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ZFP281 drives a mesenchymal-like dormancy program in early disseminated breast cancer cells that prevents metastatic outgrowth in the lung

Nature Cancer volume 3, pages 1165–1180 (2022)Cite this article

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Abstract

Increasing evidence shows that cancer cells can disseminate from early evolved primary lesions much earlier than the classical metastasis models predicted. Here, we reveal at a single-cell resolution that mesenchymal-like (M-like) and pluripotency-like programs coordinate dissemination and a long-lived dormancy program of early disseminated cancer cells (DCCs). The transcription factor ZFP281 induces a permissive state for heterogeneous M-like transcriptional programs, which associate with a dormancy signature and phenotype in vivo. Downregulation of ZFP281 leads to a loss of an invasive, M-like dormancy phenotype and a switch to lung metastatic outgrowth. We also show that FGF2 and TWIST1 induce ZFP281 expression to induce the M-like state, which is linked to CDH1 downregulation and upregulation of CDH11. We found that ZFP281 not only controls the early dissemination of cancer cells but also locks early DCCs in a dormant state by preventing the acquisition of an epithelial-like proliferative program and consequent metastases outgrowth.

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Fig. 1: Early DCCs maintain a global M-like phenotype.
Fig. 2: Early DCCs turn on M-like and pluripotency-like programs that associate with dormancy gene signatures.
Fig. 3: Identification of the TF ZFP281 as upregulated in ELs and early DCCs.
Fig. 4: ZFP281 regulates EMT, Wnt, FGF and cell cycle programs in both primed mEpiSCs and early DCCs.
Fig. 5: ZFP281 induces an M-like invasive and slow-cycling phenotype in vitro.
Fig. 6: ZFP281 favors dissemination but serves as a barrier to metastasis initiation by maintaining an M-like dormancy program in early DCCs in vivo.
Fig. 7: Role of FGF2, TWIST1 and CDH11 in ZFP281-mediated regulation of early DCC fate.

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Data availability

Source data are provided with this paper. All sequencing data are available in a public data repository (GSE165431, RNAseq of MMTV-Neu EL and PT spheres; GSE165444, ZFP281 ChIPseq of MMTV-Neu EL and PT spheres; GSE165456, scRNAseq of MMTV-Neu primary site and lung cancer cells in early and late stage; GSE165459, scRNAseq of MMTV-Neu lung cancer cells in early and late stage). All other data supporting the findings of this study are available from the corresponding authors on reasonable request.

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Acknowledgements

We thank the Aguirre-Ghiso lab for helpful discussions and thank the expertise and assistance of the Dean’s Flow Cytometry CoRE and Microscope CoRE, Icahn School of Medicine at Mount Sinai. We thank J.K. Gregory, medical illustrator at Mount Sinai, for the graphical abstract. This work was supported by the National Institutes of Health/National Cancer Institute (CA109182, CA216248, CA218024 and CA196521) and the Samuel Waxman Cancer Research Foundation Tumor Dormancy Program. A.R.N. was funded by the Portuguese Foundation for Science and Technology (SFRH/BD/100380/2014). E.D. was funded by the National Institutes of Health/National Cancer Institute (T32 CA078207). L.W. was funded by the Federal Ministry of Education and Research (DFG project number 431474090). Research in the laboratory of J.W. was supported by grants from NYSTEM (C32569GG and C32583GG) and the National Institutes of Health (R01GM129157, R01HD095938, R01HD097268 and R01HL146664).

Author information

Author notes

  1. Ana Rita Nobre

    Present address: Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

Authors and Affiliations

  1. Division of Hematology and Oncology, Department of Medicine and Department of Otolaryngology, Department of Oncological Sciences, Black Family Stem Cell Institute, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Ana Rita Nobre, Erica Dalla, Lena Wullkopf, Emma Risson, Melisa Lopez Anton & Julio A. Aguirre-Ghiso

  2. Abel Salazar Biomedical Sciences Institute, University of Porto, Porto, Portugal

    Ana Rita Nobre

  3. Department of Medicine, Columbia Center for Human Development, Columbia University Irving Medical Center, New York, NY, USA

    Jihong Yang, Xin Huang & Jianlong Wang

  4. Zhang Boli Intelligent Health Innovation Lab, Hangzhou, China

    Jihong Yang

  5. Department of Cell Biology, Albert Einstein College of Medicine, Bronx, NY, USA

    Pedram Razghandi, Wei Zheng & Julio A. Aguirre-Ghiso

  6. Cancer Dormancy and Tumor Microenvironment Institute, Albert Einstein College of Medicine, Bronx, NY, USA

    Pedram Razghandi, Wei Zheng & Julio A. Aguirre-Ghiso

  7. Cancer Computational Biology Group, Vall d´Hebron Institute of Oncology, Barcelona, Spain

    Jose A. Seoane

  8. Department of Medicine and Department of Genetics, Stanford University, Stanford, CA, USA

    Jose A. Seoane & Christina Curtis

  9. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Ephraim Kenigsberg

  10. The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Ephraim Kenigsberg

  11. Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Ephraim Kenigsberg

  12. Montefiore Einstein Cancer Center, Albert Einstein College of Medicine, Bronx, NY, USA

    Julio A. Aguirre-Ghiso

  13. Gruss-Lipper Biophotonics Center, Albert Einstein College of Medicine, Bronx, NY, USA

    Julio A. Aguirre-Ghiso

  14. Ruth L. and David S. Gottesman Institute for Stem Cell Research and Regenerative Medicine, Albert Einstein College of Medicine, Bronx, NY, USA

    Julio A. Aguirre-Ghiso

  15. Institute for Aging Research, Albert Einstein College of Medicine, Bronx, NY, USA

    Julio A. Aguirre-Ghiso

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Contributions

A.R.N. designed, planned and conducted experiments, analyzed data and wrote the manuscript; E.D., J.Y., X.H., L.W., E.R., P.R., M.L.A. and W.Z. conducted experiments; J.Y. and E.K. did the bioinformatics analysis; J.A.S. did the bioinformatics analysis in human datasets; CC provided expertise on analysis of human datasets; J.W. provided necessary reagents and developmental biology expertise; and J.A.A.-G. designed experiments, analyzed data and wrote the manuscript.

Corresponding authors

Correspondence to Ana Rita Nobre or Julio A. Aguirre-Ghiso.

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Competing interests

J.A.A.-G. is a scientific co-founder of scientific advisory board member and equity owner in HiberCell and receives financial compensation as a consultant for HiberCell, a Mount Sinai spin-off company focused on the research and development of therapeutics that prevent or delay the recurrence of cancer. The remaining authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Model description, Enrichr analysis, FACS controls and MMTV-HER2 scRNASeq data distribution.

(a) Experimental design of MMTV-HER2 bulk and single-cell RNA sequencing experiments. (b) Enrichr analysis30,31 of differentially expressed genes (DEG) in MMTV-HER2 early lesion (EL) and primary tumor (PT) 7-day spheres bulk RNAseq. Full table in Supplementary Table 2. Orange, terms mentioned in the text. (c) Biological negative controls used for FACS gating strategy. FvB mammary gland (MG) was used to set the MMTV-HER2 EL and PT gate and FvB lungs for MMTV-HER2 eL and LL DCCs (see Fig. 1f). (d) Number of cells per cluster analyzed in the single-cell RNAseq of MMTV-HER2 EL (teal), PT (red), eL (early lungs, blue) and LL (late lungs, orange) DCCs (see Fig. 1d, e). (e) Number of UMIs per cluster (left) and per sample (right) analyzed in the single-cell RNAseq (see Fig. 1d, e). (f) Percentage of epithelial (EpCAM+Eng−), hybrid (EpCAM+Eng+) and mesenchymal (EpCAM−Eng+) populations in CD45−HER2+ MMTV-HER2 EL, PT and eL (early lungs) and LL (late lungs) DCCs after tissue dissociation (representative FACS plots in Fig. 1f). Graph shows n = 5 mice/condition, median, SEM and 2−tailed multiple t-tests.

Source data

Extended Data Fig. 2 MMTV-HER2 scRNASeq data distribution of phenotypes across clusters.

(a) Number of cells per cluster analyzed in the single-cell RNAseq of HER2-, HER2+ eL (early lungs) and LL (late lungs) DCCs (see Fig. 2b, c). (b) Number of UMIs per cluster (left) and per sample (right) analyzed in the single-cell RNAseq (see Fig. 2b, c). (c) Scatterplots of single-cell RNAseq datasets (see Fig. 2b, c) using UMAP projections, color coded by per cluster (left) and per sample (right). (d) Distribution of Epithelial (Ep) and Mesenchymal (M) scores (gene lists in Supplementary Table 4, showed in Fig. 2a) in MMTV-HER2 lung DCC clusters. Cell clusters were subgrouped as M-like (1−4, higher M-like score), Hybrid (5−8) and Ep-like (9–15). (e) Distribution of gene modules B and D (M-like) in all DCC clusters. Dots represent single cells color-coded by cluster (left), sample origin (eL or LL, middle) and sub-group (Ep-like, hybrid, M-like, right). Gene module lists in Supplementary Table 4. (f) Distribution of gene modules I (Ep-like) and B (M-like) in all DCC clusters. Dots represent single cells color-coded by cluster (left), sample origin (eL or LL, middle) and sub-group (Ep-like, hybrid, M-like, right). Gene module lists in Supplementary Table 4.

Extended Data Fig. 3 High resolution plots for scRNASeq clusters and projections on to M-like signatures.

(a) Heatmap of UMI counts of selected genes (gene lists in Supplementary Table 4) in MMTV-HER2 eL (early lungs,) and LL (late lungs) DCCs single-cell RNAseq after unsupervised clustering on the DEGs and down-sampling to 500 UMI per cell. ‘Per cell’ representation of Fig. 2b heatmap, which shows UMI averages. (b)Single cells color-coded by gene expression and distributed by gene modules B and D (M-like). Examples of EMT- and dormancy-associated genes were selected.

Extended Data Fig. 4 Functional readouts of ZFP281 gain and loss of function, ZFP281 basal expression in FvB, EL and PT tissues and DCC location in lungs.

(a) mRNA expression of ZFP281, its predicted targets (Fig. 3a) and EMT genes in MMTV-HER2 EL versus PT cells, EL shCt, EL shZFP281 and PT ZFP281-OE. Red, upregulated genes; Blue, downregulated genes; two-tailed Mann–Whitney test, *p-value <0.05. (b) Representative images of ZFP281 (1st column, green), E-cadherin (2nd column, green) and Twist1 (3rd column, green) protein expression in consecutive sections of FvB mammary gland (biological negative control) and MMTV-HER2 EL and PT tissues. HER2 expression in red. Arrows point to ZFP281+EcadlowTwist1+ cells in EL. Dashed arrow points to ZFP281+ adipocytes (internal control). As previously described, stromal adipocytes express high levels of ZFP281. Scales, 20 μm. (c) ZFP281 expression in MMTV-PyMT EL and PT tissues. Graph shows n = 9 slides from 5 mice per group, median and two-tailed Mann–Whitney test. (d) Representative images of the location of lung DCCs in relation to alveolar type II (AT2) cells and CD31+ vessels. Scales, 25 μm. (e) Quantification of lung DCCs in contact with alveolar type II (AT2) cells in MMTV-HER2 eL and LL. Graph shows n = 3 mice per group, median and two-tailed Mann–Whitney test.

Source data

Extended Data Fig. 5 RNAseq and ChIP-seq analysis for ZFP281 targets and projection of ZFP281 binding score on to M-, H- and Ep-like clusters.

(a) Volcano plot from RNAseq data on MMTV-HER2 siZFP281 cells. 436 downregulated genes (green), p-value<0.05 & log2F < −0.5); 493 upregulated genes (red), p-value<0.05 & log2FC > 0.5. Gene lists in Supplementary Table 10. (b) Distribution of ZFP281 binding peaks localization in both MMTV-HER2 EL (top) and PT (bottom) cells. Graph shows n = 3 and mean. (c) Global analysis on ZFP281 targets in MMTV-HER2 EL versus PT cells. Dotted line, p-value=0.05. (d) Volcano plot of combined RNAseq (x) and ChIPseq (y) from MMTV-HER2 EL versus PT cells. 143 genes show lower ZFP281 binding and higher expression in EL versus PT cells (DW_UP); 759 genes show higher ZFP281 binding and higher expression (UP_UP); 177 genes show lower ZFP281 binding and lower expression (DW_DW); 121 genes show higher ZFP281 binding and lower expression (UP_DW). Gene lists in Supplementary Table 11. (e) Global analysis on genes with high ZFP281 binding and high expression (red, UP_UP) and low ZFP281 binding and low expression (green, DW_DW) in MMTV-HER2 EL vs PT cells (identified in C). (f) Venn diagram of MMTV-HER2 EL versus PT RNAseq (Fig. 1a), ZFP281 node (Fig. 3a) and ChIPseq (Fig. 4b) data. Targets of ZFP281 in EL cells and EpiSCs were identified from ChIP-seq data and further used to compare with EMT, Wnt, FGFR, and cell cycle arrest genes. (g) Representative tracks of MMTV-HER2 EL/PT ChIPseq (Fig. 4b, c). EMT genes: Snai1, Vim, Zeb1, Cdh11, Twist1; Cell cycle associated genes: Cdkn2d, Cdkn1a; Dormancy-associated genes: Tgfbr1, NR2f1. (h) Frequency of ZFP281 target (ChIP) score, summarizing the averaged expression of ZFP281 targets, in all cells analyzed by scRNAseq (Fig. 2). (i, j) Distribution of ZFP281 target scores and modules D and B (M-like) (I) or modules I (Ep-like) and B (M-like) (J) in all DCC clusters. Dots represent single cells color-coded by ZFP281 target scores (low, red to high, green).

Extended Data Fig. 6 Modulation of M-, Hybrid and Ep-like phenotypes upon ZFP281 gain and loss of function in 3D cultures and mammospheres.

(a) Representative images of the mammosphere phenotype of MMTV-HER2 EL, PT and EL shControl±DOX cells. Scale 50 μm. (b) Quantification of mammosphere (MS) frequency of MMTV-HER2 EL, PT and EL shControl±DOX cells. Graph shows n = 3, median and two-tailed Mann–Whitney test. (c) Quantification of mammosphere (MS) size, as number of cells per sphere after dissociation of MMTV-HER2 EL, PT and EL shControl±DOX spheres. Graph shows n = 3, median and two-tailed Mann–Whitney test. (d) EpCAM (epithelial marker) and Eng/CD105 (mesenchymal marker) expression in MMTV-HER2 EL, PT and EL shControl±DOX cells. Representative experiment. (e) Fold change of Ep-like (EpCAM + Eng-), hybrid (EpCAM + Eng + ) and M-like (EpCAM-Eng + ) populations in EL over PT and EL shControl±DOX spheres. Graph shows n = 4, mean, SEM and two-tailed Mann–Whitney test. (f, g) Representative images and quantification of 3D-Matrigel invasive phenotype of MMTV-HER2 EL, PT and EL shControl±DOX organoids. Scale 50 μm. Graph shows n = 4, median and two-tailed Mann–Whitney test. (h) mRNA expression of ZFP281 in MMTV-Neu EL siControl and siZFP281. Graph shows n = 2, and median. (i) Quantification of 3D-Matrigel invasive phenotype of MMTV-HER2 siControl and siZFP281. Graph shows n = 4, median and two-tailed Mann–Whitney test. (j, k) Quantification of mammosphere (MS) frequency (J) and size (as number of cells per sphere after dissociation, K) of MMTV-PyMT EL and PT spheres. Graph shows n = 5 experiments for MMTV-PyMT EL conditions and n = 3 for MMTV-PyMT PT conditions, median and two-tailed Mann–Whitney test. (l) mRNA expression of ZFP281 in MMTV-PyMT EL and PT spheres. Graph shows n = 3, median and two-tailed Mann–Whitney test.

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Extended Data Fig. 7 Dormant versus metastatic fate and M- versus Ep-like phenotypes of DCCs in target organs.

(a) ZFP281 expression of MMTV-HER2 EL-shZFP281 –DOX, + DOX and -DOX + DOX cells in the mammary fat pad, 5 month after injection. Graph shows n = 4 mice per condition, median and two-tailed Mann–Whitney test. (b) Frequency of lung metastasis and area, 3 month after MMTV-HER2 EL-shZFP281 sphere injections. Graph shows n = 5 per condition, median and two-tailed Mann–Whitney test. (c) Quantification of Ki67+ cells in lung metastasis 5 months after MMTV-HER2 EL shZFP281 sphere injections. Graph shows n = 5 mice per condition, median and two-tailed Mann–Whitney test. (d, e) Quantification and representative images of Twist1+ and Ecad+ cells in lung metastasis 3 months after MMTV-HER2 EL shZFP281 sphere injections. Graph shows n = 3 mice per condition for Twist quantifications and n = 5 mice per condition for Ecad quantifications, median and two-tailed Mann–Whitney test. Scales 25 μm. (f) ZFP281 expression of MMTV-HER2 PT Control or PT ZFP281-OE primary tumors. Graph shows n = 4 mice per condition, median and two-tailed Mann–Whitney test. (g) Quantification of Ki67+ cells in lung metastasis 5 months after MMTV-HER2 PT Control or PT ZFP281-OE sphere injections. Graph shows n = 5 mice per condition, median and two-tailed Mann–Whitney test. (h, i) Quantification and representative images of Twist1+ and Ecad+ cells in lung metastasis 5 months after MMTV-HER2 PT Control or PT ZFP281-OE sphere injections. Graph shows n = 3 mice per condition for Twist quantifications and n = 5 mice per condition for Ecad quantifications, median and two-tailed Mann–Whitney test. Scales 25 μm.

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Extended Data Fig. 8 Characterization and functional analysis of CDH11 expression in EL and PT lesions.

(a, b) CDH11 (green) protein expression in MMTV- HER2 (HER2, red) EL and PT cells. Scales, 25 μm. Graph shows n = 3 mice per group, median and two-tailed Mann–Whitney test. (c) CDH11 mRNA expression in MMTV- HER2 EL and PT spheres. Graph shows n = 3, median and two-tailed Mann–Whitney test. (d, e) mRNA expression of CDH11 (D) and 3D-Matrigel invasive phenotype (E) of MMTV-HER2 EL organoids transfected with siControl or siCDH11. Graphs show n = 2 (D) and n = 4 (E), median and two-tailed Mann–Whitney test. (f, g) mRNA expression of CDH11 (F) and 3D-Matrigel invasive phenotype (G) of MMTV-HER2 PT organoids 7 days after CDH11-OE. Graphs show n = 3 (F) and n = 4 (G), median and two-tailed Mann–Whitney test. (h) Representative images of CDH11 (red or gray) protein expression in primary tumors and lung metastasis of mice injected with PT Control and PT CDH11-OE spheres. Scales 20 μm. (i) Tumor volume over time of PT Control and PT CDH11-OE mice, until the primary tumor reached size for surgery. Graph shows n = 6 PT Control and 8 PT CDH11-OE mice, median, interquartile range and two-tailed multiple t-tests.

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Supplementary Tables

Supplementary Tables 1–13.

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Nobre, A.R., Dalla, E., Yang, J. et al. ZFP281 drives a mesenchymal-like dormancy program in early disseminated breast cancer cells that prevents metastatic outgrowth in the lung. Nat Cancer 3, 1165–1180 (2022). https://doi.org/10.1038/s43018-022-00424-8

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