Although metastasis remains the cause of most cancer-related mortality, mechanisms governing seeding in distal tissues are poorly understood. Here, we establish a robust method for the identification of global transcriptomic changes in rare metastatic cells during seeding using single-cell RNA sequencing and patient-derived-xenograft models of breast cancer. We find that both primary tumours and micrometastases display transcriptional heterogeneity but micrometastases harbour a distinct transcriptome program conserved across patient-derived-xenograft models that is highly predictive of poor survival of patients. Pathway analysis revealed mitochondrial oxidative phosphorylation as the top pathway upregulated in micrometastases, in contrast to higher levels of glycolytic enzymes in primary tumour cells, which we corroborated by flow cytometric and metabolomic analyses. Pharmacological inhibition of oxidative phosphorylation dramatically attenuated metastatic seeding in the lungs, which demonstrates the functional importance of oxidative phosphorylation in metastasis and highlights its potential as a therapeutic target to prevent metastatic spread in patients with breast cancer.
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The authors declare that all data supporting the findings of this study are available within the article and its supplementary information files or from the corresponding author on reasonable request. All RNA-seq data files along with their associated metadata have been deposited in the GEO database under the accession code GSE123837. Previously published microarray data that were reanalysed here with KM Plotter26,67 are available under the following accession codes: E-MTAB-365, E-TABM-43, GSE11121, GSE12093, GSE12276, GSE1456, GSE16391, GSE16446, GSE16716, GSE17705, GSE17907, GSE18728, GSE19615, GSE20194, GSE20271, GSE2034, GSE20685, GSE20711, GSE21653, GSE2603, GSE26971, GSE2990, GSE31448, GSE31519, GSE32646, GSE3494, GSE37946, GSE41998, GSE42568, GSE45255, GSE4611, GSE5327, GSE6532, GSE7390 and GSE9195.
Custom scripts are available at https://github.com/lawsonlab/Single_Cell_Metastasis.
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We thank G. Gutierrez and M. Masoud for technical assistance and animal handling. We thank N. Pervolarakis for helpful advice on data analysis. We thank K. Kessenbrock for thoughtful feedback on experimental design and careful review of the manuscript. We thank A. L. Welm for providing the PDX samples. Image acquisition was made possible in part through access to the Optical Biology Core Facility of the Developmental Biology Center, a shared resource supported by the Cancer Center Support Grant (grant no. CA-62203), with assistance from A. Syed and a Center for Complex Biological Systems Support Grant (grant no. GM-076516) at the University of California, Irvine. This study was supported by funds from the National Cancer Institute (grant nos R01 CA057621 and U01 CA199315 to Z.W., and grant no. K22 CA190511 to D.A.L.), National Institutes of Health (grant no. R01HD073179 to E.M., P41-GM103540 to M.A.D. and A.E.Y.T.L,, and T32CA009054 to M.B.G. and R.T.D., through matched university funds through matched university funds), National Science Foundation (grant no. 1847005 to M.A.D. and NSF GRFP DGE-1839285 to A.E.Y.T.L.), Team Michelle and Friends non-profit organization, Suzette Kirby Breast Cancer Research Fund, V Foundation (grant no. V2019-019) as well as an Opportunity Award funded by the UCI Center for Complex Biological Systems (CCBS; NIGMS, grant no. P50-GM076516 to R.T.D., K.B., D.Maurer, E.M. and D.A.L.). H.A. was supported by the University of Hail, Hail, Saudi Arabia for the PhD Fellowship. D.Ma was supported by a Canadian Institutes of Health Research Postdoctoral Fellowship.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
a, Identification and removal of poor-quality cell libraries. Plots show the number of genes detected in each cell from each PDX model. Cells (x axis) are ordered from fewest to most genes detected. Cells with fewer than 2500 genes detected (horizontal line) were excluded. b, Identification and removal of noisy/background genes. Plots show the distribution of genes detected per cell. Vertical line indicates that genes detected in fewer than 8 cells were excluded. c, Violin plots indicate the expression of mitochondrial genes as a percentage of total gene expression in each cell separated by HCI001 (n = 375 cells), HCI002 (n = 576 cells), and HCI010 (n = 756 cells). Cells were excluded if > 50% of genes detected were mitochondrial (horizontal line). Black line with dot indicates median. (d, e) Cells from HCI001 (d) and HCI010 (e) are plotted based on the relative expression of gene sets associated with G1/S (x-axis) and G2/M (y- axis) stages of the cell cycle. Left plots: Relative expression of the proliferation-associated gene MKI67 is shown in HCI001 and HCI010. Middle plots: Cell clustering before cell cycle regression. Cluster identities are shown in grey. Right plots: Cell clustering after cell cycle regression. Colours indicate new cluster identity of each cell and correspond with clusters shown in Fig. 2a. Bar graphs show quantification of cell clusters by cycling status before and after regression. Source data
a, tSNE plots display clustering of cells coloured by mouse of origin from PDX models HCI001 (n = 247 cells), HCI002 (n = 401 cells) and HCI010 (n = 471 cells). b, tSNE plots display clustering of cells coloured by tissue of origin from PDX models HCI001 (n = 247 cells), HCI002 (n = 401 cells) and HCI010 (n = 471 cells). c, Bar plots show selected top GO terms determined by the marker genes identified for each cell cluster. P values are determined by the Fisher exact test. Full marker gene lists utilized are listed in Supplementary Table 1. Specifically, for HCI001, n = 162 A1 genes, n = 107 A2 genes, and n = 199 A3 genes. For HCI002, n = 490 B1 genes, n = 173 B2 genes, n = 34 B3 genes, n = 181 B4 genes, and n = 194 B5 genes. For HCI010, n = 96 C1 genes, n = 247 C2 genes, n = 198 C3 genes, n = 357 C4 genes, n = 54 C5 genes, n = 110 C6 genes. d, Bar graphs show the log fold change (logFC) for selected genes from GO term pathways. Values indicate the logFC of the average gene expression for the indicated cell cluster relative to all other clusters within that PDX model. Source data
Extended Data Fig. 3 Prognostic value of micrometastasis- associated genes in basal-like breast cancer patients.
a, Kaplan-Meier curves show relapse free survival (RFS) in basal-like breast cancer patients from the KM plotter database (879 patients), based on their primary tumour expression of specified micrometastasis-associated genes. P values were determined via a log-rank test. b, Schematic for the construction of a stepwise logistic regression model to identify top biomarker candidates descriptive of primary tumour or micrometastatic cells. The data was subsampled to analyse equal numbers of micrometastatic and tumour cells from each mouse. The model was run on 10 subsamplings of the data, with the number of genes in each model determined by AIC. c, Plot demonstrating the AIC versus the number of genes included in the model. AIC is used to balance parameter additions (that is gene additions) with the descriptive power of a model. Data is presented as the 10% and 90% quantiles of the 10 data subsamplings. d, Bar plot showing the number of model appearances for each gene out of 10 data subsamplings.
Extended Data Fig. 4 Gene scoring identifies OXPHOS and glycolysis as top metabolic pathways differentially expressed between micrometastases and primary tumour cells.
Gene scores for each metabolic pathway in micrometastatic (red, n = 435 cells) or primary tumour cells (blue, n = 684 cells). Each cell in the dataset was scored by calculating the difference between the average gene expression for all the genes in each metabolic pathway versus the average gene expression of a randomly selected background gene set. Dotted line represents a zero score, which indicates the metabolic pathway is not differentially expressed relative to the background gene set. The boxed value (top right of each plot) indicates the percent of genes in the pathway that was detected in the dataset. The lower and upper hinges correspond to the first and third quartiles, and the midline represents the median. The upper and lower whiskers extend from the hinge up to 1.5 * IQR (inter-quartile range). Outlier points are indicated if they extend beyond this range.
a,b, Schematic of the experimental setup to determine the effects of oligomycin treatment on MDA-MB-231 (a) or 4T1-GFP (b) cells. Oligo. = oligomycin. (c-d) Bar graph (left) of the viability of MDA-MB-231 (c) or 4T1- GFP (d) cells 72-hours post-treatment determined via flow cytometry. Data is presented as the mean ± s.d. for n = 3 replicates. P values determined by unpaired, two- sided Student’s t-test. Brightfield images (right) of in vitro MDA-MB-231 (c) or 4T1-GFP (d) cells 72 hours after cessation of oligomycin treatment. Scale bar = 100 µm. e, Growth rate of MDA-MB-231 cells for the indicated time period after cessation of oligomycin treatment. Data is presented as mean ± s.d. of n = 3 replicates. P values determined by unpaired, two-sided Students t-test. f, Extracellular acidification rate (ECAR) of MDA-MB-231 treated with oligomycin compared to control cells. ECAR was measured at the conclusion of treatment with oligomycin as described in (a). Arrows indicate when drugs were added. O=oligomycin, F=FCCP, ROT/AA=Rotenone/ Antimycin A. Data is presented as mean ± s.d. of n = 4 replicates. g, Same as (e) for 4T1-GFP cells. h, Same as (f) for 4T1-GFP cells. Data is presented as mean ± s.d. of n = 3 replicates. P values determined by unpaired, two-sided Students t-test. (i, j) Additional FLIM images of the fluorescence lifetime of NADH in cultured MDA-MB-231 (i) or 4T1-GFP cells (j) as shown in Fig. 5e, f. Fields do not represent consecutive images of the same cell. O=oligomycin, C=control. Scale bar=10 µm. (k-l) Brightfield images of tumours from orthotopically injected control or oligomycin treated MDA-MB-231 (k) or 4T1-GFP (l) cells. Bar graphs indicate tumour weights (right). Data presented as mean ± s.d. of MDA-MB-231 (n = 6 oligomycin-treated, n = 6 control) and 4T1-GFP (n = 6 oligomycin-treated, n = 6 control) tumours. P-values were determined by unpaired, two-sided Student’s t-test. Scale bar = 0.5 cm. Source data
Metastatic cells in the lung and lymph nodes display increased OXPHOS, in contrast to primary tumour cells that express higher levels of genes associated with aerobic glycolysis. Pharmacological inhibition of OXPHOS with the complex V inhibitor oligomycin substantially attenuates lung metastasis in experimental models of TNBC, showing that OXPHOS is functionally important for metastasis.
Marker genes characteristic of each cancer cell subpopulation (A1–A3; B1–B5; C1–C6) identified by clustering analysis using the Seurat pipeline. Values indicate the log[FC] of the average gene expression for the indicated cell cluster relative to all other clusters within that PDX model. Specifically, for HCI001, n = 87 A1 cells, n = 83 A2 cells and n = 77 A3 cells. For HCI002, n = 54 B1 cells, n = 102 B2 cells, n = 81 B3 cells, n = 76 B4 cells and n = 88 B5 cells. For HCI010, n = 102 C1 cells, n = 73 C2 cells, n = 64 C3 cells, n = 88 C4 cells, n = 84 C5 cells and n = 60 C6 cells. P values were determined with the bimod test in Seurat, which utilizes a likelihood-ratio test. Adjusted P values were determined using the default Bonferonni adjustment in the Seurat FindAllMarkers() function.
GO terms associated with each cancer cell subpopulation (A1–A3; B1–B5; C1–C6). Full marker gene lists utilized for GO analysis are listed in Supplementary Table 1. Specifically, for HCI001, n = 162 A1 genes, n = 107 A2 genes and n = 199 A3 genes. For HCI002, n = 490 B1 genes, n = 173 B2 genes, n = 34 B3 genes, n = 181 B4 genes and n = 194 B5 genes. For HCI010, n = 96 C1 genes, n = 247 C2 genes, n = 198 C3 genes, n = 357 C4 genes, n = 54 C5 genes and n = 110 C6 genes. For HCI001, n = 162 A1 genes, n = 107 A2 genes and n = 199 A3 genes. For HCI002, n = 490 B1 genes, n = 173 B2 genes, n = 34 B3 genes, n = 181 B4 genes and n = 194 B5 genes. For HCI010, n = 96 C1 genes, n = 247 C2 genes, n = 198 C3 genes, n = 357 C4 genes, n = 54 C5 genes and n = 110 C6 genes. P values were determined by the Fisher’s exact test in Enrichr. Adjusted P values were determined by Benjamini–Hochberg (BH) correction in Enrichr.
330 genes differentially expressed between micrometastases and primary tumour cells. Differential expression analysis of micrometastatic (n = 435) and primary tumour (n = 684) cells was performed using the tobit test in Seurat (additional details in Methods). P values were determined by the tobit test in Seurat, which utilizes a likelihood-ratio test. Adjusted P values were determined by the default Bonferonni adjustment in the Seurat FindAllMarkers() function.
GO terms for the 330 genes differentially expressed between micrometastases and primary tumour cells and conserved in all three PDX models. GO terms identified for primary tumour (n = 214 genes) and micrometastatic cells (n = 116 genes) based on the 330 micrometastasis gene signature. P values are determined by the Fisher’s exact test in Enrichr. Adjusted P values were determined by Benjamini–Hochberg (BH) correction in Enrichr.
Genes associated with the 37 metabolic pathways for gene scoring analysis.
Integrated peak intensities for metabolites detected in micrometastases and primary tumour cells by LC–HRMS.
Primer sequences used for quantitative real-time PCR.
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Davis, R.T., Blake, K., Ma, D. et al. Transcriptional diversity and bioenergetic shift in human breast cancer metastasis revealed by single-cell RNA sequencing. Nat Cell Biol 22, 310–320 (2020). https://doi.org/10.1038/s41556-020-0477-0