Rearrangement of 3D genome organization in breast cancer epithelial to mesenchymal transition and metastasis organotropism
- UT-ORNL Graduate School of Genome Science and Technology, University of Tennessee, United States
- Biochemistry & Cellular and Molecular Biology, University of Tennessee, United States
Figures
Figure 1
Spatial compartment identity segregates cells by subtype and epithelial–mesenchymal status.
(a) Primary cells and cell lines used in this study are arranged depending on their molecular subtypes and malignancy status based on prior available characteristics. Green: normal breast and lung epithelium, red: luminal subtype, blue: HER2-enriched subtype, pink: triple-negative subtype, brown: localized lung cancer. (b) Hierarchical clustering of genome-wide compartment identity data for all breast and lung cell types at 250 kb resolution; colors as in 1 a. (c) Principal component analysis (PCA) of genome-wide compartment identity data for breast and lung cell types at 250 kb resolution; colors as in (a). The PC2 axis is divided into two subspaces that mostly segregate cell lines based on HER2 status (blue thick dotted line). (d) GO term enrichment (biological processes) of the genes in genomic regions corresponding to the top 100 positive and 100 negative elements of the first eigenvector of genome-wide compartment identity PCA.
Figure 2 with 1 supplement
Gene expression differences also segregate breast cancer cell lines along an epithelial–mesenchymal transition (EMT) axis.
(a) Principal component analysis (PCA) of transcriptome data for breast and lung cell types in this study. Cell types are colored based on their molecular subtypes as represented in Figure 1a. (b) GO term enrichment (biological processes) of the genes corresponding to top 100 positive and 100 negative elements of the first eigenvector of the transcriptome profile PCA. Projection of breast and lung cell types on a curated epithelial and mesenchymal axis based on their gene expression using gene set variation analysis (GSVA) (c) and non-negative principal component analysis (nnPCA) (d) methods.
Figure 2—figure supplement 1
Concordance between epithelial–mesenchymal transition (EMT) classification of cell types by gene expression and Hi-C compartments.
(a) Breast cancer cell lines are arranged depending on their epithelial–mesenchymal status. The states are calculated based on their gene expression profile using the method described in Tan et al., 2014. This figure has been adapted from Le et al., 2018, reused as consistent with CC-BY License. Stars indicate cell lines used in the current study. (b) Scatter plot showing relation between compartment PC1 (from Figure 1c) and gene expression EMT score (as in a) for breast cancer cell lines considered in this manuscript. (c) Example of compartment changes near but not on top of a gene that changes expression across the EMT axis. TFF3 (yellow highlighted position) is downregulated in more mesenchymal-like cancer cell lines (top 4) as compared to epithelial-like (bottom 4). The gene itself is in the A compartment (red, positive PC1 values) in all but 1 cell line, but many regions nearby switch to the B compartment in the top 4 vs. bottom 4 cell lines.
Figure 3 with 1 supplement
Transcription and compartment changes capture distinct sets of epithelial–mesenchymal transition (EMT)-related genomic regions.
(a) Overlap analysis of the genes obtained from the compartmental analysis PC1 (red, same genes analyzed in Figure 1d), the transcriptome analysis PC1 (green, same genes analyzed in Figure 2b), and a curated breast cancer epithelial–mesenchymal gene set (blue). See Supplementary file 2 for gene lists. Hierarchical clustering of compartment profiles (top row) and gene expression (bottom row) of the genes obtained from either (b) the compartmental analysis PC1, (c) transcriptome analysis PC1, or (d) the curated set of breast cancer epithelial and mesenchymal genes. For (b), the clustering order is determined by the compartment profile and then the same order of genes and cell lines is shown for the expression data. For (c) and (d), the clustering order is based on gene expression profiles and then the same order is shown for compartment profiles. Cell line names are colored according to position along the EMT axis (purple = more epithelial, yellow = more mesenchymal) as presented in Le et al., 2018 and shown in Figure 2—figure supplement 1a.
Figure 3—figure supplement 1
Conservation vs. change in breast cancer cell compartment identity for regions that differ between breast and lung epithelial cells.
(a) Out of all genomic bins that are in the A compartment in normal breast epithelial cell lines but B compartment in normal lung epithelial cells, the graph shows the proportion that switch compartments to match lung (green) for each category of breast cancer vs. those that remain like breast (pink). (b) Same as (a), but for genomic regions that are in the B compartment in normal breast epithelial cells and A compartment in normal lung.
Figure 4 with 3 supplements
Breast cancer cells that metastasize to the lung show increased lung-permissive compartment changes with concordant gene expression alteration in patients.
(a) Normal breast epithelium (HMEC and MCF10A), localized breast cancer (BT474, HCC1954, and BT549), and lung metastatic breast cancer (MCF7, T47D, SKBR3, and MDA-MB-231) cells are used to model different stages of breast cancer progression. To model diseased lung, normal lung epithelium (HTBE) and localized lung cancer (A549 and H460) cells are used. (b) Schematic diagram representing breast cancer lung-permissive changes calculation. First, for both breast and lung, cancer cell compartment identity is compared with the normal epithelial cell. This results in four possible compartment identity combinations: AA (A compartment in both normal and cancer), AB (A compartment in normal and B compartment in cancer), etc. Then, a cross-comparison between the breast and lung systems leads to 16 compartment identity combinations. Two specific combinations: AB_BB and BA_AA are defined as lung-permissive changes (yellow arrows). (c) Fraction of lung-permissive changes shown by localized and metastatic cancers from different breast cancer subtypes. TCGA patient gene expression (left) and GO term enrichment (biological processes) (right) for genes from regions exhibiting (d) BA_AA lung-permissive changes in the luminal metastatic breast cancer subtype, (e) AB_BB lung-permissive changes in case of HER2-enriched metastatic breast cancer subtype, or (f) BA_AA lung-permissive changes in case of triple-negative metastatic breast cancer subtype. TCGA data from BRCA and LUAD tumor vs. normal sets used and sample size indicated. Points indicate individual samples. Boxes indicate 25th percentile, median, and 75th percentile.
Figure 4—figure supplement 1
Calculation to adjust organ-permissive compartment switch calculation to account for different background levels of certain compartments.
(a) Calculation of the probability of a genomic region to be in a specific compartment in localized lung cancer given that the region belongs to a certain compartment in normal lung epithelial cell. For the A compartment, p(TA|NA) represents the probability of a region in the A compartment in localized lung cancer cell given that region also belongs to the A compartment in normal lung epithelial cell. Similarly, p(TB|NB) for the B compartment. (b) Calculation of the probability of a genomic region to be in a specific compartment in localized brain cancer (SF9427) given that the region belongs to a certain compartment in normal brain cell (NHA).
Figure 4—figure supplement 2
Comparison of brain- and lung-permissive changes in lung metastatic breast cancer.
(a) Normal human astrocyte (NHA) and glioblastoma (SF9427) cells are used for compartment comparisons to breast cancer changes. (b) Schematic diagram representing breast cancer brain-permissive changes calculation. Detailed steps are mentioned in Figure 4b. (c) Adjusted (see Figure 4—figure supplement 1 and Methods) levels of different brain-permissive changes shown by localized and metastatic cancers from different breast cancer subtypes. (d) Adjusted levels of different lung-permissive changes shown by localized and metastatic cancers from different breast cancer subtypes.
Figure 4—figure supplement 3
Prostate cancer cells that metastasize to the brain show increased brain-permissive compartment changes at neuronal-related genes.
(a) Normal epithelial (RWPE), localized prostate cancer (LNCaP), and brain metastatic prostate cancer (DU145) Hi-C datasets are used for compartment comparisons to glioblastoma/normal brain changes. (b) Schematic diagram representing prostate cancer brain-permissive changes calculation. Detailed steps are mentioned in Figure 4b. (c) Adjusted (see Figure 4—figure supplement 1 and Methods) levels of different brain-permissive changes shown by localized and metastatic prostate cancer. (d) Gene Ontology term enrichment for genes in regions in each category of comparisons. Note brain-relevant terms such as regulation of synapse organization in genes switched toward the A compartment in cancer that match brain A compartment and keratinization in genes switched toward the B compartment in cancer that match brain B compartment.
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Tables
Table 1
Quantifying overlap between epithelial–mesenchymal transition (EMT) and organotropic compartment switched genomic regions.
Each entry represents the fraction of the top 100 positive (left) or negative (right) compartment PC1 regions which are also found in the set of genomic bins that show organotropic compartment switches in the categories shown.
| PC1 positive 100 significant regions | PC1 negative 100 significant regions | |
|---|---|---|
| Luminal primary AB_BB + BA_AA | 0.03 | 0.03 |
| Luminal metastatic AB_BB + BA_AA | 0.03 | 0.03 |
| Her2 primary AB_BB + BA_AA | 0.03 | 0.02 |
| Her2 metastatic AB_BB + BA_AA | 0.03 | 0.02 |
| TNBC primary AB_BB + BA_AA | 0 | 0 |
| TNBC metastatic AB_BB + BA_AA | 0.03 | 0 |
Additional files
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Supplementary file 1
Classifications of cell line models used and sources of all publicly available Hi-C and RNA-seq data used in the analyses.
Information about cell lines derived from the following sources: ATCC, Engel and Young, 1978, Gazdar et al., 1998, Cellosaurus.org, Wang et al., 2021, and Stone et al., 1978.
- https://cdn.elifesciences.org/articles/103697/elife-103697-supp1-v1.xlsx
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Supplementary file 2
Lists of genes obtained from compartmental PCA, transcriptomics PCA, and curated epithelial–mesenchymal gene set.
Compartmental_PC1_Positive: genes found within the top 100 highly varying genomic regions in compartmental PCA analysis (positive PC1); Compartmental_PC1_Negative: genes found within the top 100 highly varying genomic regions in compartmental PCA analysis (negative PC1); RNAseq_PC1_Positive: top 100 highly varying genes in transcriptomics PCA analysis (positive PC1); RNAseq_PC1_Negative: top 100 highly varying genes in transcriptomics PCA analysis (negative PC1); Curated_Epithelial: curated epithelial genes; Curated_Mesenchymal: curated mesenchymal genes.
- https://cdn.elifesciences.org/articles/103697/elife-103697-supp2-v1.xlsx
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MDAR checklist
- https://cdn.elifesciences.org/articles/103697/elife-103697-mdarchecklist1-v1.docx
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Rearrangement of 3D genome organization in breast cancer epithelial to mesenchymal transition and metastasis organotropism
eLife 13:RP103697.
https://doi.org/10.7554/eLife.103697.3
