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Cancer-associated fibroblast compositions change with breast cancer progression linking the ratio of S100A4+ and PDPN+ CAFs to clinical outcome

Abstract

Tumors are supported by cancer-associated fibroblasts (CAFs). CAFs are heterogeneous and carry out distinct cancer-associated functions. Understanding the full repertoire of CAFs and their dynamic changes as tumors evolve could improve the precision of cancer treatment. Here we comprehensively analyze CAFs using index and transcriptional single-cell sorting at several time points along breast tumor progression in mice, uncovering distinct subpopulations. Notably, the transcriptional programs of these subpopulations change over time and in metastases, transitioning from an immunoregulatory program to wound-healing and antigen-presentation programs, indicating that CAFs and their functions are dynamic. Two main CAF subpopulations are also found in human breast tumors, where their ratio is associated with disease outcome across subtypes and is particularly correlated with BRCA mutations in triple-negative breast cancer. These findings indicate that the repertoire of CAF changes over time in breast cancer progression, with direct clinical implications.

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Fig. 1: Breast CAFs consist of distinct subsets with diverse transcriptional profiles.
Fig. 2: CAF composition and gene expression changes with tumor growth and metastasis.
Fig. 3: sCAFs show a continuum of cell states which fills a tetrahedron in gene-expression space, suggesting a tradeoff between four functions.
Fig. 4: PDPN and S100A4 proteins are expressed on distinct types of breast CAFs in mouse tumors.
Fig. 5: Ly6C+ pCAFs suppress CD8+ T-cell proliferation, in vitro.
Fig. 6: PDPN and S100A4 mark distinct populations of CAFs in human breast cancer.
Fig. 7: PDPN and S100A4 stromal staining is correlated with disease outcome in human patients with breast cancer.
Fig. 8: S100A4/PDPN ratio is a classifier of recurrence-free survival in BRCA-mutated TNBC.

Data availability

Single-cell and bulk RNA-seq data that support the findings of this study have been deposited in the Gene Expression Omnibus under accession code GSE149636. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

FACS analysis was performed using FACS Diva v.8, FlowJo 10.1 and Kaluza 2.1 software. Image analysis was conducted using Fiji ImageJ 1.52g and QuPath program (v.0.2.0-m8). Read mapping of single-cell RNA-seq data was performed using HISAT v.0.1.6, followed by analysis with the custom-made MetaCell package in R (Methods). Gene-set enrichment analysis was conducted using Metascape software. Statistical analysis utilized R program (v.3.6.0; R Foundation for Statistical Computing). Packages used for analysis and visualization: tidyr v.1.0.0, reshape2 v.1.4.3, survival v.3.1-8, survminer v.0.4.6, ggplot2 v.3.2.1, ggthemes v.4.2.0, cowplot v.1.0.0 and corrplot v.0.84. Pareto data analysis was performed in Wolfram Mathematica 11.3.0, with custom-made Mathematica scripts. GO analysis was conducted with the Mathematica package MathIOmica. Scripts and auxiliary data needed to reconstruct analysis files from count matrices to full figures are available in a git repository (https://github.com/AlonLabWIS).

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Acknowledgements

We thank O. Golani and Y. Addadi (MICC Cell Observatory, WIS) for their assistance with imaging and image analysis. We thank V. Kiss (Department of Biomolecular Sciences, WIS) for his assistance with imaging. We thank Z. Granot (HUJI) and R. Alon for providing cell lines. We thank members of the Scherz-Shouval laboratory for valuable input on the manuscript. U.A. is supported by Cancer Research UK (grant C19767/A27145). I.A. is an Eden and Steven Romick Professorial Chair, supported by Merck KGaA, Darmstadt, Germany, the Chan Zuckerberg Initiative, the HHMI International Scholar award, the European Research Council Consolidator Grant (ERC-COG) 724471- HemTree2.0, an SCA award of the Wolfson Foundation and Family Charitable Trust, the Thompson Family Foundation, an MRA Established Investigator Award (509044), the Israel Science Foundation (703/15), the Ernest and Bonnie Beutler Research Program for Excellence in Genomic Medicine, the Helen and Martin Kimmel award for innovative investigation, the NeuroMac DFG/Transregional Collaborative Research Center Grant, an International Progressive MS Alliance/NMSS PA-1604 08459 and an Adelis Foundation grant. R.S.S. is supported by the Israel Science Foundation (grant nos. 401/17 and 1384/1), the European Research Council (ERC grant agreement 754320), the Israel Cancer Research Fund, the Laura Gurwin Flug Family Fund, the Peter and Patricia Gruber Awards, the Comisaroff Family Trust, the Estate of Annice Anzelewitz and the Estate of Mordecai M. Roshwal. R.S.S. is the incumbent of the Ernst and Kaethe Ascher Career Development Chair in Life Sciences.

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G.F. designed, performed and analyzed experiments and wrote the manuscript. O.L.-G., C.B., C.H., M.P.-F., H.L. and S.M. designed and performed the experiments. E.D, A.G. and A.M. designed and performed bioinformatic analysis. Y.S. designed and performed statistical and image analysis. R.N. assisted with image acquisition and designed image analysis. M.D., N.B.-L., I.B, H.R.A., C.C. and E.N.-G.-Y. provided clinical samples and intellectual input. C.C., M.D. and E.N.-G.-Y. edited the manuscript. U.A. directed and designed computational analysis and wrote the manuscript. I.A. directed, designed and analyzed experiments and wrote the manuscript. R.S.S. directed, designed and analyzed experiments and wrote the manuscript.

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Correspondence to Ido Amit or Ruth Scherz-Shouval.

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Extended data

Extended Data Fig. 1 A single cell map of breast cancer stroma.

a, Sorting strategy: All live single cells (PI negative cells after debris and doublet exclusion) staining negative for Ter119 (Red blood cells); CD45 (immune); and EpCAM (epithelial) were collected and single cell sorted. PDPN was used for index sorting of pCAFs. Data are combined from 8 independent experiments, with a total n = 15 mice. FACS plots from a representative 4 W tumor are shown. b-c, Quality control metrics of single cells analyzed in this study. b, Total unique molecular identifier (UMI) per cell. Cells are grouped by batch (plate) and color-coded by biological replicate (mouse). The time point for each batch is indicated. Cells with less than 1,000 UMI were discarded from the analysis. c, Fraction of analyzed cells/batch after filtering. Batches are grouped and color-coded as described in b. d, Single cell RNA-seq data from n = 8987 QC positive cells staining negative for Ter119, CD45 and EpCAM was analyzed and clustered using the MetaCell algorithm, resulting in a two-dimensional projection of cells from 15 mice. 88 meta-cells were associated with 4 broad clusters, annotated and marked by color code. e, Expression of the hallmark genes for the 4 clusters presented in d on top of the two-dimensional projection of breast cancer stroma. Colors indicate log transformed UMI counts normalized to total counts per cell. f, Volcano plot displaying differentially expressed genes between Pdpn+ fibroblasts and S100a4+ fibroblasts (see also supplementary table 4). Marker genes for NMF, pCAF, and sCAF are highlighted. A total of n = 8033 cells was analyzed using FDR adjusted two-sided chi square test. g, Fraction of cells originated from each mouse and subset, from all cells originated in their time point. Bar values represent the mean fraction values. Time points and subclasses are annotated and colored as in Fig. 1d. h, Squared Pearson correlation matrix for n = 1045 genes between bulk and single-cell RNA-sequencing results for NMF, pCAF, and sCAF.

Extended Data Fig. 2 Pdpn+ fibroblasts undergo dynamic changes in gene expression and subset composition during tumor progression.

a, Cell-surface PDPN protein expression levels obtained from the sorting data were used to quantify the percent of PDPN+ and PDPN cells in the CD45EpCAM stroma in the different time points. Data are combined from 7 independent experiments; n = 3 mice per group. Error bars represent 95% CI of the mean. P-value of the two-way ANOVA interaction between fibroblast subtype and time point is presented. b, Pseudo-time of expression for individual metacells (color coded by functional subclasses as in Fig. 2) included in the slingshot analysis. A total of n = 3465 cells was analyzed. Box plots display median bar, first–third quartile box and 5th–95th percentile whiskers. c, Distribution of cells across time points (color coded) within metacells included in the slingshot analysis. Metacell numbers and order are consistent across all figure panels and match the order in Fig. 2. d, Expression of hallmark NMF and pCAF genes (additional to those presented in Fig. 2e) across metacells (average UMI/cell), ordered by pseudo-time.

Extended Data Fig. 3 pCAFs and NMFs form a curve in gene-expression space, whereas a tetrahedron describes sCAF gene expression.

a, PCA analysis of NMF, and pCAF and sCAF from 2W and 4W, color coded according to the subclasses defined in Fig. 1c. n = 3703 cells. b-c, PCA analyses for NMF and pCAF (b) and for sCAF (c) color coded as in a. n = 3703 cells. d, Data projected on the four faces of the tetrahedron. e, Explained variance as a function of the number of PCs (real data) vs. shuffled. Note that the total variance explained by the first 3 PCs, about 5%, is typical of single-cell gene expression data22. f, Variance of vertex positions as a function of the number of vertices considered, using PCHA with k=3-7 vertices. g, Variation of vertex position (bootstrapping) for the real data (ellipses color-coded as in Fig. 3) vs shuffled data (grey ellipses). h, Histogram depicting the average variation of vertex positions calculated for the real data (green) vs multiple runs of shuffled data (grey). i, Histogram depicting the ratio between the volumes of the convex hull of the data and the minimal enclosing tetrahedron (t-ratio). The t-ratio of the real data (green) is compared to t-ratios of shuffled data (1000 shuffles; grey).

Extended Data Fig. 4 PDPN and S100A4 proteins mark distinct types of cells in 4T1 mouse tumors, the majority of which are CK-negative.

a, b, Representative images of normal mammary fat pads (NMF; a) and lung metastases (Mets; b) (see Fig. 4a) stained with antibodies against the indicated proteins. n = 3 mice per time point; Scale bar = 50μm, inset scale bar = 17μm. c, Quantification of the average overlap between CK, PDPN, and S100A4 staining in NMFs, primary tumors (2W and 4W) and Mets. Points represent the number of overlapping pixels between two channels, divided by the total number of pixels of the originating channels, in n = 3 biological replicates (each dot is an average of 9 images per mouse). Mean ± SEM, p-values were calculated by two-way ANOVA followed by Tukey’s multiple comparisons test.

Extended Data Fig. 5 PDPN and S100A4 proteins mark distinct types of cells in E0771 mouse tumors, the majority of which are CK-negative.

a, b, E0771 cancer cells were injected into the mammary fad pad of C57BL/6 mice. 4W post injection the tumors were excised and fixed. Formalin fixed paraffin embedded (FFPE) tissue sections were immunostained with antibodies against the indicated proteins (n = 4 mice in two independent experiments). Representative images from 2 different mice are shown in (a) and (b). Scale bar = 50 μm, inset scale bar = 17μm. c, Quantification of the average overlap between CK, PDPN, and S100A4 staining in E0771 tumors. n = 4 mice in two independent experiments, 3-7 images per mouse. Mean ±SD, P-values were calculated by two-way ANOVA correcting for multiple comparisons and were not found to be significant (p > 0.05), no multiple comparison test was performed. d, FACS analysis of Ly6C and α-SMA expression in CD45mCherryPDPN+ cells freshly harvested from 4W E0771 tumors and immediately fixed. The results from n = 3 biological replicates are quantified and analyzed utilizing one-way ANOVA followed by Tukey’s multiple comparisons test, Mean ±SEM.

Extended Data Fig. 6 Subsets of human sCAFs express MHC class II and NT5E, whereas a subset of pCAFs expresses α-SMA.

a, b, The overlap between S100A4, CK, MHC-II and NT5E stains (a; n = 12 patients, average scores of 3 images per patient) and between PDPN, CK, and α-SMA stains (b; n = 14 patients, average scores of 2-4 images per patient) in TNBC patients. Median is presented with 1st and 3rd quartiles, with untrimmed violin plot overlay. P-values were calculated by two-way ANOVA followed by Tuckey’s multiple comparisons test. c, Representative images of MxIF staining of serial sections from the same patients presented in Fig. 6a with antibodies against the indicated proteins. Scale bar = 500 μm.; inset scale bar = 90 μm.

Extended Data Fig. 7 pCAFs tend to localize to cancer-adjacent regions more often than sCAFs in human breast cancer patients.

a, Heat map showing Pearson’s correlation coefficients of the staining scores for different cell type markers (n = 70 patients). b, c, The association with overall survival of PDPN (b) or S100A4/PDPN (c) scored and classified as in Fig. 7b was assessed by KM analysis (n = 70 patients, P-values were calculated using log-rank test, two-sided). d, Illustration of the regional analysis workflow. e, The ratio of cancer-adjacent/dense stroma PDPN and S100A4 staining was determined for each core in the TNBC TMA (See also Fig. 7d). n = 70, median is presented with 1st and 3rd quartiles with trimmed violin plot overlay, P-value was calculated using two-sided Wilcoxon matched pairs signed-rank test. f, g, Cancer-adjacent regions and regions of dense stroma were determined for each core in the METABRIC TMA based on CK staining (see Methods section), PDPN and S100A4 staining in each region was scored (f) and the ratio of cancer-adjacent/dense stroma PDPN and S100A4 staining was determined (g). n = 219, median is presented with 1st and 3rd quartiles with trimmed violin plot overlay, P-value was calculated using two-sided Wilcoxon matched pairs signed rank test.

Extended Data Fig. 8 BRCA status is not significantly correlated with recurrence free survival in a cohort of TNBC patients.

a, CD3 and DAPI staining was performed on n = 68 patients from the TNBC cohort. Representative staining in a BRCA mutated (mut) patient and a BRCA WT patient is shown. b, Representative H&E stains of a BRCA mutated (mut) patient and a BRCA WT patient are shown (n = 25 BRCA WT; n = 20 BRCA mut; Serial sections of the same cores used in Fig. 8a are shown in a and b). Scale bar = 500μm; inset scale bar = 80μm. c, Box plot depicting CD3 staining scores (see Methods section) in patients with known BRCA status from our TNBC cohort (n = 23 BRCA WT; n = 20 BRCA mut) as well as the total TNBC cohort (All, n = 68). Median is presented with 1st and 3rd quartiles with trimmed violin plot overlay. P-value was calculated using a two-sided Student’s t-test. d, TNBC patients were stratified by BRCA mutational status and the association with recurrence free survival was assessed by KM analysis. n = 45, P-value was calculated using two-sided log rank test.

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Friedman, G., Levi-Galibov, O., David, E. et al. Cancer-associated fibroblast compositions change with breast cancer progression linking the ratio of S100A4+ and PDPN+ CAFs to clinical outcome. Nat Cancer 1, 692–708 (2020). https://doi.org/10.1038/s43018-020-0082-y

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