fluorescence imaging of spatially overlapping proteins without reference spectra measurements," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    20. Zhiyuan Yuan & Yisi Li & Minglei Shi & Fan Yang & Juntao Gao & Jianhua Yao & Michael Q. Zhang, 2022. "SOTIP is a versatile method for microenvironment modeling with spatial omics data," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    21. Rui Chen & Jiasu Xu & Boqian Wang & Yi Ding & Aynur Abdulla & Yiyang Li & Lai Jiang & Xianting Ding, 2024. "SpiDe-Sr: blind super-resolution network for precise cell segmentation and clustering in spatial proteomics imaging," Nature Communications, Nature, vol. 15(1), pages 1-16, December.

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