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A convolutional neural network STIFMap reveals associations between stromal stiffness and EMT in breast cancer

Author

Listed:
  • Connor Stashko

    (University of California
    University of California, San Francisco)

  • Mary-Kate Hayward

    (University of California
    University of California, San Francisco)

  • Jason J. Northey

    (University of California
    University of California, San Francisco)

  • Neil Pearson

    (Harvey Mudd College)

  • Alastair J. Ironside

    (Western General Hospital, NHS Lothian)

  • Johnathon N. Lakins

    (University of California
    University of California, San Francisco)

  • Roger Oria

    (University of California
    University of California, San Francisco)

  • Marie-Anne Goyette

    (Dana-Farber Cancer Institute)

  • Lakyn Mayo

    (University of California, San Francisco)

  • Hege G. Russnes

    (Oslo University Hospital
    University of Oslo)

  • E. Shelley Hwang

    (Duke University Medical Center)

  • Matthew L. Kutys

    (University of California, San Francisco
    University of California, San Francisco)

  • Kornelia Polyak

    (Dana-Farber Cancer Institute)

  • Valerie M. Weaver

    (University of California
    University of California, San Francisco
    University of California, San Francisco
    University of California, San Francisco)

Abstract

Intratumor heterogeneity associates with poor patient outcome. Stromal stiffening also accompanies cancer. Whether cancers demonstrate stiffness heterogeneity, and if this is linked to tumor cell heterogeneity remains unclear. We developed a method to measure the stiffness heterogeneity in human breast tumors that quantifies the stromal stiffness each cell experiences and permits visual registration with biomarkers of tumor progression. We present Spatially Transformed Inferential Force Map (STIFMap) which exploits computer vision to precisely automate atomic force microscopy (AFM) indentation combined with a trained convolutional neural network to predict stromal elasticity with micron-resolution using collagen morphological features and ground truth AFM data. We registered high-elasticity regions within human breast tumors colocalizing with markers of mechanical activation and an epithelial-to-mesenchymal transition (EMT). The findings highlight the utility of STIFMap to assess mechanical heterogeneity of human tumors across length scales from single cells to whole tissues and implicates stromal stiffness in tumor cell heterogeneity.

Suggested Citation

  • Connor Stashko & Mary-Kate Hayward & Jason J. Northey & Neil Pearson & Alastair J. Ironside & Johnathon N. Lakins & Roger Oria & Marie-Anne Goyette & Lakyn Mayo & Hege G. Russnes & E. Shelley Hwang & , 2023. "A convolutional neural network STIFMap reveals associations between stromal stiffness and EMT in breast cancer," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39085-1
    DOI: 10.1038/s41467-023-39085-1
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    References listed on IDEAS

    as
    1. Juliane Winkler & Abisola Abisoye-Ogunniyan & Kevin J. Metcalf & Zena Werb, 2020. "Concepts of extracellular matrix remodelling in tumour progression and metastasis," Nature Communications, Nature, vol. 11(1), pages 1-19, December.
    2. Anjali Rao & Dalia Barkley & Gustavo S. França & Itai Yanai, 2021. "Exploring tissue architecture using spatial transcriptomics," Nature, Nature, vol. 596(7871), pages 211-220, August.
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