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    Inference of tissue relative proportions of the breast epithelial cell types luminal progenitor, basal, and luminal mature

    Bartlett, Thomas and Jia, P. and Chandna, Swati and Roy, Sa. (2021) Inference of tissue relative proportions of the breast epithelial cell types luminal progenitor, basal, and luminal mature. Scientific Reports 11 (1), p. 23702. ISSN 2045-2322.

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    Abstract

    Single-cell analysis has revolutionised genomic science in recent years. However, due to cost and other practical considerations, single-cell analyses are impossible for studies based on medium or large patient cohorts. For example, a single-cell analysis usually costs thousands of euros for one tissue sample from one volunteer, meaning that typical studies using single-cell analyses are based on very few individuals. While single-cell genomic data can be used to examine the phenotype of individual cells, cell-type deconvolution methods are required to track the quantities of these cells in bulk-tissue genomic data. Hormone receptor negative breast cancers are highly aggressive, and are thought to originate from a subtype of epithelial cells called the luminal progenitor. In this paper, we show how to quantify the number of luminal progenitor cells as well as other epithelial subtypes in breast tissue samples using DNA and RNA based measurements. We find elevated levels of cells which resemble these hormone receptor negative luminal progenitor cells in breast tumour biopsies of hormone receptor negative cancers, as well as in healthy breast tissue samples from BRCA1 (FANCS) mutation carriers. We also find that breast tumours from carriers of heterozygous mutations in non-BRCA Fanconi Anaemia pathway genes are much more likely to be hormone receptor negative. These findings have implications for understanding hormone receptor negative breast cancers, and for breast cancer screening in carriers of heterozygous mutations of Fanconi Anaemia pathway genes.

    Metadata

    Item Type: Article
    School: School of Business, Economics & Informatics > Economics, Mathematics and Statistics
    Depositing User: Swati Chandna
    Date Deposited: 19 Jan 2022 11:09
    Last Modified: 21 Jan 2022 20:02
    URI: https://eprints.bbk.ac.uk/id/eprint/47296

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