tokenpocket钱包下载app苹果|breast

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breast 在英语-中文（简体）词典中的翻译

breastnoun uk

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/brest/

breast noun

(WOMAN)

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B1 [ C ] either of the two soft, rounded parts of a woman's chest that produce milk after she has a baby

乳房

When a woman becomes pregnant her breasts tend to grow larger.

女性怀孕后，乳房会增大。

breast cancer

乳腺癌

Do you think she's had breast implants?

你觉得她做过隆胸手术吗？

更多范例减少例句Antibodies found in breast milk protect newborn babies against infection.The study showed that one in twelve women is likely to develop breast cancer.A lot of mothers find early weaning from breast milk more convenient.a silicone breast implantThey found a malignant tumour in her breast.

breast noun

(BIRD/ANIMAL)

[ C ] the front part of a bird's body

（鸟的）前胸，胸部

A robin is easy to identify because of its red breast.

知更鸟因其胸脯是红色的很容易辨认。

[ U or C ] the meat from the front part of the body of a bird or other animal

鸡脯肉;（动物的）胸脯肉

I had a cold chicken breast and a salad for lunch.

我午饭吃的是冷鸡脯肉和色拉。

breast of turkey

火鸡的前胸肉

pigeon breasts

鸽子前胸肉

breast noun

(CLOTHING)

[ C ] the part of a piece of clothing that covers a person's chest

上衣前部

He put a silk hanky in his breast pocket (= a pocket on the top front part of a shirt or coat).

他将一条丝绸手帕放入上衣口袋。

breast noun

(CHEST)

[ C ] literary a person's chest

（人的）胸部，胸膛

The dagger entered his breast.

匕首插入了他的胸膛。

[ C ] literary the centre of a person's feelings

心窝，胸怀

A feeling of love surged in his breast.

他心中涌起一阵爱意。

(breast在剑桥英语-中文（简体）词典的翻译 © Cambridge University Press)

breast的翻译

中文（繁体）

女性, 乳房, 鳥…

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pecho, seno, mama…

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seio, peito, mama…

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स्त्रीच्या छातीच्या दोन मऊ, गोलाकार भागांपैकी एक जो तिला बाळ झाल्यानंतर दूध तयार करतो…

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（女性の）胸, 乳房, （鶏などの）胸肉…

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göğüs, meme, kuşların göğüs kısmı…

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sein [masculine], blanc [masculine], sein…

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pit…

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borst, het hoofd bieden, boven op … komen…

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ஒரு குழந்தையைப் பெற்ற பிறகு பால் உற்பத்தி செய்யும் ஒரு பெண்ணின் மார்பின் இரண்டு மென்மையான, வட்டமான பகுதிகள்…

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bryst, stille sig op imod, nå toppen…

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bröst, gå (simma) rätt emot, möta…

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buah dada, dada, mengharungi…

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die Brust, ankämpfen gegen, bewältigen…

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bryst [neuter], pupp [masculine], bryststykke [neuter]…

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پستان, چھاتی…

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грудна залоза, груди, чинити опір…

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грудь, грудка…

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ثَدي, نَهْد, لَحم الصدْر…

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เต้านมผู้หญิง, หน้าอก, เผชิญหน้า…

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vú, ngực, đối diện…

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pierś, stawiać czoło, wspiąć się…

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가슴, 가슴살…

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seno, petto, mammella…

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breast-beating, at breastbeating

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惯用语

beat your breast/chest idiom

make a clean breast of it idiom

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flexitarian

A flexitarian way of eating consists mainly of vegetarian food but with some meat.

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A spatially resolved single-cell genomic atlas of the adult human breast | Nature

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Article

Published: 28 June 2023

A spatially resolved single-cell genomic atlas of the adult human breast

Tapsi Kumar

ORCID: orcid.org/0000-0003-2825-33871,2 na1, Kevin Nee3 na1, Runmin Wei1 na1, Siyuan He1,2 na1, Quy H. Nguyen3 na1, Shanshan Bai1, Kerrigan Blake4,5, Maren Pein

ORCID: orcid.org/0000-0002-7906-59603,4, Yanwen Gong3,5, Emi Sei1, Min Hu1, Anna K. Casasent1, Aatish Thennavan1, Jianzhuo Li1, Tuan Tran1, Ken Chen

ORCID: orcid.org/0000-0003-4013-52796, Benedikt Nilges7, Nachiket Kashikar7, Oliver Braubach8, Bassem Ben Cheikh8, Nadya Nikulina8, Hui Chen9, Mediget Teshome10, Brian Menegaz

ORCID: orcid.org/0000-0002-3020-579011, Huma Javaid11, Chandandeep Nagi11, Jessica Montalvan11, Tatyana Lev4,5, Sharmila Mallya4, Delia F. Tifrea12, Robert Edwards12, Erin Lin12, Ritesh Parajuli12, Summer Hanson

ORCID: orcid.org/0000-0002-4106-981913, Sebastian Winocour14, Alastair Thompson14, Bora Lim

ORCID: orcid.org/0000-0002-4182-605815 na2, Devon A. Lawson

ORCID: orcid.org/0000-0003-1692-05174 na2, Kai Kessenbrock

ORCID: orcid.org/0000-0003-0410-61043 na2 & …Nicholas Navin

ORCID: orcid.org/0000-0002-2106-86241,2,6 na2 Show authors

Nature

volume 620, pages 181–191 (2023)Cite this article

30k Accesses

13 Citations

315 Altmetric

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Subjects

Gene expressionGene expression profilingGene regulatory networks

AbstractThe adult human breast is comprised of an intricate network of epithelial ducts and lobules that are embedded in connective and adipose tissue1,2,3. Although most previous studies have focused on the breast epithelial system4,5,6, many of the non-epithelial cell types remain understudied. Here we constructed the comprehensive Human Breast Cell Atlas (HBCA) at single-cell and spatial resolution. Our single-cell transcriptomics study profiled 714,331 cells from 126 women, and 117,346 nuclei from 20 women, identifying 12 major cell types and 58 biological cell states. These data reveal abundant perivascular, endothelial and immune cell populations, and highly diverse luminal epithelial cell states. Spatial mapping using four different technologies revealed an unexpectedly rich ecosystem of tissue-resident immune cells, as well as distinct molecular differences between ductal and lobular regions. Collectively, these data provide a reference of the adult normal breast tissue for studying mammary biology and diseases such as breast cancer.

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Fig. 1: Major cell types of the adult human breast.Fig. 2: Spatial analysis of major breast cell types.Fig. 3: Epithelial cells of the human breast.Fig. 4: Immune cell ecosystem in human breast tissues.Fig. 5: Breast fibroblasts and adipocytes.Fig. 6: Vascular, perivascular and lymphatic cells in the human breast.

Data availability

The HBCA website can be viewed at http://www.breastatlas.org. The data are available at the Gene Expression Omnibus (GSE195665). The data are also available from CZI in the CELLxGENE database (https://cellxgene.cziscience.com/collections/4195ab4c-20bd-4cd3-8b3d-65601277e731).

Code availability

The scripts associated with the analysis are available at GitHub (https://github.com/navinlabcode/HumanBreastCellAtlas).

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Download referencesAcknowledgementsThis work was supported by major funding from the Chan-Zuckerberg Initiative (CZI) SEED Network Grant (CZF2019-002432); and grants to N. Navin from the NIH National Cancer Institute (RO1CA240526, RO1CA236864, 1R01CA234496, F30CA243419), the CPRIT Single Cell Genomics Center (RP180684), the American Cancer Society (132551-RSG-18-194-01-DDC). N. Navin is an AAAS Fellow, AAAS Wachtel Scholar, Damon-Runyon Rachleff Innovator, Andrew Sabin Fellow and Jack & Beverly Randall Innovator. T.K. is funded by the NCI T32 Translational Genomics Fellowship and Rosalie B. Hite fellowship. This study was supported by the MD Anderson Sequencing Core Facility Grant (CA016672). M.P. is supported by a fellowship from the CIRM Training Grant (EDUC4-12822). The work was also supported by a Damon-Runyon Quantitative Biology Postdoctoral Fellow to R.W. We thank B. Marshall, N. Tavares and J. Cool for their guidance and support; J. Waters, S. Stingley and L. Vann from for their support on this project; J. Wiley, A. Wood, A. Alexander and A. Contreras for clinical support; A. Longworth, S. Mallya and M. Curran for their advice; Y. Lin and R. Ye at MD Anderson for help with experiments; J. Zamanian and J. Yu-Sheng Chien for assistance with data depositing; and all of the women who participated in the HBCA and donated their breast tissue to this project. We thank Enable Medicine for their help with CODEX data generation. This publication is part of the HCA (www.humancellatlas.org).Author informationAuthor notesThese authors contributed equally: Tapsi Kumar, Kevin Nee, Runmin Wei, Siyuan He, Quy H. NguyenThese authors jointly supervised this work: Bora Lim, Devon A. Lawson, Kai Kessenbrock, Nicholas NavinAuthors and AffiliationsDepartment of Systems Biology, UT MD Anderson Cancer Center, Houston, TX, USATapsi Kumar, Runmin Wei, Siyuan He, Shanshan Bai, Emi Sei, Min Hu, Anna K. Casasent, Aatish Thennavan, Jianzhuo Li, Tuan Tran & Nicholas NavinGraduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center, Houston, TX, USATapsi Kumar, Siyuan He & Nicholas NavinDepartment of Biological Chemistry, University of California, Irvine, Irvine, CA, USAKevin Nee, Quy H. Nguyen, Maren Pein, Yanwen Gong & Kai KessenbrockDepartment of Physiology and Biophysics, University of California, Irvine, Irvine, CA, USAKerrigan Blake, Maren Pein, Tatyana Lev, Sharmila Mallya & Devon A. LawsonMath, Computational & Systems Biology, University of California, Irvine, Irvine, CA, USAKerrigan Blake, Yanwen Gong & Tatyana LevDepartment of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USAKen Chen & Nicholas NavinResolve Biosciences, Monheim am Rhein, GermanyBenedikt Nilges & Nachiket KashikarAkoya Biosciences, Menlo Park, CA, USAOliver Braubach, Bassem Ben Cheikh & Nadya NikulinaDepartment of Pathology, UT MD Anderson Cancer Center, Houston, TX, USAHui ChenDepartment of Breast Surgical Oncology, UT MD Anderson Cancer Center, Houston, TX, USAMediget TeshomeDepartment of Pathology and Immunology, Baylor Medical College, Houston, TX, USABrian Menegaz, Huma Javaid, Chandandeep Nagi & Jessica MontalvanChao Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, USADelia F. Tifrea, Robert Edwards, Erin Lin & Ritesh ParajuliDepartment of Surgery, University of Chicago Medicine, Chicago, IL, USASummer HansonDepartment of Surgery, Baylor College of Medicine, Houston, TX, USASebastian Winocour & Alastair ThompsonDepartment of Medicine, Section of Hematology and Oncology, Baylor College of Medicine, Houston, TX, USABora LimAuthorsTapsi KumarView author publicationsYou can also search for this author in

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PubMed Google ScholarContributionsscRNA-seq experiments were performed by K.N., Q.H.N., S.B., T.K., E.S., J.L., M.P., T.T. and S.M. Spatial genomics experiments were performed by S.B., E.S., B.N., N.K., O.B., B.B.C. and N. Nikulina. Single-cell data analysis was performed by T.K., R.W., S. He., K.B., M.P., Y.G., M.H., A.K.C., B.N., N.K., K.C. and T.L. Spatial data analysis was performed by R.W., S. He and A. Thennavan. Tissue samples and clinical coordination was performed by O.B., B.B.C., H.C., A.K.C., M.T., B.M., H.J., J.M., R.E., D.F.T., C.N., E.L., R.P., S.W., S.M., A. Thompson, B.L. and S. Hanson. Tissue pathological analysis was performed by H.C., C.N. and A. Thennavan. Project management and manuscript writing was performed by B.L., D.A.L., N. Navin and K.K. N. Navin and K.K. are the coordinators for the Breast Atlas Bionetwork that is part of the HCA Project.Corresponding authorsCorrespondence to

Devon A. Lawson, Kai Kessenbrock or Nicholas Navin.Ethics declarations

Competing interests

N. Navin previously served on the scientific advisory board for Resolve Biosciences (2020–2022) but did not receive any compensation. O.B., B.B.C. and N. Nikulina are employees at Akoya Biosciences. B.N. and N.K. are employees of Resolve Biosciences. The other authors declare no competing interests.

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Nature thanks Itai Yanai and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Extended data figures and tablesExtended Data Fig. 1 Frequency of Major Breast Cell Types Across Women and Sample Types.a, Experimental workflow for breast tissue processing for scRNA-seq, showing different conditions used for digestion times and trypsin treatments. b, Pie chart showing ethnic backgrounds of women who provided tissue samples for the breast atlas. c, Cell type frequencies for different tissue sources (reduction mammoplasties - RM, prophylactic mastectomies - PM and contralateral mastectomies - CM), cells vs nuclei single cell RNA-seq protocols, and experimental dissociation protocol. d, Major cell type frequencies of matched left and right breasts from 22 women (left) and averages across all left and all right breast tissues (right). e, Stacked barplot showing the variation of cell type frequencies across the 126 women in scRNA-seq data. The top annotation bar shows the experimental workflow used (short/medium/long). f, Top regulons identified with SCENIC for each cell type cluster from the snRNA-seq data. g, Top regulons identified with SCENIC for each cell type cluster from the scRNA-seq data. h, Multi-dimensional scaling and Procrustes analysis to determine the concordance of left and right breast cell type frequencies and Pearson correlations for 22 women with matched breast tissue samples. P-value was calculated based on a two-sided test.Extended Data Fig. 2 Ligand-receptor Interaction Analysis Between Cell Types.Ligand-receptor interaction plots predicted from scRNA-seq data using CellPhoneDB between the major breast cell types. a, Interaction plot between the epithelial (Basal, LumHR and LumSec) cell types. b, Interaction plot between the epithelial and immune (B-cells, T-cells, Myeloid cells) cell types. c, Interaction plot between the epithelial and stromal (Fibroblasts, Perivascular and Vascular endothelial cells) cell types. d, Interaction plot within the stromal (Fibroblasts, Myeloid, Lymphatic, Vascular and Perivascular cells) cell types. e, Interaction plot between adipocytes and stromal cell types.Extended Data Fig. 3 Spatial Transcriptomic Analysis of Breast Cell Types.a, Integrated UMAP and unbiased clustering of ST data from 10 breast samples, showing 9 ST clusters. b, Histopathological images, and spatial distribution of ST clusters in the ST data from the breast tissues. c, Concordance of ST clusters and the scRNA-seq clusters of the major cell types using Fisher’s exact test. d, Pearson correlation analysis of marker gene expression levels between the ST clusters and the scRNA-seq data for different cell types. All p-values were calculated based on two-sided tests.Extended Data Fig. 4 Spatial Analysis of Breast Cell Types with CODEX and smFISH.a, Cell segmentation results of smFISH (Resolve) data across 12 tissue samples profiled from 5 different women. Cells were annotated based on combinations of markers for each cell type as described in Supplementary Table 6. b, Densities of cell types across three topographic areas using 12 tissues profiled by smFISH (Resolve). c, Heatmap of the top 5 targeted maker genes for each cell type in the smFISH (Resolve) data from 12 combined tissue samples. d, Cell segmentation results of CODEX data from 8 different women. Cells were annotated based on combinations or single protein markers to identify different cell types. e, Densities of cell types across three topographic areas from 8 different women by CODEX. f, Heatmap showing protein levels for markers that were used to identify different cell types in the CODEX data. (D: ducts, L: lobules and C: connective regions).Extended Data Fig. 5 Analysis of Single Cell and Spatial Epithelial Data.a, UMAPs of snRNA-seq data showing the expression of hormone receptor genes. b, Epithelial cell state frequencies across the 126 women in scRNA-seq data, where the top annotation bar represents the dissociation protocol. c, UMAP feature plots showing the expression of previously reported stem cell marker genes in the scRNA-seq epithelial dataset. d, Ligand-receptor interactions within the epithelial cell states predicted with CellPhoneDB. e, Cell cycle scoring of S-phase for different epithelial cell states detected in the scRNA-seq data. f, Cell cycle scoring for S-phase in the epithelial cell type clusters detected in the snRNA-seq data. g, smFISH (Resolve) data showing the expression of the MKI67 proliferation marker in the epithelial cells of the ducts and lobules from 4 different breast tissues. h, UMAP of different LumSec cell states and ELF5, LTF signature scores, respectively. i, Histopathological image of adjacent H&E section showing the anatomic annotation of ducts and lobules (left) and smFISH MERFISH (right panel) from P101 showing the spatial distribution of different LumSec cell states across different regions. j, Stacked barplot showing the distribution of different LumSec cell proportions in ducts and lobules across 3 MERFISH samples. k, Histopathological image (left panel) and smFISH MERFISH (right panel) from P101 showing the spatial distribution of the LumHR-SCGB population in a specific region of epithelium.Extended Data Fig. 6 Spatial analysis of epithelial cells in ductal and lobular structures.a, Spatial transcriptomic analysis showing clusters labelled as duct or lobule/TDLU from 3 breast tissues (P10, P35 and P47). b, smFISH (Resolve) data (P46-S1 and P46-S4) showing a subset of Keratin markers (left) and hormone receptor genes (right) and their localization to different breast tissue regions annotated as either duct or lobule/TDLU. c, CODEX data from P131 showing KRT5 in ducts and KRT19 in lobules/TDLU regions, with enlarged panels of the right. d, CODEX analysis from P130 of ductal and lobular/TDLU regions, showing differences for KRT14 levels in ducts and lobules. e, CODEX data from P131 showing protein levels of KRT8 and progesterone receptor (PR) in epithelial cells in the ducts and lobular/TDLU regions.Extended Data Fig. 7 Immune cell subtypes in the breast and their variation in women.a, H&E staining of plasma B-cells, T-cells, mast cell and macrophages (arrows) in human breast tissues. b, Stacked barplot showing the cell type frequencies of T, B and myeloid cells across 126 women in scRNA-seq data. Top annotation bar represents different tissue dissociation protocols that were utilized. c-e, Stacked barplots showing the cell state frequencies of T, B and myeloid cells across 126 women in scRNA-seq data respectively. f, Dot plot showing expression of checkpoint/exhaustion markers in NK and T cell states from the scRNA-seq data of 126 women. g, Ligand-receptor interaction analysis predicted with CellPhoneDB between the fibroblasts cell states and macrophage cell states.Extended Data Fig. 8 Spatial analysis of immune cells in human breast tissues.a, CODEX data from patient P130 and P131 showing localization of different immune cells with epithelial marker KRT19 and vascular marker CD31. Yellow and white arrows indicate CD4 Tregs and DCs, respectively. b, Frequency of T cells with the RUNX3 tissue residency marker in CODEX data. c, CODEX data (P130) showing immune cells in ductal, lobular and connective regions. d, Stacked barplots of CODEX data showing the density of immune cell types in each spatial region in 8 women. e, smFISH (Resolve) data (P46-S1) showing RNA localization of T, B and myeloid cells. f, Segmented smFISH (Resolve) data (P46-S1) showing cell localization of T, B and myeloid cells. g, smFISH (Resolve) data (P46-S1 and P47-S1) showing immune cell localization of B, T and myeloid cells across ducts, lobules and connective regions. h, Stacked barplots of smFISH (Resolve) data showing the density and proportion of immune cell types in different spatial regions. i, Adjacent histopathological tissue section (left) and segmented smFISH MERFISH data (right) from patient P91 showing the spatial distribution of m1, m2 macrophages and cDC2 populations in different regions of human breast tissue. j, Stacked barplot showing the density of m1, m2 macrophages and cDC2 populations in different regions across 3 smFISH MERFISH samples. k, Adjacent histopathological tissue section (left) and segmented smFISH MERFISH data (right) from patient P96 showing the spatial distribution of different B-cell states in different regions of human breast tissue. l, Stacked barplot showing the density of different B-cell states in different regions across three smFISH MERFISH samples.Extended Data Fig. 9 Fibroblast cell states in the human breast.a, Stacked barplot showing the fibroblasts cell state frequencies across 126 women in scRNA-seq data with top annotation bar representing the tissue dissociation protocol. b, Gene ontology enrichment analysis showing top enriched biological process gene sets associated with each cell state (Pos: positive; Neg: negative; Reg: regulation; RSTK: receptor protein serine/threonine kinase; TGF: transforming growth factor; IGF: insulin-like growth factor). c, CODEX data from P132 showing fibroblasts marked by VIM in the connective tissue (I) and interlobular (II) regions. d, smFISH (Resolve) data showing fibroblast markers in areas of connective tissue regions (I) and epithelial regions (II) from two women (P47-S1 and P69-S3). e, smFISH (Resolve) data (P35-S1) indicating spatial proximity regions with epithelial-proximal (Epi-prox), epithelial-middle (Epi-mid) and epithelial-distant (Epi-Dist) regions for 4 marker genes. f, Percentages of 4 markers that are proximal, middle or distant to the epithelial cells, quantified from the smFISH (Resolve) data. g, RNAscope in situ hybridization of breast tissues using an MMP3 probe in combination with anti-Vimentin and anti-PanCK immunofluorescent staining, with enlarged panel (right). h, Ligand-receptor interactions between fibroblasts, adipocytes and myeloid cell states predicted using CellPhoneDB.Extended Data Fig. 10 Endothelial cell diversity in the Human Breast.a, Stacked barplot showing the endothelial cell state frequencies across the 126 women in scRNA-seq data, with top annotation bar showing the tissue dissociation protocol. b, Dot plot of gene ontology enrichment results for 4 lymphatic cell states. c, Heatmap showing top gene expression for vascular and lymphatic endothelial clusters detected in the ST data. d, smFISH (Resolve) data showing veins (ACKR1) and capillaries (RBP7), as well as a canonical vascular marker (VWF) in two different HBCA samples (P46-S3 and P69-S3). e, Adjacent H&E tissue section with pathological annotations (left panel) and segmented smFISH MERFISH data (right panel) from P101 showing the spatial distribution of vascular endothelial cell states in different regions of human breast tissue. f, Stacked barplot showing the density of vascular endothelial states in different regions across 3 smFISH MERFISH samples.Extended Data Fig. 11 Perivascular cells in Human Breast Tissues.a, Stacked barplot showing the perivascular cell state frequencies across the 126 women in scRNA-seq data with top annotation bars indicating the tissue dissociation protocol. b, UMAPs of pericytes and vascular smooth muscle cells (VSMCs) and feature plots of the VSMCs marker genes (SYNM and ACTG2). c-e, smFISH (Resolve) data showing expression of pericyte marker RGS5, together with vascular marker VWF and fibroblast marker COL1A1 in lobular and ductal regions from 2 different breast tissue samples (P47-S1 and P46-S3). f, CODEX results from P131 showing vascular cells (anti-CD31) and pericytes (anti-LIF) in a TDLU region. g, smFISH MERFISH from P96 showing the spatial distribution of vascular endothelial cell states (left panel) and perivascular cell states (right panel) in different regions of human breast tissue. h, smFISH (MERFISH) data showing arteries (SOX17) and VSMCs (ATCG2 and SYNM) in breast tissue.Extended Data Fig. 12 Metadata correlations with breast cell types and states.a, Boxplots showing the major cell type frequencies across ethnicity status in the n = 69 women using Wilcoxon rank sum test (top). Significant associations of cell states with ethnicity status using Fisher’s exact test (bottom). b, Boxplots showing the major cell type frequencies across pre- and post-menopause status in the n = 71 women using Wilcoxon rank sum test (top). Significant associations of cell states with menopause status using Fisher’s exact test (bottom). c, Boxplots showing the major cell type frequencies across different age groups using Wilcoxon rank sum test, young (<50 years) and old (>50 years) for n = 76 women (top). Significant associations of cell states with age groups using Fisher’s exact test (bottom). d, Boxplots showing the major cell type frequencies across different breast density (high, low) groups in the n = 16 women using Wilcoxon rank sum test (top). Significant associations of cell states with breast density using Fisher’s exact test (bottom). e, Boxplots showing the major cell type frequencies across different BMI status in 73 women using Wilcoxon rank sum test, overweight (BMI >= 25 and < 30) and obese (BMI >= 30). f, Boxplots showing the major cell type frequencies across different parity status (nulliparous, parous) status in the n=64 women using Wilcoxon rank sum test. All p-values were calculated based on two-sided tests. Boxplots show the median with interquartile ranges (25–75%), while whiskers extend to 1.5× the interquartile range from the box.Extended Data Fig. 13 Summary of the Major Cell Types and States in Breast Tissues.This illustration summarizes all of the breast cell types and cell states that were identified in the HBCA study. a, Summary of cell lineages from cell types to cell states. b, Mapping of cell types and cell states to the four major spatial regions (Adipose, Connective, Ductal, Lobular) that were supported by the spatial technologies. Not all cell states were assigned to specific spatial regions, in cases where the data did not support their assignment. Individual figures were created with BioRender.com.Supplementary informationSupplementary InformationReporting SummaryRights and permissionsSpringer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.Reprints and permissionsAbout this articleCite this articleKumar, T., Nee, K., Wei, R. et al. A spatially resolved single-cell genomic atlas of the adult human breast.

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Breast cancer

Nadia Harbeck1, Frédérique Penault-Llorca2, Javier Cortes3,4, Michael Gnant5, Nehmat Houssami6, Philip Poortmans7,8, Kathryn Ruddy9, Janice Tsang10 & …Fatima Cardoso11 Show authors

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Breast cancerCancer therapyGenetic predisposition to diseaseRadiotherapyTumour biomarkers

AbstractBreast cancer is the most frequent malignancy in women worldwide and is curable in ~70–80% of patients with early-stage, non-metastatic disease. Advanced breast cancer with distant organ metastases is considered incurable with currently available therapies. On the molecular level, breast cancer is a heterogeneous disease; molecular features include activation of human epidermal growth factor receptor 2 (HER2, encoded by ERBB2), activation of hormone receptors (oestrogen receptor and progesterone receptor) and/or BRCA mutations. Treatment strategies differ according to molecular subtype. Management of breast cancer is multidisciplinary; it includes locoregional (surgery and radiation therapy) and systemic therapy approaches. Systemic therapies include endocrine therapy for hormone receptor-positive disease, chemotherapy, anti-HER2 therapy for HER2-positive disease, bone stabilizing agents, poly(ADP-ribose) polymerase inhibitors for BRCA mutation carriers and, quite recently, immunotherapy. Future therapeutic concepts in breast cancer aim at individualization of therapy as well as at treatment de-escalation and escalation based on tumour biology and early therapy response. Next to further treatment innovations, equal worldwide access to therapeutic advances remains the global challenge in breast cancer care for the future.

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Fig. 1: Breast cancer.Fig. 2: Molecular mutations in breast cancer.Fig. 3: Immune crosstalk in breast cancer.Fig. 4: Breast cancer imaging.Fig. 5: Breast cancer histological types and molecular alterations.Fig. 6: Algorithm for early breast cancer.Fig. 7: Breast-conserving surgery.Fig. 8: Radiation therapy for breast cancer.Fig. 9: Common metastatic sites in breast cancer.Fig. 10: Algorithm for advanced breast cancer.Fig. 11: Emerging targetable pathways in breast cancer.

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Download referencesAcknowledgementsThe authors thank N. Radosevic-Robin (Jean Perrin Comprehensive Cancer Centre, France) for her assistance in preparing Fig. 1. N. Houssami receives research support through a National Breast Cancer Foundation (NBCF, Australia) Breast Cancer Research Leadership Fellowship. K.R. acknowledges research funding from the Clinical and Translational Sciences Award (CTSA) grant number KL2 TR002379 from the National Centre for Advancing Translational Sciences, a component of the US National Institutes of Health.Author informationAuthors and AffiliationsLMU Munich, University Hospital, Department of Obstetrics and Gynecology, Breast Center and Comprehensive Cancer Center (CCLMU), Munich, GermanyNadia HarbeckDepartment of Pathology and Biopathology, Jean Perrin Comprehensive Cancer Centre, UMR INSERM 1240, University Clermont Auvergne, Clermont-Ferrand, FranceFrédérique Penault-LlorcaIOB Institute of Oncology, Quironsalud Group, Madrid and Barcelona, SpainJavier CortesVall d´Hebron Institute of Oncology, Barcelona, SpainJavier CortesComprehensive Cancer Center, Medical University of Vienna, Vienna, AustriaMichael GnantSydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, AustraliaNehmat HoussamiDepartment of Radiation Oncology, Institut Curie, Paris, FrancePhilip PoortmansUniversité PSL, Paris, FrancePhilip PoortmansDepartment of Oncology, Mayo Clinic, Rochester, MN, USAKathryn RuddyHong Kong Breast Oncology Group, The University of Hong Kong, Hong Kong, ChinaJanice TsangBreast Unit, Champalimaud Clinical Center/Champalimaud Foundation, Lisbon, PortugalFatima CardosoAuthorsNadia HarbeckView author publicationsYou can also search for this author in

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PubMed Google ScholarContributionsIntroduction (all authors); Epidemiology (J.T.); Mechanisms/pathophysiology (F.P.-L.); Diagnosis, screening and prevention (N. Houssami); Management (N. Harbeck, F.C., M.G., P.P., J.C. and N. Houssami); Quality of life (K.R.); Outlook (all authors); Overview of the Primer (N. Harbeck and F.C.).Corresponding authorCorrespondence to

Nadia Harbeck.Ethics declarations

Competing interests

N. Harbeck reports honoraria for lectures and/or consulting from Agendia, Amgen, Astra Zeneca, Celgene, Daiichi-Sankyo, Genomic Health, Lilly, MSD, Novartis, Odonate, Pfizer, Roche, Sandoz/Hexal and Seattle Genetics. F.P.-L. declares personal financial interests in Abbvie, Agendia, Astrazeneca, BMS, Genomic Health, Janssen, Lilly, Merck Lifa, MSD, Myriad, Nanostring, Novartis, Pfizer and Roche; institutional financial interests in Astrazeneca, BMS, Genomic Health, MSD, Myriad, Nanostring and Roche; and congress invitations from Abbvie, Astrazeneca, BMS, MSD and Roche. J.C. has received honoraria from Celgene, Chugai, Eisai, Novartis, Pfizer, Roche and Samsung; has served as a consultant for Astrazeneca, Biothera, Celgene, Daichii Sankyo, Erytech Pharma, Merus, Polyphor, Roche and Seattle Genetics; has received research funding from Ariad, Astrazeneca, Baxalta GMBH, Bayer, Eisai, Guardant Health, Merch Sharp & Dohme, Pfizer, Puma and Roche; and has stocks in MedSIR. M.G. reports honoraria from Amgen, AstraZeneca, Celgene, Eli Lilly, Medison, Nanostring Technologies, Novartis and Roche; advisory fees from Accelsoir; research funding from AstraZeneca, Novartis, Pfizer and Roche; and travel expenses from Amgen, AstraZeneca, Celgene, Eli Lilly, Ipsen, Medison, Novartis and Pfizer. K.R. declares previous ownership of Merck and Pfizer stock (October 2016–February 2018). J.T. reports honoraria and consultancy or advisory roles for AstraZeneca, Astellas, De Novo, Eisai, Foundation Medicine, Nanostring, Novartis, Pfizer and Roche. F.C. declares consultancy roles for Amgen, Astellas/Medivation, AstraZeneca, Celgene, Daiichi-Sankyo, Eisai, Genentech, GE Oncology, GlaxoSmithKline, Macrogenics, Medscape, Merck-Sharp, Merus BV, Mylan, Mundipharma, Novartis, Pfizer, Pierre-Fabre, prIME Oncology, Roche, Sanofi, Seattle Genetics and Teva. The remaining authors declare no competing interests.

Additional informationPeer review informationNature Reviews Disease Primers thanks T. Howell, P. Neven, M. Toi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.Publisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Related links

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European Organization for Research and Treatment of Cancer:

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EuroQol 5-Dimensions:

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Functional Assessment of Cancer Therapy:

http://www.facit.org/FACITOrg

Patient-Reported Outcomes Measurement Information System:

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Short Form Health Survey-36:

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Nat Rev Dis Primers 5, 66 (2019). https://doi.org/10.1038/s41572-019-0111-2Download citationAccepted: 22 July 2019Published: 23 September 2019DOI: https://doi.org/10.1038/s41572-019-0111-2Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard

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Breast cancer - Symptoms and causes - Mayo Clinic

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Overview

Breast anatomy

Enlarge image

Breast anatomy

Each breast contains 15 to 20 lobes of glandular tissue, arranged like the petals of a daisy. The lobes are further divided into smaller lobules that produce milk for breastfeeding. Small tubes, called ducts, conduct the milk to a reservoir that lies just beneath your nipple.

Breast cancer is a kind of cancer that begins as a growth of cells in the breast tissue.

After skin cancer, breast cancer is the most common cancer diagnosed in women in the United States. But breast cancer doesn't just happen in women. Everyone is born with some breast tissue, so anyone can get breast cancer.

Breast cancer survival rates have been increasing. And the number of people dying of breast cancer is steadily going down. Much of this is due to the widespread support for breast cancer awareness and funding for research.

Advances in breast cancer screening allow healthcare professionals to diagnose breast cancer earlier. Finding the cancer earlier makes it much more likely that the cancer can be cured. Even when breast cancer can't be cured, many treatments exist to extend life. New discoveries in breast cancer research are helping healthcare professionals choose the most effective treatment plans.

Breast cancer care at Mayo ClinicProducts & ServicesA Book: Beyond Breast CancerA Book: Taking Care of YouShow more products from Mayo Clinic

Types

Angiosarcoma

Ductal carcinoma in situ (DCIS)

Inflammatory breast cancer

Invasive lobular carcinoma

Lobular carcinoma in situ (LCIS)

Male breast cancer

Paget's disease of the breast

Recurrent breast cancer

Symptoms

Nipple changes

Enlarge image

Nipple changes

Breast and nipple changes can be a sign of breast cancer. Make an appointment with a healthcare professional if you notice any changes.

Signs and symptoms of breast cancer may include:

A breast lump or thickened area of skin that feels different from the surrounding tissue.

A nipple that looks flattened or turns inward.

Changes in the color of the breast skin. In people with white skin, the breast skin may look pink or red. In people with brown and Black skin, the breast skin may look darker than the other skin on the chest or it may look red or purple.

Change in the size, shape or appearance of a breast.

Changes to the skin over the breast, such as skin that looks dimpled or looks like an orange peel.

Peeling, scaling, crusting or flaking of the skin on the breast.

When to see a doctorIf you find a lump or other change in your breast, make an appointment with a doctor or other healthcare professional. Don't wait for your next mammogram to see if the change you found is breast cancer. Report any changes in your breasts even if a recent mammogram showed there was no breast cancer.

Request an appointment

CausesThe exact cause of most breast cancers isn't known. Researchers have found things that increase the risk of breast cancer. These include hormones, lifestyle choices and things in the environment. But it's not clear why some people who don't have any factors get cancer, yet others with risk factors never do. It's likely that breast cancer happens through a complex interaction of your genetic makeup and the world around you.

Healthcare professionals know that breast cancer starts when something changes the DNA inside cells in the breast tissue. A cell's DNA holds the instructions that tell a cell what to do. In healthy cells, the DNA gives instructions to grow and multiply at a set rate. The instructions tell the cells to die at a set time. In cancer cells, the DNA changes give different instructions. The changes tell the cancer cells to make many more cells quickly. Cancer cells can keep living when healthy cells would die. This causes too many cells.

The cancer cells might form a mass called a tumor. The tumor can grow to invade and destroy healthy body tissue. In time, cancer cells can break away and spread to other parts of the body. When cancer spreads, it's called metastatic cancer.

The DNA changes that lead to breast cancer most often happen in the cells that line the milk ducts. These ducts are tubes designed to carry milk to the nipple. Breast cancer that starts in the ducts is called invasive ductal carcinoma. Breast cancer also can start in cells in the milk glands. These glands, called lobules, are designed to make breast milk. Cancer that happens in the lobules is called invasive lobular carcinoma. Other cells in the breast can become cancer cells, though this isn't common.

Risk factorsFactors that may increase the risk of breast cancer include:

A family history of breast cancer. If a parent, sibling or child had breast cancer, your risk of breast cancer is increased. The risk is higher if your family has a history of getting breast cancer at a young age. The risk also is higher if you have multiple family members with breast cancer. Still, most people diagnosed with breast cancer don't have a family history of the disease.

A personal history of breast cancer. If you've had cancer in one breast, you have an increased risk of getting cancer in the other breast.

A personal history of breast conditions. Certain breast conditions are markers for a higher risk of breast cancer. These conditions include lobular carcinoma in situ, also called LCIS, and atypical hyperplasia of the breast. If you've had a breast biopsy that found one of these conditions, you have an increased risk of breast cancer.

Beginning your period at a younger age. Beginning your period before age 12 increases your risk of breast cancer.

Beginning menopause at an older age. Beginning menopause after age 55 increases the risk of breast cancer.

Being female. Women are much more likely than men are to get breast cancer. Everyone is born with some breast tissue, so anyone can get breast cancer.

Dense breast tissue. Breast tissue is made up of fatty tissue and dense tissue. Dense tissue is made of milk glands, milk ducts and fibrous tissue. If you have dense breasts, you have more dense tissue than fatty tissue in your breasts. Having dense breasts can make it harder to detect breast cancer on a mammogram. If a mammogram showed that you have dense breasts, your risk of breast cancer is increased. Talk with your healthcare team about other tests you might have in addition to mammograms to look for breast cancer.

Drinking alcohol. Drinking alcohol increases the risk of breast cancer.

Having your first child at an older age. Giving birth to your first child after age 30 may increase the risk of breast cancer.

Having never been pregnant. Having been pregnant one or more times lowers the risk of breast cancer. Never having been pregnant increases the risk.

Increasing age. The risk of breast cancer goes up as you get older.

Inherited DNA changes that increase cancer risk. Certain DNA changes that increase the risk of breast cancer can be passed from parents to children. The most well-known changes are called BRCA1 and BRCA2. These changes can greatly increase your risk of breast cancer and other cancers, but not everyone with these DNA changes gets cancer.

Menopausal hormone therapy. Taking certain hormone therapy medicines to control the symptoms of menopause may increase the risk of breast cancer. The risk is linked to hormone therapy medicines that combine estrogen and progesterone. The risk goes down when you stop taking these medicines.

Obesity. People with obesity have an increased risk of breast cancer.

Radiation exposure. If you received radiation treatments to your chest as a child or young adult, your risk of breast cancer is higher.

PreventionThings you can do to lower your risk of breast cancer

Breast self-exam

Enlarge image

Breast self-exam

To perform a breast self-exam for breast awareness, use a methodical approach that ensures you cover your entire breast. For instance, imagine that your breasts are divided into equal wedges, like pieces of a pie, and sweep your fingers along each piece in toward your nipple.

Making changes in your daily life may help lower your risk of breast cancer. Try to:

Ask about breast cancer screening. Talk with your doctor or other healthcare professional about when to begin breast cancer screening. Ask about the benefits and risks of screening. Together, you can decide what breast cancer screening tests are right for you.

Become familiar with your breasts through breast self-exam for breast awareness. You may choose to become familiar with your breasts by occasionally inspecting them during a breast self-exam for breast awareness. If there is a new change, a lump or something not typical in your breasts, report it to a healthcare professional right away.

Breast awareness can't prevent breast cancer. But it may help you to better understand the look and feel of your breasts. This might make it more likely that you'll notice if something changes.

Drink alcohol in moderation, if at all. Limit the amount of alcohol you drink to no more than one drink a day, if you choose to drink. For breast cancer prevention, there is no safe amount of alcohol. So if you're very concerned about your breast cancer risk, you may choose to not drink alcohol.

Exercise most days of the week. Aim for at least 30 minutes of exercise on most days of the week. If you haven't been active lately, ask a healthcare professional whether it's OK and start slowly.

Limit menopausal hormone therapy. Combination hormone therapy may increase the risk of breast cancer. Talk with a healthcare professional about the benefits and risks of hormone therapy.

Some people have symptoms during menopause that cause discomfort. These people may decide that the risks of hormone therapy are acceptable in order to get relief. To reduce the risk of breast cancer, use the lowest dose of hormone therapy possible for the shortest amount of time.

Maintain a healthy weight. If your weight is healthy, work to maintain that weight. If you need to lose weight, ask a healthcare professional about healthy ways to lower your weight. Eat fewer calories and slowly increase the amount of exercise.

Medicines and operations for those a high risk of breast cancerIf you have a high risk of breast cancer, you might consider other options to lower the risk. You might have a high risk if you have a family history of breast cancer. Your risk also might be higher if you have a history of precancerous cells in the breast tissue. Talk about your risk with your healthcare team. Your team might have options for lowering your risk, such as:

Preventive medicines. Using estrogen-blocking medicines can lower the risk of breast cancer in those who have a high risk. Options include medicines called selective estrogen receptor modulators and aromatase inhibitors. These medicines also are used as hormone therapy treatment for breast cancer.

These medicines carry a risk of side effects. For this reason, they're only used in those who have a very high risk of breast cancer. Discuss the benefits and risks with your healthcare team.

Preventive surgery. If you have a very high risk of breast cancer, you may consider having surgery to lower the risk of breast cancer. One option might be surgery to remove the breasts, called prophylactic mastectomy. Another option is surgery to remove the ovaries, called prophylactic oophorectomy. This operation lowers the risk of breast cancer and ovarian cancer.

More InformationBreast cancer care at Mayo ClinicBreast cancer chemopreventionGenetic testing for breast cancer: Psychological and social impact

By Mayo Clinic Staff

Breast cancer care at Mayo Clinic

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Diagnosis & treatment

Feb. 10, 2024

Show references

Cancer facts and figures 2023. American Cancer Society. https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/2023-cancer-facts-figures.html. Accessed Aug. 9, 2023.

Abraham J, et al., eds. Breast cancer. In: The Bethesda Handbook of Clinical Oncology. 6th ed. Kindle edition. Wolters Kluwer; 2023. Accessed March 30, 2023.

Breast cancer. Cancer.Net. https://www.cancer.net/cancer-types/breast-cancer/view-all. Accessed Aug. 2, 2023.

Mukwende M, et al. Erythema. In: Mind the Gap: A Handbook of Clinical Signs in Black and Brown Skin. St. George's University of London; 2020. https://www.blackandbrownskin.co.uk/mindthegap. Accessed Aug. 10, 2023.

Townsend CM Jr, et al. Diseases of the breast. In: Sabiston Textbook of Surgery: The Biological Basis of Modern Surgical Practice. 21st ed. Elsevier; 2022. https://www.clinicalkey.com. Accessed Aug. 2, 2023.

Breast cancer risk reduction. National Comprehensive Cancer Network. https://www.nccn.org/guidelines/guidelines-detail?category=2&id=1420. Accessed Aug. 2, 2023.

Breast cancer prevention (PDQ) – Patient version. National Cancer Institute. https://www.cancer.gov/types/breast/patient/breast-prevention-pdq. Accessed Aug. 2, 2023.

Breast cancer. National Comprehensive Cancer Network. https://www.nccn.org/guidelines/guidelines-detail?category=1&id=1419. Accessed Aug. 2, 2023.

Klimberg VS, et al., eds. Breast cancer diagnosis and techniques for biopsy. In: Bland and Copeland's The Breast: Comprehensive Management of Benign and Malignant Diseases. 6th ed. Elsevier; 2024. https://www.clinicalkey.com. Accessed Aug. 2, 2023.

Palliative care. National Comprehensive Cancer Network. https://www.nccn.org/guidelines/guidelines-detail?category=3&id=1454. Accessed Aug. 2, 2023.

Cancer-related fatigue. National Comprehensive Cancer Network. https://www.nccn.org/guidelines/guidelines-detail?category=3&id=1424. Accessed Aug. 2, 2023.

Breast SPOREs. National Cancer Institute. https://trp.cancer.gov/spores/breast.htm. Accessed Aug. 9, 2023.

Ami TR. Allscripts EPSi. Mayo Clinic. Jan. 31, 2023.

Ami TR. Allscripts EPSi. Mayo Clinic. April 5, 2023.

Member institutions. Alliance for Clinical Trials in Oncology. https://www.allianceforclinicaltrialsinoncology.org/main/public/standard.xhtml?path=%2FPublic%2FInstitutions. Accessed Aug. 9, 2023.

Giridhar KV (expert opinion). Mayo Clinic. Oct. 18, 2023.

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Infographic: Breast Cancer Risk

Lumpectomy

Modified radical mastectomy

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Associated Procedures

3D mammogram

Bone scan

Brachytherapy

BRCA gene test

Breast cancer risk assessment

Breast cancer supportive therapy and survivorship

Breast cancer surgery

Breast MRI

Breast self-exam for breast awareness

Chemotherapy

Chemotherapy for breast cancer

Chest X-rays

Complete blood count (CBC)

CT scan

Hormone therapy for breast cancer

Lumpectomy

Mammogram

Mastectomy

Molecular breast imaging

MRI

Positron emission tomography scan

Precision medicine for breast cancer

Radiation therapy

Radiation therapy for breast cancer

Sentinel node biopsy

Ultrasound

X-ray

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breast noun

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B1 [ C ] either of the two soft, rounded parts of a woman's chest that produce milk after she has a baby

乳房

When a woman becomes pregnant her breasts tend to grow larger.

女性懷孕後，乳房會增大。

breast cancer

乳癌

Do you think she's had breast implants?

你覺得她做過隆胸手術嗎？

更多範例减少例句Antibodies found in breast milk protect newborn babies against infection.The study showed that one in twelve women is likely to develop breast cancer.A lot of mothers find early weaning from breast milk more convenient.a silicone breast implantThey found a malignant tumour in her breast.

breast noun

(BIRD/ANIMAL)

[ C ] the front part of a bird's body

（鳥的）前胸，胸部

A robin is easy to identify because of its red breast.

知更鳥因其胸脯是紅色的很容易辨認。

[ U or C ] the meat from the front part of the body of a bird or other animal

雞胸肉;（動物的）胸肉

I had a cold chicken breast and a salad for lunch.

我午飯吃的是冷雞胸肉和沙拉。

breast of turkey

火雞的前胸肉

pigeon breasts

鴿子的前胸肉

breast noun

(CLOTHING)

[ C ] the part of a piece of clothing that covers a person's chest

上衣前部

He put a silk hanky in his breast pocket (= a pocket on the top front part of a shirt or coat).

他將一條絲綢手帕放入上衣口袋。

breast noun

(CHEST)

[ C ] literary a person's chest

（人的）胸部，胸膛

The dagger entered his breast.

匕首插入了他的胸膛。

[ C ] literary the centre of a person's feelings

心窩，胸懷

A feeling of love surged in his breast.

他心中湧起一陣愛意。

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Anatomy of the Breast

What does the inside of the breast look like? This illustration shows the makeup of breast anatomy both inside and outside.

What’s inside your breasts? Most people aren’t really sure, but if you want to understand breast cancer prevention, risk, or diagnosis, it’s important to know the kinds of tissue and structures breasts are made of. This information will help you visualize what parts of the breast your doctor is referring to. If you have been diagnosed with breast cancer, knowing this will help you talk to your doctor about surgery and other treatment options.

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What is a breast made of?

If you were assigned female at birth, your breasts contain different types of tissue:

glandular tissue, which includes the breast lobes and breast ducts

fibrous, or supportive or connective tissue, which is the same tissue that ligaments and scar tissue are made of

fatty tissue fills in the spaces between glandular and fibrous tissue and largely determines your breast size

Doctors refer to all non-fatty tissue as fibroglandular tissue. There are also bands of supportive, flexible connective tissue called ligaments, which stretch from the skin to the chest wall to hold the breast tissue in place. Muscle plays an important role too. The pectoral muscle lies against the chest wall underneath both breasts, giving them support. Blood vessels provide oxygen to the breast tissue and carry away waste.

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Embedded in the breast’s fatty and fibrous tissue are 15 to 20 glands called lobes, each of which has many smaller lobules, or sacs, that produce milk. Lobules are arranged in clusters, like bunches of grapes.

Ducts are thin tubes that carry milk to the nipple. The nipple is located in the middle of the areola, which is the darker area surrounding the nipple. Breast cancers can form in the ducts and the lobes.

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Lymph vessels filter and carry lymph fluid from the breast to the lymph nodes. Clusters of lymph nodes near the breast are located in the armpit (known as axillary lymph nodes), above the collarbone, in the neck, and in the chest.

What does a mammogram show?

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A doctor will tell you that your breasts are dense if most of the tissue seen on your mammogram is fibrous or glandular breast tissue. These tissue types appear thicker and denser than fatty tissue and will show up white on a mammogram. Because cancer cells also appear white on the image, it may be harder for radiologists to see disease in people with dense breasts. So that’s why some people with dense breasts may be asked to have extra imaging tests, such as ultrasound or MRI, which can pick up some cancers that may be missed on a mammogram.

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A single-cell and spatially resolved atlas of human breast cancers | Nature Genetics

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Published: 06 September 2021

A single-cell and spatially resolved atlas of human breast cancers

Sunny Z. Wu

ORCID: orcid.org/0000-0002-6153-04491,2 na1, Ghamdan Al-Eryani1,2 na1, Daniel Lee Roden

ORCID: orcid.org/0000-0003-2393-58051,2 na1, Simon Junankar

ORCID: orcid.org/0000-0002-3965-82781,2, Kate Harvey

ORCID: orcid.org/0000-0002-2155-52031, Alma Andersson3, Aatish Thennavan4, Chenfei Wang5, James R. Torpy

ORCID: orcid.org/0000-0002-5392-16421,2, Nenad Bartonicek

ORCID: orcid.org/0000-0003-2144-18871,2, Taopeng Wang1,2, Ludvig Larsson

ORCID: orcid.org/0000-0003-4209-29113, Dominik Kaczorowski

ORCID: orcid.org/0000-0002-9205-19346, Neil I. Weisenfeld7, Cedric R. Uytingco7, Jennifer G. Chew7, Zachary W. Bent7, Chia-Ling Chan6, Vikkitharan Gnanasambandapillai6, Charles-Antoine Dutertre8,9, Laurence Gluch10, Mun N. Hui1,11, Jane Beith11, Andrew Parker2,12, Elizabeth Robbins13, Davendra Segara12, Caroline Cooper

ORCID: orcid.org/0000-0002-3630-001914,15, Cindy Mak16,17, Belinda Chan16, Sanjay Warrier16,17, Florent Ginhoux

ORCID: orcid.org/0000-0002-2857-775518,19,20, Ewan Millar21,22,23, Joseph E. Powell6,24, Stephen R. Williams7, X. Shirley Liu

ORCID: orcid.org/0000-0003-4736-73395, Sandra O’Toole1,13,23,25, Elgene Lim

ORCID: orcid.org/0000-0001-8065-88381,2,12, Joakim Lundeberg

ORCID: orcid.org/0000-0003-4313-16013, Charles M. Perou

ORCID: orcid.org/0000-0001-9827-22474 & …Alexander Swarbrick

ORCID: orcid.org/0000-0002-3051-56761,2 Show authors

Nature Genetics

volume 53, pages 1334–1347 (2021)Cite this article

81k Accesses

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Breast cancerCancer microenvironment

AbstractBreast cancers are complex cellular ecosystems where heterotypic interactions play central roles in disease progression and response to therapy. However, our knowledge of their cellular composition and organization is limited. Here we present a single-cell and spatially resolved transcriptomics analysis of human breast cancers. We developed a single-cell method of intrinsic subtype classification (SCSubtype) to reveal recurrent neoplastic cell heterogeneity. Immunophenotyping using cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) provides high-resolution immune profiles, including new PD-L1/PD-L2+ macrophage populations associated with clinical outcome. Mesenchymal cells displayed diverse functions and cell-surface protein expression through differentiation within three major lineages. Stromal-immune niches were spatially organized in tumors, offering insights into antitumor immune regulation. Using single-cell signatures, we deconvoluted large breast cancer cohorts to stratify them into nine clusters, termed ‘ecotypes’, with unique cellular compositions and clinical outcomes. This study provides a comprehensive transcriptional atlas of the cellular architecture of breast cancer.

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Fig. 1: Cellular composition of primary breast cancers and identification of malignant epithelial cells.Fig. 2: Identifying drivers of neoplastic breast cancer cell heterogeneity.Fig. 3: T cell and innate lymphoid cell landscape of breast cancers.Fig. 4: Myeloid landscape of breast cancers.Fig. 5: Transcriptional profiling and phenotyping of diverse mesenchymal differentiation states across breast cancers.Fig. 6: Mapping breast cancer heterogeneity using spatially resolved transcriptomics.Fig. 7: Spatially mapping new heterotypic cellular interactions.Fig. 8: Deconvolution of breast cancer cohorts using single-cell signatures reveals robust ecotypes associated with patient survival and intrinsic subtypes.

Data availability

All processed scRNA-seq data are available for in-browser exploration and download through the Broad Institute Single Cell portal at https://singlecell.broadinstitute.org/single_cell/study/SCP1039. Processed scRNA-seq data from this study are also available through the Gene Expression Omnibus under accession number GSE176078. Raw scRNA-seq data from this study have been deposited with the European Genome-phenome Archive, which is hosted by the European Bioinformatics Institute and Centre for Genomic Regulation under accession no. EGAS00001005173. All spatially resolved transcriptomics data from this study are available from the Zenodo data repository (https://doi.org/10.5281/zenodo.4739739). Spatially resolved transcriptomics data from Andersson et al.56 can be downloaded from the Zenodo data repository (https://doi.org/10.5281/zenodo.3957257).

Code availability

Code related to the analyses in this study can be found on GitHub at https://github.com/Swarbricklab-code/BrCa_cell_atlas (ref. 72).

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Swarbrick, A., Wu, S., Al-Eryani, G., Roden, D. & Bartonicek, N. BrCa_cell_atlas. Version 1.0.0 (analysis code) https://doi.org/10.5281/zenodo.5031502 (2021).Download referencesAcknowledgementsThis work is supported by a research grant from the National Breast Cancer Foundation (NBCF) of Australia (no. IIRS-19-106) and supported by the generosity of J. McMurtrie, AM and D. McMurtrie, the Petre Foundation, White Butterfly Foundation, Sydney Breast Cancer Foundation, Skipper Jacobs Charitable Trust, G. P. Harris Foundation and The National Health and Medical Research Council (NHMRC). A.S. is the recipient of a Senior Research Fellowship from the NHMRC (no. APP1161216). S.Z.W., G.A.-E. and J.T. are supported by the Australian Government Research Training Program Scholarship. S.O.T. is supported by the NBCF (PRAC 16-006; no. IIRS-19-084), Sydney Breast Cancer Foundation and the Family and Friends of M. O’Sullivan. S.J. is supported by a research fellowship from the NBCF. X.S.L. is supported by the Breast Cancer Research Foundation (no. BCRF-19-100) and National Institutes of Health (no. R01CA234018). C.M.P. and A.T. were supported by the National Cancer Institute Breast SPORE program (no. P50-CA58223), grant no. RO1-CA148761, and Breast Cancer Research Foundation. This work was supported by the Australian Centre for Translational Breast Cancer Research, Walter and Eliza Hall Institute, with funding support from the NHMRC Centre for Research Excellence grant no. APP1153049. E.L. is supported as a National Breast Cancer Foundation Endowed Chair and by the Love Your Sister foundation. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank the following people for their assistance in the experimental part of this manuscript: J. Yang; G. Lehrbach from the Garvan Institute of Medical Research Tissue Culture Facility; A. Zaratzian from the Garvan Histopathology Facility for tissue processing and IHC staining and guidance on the Visium experiments; the Garvan–Weizmann Centre for Cellular Genomics, including E. Lam, H. Saeed and M. Armstrong for the expertise in flow sorting. We thank H. Holliday for the incredible illustration in Fig. 8g. We thank H. H. Milioli for providing guidance for analyzing the METABRIC cohort dataset. We thank I. Shapiro and C. Grant as consumer advocates. This manuscript was edited at Life Science Editors.Author informationAuthor notesThese authors contributed equally: Sunny Z. Wu, Ghamdan Al-Eryani, Daniel Lee Roden.Authors and AffiliationsThe Kinghorn Cancer Centre and Cancer Research Theme, Garvan Institute of Medical Research, Darlinghurst, New South Wales, AustraliaSunny Z. Wu, Ghamdan Al-Eryani, Daniel Lee Roden, Simon Junankar, Kate Harvey, James R. Torpy, Nenad Bartonicek, Taopeng Wang, Mun N. Hui, Sandra O’Toole, Elgene Lim & Alexander SwarbrickSt Vincent’s Clinical School, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, AustraliaSunny Z. Wu, Ghamdan Al-Eryani, Daniel Lee Roden, Simon Junankar, James R. Torpy, Nenad Bartonicek, Taopeng Wang, Andrew Parker, Elgene Lim & Alexander SwarbrickScience for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Solna, SwedenAlma Andersson, Ludvig Larsson & Joakim LundebergLineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USAAatish Thennavan & Charles M. PerouDepartment of Data Science, Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Harvard T.H. Chan School of Public Health, Boston, MA, USAChenfei Wang & X. Shirley LiuGarvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, AustraliaDominik Kaczorowski, Chia-Ling Chan, Vikkitharan Gnanasambandapillai & Joseph E. Powell10x Genomics, Pleasanton, CA, USANeil I. Weisenfeld, Cedric R. Uytingco, Jennifer G. Chew, Zachary W. Bent & Stephen R. WilliamsGustave Roussy Cancer Campus, Villejuif, FranceCharles-Antoine DutertreInstitut National de la Santé Et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée—Ligue Nationale contre le Cancer, Villejuif, FranceCharles-Antoine DutertreThe Strathfield Breast Centre, Strathfield, New South Wales, AustraliaLaurence GluchChris O’Brien Lifehouse, Camperdown, New South Wales, AustraliaMun N. Hui & Jane BeithSt Vincent’s Hospital, Darlinghurst, New South Wales, AustraliaAndrew Parker, Davendra Segara & Elgene Lim Department of Tissue Pathology and Diagnostic Pathology, New South Wales Health Pathology, Royal Prince Alfred Hospital, Camperdown, New South Wales, AustraliaElizabeth Robbins & Sandra O’ToolePathology Queensland, Princess Alexandra Hospital, Brisbane, Queensland, AustraliaCaroline CooperSouthside Clinical Unit, Faculty of Medicine, University of Queensland, Brisbane, Queensland, AustraliaCaroline CooperDepartment of Breast Surgery, Chris O’Brien Lifehouse, Camperdown, New South Wales, AustraliaCindy Mak, Belinda Chan & Sanjay WarrierRoyal Prince Alfred Institute of Academic Surgery, University of Sydney, Sydney, New South Wales, AustraliaCindy Mak & Sanjay WarrierSingapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Singapore, SingaporeFlorent GinhouxShanghai Institute of Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaFlorent GinhouxTranslational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, SingaporeFlorent GinhouxNew South Wales Health Pathology, Department of Anatomical Pathology, St George Hospital, Kogarah, New South Wales, AustraliaEwan MillarSchool of Medical Sciences, University of New South Wales, Sydney, New South Wales, AustraliaEwan MillarFaculty of Medicine & Health Sciences, Western Sydney University, Campbelltown, New South Wales, AustraliaEwan Millar & Sandra O’TooleUniversity of New South Wales Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, AustraliaJoseph E. PowellSydney Medical School, Sydney University, Sydney, New South Wales, AustraliaSandra O’TooleAuthorsSunny Z. WuView author publicationsYou can also search for this author in

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PubMed Google ScholarContributionsA.S. conceived the project and directed the study with input from all authors. E.L., S.W., M.N.H., B.C., C.C., C.M., D.S., E.R., A.P., J.B., S.O.T., E.M. and L.G. contributed to the experimental design, procured the patient tumor tissue and assisted with interpreting the data. S.Z.W., G.A.-E. and K.H. performed the single-cell captures. K.H. analyzed all the clinical information. S.Z.W., K.H. and G.A.-E. optimized and performed the tumor dissociation experiments. G.A.-E. optimized and performed the antibody staining for the CITE-seq experiments. N.B. and G.A.-E. performed the CITE-seq data processing. C.-L.C. and S.Z.W. performed the scRNA-seq experiments on the Chromium Controller. C.-L.C. helped perform the next-generation sequencing of the scRNA-seq libraries. S.Z.W. performed the preprocessing, data integration and reclustering steps for the scRNA-seq data. J.T. performed the analysis and benchmarking of inferCNV. A.T. and C.M.P. led the development of SCSubtype. D.R. interpreted and led the analyses for the breast cancer GM analyses. K.H. and T.W. performed the H&E and IHC experiments. S.O.T. independently assessed and scored all histology in this study. G.A.-E. interpreted and performed the analyses of the immune cells with intellectual input from S.J. C.-A.D. and F.G. provided intellectual input related to myeloid cluster annotation. S.Z.W. interpreted and performed all the analyses of stromal cells. D.K. and C.-L.C. performed the Visium experiments with input from J.E.P. V.G. helped perform preprocessing of the Visium datasets. S.R.W., N.I.W., C.R.U., J.G.C. and Z.W.B. performed the Visium experiments and data processing from an independent laboratory. A.A. performed the Stereoscope deconvolution with input from J.L. S.Z.W. performed the downstream analysis of the Visium data with guidance from A.A., L.L., G.A.-E. and J.L. D.R. interpreted and performed the CIBERSORTx analysis. S.Z.W. and D.R. performed the survival analyses. C.W. and X.S.L. provided intellectual input and guidance on bulk deconvolution and survival analyses. S.Z.W., A.S., D.R., G.A.-E. and S.J. wrote the manuscript with input from all authors.Corresponding authorCorrespondence to

Alexander Swarbrick.Ethics declarations

Competing interests

C.M.P. is an equity stockholder and consultant for BioClassifier; he is also listed as an inventor on patent applications for the Breast PAM50 Subtyping assay. J.L. is an author on patents owned by Spatial Transcriptomics AB covering technology presented in this paper. The other authors declare no competing interests.

Additional informationPeer review information Nature Genetics thanks Itai Yanai and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Extended dataExtended Data Fig. 1 Identification of malignant cells, single-cell RNA sequencing metrics and non-integrated data of stromal and immune cells.a-b, Number of unique molecular identifiers (a) and genes (b) per tumor analyzed by scRNA-Seq in this study. Tumors are stratified by the clinical subtypes TNBC (red), HER2 (pink) and ER (blue). Diamond points represent the mean. c-d, Number of unique molecular identifiers (UMIs;c) and genes (d) per major lineage cell types identified in this study. These major lineage tiers are grouped by T-cells, B-cells, Plasmablasts, Myeloid, Epithelial, Cycling, Mesenchymal (cancer-associated fibroblasts and perivascular-like cells) and Endothelial. Diamond points represent the mean. e-f, UMAP visualization of all 71,220 stromal and immune cells without batch correction and data integration. UMAP dimensional reduction was performed using 100 principal components in the Seurat v3 package. Cells are grouped by tumor (e) and major lineage tiers (f) as identified using the Garnett cell classification method. g, InferCNV heatmaps of all malignant cells grouped by clinical subtypes. Common subtype-specific CNVs and a chr6 artefact reported by Tirosh et. al. are marked (Tirosh et al., 2016b).Extended Data Fig. 2 Supplementary data for SCSubtype classifier.a-b, Hierarchical Clustering of Allcells-Pseudobulk (indicated by yellow stars) and Ribozero mRNA-Seq (indicated by blue stars) profiles of the patient samples with TCGA patient mRNA-Seq data. a, View of the basal cluster showing pairing of Allcells-Pseudobulk and Ribozero mRNA-Seq profiles of 2 representative tumors (CID4495 and CID4515) in the present study. b, View of the luminal cluster showing pairing of Allcells-Pseudobulk and Ribozero mRNA-Seq profiles of 4 representative tumors (CID4067, CID4463, CID4290 and CID3948) in the present study. c, Heatmap of SCSubtype gene sets across the training and test samples in each individual group. Colored outlined boxes highlighting the top expressed genes per group. d, Barplot representing proportions of SCSubtype calls in individual samples. Test dataset samples are highlighted within the golden colored outline. e, Scatterplot of individual cancer cells plotted according to the Proliferation score (x-axis) and Differentiation – DScore (y-axis). Individual cells are colored based on the SCSubtype calls. f, Scatterplot of individual TCGA breast tumors plotted according to the Proliferation score (x-axis) and Differentiation – DScore (y-axis). Individual patients are colored based on the PAM50 subtype calls.Extended Data Fig. 3 Supplementary data for breast cancer gene modules.a, Spherical k-means (skmeans) based consensus clustering of the Jaccard similarities between 574 signatures of neoplastic cell ITTH. This showed the probability (p1-p7) of each signature of ITTH being assigned to one of seven clusters/classes. Silhouette scores are shown for each signature. b, Heatmap of pair-wise Pearson correlations of the scaled AUCell signature scores, across all individual neoplastic cells, for each of the seven ITTH gene-modules (bolded) and a curated set of breast cancer related gene-signatures. Hierarchical clustering was performed using Pearson correlations and average linkage c, Heatmap showing the scaled AUCell signature scores of each of the seven ITTH gene-modules (rows) across all individual neoplastic cells (columns). Hierarchical clustering was done using Pearson correlations and average linkage. (HER2_AMP = Clinical HER2 amplification status). d, Distributions of signature scores (z-score scaled) for each of the gene-module signatures (24,489 cells from 21 tumors). Cells are grouped according to the gene-module (GM1-7) cell-state. e, Barchart showing the proportion of cells assigned to each of the gene-module cell-states (GM1-7) with cells grouped according to the SCSubtypes. f, Distributions of SCSubtype scores for each of the gene-module signatures (24,489 cells from 21 tumors). Cells are grouped according to the gene-module (GM1-7) cell-state. Kruskal-Wallis tests were performed to calculate the significance between the four SCSubtype score groups in each of the gene-module groups, p-value shown. Wilcox tests were used to identify which SCSubtype had significantly increased SCSubtype scores in the cells assigned to each gene-module, the scores of each SCSubtype were compared to the rest of the SCSubtype scores (****: Holm adjusted p-value < 0.0001, ns: Holm adjusted p-value > 0.05). Box plots in d and f depict the first and third quartiles as the lower and upper bounds, respectively. The whiskers represent 1.5x the interquartile range and the centre depicts the median.Extended Data Fig. 4 CITE-seq vignette.a, UMAP Visualization of a TNBC sample with 157 DNA barcoded antibodies (Supplementary Table 11). Cluster annotations were extracted from our final breast cancer atlas cell annotations. b, Heatmap visualization of the cluster averaged antibody derived tag (ADT) values for the 157 CITE-seq antibody panel. Only immune cells are shown. c-d, Expression featureplots of measured experimental ADT values (shown in top rows) against the CITE-seq imputation ADT levels (shown in bottom rows), as determined using the seurat v3 method. Selected markers for immunophenotyping T-cells (c; CD4, CD8A, PD-1 and CD103) and myeloid cells (d; PD-L1, CD86, CD49f and CD14) are shown.Extended Data Fig. 5 Supplementary data for T-cells and innate lymphoid cells.a, Dotplot visualizing averaged expression of canonical markers across T-cell and innate lymphoid clusters. b, Cytotoxic and dysfunctional gene signature scores across T-cell and innate lymphoid clusters. A Kruskal-Wallis test was performed to compare significance between (pairwise two-sided t-test for each cluster compared to the mean, p-values denoted by asterisks: *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001). Red line indicates the median expression. c, Dysfunctional gene signature scores of CD8 : LAG3 and CD8+ T : IFNG clusters across clinical subtypes (n = 26; 11 TNBC, 10 ER+ and 5 HER2+). A pairwise two-sided t-test for each cluster was performed to determine significance. P-values denoted by asterisks: *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001. d, Differentially expressed immune modulator genes, stratified by T-cell and Myeloid clusters, compared across breast cancer subtypes. A pairwise MAST comparison was performed to obtain bonferroni corrected p-values. All genes displayed are statistically significant (p-value < 0.05). e, Pairwise two-sided t-test comparison of LAG3, CD27, PD-1 (PDCD1) and CD70 log-normalised expression values in LAG3/c8 T-cells across breast cancer subtypes (n = 26; 11 TNBC, 10 ER+ and 5 HER2+). f, Enrichment of PDCD1, CD27, LAG3 and CD70 expression in the METABRIC cohort between clinical subtypes (n = 1,608; 209 Basal, 224 Her2, 700 LumA and 475 LumB). A pair-wise Wilcox test was performed to identify statistical significance. P‐values denoted by asterisks: *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001. Box plots in b and f depict the first and third quartiles as the lower and upper bounds, respectively. The whiskers represent 1.5x the interquartile range and the centre depicts the median.Extended Data Fig. 6 Gene expression of immune cell surface receptors across malignant, immune and mesenchymal clusters and breast cancer clinical subtypes.a, Averaged expression and clustering of 133 clinically targetable receptor or ligand immune modulator markers across all cell types grouped by clinical breast cancer subtypes (TNBC, HER2+ and ER+). Gene lists were manually curated through systematic literature search of known immune modulating proteins expressed on the surface of cells. Default parameters for hierarchical clustering were used via the ‘pheatmap’ package for the visualization of gene expression values.Extended Data Fig. 7 Supplementary data for B-cells, Plasmablasts and Myeloid cells.a, UMAP visualization of all reclustered B-cells (n = 3,202 cells) and Plasmablasts (n = 3,525 cells) as annotated using canonical gene expression markers. b, Featureplots of CD27, IGHD, IGKC and IGLC2 across naïve B cells, memory B cells, and Plasmablasts. c, Tumour associated macrophage (TAM) signature score obtained from Cassetta et al. 2019 and the expression of log-normalised levels of CCL8 across all myeloid clusters (9,675 cells from 26 tumors). A pairwise two-sided t-test was performed to determine statistical significance for clusters of interest. P-values denoted by asterisks: *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001. Dashed red line marks median TAM module score or gene expression. A Kruskal-Wallis test was performed to compare significance between groups’. d, LAM and DC : LAMP3 gene expression signatures acquired from Jaitin et al. 2019 and Zhang et al. 2019 respectively, visualized on the myeloid UMAP clusters. e, Heatmap visualizing GO enrichment pathways across myeloid clusters. f, Proportion of myeloid clusters across clinical subtypes. Statistical significance was determined using a two-sided t-test in a pairwise comparison of means between groups (n = 26; 11 TNBC, 10 ER+ and 5 HER2+). P‐values denoted by asterisks: *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001. g, Violin plots of imputed CITE-seq PD-L1 and PD-L2 expression values found on myeloid cells. Box plots in c and f depict the first and third quartiles as the lower and upper bounds, respectively. The whiskers represent 1.5x the interquartile range and the centre depicts the median.Extended Data Fig. 8 Supplementary data for mesenchymal cell states and subclusters.a, t-SNEvisualization CAFs, PVL cells and endothelial cells using Seurat reclustered with default resolution parameters (0.8). b, Pseudotime plot for CAFs, PVL cells and endothelial cells, as determined using monocle. Coordinates are as in main Figs. 5c, 5e and 5g. c, t-SNE visualizations for CAFs, PVL cells and endothelial cells with monocle derived cell states overlaid. d, Heatmaps for CAFs, PVL cells and endothelial cells show cell state averaged log normalised expression values for all differentially expressed genes determined using the MAST method, with select stromal markers highlighted. e, Top 10 gene ontologies (GO) of each mesenchymal cell state, as determined using pathway enrichment with ClusterProfiler with all differentially expressed genes as input. f, Stromal cell state averaged signature scores for pancreatic ductal adenocarcinoma myofibroblast-like, inflammatory-like and antigen-presenting CAF sub-populations, as determined using AUCell. g, Enrichment of antigen-presenting CAF markers CLU, CD74 and CAV1 in various stromal cell states. h, Subclusters of CAFs, PVL cells and endothelial cells determined using Seurat show a strong integration with three normal breast tissue datasets, highlighting similarities in subclusters across disease status and clinical subtypes of breast cancer. i, Cell states of CAFs, PVL cells and endothelial cells determined using monocle show a strong integration with three normal breast tissue datasets and breast cancer clinical subtypes.Extended Data Fig. 9 Supplementary data for spatial transcriptomics.a, H&E images for the remaining five breast tumors analysed using Visium (TNBC: CID4465, 1142243F and 1160920F; ER+: CID4535 and CID4290). Scale bars represent 500 μm. b, Histograms of cancer deconvolution values, as estimated using Stereoscope. Red line indicates the 10% cutoff used to select spots for scoring breast cancer gene-modules. Spots are colored by the pathology annotation. c, Box plots of gene module scores for all cancer filtered spots, as determined using AUCell, grouped by sample (TNBC=red; ER=blue). Statistical significance was determined using a two-sided t-test, with p-values adjusted using the Benjamini–Hochberg procedure. Box plots depict the first and third quartiles as the lower and upper bounds, respectively. The whiskers represent 1.5x the interquartile range and the centre depicts the median. P‐values denoted by asterisks: *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001. d, Clustered gene module correlations across all cancer filtered spots. Color scales represent Pearson correlation values and are scaled per GM (‘n.s’ denotes not significant; two-sided correlation coefficient, Benjamini–Hochberg adjusted p-value < 0.05). e, Heatmap of the deconvolution values for inflammatory-like CAFs, myofibroblast-like CAFs, Macrophage CXCL10/c9, LAM1 and LAM2 clusters. Spots (columns) are grouped by sample and pathology. Deconvolution abundances (rows) are scaled by cell type. f, Predicted signaling in tissue spots enriched for iCAFs and CD4/CD8+ T-cells. Spots filtered for CAF-ligands and T-cell receptors detected by scRNA-Seq. The mean interaction scores of cell-signaling pairs are defined as the product of the ligand and receptor expression. g, Plots of PD-1 (PDCD1; y axis) expression with PD-L1 (CD274; x axis) or PD-L2 (PDCD1LG2; x axis) expression in spots enriched for CD4/CD8+ T-cells and LAM2 cells, as determined by Stereoscope. Abundance of CD4/CD8 T-cells (combined as T_cell here) and LAM2 are overlaid on the expression plots.Extended Data Fig. 10 Supplementary figure for CIBERSORTx cell-type deconvolution.a, Bar and boxplot (inset) of the Pearson correlation for 45 cell-types between the actual cell-fractions captured by scRNA-Seq and the CIBERSORTx predicted fractions from pseudo-bulk expression profiles (*denotes significance p < 0.05, two-sided correlation coefficient). Inset box plot depicts the first and third quartiles as the lower and upper bounds, respectively. The whiskers represent 1.5x the interquartile range and the centre depicts the median. b, Barplot comparing the Pearson correlation for cell-types between the actual cell-fractions captured by scRNA-Seq and the CIBERSORTx (red) and DWLS (blue) predicted fractions from pseudo-bulk expression profiles (*denotes significance p < 0.05, two-sided correlation coefficient). c, Boxplot comparing the CIBERSORTx predicted SCSubtype and Cycling cell-fractions in each METABRIC tumor, stratified by PAM50 subtypes (n = 1,608; 209 Basal, 224 Her2, 700 LumA and 475 LumB). Box plots depicted as described in b. d, Heatmap of ecotypes formed from the common METABRIC tumors (columns) identified from combining ecotypes generated using CIBERSORTx with all 32 significantly correlated cell-types (rows), when using CIBERSORTx on pseudo-bulk samples. e-f, Relative proportion of the PAM50 subtypes (e) and major cell-types (f) in each ecotype, when combining CIBERSORTx consensus clustering results. g-h, Kaplan-Meier (KM) plot of all patients with common tumors in each of the ecotypes (g) and patients with tumors in ecotypes E4 and E7 (h), when combining CIBERSORTx consensus clustering results. p-values calculated using the log-rank test. i-j, Relative proportion of the PAM50 molecular subtypes (i) and major cell-types (j) of the common tumors from combining CIBERSORT and DWLS generated ecotypes. k, KM plot of the patients with tumors in ecotypes E4 and E7, formed from combining CIBERSORT and DWLS generated ecotypes. p-value calculated using the log-rank test. l, Relative proportion of the METABRIC integrative cluster annotations of the tumors in each ecotype, as determined using CIBERSORTx across all cell-types.Supplementary informationSupplementary InformationSupplementary Note, containing Methods and one figure.Reporting SummaryPeer Review InformationSupplementary TablesSupplementary TablesRights and permissionsReprints and permissionsAbout this articleCite this articleWu, S.Z., Al-Eryani, G., Roden, D.L. et al. A single-cell and spatially resolved atlas of human breast cancers.

Nat Genet 53, 1334–1347 (2021). https://doi.org/10.1038/s41588-021-00911-1Download citationReceived: 11 September 2020Accepted: 08 July 2021Published: 06 September 2021Issue Date: September 2021DOI: https://doi.org/10.1038/s41588-021-00911-1Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard

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BREAST中文(简体)翻译：剑桥词典

A spatially resolved single-cell genomic atlas of the adult human breast | Nature

Breast cancer | Nature Reviews Disease Primers

Development of the Human Breast - PMC

Breast cancer - Symptoms and causes - Mayo Clinic

BREAST中文(繁體)翻譯：劍橋詞典

Anatomy of the Breast | Memorial Sloan Kettering Cancer Center

A single-cell and spatially resolved atlas of human breast cancers | Nature Genetics

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BREAST中文(简体)翻译：剑桥词典

A spatially resolved single-cell genomic atlas of the adult human breast | Nature

Breast cancer | Nature Reviews Disease Primers

Development of the Human Breast - PMC

Breast cancer - Symptoms and causes - Mayo Clinic

BREAST中文(繁體)翻譯：劍橋詞典

Anatomy of the Breast | Memorial Sloan Kettering Cancer Center

A single-cell and spatially resolved atlas of human breast cancers | Nature Genetics

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