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Owen Qu

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Proceedings Article | 29 May 2024 Paper

Spatial analysis of immune cells in breast cancer using k-nearest neighbor graphs and Louvain-community clustering of immunofluorescent protein multiplexing images

Alison Cheung, Wenchao Han, Snow Zhou, Dan Wang, Vishwesh Ramanathan, Owen Qu, Anne Martel, Martin Yaffe

Proceedings Volume 13174, 1317414 (2024) https://doi.org/10.1117/12.3025909

KEYWORDS: Breast cancer, Cancer, Proteins, Multiplexing, Tumors, Tissues, Pathology, Cell phenotyping, Antibodies

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Immune phenotype data, specifically the description of densities and spatial distribution of immune cells are now frequently included in the clinical pathology report as these features of the cells in the tumor microenvironment (TME) have shown to be associated with prognosis. In addition, immune-therapeutics, which aim at manipulating the patients’ immune system to kill cancer cells, have recently been approved for treatment of triple-negative breast cancers (TNBCs). Thus, quantifying the immune phenotype of the cancer could be important both for prognostication, and for prediction of therapy response. We have studied the immune phenotype of 42 breast cancers using immunofluorescence protein multiplexing and quantitative image analysis. After sectioning, formalin-fixed paraffin-embedded tissues were sequentially stained with a panel of fluorescently-labelled antibodies and imaged with the multiplexer (Cell DIVE, Leica Biosystems). Composite images of antibody-stained sections were then analysed using specialized digital pathology software (HALO, Indica Labs). Binary thresholding was conducted to identify and quantify densities of various immune lineage subsets (T lymphocytes and macrophages). Their cellular localisation was mapped and the spatial features of cellular arrangement were evaluated using a k-nearest neighbor graph (KNNG) method and Louvain community-proximity clustering. The spatial relationship of various immune and cancer cell types was quantified to assess whether cellular arrangements and structures differed among breast cancer subtypes. Our work demonstrates the use of molecular and cellular imaging in quantifying features of the tumor microenvironment in breast cancer classification, and the application of KNNG in studying spatial biology.

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