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Preneoplastic stromal cells promote BRCA1-mediated breast tumorigenesis

Nature Genetics volume 55, pages 595–606 (2023)Cite this article

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Abstract

Women with germline BRCA1 mutations (BRCA1+/mut) have increased risk for hereditary breast cancer. Cancer initiation in BRCA1+/mut is associated with premalignant changes in breast epithelium; however, the role of the epithelium-associated stromal niche during BRCA1-driven tumor initiation remains unclear. Here we show that the premalignant stromal niche promotes epithelial proliferation and mutant BRCA1-driven tumorigenesis in trans. Using single-cell RNA sequencing analysis of human preneoplastic BRCA1+/mut and noncarrier breast tissues, we show distinct changes in epithelial homeostasis including increased proliferation and expansion of basal-luminal intermediate progenitor cells. Additionally, BRCA1+/mut stromal cells show increased expression of pro-proliferative paracrine signals. In particular, we identify pre-cancer-associated fibroblasts (pre-CAFs) that produce protumorigenic factors including matrix metalloproteinase 3 (MMP3), which promotes BRCA1-driven tumorigenesis in vivo. Together, our findings demonstrate that precancerous stroma in BRCA1+/mut may elevate breast cancer risk through the promotion of epithelial proliferation and an accumulation of luminal progenitor cells with altered differentiation.

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Fig. 1: Single-cell transcriptomics analysis of human breast tissues from BRCA1+/mut and noncarriers.
Fig. 2: Increased proliferation and accumulation of a luminal epithelial progenitor subset with altered differentiation in BRCA1+/mut breast tissues.
Fig. 3: Receptor–ligand interaction analysis reveals increased stromal cell-induced epithelial proliferation in BRCA1+/mut breast tissue.
Fig. 4: Expansion of CAF-like, MMP3-expressing fibroblasts in premalignant BRCA1+/mut breast tissues.
Fig. 5: MMP3-expressing pre-CAFs promote breast epithelial proliferation and altered differentiation in primary human cocultures in vitro.
Fig. 6: MMP3-expressing fibroblasts promote mutant BRCA1-driven breast cancer initiation in vivo.
Fig. 7: Mathematical modeling predicts that stromal cell-induced epithelial proliferation leads to increased lifetime breast cancer risk in BRCA1+/mut.

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Data availability

Reagents and resources generated in this study are available upon request. All data are available at Gene Expression Omnibus (GEO) database, including raw fastq files and quantified data matrices under accession code GSE174588. Source data are provided with this paper.

Code availability

No specific code was developed in this study and all data was processed and analyzed using existing code and software whose full details are provided in the Methods section.

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Acknowledgements

We thank D. Lawson and X. Dai for carefully reading the manuscript. Thank you to L. Hosohama, S.M.-Q. Nguyen and N.R. James for their assistance on this project. This study was supported by funds from the National Institutes of Health (NIH)/National Cancer Institute (NCI) (1R01CA234496; 4R00CA181490 to K.K., and T32CA009054; T32GM008620; F30CA243419 to K.N.), the American Cancer Society (132551-RSG-18-194-01-DDC to K.K.), the NSF (DMS1763272 to Q.N.), The Simons Foundation (594598 to Q.N.), and a grant from Breast Cancer Research Foundation joint with Jayne Kosinas Ted Giovanis Foundation for Health and Policy (to Q.N.). D.M. was supported by the Canadian Institutes of Health Research Postdoctoral Fellowship, and the NIH/NCI K99/R00 Transition to Independence Award (1K99CA267160-01). S.S. and M.A.L. were supported by the Department of Defense (CDMRP BC181737). M.P. was supported by a fellowship from the CIRM Training Grant (EDUC4-12822). The content is solely the responsibility of the authors and does not necessarily represent the official views of the California Institute for Regenerative Medicine. J.I.R. was supported by a Feodor-Lynen fellowship from the Alexander-von-Humboldt Stiftung. We also wish to acknowledge the support of the Chao Family Comprehensive Cancer Center (CFCCC) at the University of California, Irvine, which is supported by the NIH/NCI (grant P30CA062203). Shared resources utilized through the CFCCC include the Experimental Tissue Resource (ETR) as well as the Optical Biology Core (OBC). Finally, we are grateful to the late Z. Werb for her continuous interest and support of this project.

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  1. These authors contributed equally: Kevin Nee, Dennis Ma.

Authors and Affiliations

  1. Department of Biological Chemistry, University of California, Irvine, CA, USA

    Kevin Nee, Dennis Ma, Quy H. Nguyen, Maren Pein, Nicholas Pervolarakis, Jacob Insua-Rodríguez, Yanwen Gong, Grace Hernandez, Hamad Alshetaiwi, Justice Williams, Maha Rauf, Kushal Rajiv Dave, Keerti Boyapati, Aliza Hasnain, Christian Calderon, Anush Markaryan & Kai Kessenbrock

  2. Center for Complex Biological Systems, University of California, Irvine, CA, USA

    Nicholas Pervolarakis & Yanwen Gong

  3. Department of Pathology, University of Hail, Hail, Saudi Arabia

    Hamad Alshetaiwi

  4. Department of Pathology and Laboratory Medicine, University of California Irvine Medical Center, Orange, CA, USA

    Robert Edwards

  5. Department of Surgery, University of California Irvine Medical Center, Orange, CA, USA

    Erin Lin & Ritesh Parajuli

  6. Department of Mathematics, University of California, Irvine, CA, USA

    Peijie Zhou & Qing Nie

  7. The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA, USA

    Peijie Zhou & Qing Nie

  8. Beckman Research Institute at City of Hope, Duarte, CA, USA

    Sundus Shalabi & Mark A. LaBarge

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Contributions

K.K., K.N. and D.M. designed research and supervised research; K.N., D.M., Q.H.N., M.P., J.I.-R.,, G.H., H.A., J.W., M.R., K.R.D., K.B., C.C., A.M., P.S. and D.J. performed research; M.A.L., S.S., X.D., J.R., G.X.Y.Z., C.M.N. and Q.N. contributed new reagents and analytic tools; K.N., N.P., P.Z. and Q.H.N. performed computational analyses; K.N., D.M. and K.K. wrote the paper manuscript; and all authors discussed the results and provided comments and feedback.

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Correspondence to Kai Kessenbrock.

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Extended data

Extended Data Fig. 1 Flow cytometry gating strategy and quality control metrics for scRNA-seq analysis of breast tissues.

a) FACS plots showing gating strategy of mammary epithelial cells in forward and side scatter, singlets gate, dead cell (Sytox + ) and lineage (CD31 + , CD45 + ) exclusion gate. FACS plot on the right-hand side shows gating strategy for basal (Epcam + , CD49f-high) and luminal (Epcam-high, CD49f-low) epithelial cells as well as for stromal cells (Epcam-). b) Faceted UMAP projections of n = 22 NonCarrier and BRCA1+/mut patient scRNA-seq libraries. Each faceted UMAP projection represents all cells per individual patient. NC – NonCarrier, BRCA1 – BRCA1 germline mutation carrier. c) Combined UMAP projection of all cells colored by patient. d) Violin plots depicting UMI counts (number of individual molecules interrogated/droplet) (top) and gene counts (number of unique genes detected/droplet) (bottom) of each individual patient scRNA-seq library. e) Stacked bar plots indicating proportions of cell types detected in individual BRCA1+/mut or NonCarrier samples.

Extended Data Fig. 2 Differential gene expression analysis between fibroblasts and pericytes in the human breast.

a) Heatmap showing expression of top 20 marker genes specifically expressed in fibroblasts and pericytes from scRNA-seq dataset (rows=genes, columns=cells). Yellow represents a positive z-score, purple represents a negative z-score. b) Venn diagram illustrating the number of genes that are mutually or exclusively expressed in fibroblasts and pericytes. Selected marker genes for each category are shown. c) Volcano plot depicting differential gene expression analysis of fibroblasts (green) and pericytes (violet), the Wilcoxon rank sum test is used to determine differentially expressed genes, adjusted p values are determined using the Bonferroni method for multiple testing correction. d) Bar chart showing top 10 GO Terms enriched in all 367 fibroblast-specific genes. e) Bar chart showing top 10 GO Terms enriched in all 217 pericyte-specific genes. f) Dot plot illustrating mRNA expression levels of PDPN and PROCR by fibroblasts and pericytes, respectively. g) FACS plot gated on live cells, singlets, lin-, EpCAM− stromal cells showing distinct populations of PDPN + and PROCR + stromal cells. h) Gene expression analysis of FACS-isolated PROCRmid PDPN + stromal cells by qPCR for selected fibroblast-specific genes. Gene expression normalized to GAPDH and relative expression versus PDPN-PROCR + stromal cells from FACS is shown. Each bar graph shows three points (n = 3), each point represents 1 biologically independent patient’s averaged fold change of a technical triplicate (n = 3), Whisker plots represent the mean and the 25th and 75th quantiles. i) Gene expression analysis of FACS-isolated PROCR + PDPN- stromal cells by qPCR for selected pericyte-specific genes. Gene expression normalized to GAPDH and relative expression versus PROCRmidPDPN + stromal cells from FACS is shown. Each bar graph shows three points (n = 3), each point represents 1 biologically independent patient’s averaged fold change of a technical triplicate (n = 3), Whisker plots represent the mean and the 25th and 75th quantiles.

Extended Data Fig. 3 High-resolution scRNA-seq analysis of BRCA1+/mut epithelial cells shows increase of basal epithelial cells with altered differentiation.

a) Top 10 marker gene heatmap for epithelial cell states in BRCA1+/mut breast tissues. b) Volcano plot showing differentially expressed genes between NonCarrier and BRCA1+/mut basal epithelial cells. P values were determined using the Seurat tobit likelihood-ratio test, the wilcoxon rank sum test is used to determine differentially expressed genes, adjusted p values are determined using the Bonferroni method for multiple testing correction. c) Single-cell western blot (scWB) analyses for KRT14 and KRT19 on FACS-isolated basal epithelial cells from NonCarrier and BRCA1+/mut individuals. Representative regions of scWB chips post electrophoresis and antibody probing. d) Quantification of scWBs of all basal cells analyzed. Data is represented as mean ± SD from at least 1000 cells/individual; NonCarrier n = 3, BRCA1+/mut n = 3. P value was determined with an unpaired two-tailed t-test. e) Relative fluorescence intensity of KRT14 and KRT19 of selected lanes in scWB as indicated in c).

Extended Data Fig. 4 BRCA1+/mut tissues harbor increased numbers of KRT19 + cells that co-express KRT14.

a) Representative IF staining for KRT14 (green) and KRT19 (red) in human mammary tissues from NonCarrier (n = 6) and BRCA1+/mut (n = 6) individuals. Yellow staining indicates epithelial cells that are KRT14/KRT19-double-positive. Scale bar= 50 μm. b) Bar graph depicting percentages of KRT14/KRT19-double-positive cells in lobular and ductal epithelial regions and whole tissue (lobular + ductal regions) of human mammary tissues from NonCarrier (n = 6) and BRCA1+/mut (n = 6) individuals. Values are represented as mean ± SD from counts of at least 5 different random fields per tissue. P values were determined by unpaired two-tailed t-tests.

Extended Data Fig. 5 BRCA1+/mut tissues harbor increased numbers of KRT19 + cells that co-express KRT23.

a) Representative immunofluorescence staining for KRT23 (red) and KRT19 (green) in human mammary tissues from NonCarrier (n = 6) and BRCA1+/mut (n = 6) individuals. Yellow staining indicates epithelial cells that are KRT19/KRT23 double-positive. Scale bar = 50 μm. b) Bar graph depicting percentages of KRT19/KRT23 double-positive cells in lobular and ductal regions of epithelial tissues and whole tissue (lobular + ductal regions) of human mammary tissues from NonCarrier (n = 6) and BRCA1+/mut (n = 6) individuals. Values are represented as mean ± SD from counts of at least 5 different random fields per tissue. P values were determined by unpaired two-tailed t-tests.

Extended Data Fig. 6 Ligand–receptor interaction analysis in NonCarrier breast tissues.

a) Ligand–receptor interactions depicted in Circos plots of ligands expressed by fibroblasts, pericytes or endothelial cells interacting with receptors on epithelial cells in NonCarrier breast tissues. b) Receptor–ligand interaction enrichment scores of GO Terms (GO-Biological Processes 2018) of ligands from NonCarrier fibroblasts (left), pericytes (center), and endothelial cells (right), and epithelial receptors are shown.

Extended Data Fig. 7 BRCA1+/mut vascular cells express elevated levels of NGF which increases branching morphogenesis.

a) UMAP projection of vascular cell states identifying 3 endothelial cell states, 2 pericyte cell states, and lymphatic cells. b) Violin plots of marker genes with enhanced expression in each vascular cell state cluster. c) Volcano plot showing differentially expressed genes between NonCarrier and BRCA1+/mut pericytes, the Wilcoxon rank sum test is used to determine differentially expressed genes, adjusted p values are determined using the Bonferroni method for multiple testing correction. d) Schematic for the generation of hydrogel branching assays. e) Representative images of a BRCA1+/mut organoid in hydrogel branching assay at days 6-9 after seeding. Scale bars = 200 μm. f) Branch growth curves of n = 6 control and n = 6 NGF (100 ng/ml) treated hydrogel branching assay. P value was calculated using CGGC permutation (two-sided) test43.

Extended Data Fig. 8 Additional analyses of pre-CAF signature by type of BRCA1 mutation, parity status and using Kaplan–Meier survival analysis in breast cancer.

a) Pre-CAF gene signature scoring in fibroblast from nulliparous versus parous NonCarrier and BRCA1+/mut patients. Libraries with representation of less than 250 fibroblasts were excluded. Boxplots indicate median and 25% and 75% quantiles respectively, minima and maxima represent the 10th and 90th percentile respectively, p values were determined by Welch two sample t-test. b) Kaplan–Meier (KM) analyses in breast cancer patients, associating the NonCarrier fibroblast signature (left) or pre-CAF fibroblast signature (right) with overall survival. Auto cutoff was used to group samples into signature low and high. HR hazard ratio. P values were determined by log-rank test. KM plots are shown for breast cancer patients with all subtypes, TNBC (ER-,PR-,HER2-), HER2 + , or ER + PR + breast cancers.

Extended Data Fig. 9 Additional data from in situ analysis of MMP3-expressing stromal cells in BRCA1+/mut and NonCarrier samples.

a) Additional representative IF images from ductal and lobular regions in NonCarrier breast tissues stained with anti-MMP3 (red) and anti-PanCK (green) antibodies. DAPI staining is shown in blue. Percentages are indicated of stromal cells that are positive for MMP3. Scale bar = 50 µm. b) Additional representative IF images from ductal and lobular regions in BRCA1+/mut breast tissues stained with anti-MMP3 (red) and anti-PanCK (green) antibodies. DAPI staining is shown in blue. Percentages are indicated of stromal cells that are positive for MMP3. Scale bar = 50 µm.

Extended Data Fig. 10 Fibroblast-derived MMP3 promotes epithelial growth in vitro.

a) FACS plots showing gating strategy for isolation of GFP-transduced human fibroblasts isolated from patient breast tissue in forward and side scatter, singlets gate, and GFP gate. b) Representative images of cocultures after 5 days of seeding. 4000 primary mammary epithelial cells (NonCarrier 32) were cultured alone (No Fibroblasts) or with 1×105 primary mammary fibroblasts (NonCarrier 33 or BRCA1+/mut 19) transduced with lentivirus to express GFP only (+GFP) or GFP and MMP3 ( + MMP3) in Matrigel for 5 days. Fibroblasts are distinguished from epithelial spheres (GFP-negative) with GFP fluorescence. Scale bar = 400 μm. c) Quantification of spheres after 5 days. Values are represented as mean ± SD from 3 separate experiments with 3 triplicate wells per experiment. P values were determined by unpaired two-tailed t-tests. d) Mean values of sphere counts pooled from 3 separate experiments from (c) and Fig. 5c. Values are represented as mean ± SD. Statistical significance between all groups was determined with a one-way ANOVA test. e) 10×105 FACS-isolated epithelial cells from 4 patient samples were seeded in Matrigel and treated with 0.5 µg/mL or 1 μg/mL recombinant MMP3 and spheres were counted after 5 and 10 days. Bar chart values are represented as mean ± SD from triplicates from three separate experiments. P values were determined using unpaired two-tailed t-tests. Representative bright field images of mammospheres after 10 days of culture are shown on the right (scale bar = 400 μm). f) Bar graph depicting fold change in sphere count after 10 days of culture with human recombinant MMP3 compared to control (dotted red line). Values are displayed as mean ± SD from 15 independent experiments (5 different patient samples with 3 separate experiments each). P values were determined using unpaired two-tailed t-tests. g) Primary human breast fibroblasts isolated by FACS from patient sample “NonCarrier 27” were transduced to express mouse MMP3 (mMMP3) and GFP or GFP only. qPCR analysis was performed on transduced fibroblasts in two separate trials with three replicates per group. Amplification plot is shown with the difference in the normalized reporter value of the experimental reaction minus the normalized reporter value generated by the instrument (ΔRn) on the y-axis and the cycle number on the x-axis.

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Supplementary Fig. 1—Western blot images.

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Supplementary Data

Modeling breast cancer risk using a hierarchical cancer progenitor cell model. (a) Comparison of simulated cell population dynamics in 2-fold increased proliferation and control groups for germline progenitor cells (top left), precancerous progenitor cells (top right), precancerous stem cells (bottom left) and cancer stem cells (bottom right). Thick lines: Averaged population dynamics of 2-fold increased proliferation (red) and control group (blue). Gray thin lines indicate stochastic simulation trajectories (sample n = 50 for each group). (b) Schematic model of the assumptions and parameters used to simulate the acquisition of random mutations and consequential fitness change in BRCA1+/mut cells. rcycle is the baseline cell division rate, rdeath is the cell date rate. Parameters are further defined in Supplementary Table 16. (c) The robustness of results with respect to parameter α in the double-exponential distribution of fitness change (Supplementary Table 17) in the calculation of risk ratio with or without increased proliferation (2X and 1X Epithelial proliferation, respectively). The simulation is performed in the time range of 20 years. Violin plots show the distribution of risk ratios of n = 20 patients in each group and boxplots indicate median and 25 and 75% quantiles respectively.

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Unprocessed western blots.

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Nee, K., Ma, D., Nguyen, Q.H. et al. Preneoplastic stromal cells promote BRCA1-mediated breast tumorigenesis. Nat Genet 55, 595–606 (2023). https://doi.org/10.1038/s41588-023-01298-x

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