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Identification of aberrant luminal progenitors and mTORC1 as a potential breast cancer prevention _target in BRCA2 mutation carriers

Nature Cell Biology volume 26, pages 138–152 (2024)Cite this article

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Abstract

Inheritance of a BRCA2 pathogenic variant conveys a substantial life-time risk of breast cancer. Identification of the cell(s)-of-origin of BRCA2-mutant breast cancer and _targetable perturbations that contribute to transformation remains an unmet need for these individuals who frequently undergo prophylactic mastectomy. Using preneoplastic specimens from age-matched, premenopausal females, here we show broad dysregulation across the luminal compartment in BRCA2mut/+ tissue, including expansion of aberrant ERBB3lo luminal progenitor and mature cells, and the presence of atypical oestrogen receptor (ER)-positive lesions. Transcriptional profiling and functional assays revealed perturbed proteostasis and translation in ERBB3lo progenitors in BRCA2mut/+ breast tissue, independent of ageing. Similar molecular perturbations marked tumours bearing BRCA2-truncating mutations. ERBB3lo progenitors could generate both ER+ and ER− cells, potentially serving as cells-of-origin for ER-positive or triple-negative cancers. Short-term treatment with an mTORC1 inhibitor substantially curtailed tumorigenesis in a preclinical model of BRCA2-deficient breast cancer, thus uncovering a potential prevention strategy for BRCA2 mutation carriers.

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Fig. 1: Premenopausal BRCA2mut/+ breast tissue harbours expanded ERBB3lo LP and ML cell populations.
Fig. 2: Characterization of LP subsets by single-cell proteomic and colony forming assays.
Fig. 3: BRCA2mut/+ ERBB3lo LPs transcriptionally upregulate pathways related to translation and proteostasis.
Fig. 4: Dysregulated protein synthesis in BRCA2-deficient breast epithelial cells.
Fig. 5: Ageing-independent features of BRCA2mut/+ ERBB3lo LP cells.
Fig. 6: mTORC1 signature unique to tumours with BRCA2-truncating mutations.
Fig. 7: Mouse model of Brca2 deletion recapitulates features of BRCA2 mutation carriers.
Fig. 8: mTORC1 inhibition delays tumorigenesis in Brca2/Trp53-deficient mice in vivo.

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Data availability

Human RNA-seq, mouse RNA-seq and spatial transcriptomic data that support the findings of this study have been deposited in the Gene Expression Omnibus (GEO) under accession codes GSE233505, GSE232631 and GSE246518, respectively. The human breast cancer data were derived from the TCGA Research Network: http://cancergenome.nih.gov/. The dataset supporting the findings of this study is available at https://www.cbioportal.org/study/summary?id=brca_tcga_pub. Curated gene sets were obtained from the Molecular Signatures Database, https://www.gsea-msigdb.org/gsea/msigdb/index.jsp. The data supporting the findings of this study are available within this paper, the extended data, the source data and the supplementary information. Source data are provided with this paper.

References

  1. Petr, M. A., Tulika, T., Carmona-Marin, L. M. & Scheibye-Knudsen, M. Protecting the aging genome. Trends Cell Biol. 30, 117–132 (2020).

    Article  CAS  PubMed  Google Scholar 

  2. Lord, C. J. & Ashworth, A. BRCAness revisited. Nat. Rev. Cancer 16, 110–120 (2016).

    Article  CAS  PubMed  Google Scholar 

  3. Roy, R., Chun, J. & Powell, S. N. BRCA1 and BRCA2: different roles in a common pathway of genome protection. Nat. Rev. Cancer 12, 68–78 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Venkitaraman, A. R. Cancer suppression by the chromosome custodians, BRCA1 and BRCA2. Science 343, 1470–1475 (2014).

    Article  CAS  PubMed  Google Scholar 

  5. Kuchenbaecker, K. B. et al. Risks of breast, ovarian, and contralateral breast cancer for BRCA1 and BRCA2 mutation carriers. JAMA 317, 2402–2416 (2017).

    Article  CAS  PubMed  Google Scholar 

  6. Breast Cancer Association Consortium. Pathology of tumors associated with pathogenic germline variants in 9 breast cancer susceptibility genes. JAMA Oncol. 8, e216744 (2022).

    Article  PubMed Central  Google Scholar 

  7. Visvader, J. E. Cells of origin in cancer. Nature 469, 314–322 (2011).

    Article  CAS  PubMed  Google Scholar 

  8. Molyneux, G. et al. BRCA1 basal-like breast cancers originate from luminal epithelial progenitors and not from basal stem cells. Cell Stem Cell 7, 403–417 (2010).

    Article  CAS  PubMed  Google Scholar 

  9. Nolan, E. et al. RANK ligand as a potential _target for breast cancer prevention in BRCA1-mutation carriers. Nat. Med. 22, 933–939 (2016).

    Article  CAS  PubMed  Google Scholar 

  10. Lim, E. et al. Aberrant luminal progenitors as the candidate _target population for basal tumor development in BRCA1 mutation carriers. Nat. Med. 15, 907–913 (2009).

    Article  CAS  PubMed  Google Scholar 

  11. Sigl, V. et al. RANKL/RANK control Brca1 mutation-driven mammary tumors. Cell Res. 26, 761–774 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Karaayvaz-Yildirim, M. et al. Aneuploidy and a deregulated DNA damage response suggest haploinsufficiency in breast tissues of BRCA2 mutation carriers. Sci. Adv. 6, eaay2611 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Shalabi, S. F. et al. Evidence for accelerated aging in mammary epithelia of women carrying germline BRCA1 or BRCA2 mutations. Nat. Aging 1, 838–849 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Balko, J. M. et al. The receptor tyrosine kinase ErbB3 maintains the balance between luminal and basal breast epithelium. Proc. Natl Acad. Sci. USA 109, 221–226 (2012).

    Article  CAS  PubMed  Google Scholar 

  15. Shehata, M. et al. Phenotypic and functional characterization of the luminal cell hierarchy of the mammary gland. Breast Cancer Res. 14, R134 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Teh, C. E. et al. Deep profiling of apoptotic pathways with mass cytometry identifies a synergistic drug combination for killing myeloma cells. Cell Death Differ. 27, 2217–2233 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Gray, G. K. et al. A human breast atlas integrating single-cell proteomics and transcriptomics. Dev. Cell 57, 1400–1420.e7 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Knapp, D., Kannan, N., Pellacani, D. & Eaves, C. J. Mass cytometric analysis reveals viable activated caspase-3+ luminal progenitors in the normal adult human mammary gland. Cell Rep. 21, 1116–1126 (2017).

    Article  CAS  PubMed  Google Scholar 

  19. Kelly, P. N. & Strasser, A. The role of Bcl-2 and its pro-survival relatives in tumourigenesis and cancer therapy. Cell Death Differ. 18, 1414–1424 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Cha, J. H., Chan, L. C., Li, C. W., Hsu, J. L. & Hung, M. C. Mechanisms controlling PD-L1 expression in cancer. Mol. Cell 76, 359–370 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Bernardo, G. M. et al. FOXA1 represses the molecular phenotype of basal breast cancer cells. Oncogene 32, 554–563 (2013).

    Article  CAS  PubMed  Google Scholar 

  22. Carroll, J. S. et al. Chromosome-wide mapping of estrogen receptor binding reveals long-range regulation requiring the forkhead protein FoxA1. Cell 122, 33–43 (2005).

    Article  CAS  PubMed  Google Scholar 

  23. Mote, P. A. et al. Germ-line mutations in BRCA1 or BRCA2 in the normal breast are associated with altered expression of estrogen-responsive proteins and the predominance of progesterone receptor A. Genes Chromosomes Cancer 39, 236–248 (2004).

    Article  CAS  PubMed  Google Scholar 

  24. Shoker, B. S., Jarvis, C., Sibson, D. R., Walker, C. & Sloane, J. P. Oestrogen receptor expression in the normal and pre-cancerous breast. J. Pathol. 188, 237–244 (1999).

    Article  CAS  PubMed  Google Scholar 

  25. Rios, A. C. et al. Intraclonal plasticity in mammary tumors revealed through large-scale single-cell resolution 3D imaging. Cancer Cell 35, 618–632 (2019).

    Article  CAS  PubMed  Google Scholar 

  26. Hetz, C. & Saxena, S. ER stress and the unfolded protein response in neurodegeneration. Nat. Rev. Neurol. 13, 477–491 (2017).

    Article  CAS  PubMed  Google Scholar 

  27. Liu, G. Y. & Sabatini, D. M. mTOR at the nexus of nutrition, growth, ageing and disease. Nat. Rev. Mol. Cell Biol. 21, 183–203 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Mossmann, D., Park, S. & Hall, M. N. mTOR signalling and cellular metabolism are mutual determinants in cancer. Nat. Rev. Cancer 18, 744–757 (2018).

    Article  CAS  PubMed  Google Scholar 

  29. Elkon, R. et al. Myc coordinates transcription and translation to enhance transformation and suppress invasiveness. EMBO Rep. 16, 1723–1736 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Pourdehnad, M. et al. Myc and mTOR converge on a common node in protein synthesis control that confers synthetic lethality in Myc-driven cancers. Proc. Natl Acad. Sci. USA 110, 11988–11993 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Pal, B. et al. A single-cell RNA expression atlas of normal, preneoplastic and tumorigenic states in the human breast. EMBO J. 40, e107333 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Blanco, S. et al. Stem cell function and stress response are controlled by protein synthesis. Nature 534, 335–340 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Liu, J., Xu, Y., Stoleru, D. & Salic, A. Imaging protein synthesis in cells and tissues with an alkyne analog of puromycin. Proc. Natl Acad. Sci. USA 109, 413–418 (2012).

    Article  CAS  PubMed  Google Scholar 

  34. Signer, R. A., Magee, J. A., Salic, A. & Morrison, S. J. Haematopoietic stem cells require a highly regulated protein synthesis rate. Nature 509, 49–54 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Lewis, C. M. et al. Telomerase immortalization of human mammary epithelial cells derived from a BRCA2 mutation carrier. Breast Cancer Res. Treat. 99, 103–115 (2006).

    Article  CAS  PubMed  Google Scholar 

  36. Johnson, S. C., Rabinovitch, P. S. & Kaeberlein, M. mTOR is a key modulator of ageing and age-related disease. Nature 493, 338–345 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Osako, T. et al. Age-correlated protein and transcript expression in breast cancer and normal breast tissues is dominated by host endocrine effects. Nat. Cancer 1, 518–532 (2020).

    Article  CAS  PubMed  Google Scholar 

  38. Szklarczyk, D. et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 51, D638–D646 (2023).

    Article  CAS  PubMed  Google Scholar 

  39. The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).

    Article  PubMed Central  Google Scholar 

  40. Hetz, C., Zhang, K. & Kaufman, R. J. Mechanisms, regulation and functions of the unfolded protein response. Nat. Rev. Mol. Cell Biol. 21, 421–438 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Luo, B. & Lee, A. S. The critical roles of endoplasmic reticulum chaperones and unfolded protein response in tumorigenesis and anticancer therapies. Oncogene 32, 805–818 (2013).

    Article  CAS  PubMed  Google Scholar 

  42. Casas, C. GRP78 at the centre of the stage in cancer and neuroprotection. Front. Neurosci. 11, 177 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Shapiro, D. J., Livezey, M., Yu, L., Zheng, X. & Andruska, N. Anticipatory UPR activation: a protective pathway and _target in cancer. Trends Endocrinol. Metab. 27, 731–741 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Jamieson, P. R. et al. Derivation of a robust mouse mammary organoid system for studying tissue dynamics. Development 144, 1065–1071 (2017).

    CAS  PubMed  Google Scholar 

  45. Cerezo, M. et al. Compounds triggering ER stress exert anti-melanoma effects and overcome BRAF inhibitor resistance. Cancer Cell 29, 805–819 (2016).

    Article  CAS  PubMed  Google Scholar 

  46. Melchor, L. et al. Identification of cellular and genetic drivers of breast cancer heterogeneity in genetically engineered mouse tumour models. J. Pathol. 233, 124–137 (2014).

    Article  CAS  PubMed  Google Scholar 

  47. Aapro, M. et al. Adverse event management in patients with advanced cancer receiving oral everolimus: focus on breast cancer. Ann. Oncol. 25, 763–773 (2014).

    Article  CAS  PubMed  Google Scholar 

  48. Lee, B. J. et al. Selective inhibitors of mTORC1 activate 4EBP1 and suppress tumor growth. Nat. Chem. Biol. 17, 1065–1074 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Xie, Z. et al. Gene set knowledge discovery with Enrichr. Curr. Protoc. 1, e90 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Jonkers, J. et al. Synergistic tumor suppressor activity of BRCA2 and p53 in a conditional mouse model for breast cancer. Nat. Genet. 29, 418–425 (2001).

    Article  CAS  PubMed  Google Scholar 

  51. Shultz, L. D. et al. Human lymphoid and myeloid cell development in NOD/LtSz-scid IL2Rγnull mice engrafted with mobilized human hemopoietic stem cells. J. Immunol. 174, 6477–6489 (2005).

    Article  CAS  PubMed  Google Scholar 

  52. Zunder, E. R. et al. Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm. Nat. Protoc. 10, 316–333 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Crowell, H. L. et al. An R-based reproducible and user-friendly preprocessing pipeline for CyTOF data. F1000Res. 9, 1263 (2020).

    Article  PubMed  Google Scholar 

  54. Finck, R. et al. Normalization of mass cytometry data with bead standards. Cytometry A 83, 483–494 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Nowicka, M. et al. CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets. F1000Res. 6, 748 (2017).

    Article  PubMed  Google Scholar 

  56. Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Liao, P. S., Chew, T. S. & Chung, P. C. A fast algorithm for multilevel thresholding. J. Inf. Sci. Eng. 17, 713–727 (2001).

    Google Scholar 

  58. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  60. McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Chen, Y., Lun, A. T. & Smyth, G. K. From reads to genes to pathways: differential expression analysis of RNA-seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Res. 5, 1438 (2016).

    PubMed  PubMed Central  Google Scholar 

  62. Dekkers, J. F. et al. Modeling breast cancer using CRISPR-Cas9-mediated engineering of human breast organoids. J. Natl Cancer Inst. 112, 540–544 (2020).

    Article  PubMed  Google Scholar 

  63. Shackleton, M. et al. Generation of a functional mammary gland from a single stem cell. Nature 439, 84–88 (2006).

    Article  CAS  PubMed  Google Scholar 

  64. Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. JCO Precis. Oncol. 1, 1–16 (2017).

  65. Liao, Y., Smyth, G. K. & Shi, W. The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads. Nucleic Acids Res. 47, e47 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank C. Lewis and D. Livingston for providing cell lines and mice, M. Lazarou and C. Pratt for discussions and WEHI Bioservices, FACS, Imaging, Histology, Genomics, Information Technology and SCORE facilities. We thank B. Mann, L. Taylor and colleagues from the RMH Tissue Bank and the Victorian Cancer Biobank; H. Thorne, E. Niedermayr and kConFab staff; the University of Melbourne MCFP; and the Victorian Node of ANFF. We thank Wurundjeri Elders, community and country. This work was supported by the NBCF (grant no. IIRS-20-022) and NHMRC grants (nos. 1054618, 1078730, 1100807, 1113133, 1153049, 1175960), NHMRC IRIISS, the Victorian State Government Operational Infrastructure Support, the Breast Cancer Research Foundation, the Two Sisters Foundation and M. Heine and family. Y.C. was supported by an MRFF Investigator Grant (no. 1176199); C.E.T. by VCA Fellowship no. MCRF20026; and G.J.L., G.K.S. and J.E.V. by NHMRC Fellowships (G.J.L. nos. 1078730, 1175960; G.K.S. no. 1058892; J.E.V. nos. 1037230, 1102742).

Author information

Author notes

  1. These authors contributed equally: Rachel Joyce, Rosa Pascual.

Authors and Affiliations

  1. ACRF Cancer Biology and Stem Cells Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia

    Rachel Joyce, Rosa Pascual, Luuk Heitink, Bianca D. Capaldo, François Vaillant, Minhsuang Tsai, Elliot Surgenor, Felicity C. Jackling, Michael J. G. Milevskiy, Xiaoyu Song, Yunshun Chen, Geoffrey J. Lindeman & Jane E. Visvader

  2. Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia

    Rachel Joyce, Rosa Pascual, Luuk Heitink, Bianca D. Capaldo, François Vaillant, Pradeep Rajasekhar, Marie Trussart, Michael J. G. Milevskiy, Xiaoyu Song, Mengbo Li, Charis E. Teh, Daniel H. D. Gray, Yunshun Chen & Jane E. Visvader

  3. Department of Anatomical Pathology, Royal Melbourne Hospital, Parkville, Victoria, Australia

    Michael Christie

  4. Advanced Technology and Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia

    Casey J. A. Anttila & Pradeep Rajasekhar

  5. Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia

    Marie Trussart, Mengbo Li, Gordon K. Smyth & Yunshun Chen

  6. Immunology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia

    Charis E. Teh & Daniel H. D. Gray

  7. School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria, Australia

    Gordon K. Smyth

  8. Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia

    Geoffrey J. Lindeman

  9. Parkville Familial Cancer Centre and Department of Medical Oncology, The Royal Melbourne Hospital and Peter MacCallum Cancer Centre, Parkville, Victoria, Australia

    Geoffrey J. Lindeman

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Contributions

R.J., R.P., G.J.L. and J.E.V. designed the study. R.J., R.P., L.H., F.V., B.D.C., M.T., X.S., E.S., F.C.J., C.J.A.A. and M.J.G.M. performed experiments. M.C. performed the pathology scoring and analysis. M.L., M.T., Y.C. and G.K.S. performed bioinformatic analyses. C.E.T. and D.H.D.G. provided tools and advice. R.J., R.P., G.J.L. and J.E.V. interpreted data. R.J., R.P. and J.E.V. wrote the manuscript.

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Correspondence to Geoffrey J. Lindeman or Jane E. Visvader.

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Extended data

Extended Data Fig. 1 Pre-menopausal BRCA2mut/+ breast tissue harbours aberrant luminal cell populations.

a, Dot plot of ages (n = 22 BRCA2 mutation carriers and n = 36 non-carriers) for breast tissue specimens used for FACS experiments presented in Fig. 1; two-tailed unpaired t-test. Error bars, mean ± s.e.m. b, FACS gating strategy for isolation of human mammary epithelial cell subsets from breast tissue; cells were gated using forward and side-scatter areas, doublets were removed by gating on single cells using forward-scatter height and areas, then propidium iodine-negative live cells were gated. Lineage (CD31, CD45, CD235α)-negative cells were then selected and fractionated into mature luminal (ML), luminal progenitor (LP) and basal cells on the basis of EPCAM and CD49f expression. c, Bar graph of ERBB3 expression measured as fold change in ERBB3 median fluorescence intensity (MFI) in mammary epithelial cell populations compared to ERBB3– FMO controls, from n = 5 premenopausal WT individuals. ***P = 0.0001, ordinary one-way ANOVA. Error bars, mean ± s.e.m. d, Bar graph of the percentage of ERBB3lo cells within the LP and ML compartments of age-matched, >20 to <45 year-old pre-menopausal WT (n = 28) and BRCA2mut/+ individuals (n = 13). Error bars, mean ± s.e.m. **P = 0.0016, *P = 0.0117, two-tailed unpaired t-test. e, Representative images of comet tails from untreated versus 3 Gy-irradiated luminal subsets from premenopausal BRCA2 mutation carriers (n = 3) and non-carriers (n = 3). UT, untreated. IR, irradiated. Representative comet tails are manually outlined in red. Scale bar, 50 μm. f, Bar graph of mean normal olive tail moments of comet tails visualised in untreated and 3 Gy-irradiated ERBB3lo LP, ERBB3hi LP and ML cells (n = 3 BRCA2mut/+ and n = 3 WT individuals). A minimum of 50 cells were quantified per sample. **P = 0.0011, ****P < 0.0001, 2-way ANOVA. Error bars, mean ± s.e.m.

Source data

Extended Data Fig. 2 Characterization of LP subsets by single-cell proteomic analysis.

a, UMAP for WT (n = 5) lineage-depleted (total) cells coloured by cell cluster: ML (blue), LP (green), basal (pink) and stroma (purple) (left). UMAP for WT epithelial cells (centre) and UMAP for WT luminal cells (right). b, WT epithelial UMAPs coloured according to protein expression of lineage markers. c, Dot plot showing protein expression in basal, LP and ML cells. Colour intensity corresponds to median marker expression; dot size corresponds to the percentage of positive cells within each population. d, UMAP showing integrated CyTOF profiles of epithelial cells from WT (n = 5) and BRCA2mut/+ (n = 5) individuals. e, Stacked bar plot indicating relative abundance of each epithelial cluster in WT and BRCA2mut/+ samples. f, Boxplots showing marker expression within the ERBB3lo and ERBB3hi in ML (blue) and LP (green) compartments for BRCA2mut/+ (yellow, n = 5) and WT (grey, n = 5) individuals. Boxplots show median, quartiles, minimum and maximum. P-values, two-tailed unpaired t-test.

Source data

Extended Data Fig. 3 Strong contiguous ER staining resolved by 3D confocal imaging in BRCA2 mutant tissue.

a, Graph of ages (years) for anti-ER-immunostained breast tissue sections from n = 38 WT and n = 38 BRCA2mut/+ females. Error bars, mean ± s.e.m, two-tailed unpaired t-test. b, Graphs showing quantification of the percentage of ER+ cells in lobules and ducts (left) and ER expression (H-score, right) in breast tissue sections from age-matched WT and BRCA2mut/+ individuals (n = 38 for each genotype). Error bars, mean ± s.e.m, two-tailed unpaired t-test. c, 3D confocal overview (wholemount) images (left panels) and optical sections (right panels) of ER (green), E-cadherin (orange) and Keratin 5 (blue)-immunostained breast tissue from a 40 year-old BRCA2mut/+ carrier (BRCA2mut/+ Pt. 1), a 46 year-old BRCA2mut/+ female (BRCA2mut/+ Pt. 2), and 29 year-old non-carrier (WT Pt. 1). BRCA2mut/+ (n = 6) and WT (n = 5) age-matched individuals. Scale bar, 200 μm.

Source data

Extended Data Fig. 4 BRCA2mut/+ ERBB3lo luminal progenitors transcriptionally upregulate pathways related to translation and proteostasis.

a, Bar graphs of mean MKI67 and FOXA1 gene expression (log counts per million, log cpm) in ERBB3lo/hi LP cells from BRCA2mut/+ (n = 6) and ERBB3lo (n = 6) and ERBB3hi (n = 8) LP cells from WT individuals. b, STRING graphic (left) of protein-protein interactions for proteins encoded by genes upregulated in ERBB3hi versus ERBB3lo LP cells in WT individuals only. Lines reflect full STRING network (edges indicate both functional and physical protein associations), network edges reflect confidence (line thickness indicates strength of data support). Minimum required interaction score: highest confidence (0.900). Selected significantly enriched GO Biological Processes ‘Cell cycle’ and ‘Cell division’ are highlighted as coloured nodes in the network and correspond to the bar graph (right) of enrichment. c, Mean-difference plots (left) of significantly upregulated (red), downregulated (blue) and non-differentially expressed (black) genes between ERBB3hi and ERBB3lo LP cells from pre-menopausal WT (n = 6 ERBB3lo, n = 8 ERBB3hi) and BRCA2mut/+ (n = 6 ERBB3lo/hi) individuals; and bar graph (right) showing total numbers of differentially expressed genes (DEG) between ERBB3hi and ERBB3lo LP cells in BRCA2mut/+ and WT females. d, Bar graph of significantly enriched GSEA Hallmarks in ERBB3lo vs ERBB3hi BRCA2mut/+ LP cells. P-value by two-sided competitive gene-set test, no multiple comparisons adjustment. e, Significantly enriched GO and KEGG groups in WT ERBB3lo versus ERBB3hi LP cells. P-value by one-sided hypergeometric test, no multiple comparisons adjustment. f, Representative signature expression heatmaps of various cell types (SingleR) overlaid onto hematoxylin-eosin-stained breast tissue from n = 2 BRCA2mut/+ and n = 2 WT individuals. g, Bar graph of significantly enriched MSigDB C2 gene-sets in BRCA2mut/+ vs WT lobules (Visium 10x). P-value by two-sided competitive gene-set test, FDR adjusted P-values. h, Graph of patient ages (in years) for preneoplastic breast tissue (n = 7 WT, n = 9 BRCA2mut/+) that was immunostained for phospho-S6 expression; two-tailed unpaired t-test. Error bars, mean ± s.e.m. i, Western blot analysis of phospho-p70 S6KThr389, total p70 S6K and GAPDH expression in BRCA2+/+ or BRCA2mut/+ hTERT-immortalised cell lines (n = 4 per condition, n = 2 independent experiments). j, Western blot analysis of phospho-AKTSer473, pan-AKT and vinculin expression in BRCA2+/+ or BRCA2mut/+ hTERT-immortalised cell lines (n = 4 for each condition, n = 2 independent experiments).

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Extended Data Fig. 5 Molecular alterations associated with BRCA2 mutation status and aging.

a, Dot plots showing the contribution of LP, ML and basal cells to Lin– breast cells from post-menopausal (postmen) WT (n = 10) and BRCA2mut/+ (n = 9) and pre-menopausal (premen) WT (n = 48) and BRCA2mut/+ individuals (n = 25 ML, LP; n = 26 basal) **P = 0.0063, 2-way ANOVA. Error bars, mean ± s.e.m. b, Bar graph of significantly upregulated KEGG pathways in post- vs pre-menopausal mammary subsets. c, Barcode plots for gene-set enrichment in post-menopausal vs pre-menopausal LP (above) or ML (below) cells. d, STRING graphic (left) of protein-protein interactions for proteins encoded by genes upregulated by both BRCA2mut/+ ERBB3lo LP cells (vs ERBB3hi LP) and post-menopausal LP cells (vs pre-menopausal LP) in WT individuals only. Lines reflect full STRING network (edges indicate both functional and physical protein associations), network edges reflect confidence (line thickness indicates strength of data support). Minimum required interaction score: highest confidence (0.900). Significantly enriched KEGG pathway ‘spliceosome’ genes are highlighted as coloured nodes in the network and correspond to the bar graph (right) of enrichment. e, Western blot analysis for BRCA2 following CRISPR-Cas9 editing in MCF-10A cells using two independent sgRNAs BRCA2 #1 and #2 versus a control (sgRNA non-_target). Each lane represents an independent experiment. Vinculin was used as a loading control. f, Representative western blot analysis (left) and quantification (right) of phospho-S6Ser235/236 in MCF10-A cells normalized to vinculin loading control and to the non-_target (NT) control values in the same blot (n = 4 for each group). α-tubulin is shown as an additional loading control. Data points represent independent experiments, error bars indicate mean ± s.e.m. *P = 0.0277, **P = 0.0015, two-tailed unpaired t-test. g, Western blot analysis for BRCA1 and phospho-S6 Ser235/236 following CRISPR-Cas9 editing in MCF-10A cells using four independent Doxycycline-inducible sgRNAs (sgRNA BRCA1) compared to control cells (sgRNA non-_target). One replicate per guide. Vinculin and Hsp70 were used as loading control. Same samples are shown for both blots.

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Extended Data Fig. 6 Characterization of a Brca2/Trp53-deficient mouse model of tumorigenesis.

a, Representative immunostaining of an ER+KI67+ Brca2Δ/ΔTrp53Δ/fl mammary tumour (n = 21). Scale bar, 50 μm. b, Graphs showing QuPath quantification of the percentage of ER+ (left) and KI67+ (right) cells in tumours from Brca2Δ/ΔTrp53Δ/fl mice (n = 12), where dots of the same colour reflect multiple tumours isolated from one mouse. A single intensity measurement threshold of 0.2 was set to distinguish DAB-positive versus negative cells, and the Nucleus: DAB OD mean intensity feature was measured. c, Representative H&E staining of preneoplastic (14-week-old, n = 10 Brca2fl/flTrp53fl/+ and n = 11 Brca2Δ/ΔTrp53Δ/fl mice) and hyperplastic (20-week-old, n = 12 Brca2fl/flTrp53fl/+ and n = 8 Brca2Δ/ΔTrp53Δ/fl mice) mammary gland sections. Scale bar, 50 μm. d, FACS gating strategy for isolating mammary epithelial cell subsets from murine mammary glands. Cells were gated using forward and side-scatter areas, doublets were removed, and then 7-AAD-negative (live) cells were gated. Lineage-negative cells were fractionated based on CD24 and CD29 expression. CD24hiCD29lo luminal cells were further fractionated into HR+/– LP and ML cells according to Sca-1 and CD14 expression.

Extended Data Fig. 7 Molecular profiling of Brca2/Trp53-deficient epithelial subsets and mTORC1 inhibition as a potential chemoprevention agent in Brca2-deficient mice.

a, Multidimensional scaling plot of the transcriptomic profiles for ML, LP and basal cells from 14-week-old control (Brca2fl/flTrp53fl/+) and Brca2KO (Brca2Δ/ΔTrp53Δ/fl) mice. b, Bar graph of log-fold change (FC) mean RNA expression in Brca2KO compared to control ML cells. c, Bar plot for the top 5 significantly upregulated KEGG pathways in Brca2KO versus control ML cells. d, Quantification of p-4E-BP1 levels normalized to total 4E-BP1 in sorted LP cells for western blot data shown in Fig. 7m. Error bars, mean ± s.e.m. *P = 0.0239, two-tailed unpaired t-test. e, Representative western blot analysis for BiP and vinculin in sorted LP cells from 14-week-old mice (n = 5 control, n = 5 Brca2KO). f, Western blot analysis for phospho-S6Ser235/236 and vinculin (loading control) in control and Brca2Het (MMTV-creT/+Brca2fl/+Trp53fl/+) organoids treated either with vehicle (ethanol) or rapamycin (1 nM, 48 hours) (n = 2 experiments). g, Graph of prevention study mouse weights (n = 12 mice per arm, showing one experimental replicate out of three). h, Representative immunostaining for phospho-S6 Ser235/236 in lung from vehicle- and everolimus-treated mice (8 weeks) (n = 2). Scale bar, 50 μm. i, Kaplan-Meier curves showing tumour-free rates of everolimus (median survival = 133 days) and vehicle treated mice (median survival = 110 days) following 9-week treatment (n = 12 mice per arm). Vertical tick represents censored animal. P-value, Log-rank (Mantel-Cox) test.

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Supplementary information

Reporting Summary

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Supplementary Tables 1–7. Individual titles are included in the Excel file.

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Joyce, R., Pascual, R., Heitink, L. et al. Identification of aberrant luminal progenitors and mTORC1 as a potential breast cancer prevention _target in BRCA2 mutation carriers. Nat Cell Biol 26, 138–152 (2024). https://doi.org/10.1038/s41556-023-01315-5

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