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Cited by 6Single-cell and spatial transcriptomicsBreast Cancer Treatment StudiesGene expression and cancer classification

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

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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.

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Crossref · 10.1038/s41598-021-03161-7

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Figures

Figure 1 (a) DNA methylation: a methyl group is added to some cytosine DNA bases, giving tissue and cell-type specific patterns at the genome-wide scale that allow accurate inference of cell-type proportions in bulk-tissue genomic (i.e., convolved) data. (b) We use a two-step procedure to first estimate the reference matrix X, before second estimating the mixing matrix W (representing the cell-type proportions). (c) DNAme data are used to estimate general cell-type proportions with an earlier tool1, before DNAme and RNA-seq data are used to estimate breast epithelial subtype proportions. Illustration by Christoph Bock, Max Planck Institute for Informatics; adapted under licence CC BY-SA 3.0 from https://en.wikipedia.org/wiki/DNA_methylation.
Figure 1 (a) DNA methylation: a methyl group is added to some cytosine DNA bases, giving tissue and cell-type specific patterns at the genome-wide scale that allow accurate inference of cell-type proportions in bulk-tissue genomic (i.e., convolved) data. (b) We use a two-step procedure to first estimate the reference matrix X, before second estimating the mixing matrix W (representing the cell-type proportions). (c) DNAme data are used to estimate general cell-type proportions with an earlier tool1, before DNAme and RNA-seq data are used to estimate breast epithelial subtype proportions. Illustration by Christoph Bock, Max Planck Institute for Informatics; adapted under licence CC BY-SA 3.0 from https://en.wikipedia.org/wiki/DNA_methylation.CC BY · PubMed Central
Figure 2 Robustness of inference. A data-set with n=100 bulk-tissue samples was simulated, and data-points were removed and replaced at random with noise; this was repeated b=1000 times. (a) Mean square error (MSE) comparing predicted proportions of epithelial subtypes with the ground-truth, as an increasing number of data-points are removed. Dashed lines indicate 95% C.I.s. (b) Percentage of mixing proportions l∈{1,k}, Wlj, j∈{1,n}, which cannot be estimated, as an increasing number of data-points are removed.
Figure 2 Robustness of inference. A data-set with n=100 bulk-tissue samples was simulated, and data-points were removed and replaced at random with noise; this was repeated b=1000 times. (a) Mean square error (MSE) comparing predicted proportions of epithelial subtypes with the ground-truth, as an increasing number of data-points are removed. Dashed lines indicate 95% C.I.s. (b) Percentage of mixing proportions l∈{1,k}, Wlj, j∈{1,n}, which cannot be estimated, as an increasing number of data-points are removed.CC BY · PubMed Central
Figure 3 UMAP projections of single-cell RNA-seq data from n=13,909 breast epithelial cells. (a) Inferred GMM-LE clusters. Colour intensity matching shows strong overlap of (a) with (b)–(d) in which colour intensities correspond to mean log expression levels from independent and pre-defined sets of marker genes (Table S1) for luminal progenitor, luminal mature, and basal cells (respectively).
Figure 3 UMAP projections of single-cell RNA-seq data from n=13,909 breast epithelial cells. (a) Inferred GMM-LE clusters. Colour intensity matching shows strong overlap of (a) with (b)–(d) in which colour intensities correspond to mean log expression levels from independent and pre-defined sets of marker genes (Table S1) for luminal progenitor, luminal mature, and basal cells (respectively).CC BY · PubMed Central
Figure 4 Inferred proportions of luminal progenitor (lum prog), basal, and mature luminal (lum mat) cells, in (a) hormone receptor positive/negative cancers. We use ‘like’ as a suffix to indicate similar genomic characteristics of tumour cells and their related healthy epithelial cells. (b) Inferred proportions of luminal progenitor, basal, and mature luminal cells, and in (b) BRCA1 (FANCS) mutation carriers. Inferences are based on DNAme data.
Figure 4 Inferred proportions of luminal progenitor (lum prog), basal, and mature luminal (lum mat) cells, in (a) hormone receptor positive/negative cancers. We use ‘like’ as a suffix to indicate similar genomic characteristics of tumour cells and their related healthy epithelial cells. (b) Inferred proportions of luminal progenitor, basal, and mature luminal cells, and in (b) BRCA1 (FANCS) mutation carriers. Inferences are based on DNAme data.CC BY · PubMed Central
Figure 5 Linear modelling (with 95% C.I.s indicated) of estimated proportions of (a) luminal progenitor-like, (b) mature luminal-like, (c) basal-like, and (d) overall epithelial-like cells, in n=257 tumour biopsies. The horizontal bars arranged along the vertical axis in each plot represent linear model predictors as follows: FA-gene (non-BRCA, FANC suffix) mutation carrier status, disease stage, and volunteer age. (e) Biopsies of FA-gene mutation carriers are mostly hormone receptor (HR) triple-negative, whereas those of (f) FA-gene wild-type are mostly HR-positive. Inferences are based on DNAme data.
Figure 5 Linear modelling (with 95% C.I.s indicated) of estimated proportions of (a) luminal progenitor-like, (b) mature luminal-like, (c) basal-like, and (d) overall epithelial-like cells, in n=257 tumour biopsies. The horizontal bars arranged along the vertical axis in each plot represent linear model predictors as follows: FA-gene (non-BRCA, FANC suffix) mutation carrier status, disease stage, and volunteer age. (e) Biopsies of FA-gene mutation carriers are mostly hormone receptor (HR) triple-negative, whereas those of (f) FA-gene wild-type are mostly HR-positive. Inferences are based on DNAme data.CC BY · PubMed Central

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Research works printed but not linked: 1

  1. van Dijk, D., Nainys, J., Sharma, R., Kaithail, P., Carr, A. J., Moon, K. R. et al. MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data. BioRxiv, 111591 (2017). · DOI

Metadata

Authors
Bartlett, T. E., Jia, P., Chandna, S., & Roy, S.
Affiliations
  • University College London (GB)
  • Birkbeck, University of London (GB)
  • University of Bath (GB)
Corresponding author
Bartlett, Thomas E.
Venue
Scientific Reports
Published
2021-12-08
Volume
11
Issue
1
Pages
23702-23702
ISSN
2045-2322 (ISSN-L)
OpenAlex
W4226107866
Cited by
6
References
44
Citations by year
2026: 1 · 2022: 5
FWCI
0.39
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • BIROn (Birkbeck, University of London) (acceptedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • UCL Discovery (University College London) (submittedVersion)
Funders
Medical Research Council (MR/P014070/1)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords
luminal progenitor cells, basal cells, cell type deconvolution, hormone receptor-negative breast cancer, BRCA1 mutation carriers, heterozygous mutation, breast cancer screening
MeSH terms
Algorithms, Biopsy, Breast, Breast Neoplasms, Disease Susceptibility, Epithelial Cells, Female, Humans, Immunohistochemistry, Models, Biological, Mutation, Phenotype, Stem Cells, Biomarkers, Tumor, Genetic Predisposition to Disease, Single-Cell Analysis

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CoChat. (2026). Citation check: Inference of tissue relative proportions of the breast epithelial cell types luminal progenitor, basal, and luminal mature. https://app.cochat.ai/papers/10.1038/s41598-021-03161-7
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Bartlett, T. E., Jia, P., Chandna, S., & Roy, S. (2021). Inference of tissue relative proportions of the breast epithelial cell types luminal progenitor, basal, and luminal mature. Scientific Reports. https://doi.org/10.1038/s41598-021-03161-7

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