Data from: Identification of a minority population of LMO2+ breast cancer cells that integrate into the vasculature and initiate metastasis.
Data files
Oct 05, 2022 version files 1.60 GB
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clonally_extinct_exprs.txt
254.24 MB
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clonally_extinct_pheno.txt
60.64 KB
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dbEMT.txt
8.34 KB
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Figure1_results.txt
329.29 KB
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HUGO.txt
122.51 KB
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ISPY_dataset.txt
102.95 MB
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ISPY_pheno.txt
9.70 KB
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ISPY_survival.txt
8.74 KB
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LMO2_CIBERSORT_ReferenceMatrix.txt
429.81 MB
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Metabric_discovery_data.txt
148.64 MB
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Metabric_discovery_pheno.txt
29.33 KB
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Metabric_discovery_survival.txt
16.25 KB
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Metabric_validation_data.txt
147.31 MB
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Metabric_validation_pheno.txt
26.61 KB
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Metabric_validation_survival.txt
16.09 KB
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msigdb.txt
114.05 MB
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PYMT.rds
11.17 MB
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README.txt
6.42 KB
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singleCellFunctions.R
4.80 KB
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TCGA_data.txt
390.52 MB
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TCGA_pheno.txt
71.97 KB
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TCGA_survival.txt
32.18 KB
Abstract
Metastasis is responsible for the majority of breast cancer-related deaths, however, identifying the cellular determinants of metastasis has remained challenging. Here, we identified a minority population of immature THY1+/VEGFA+ tumor epithelial cells in human breast tumor biopsies that display angiogenic features and are marked by the expression of the oncogene, LMO2. Higher abundance of LMO2+ basal cells correlated with tumor endothelial content and predicted poor distant recurrence-free survival in patients. Using MMTV-PyMT/Lmo2CreERT2 mice, we demonstrated that Lmo2 lineage-traced cells integrate into the vasculature and have a higher propensity to metastasize. LMO2 knockdown in human breast tumors reduced lung metastasis by impairing intravasation, leading to a reduced frequency of circulating tumor cells. Mechanistically, we find that LMO2 binds to STAT3 and is required for STAT3 activation by TNFα and IL6. Collectively, our study identifies a population of metastasis-initiating cells with angiogenic features and establishes the LMO2-STAT3 signaling axis as a therapeutic target in breast cancer metastasis.
Brief overview
Please find enclosed in this repository two main folders:
(1) 'data', which contains raw and processed data used for the genomic analysis done in the manuscript including original scRNA-seq data from our laboratory and publicly available scRNA-seq data from other labs, and
(2) 'scripts', which contains the code and scripts used to generate the results shown in the figures. Please reach out directly to us if you have any questions about the data and scripts enclosed here for the manuscript.
All data are stored as tab-delimited text files that can be opened with any spreadsheet software program. All code was written in either R or bash.