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. Author manuscript; available in PMC: 2022 Dec 1.
Published in final edited form as: Mol Carcinog. 2021 Sep 17;60(12):874–885. doi: 10.1002/mc.23352

Common Tumor Suppressive Signaling of Thyroid Hormone Receptor Beta in Breast and Thyroid Cancer Cells

Eric L Bolf 1,2, Noelle E Gillis 1,2, Cole D Davidson 1,2, Lauren Cozzens 1, Sophie Kogut 3, Jennifer A Tomczak 1, Seth Frietze 2,3,4, Frances E Carr 1,2,4
PMCID: PMC8585704  NIHMSID: NIHMS1739932  PMID: 34534367

Abstract

The thyroid hormone receptor beta (TRβ) is a tumor suppressor in multiple types of solid tumors, most prominently in breast and thyroid cancer. An increased understanding of the molecular mechanisms by which TRβ abrogates tumorigenesis will aid in understanding the core tumor suppressive functions of TRβ. Here, we restored TRβ expression in the MDA-MB-468 basal-like breast cancer cell line and perform RNA-sequencing to determine the TRβ-mediated changes in gene expression and associated signaling pathways. The TRβ expressing MDA-MB-468 cells exhibit a more epithelial character as determined by PCA-PAM50 score and through reduced expression of mesenchymal cytokeratins. The epithelial to mesenchymal transition (EMT) pathway is also significantly reduced. The MDA-MB-468 dataset was further compared to RNA sequencing results from TRβ expressing thyroid cancer cell line SW1736 to determine which genes are TRβ correspondingly regulated across both cell types. Several pathways including lipid metabolism and chromatin remodeling processes were observed to be altered in the shared gene set. These data provide novel insights into the molecular mechanisms by which TRβ suppresses breast tumorigenesis.

Keywords: Breast Cancer, Thyroid Hormone, Thyroid Cancer, Gene Expression

Introduction

The nuclear hormone receptor thyroid hormone receptor beta (TRβ) is emerging as an important tumor suppressor in a variety of solid tumors, most prominently in breast and thyroid cancer1. Notably, loss of TRβ expression is common in both breast and thyroid cancer2,3. Both of these diseases are critical public health issues; breast cancer is the most frequently diagnosed cancer in women and thyroid cancer is projected to be the 4th most common cancer in the United States by 2030 and the 2nd most commonly diagnosed cancer in women, after breast4.

The tumor suppression activity of TRβ has been examined through in vitro and in vivo studies. In xenograft experiments, restoration of TRβ expression limits tumor growth in follicular thyroid cancer cell lines5, in a luminal A breast cancer cell line6, and in a basal-like breast cancer cell line7. Deletion or mutation of TRβ also leads to the development of thyroid tumors in mice8,9. Although the mechanisms by which TRβ represses tumorigenesis are not well-defined, TRβ is known to regulate several pathways that are drivers of cancer, including repression of PI3K signaling and repression of RUNX2 expression2,3,5,10,11. Our recent transcriptomic analysis in an anaplastic thyroid cancer (ATC) cell line revealed TRβ functions to regulate the cancer stem cell population and induction of the JAK1/STAT1 pathway12.

Prior studies have demonstrated that TRβ exerts a strong anti-tumor effect in breast cancer cells7,10,13. Increased expression of TRβ in the basal-like human MDA-MB-468 triple negative breast cancer cell line reduces cellular proliferation and promotes apoptosis in vitro14 and reduced the tumor burden in vivo7,10,13. Importantly, this anti-tumor activity included repression of growth markers and increased anti-metastatic potential; mice with tumors expressing TRβ exhibited fewer metastatic lesions than control animals7,13. Additionally, when the animals were rendered hypothyroid, the number of metastatic lesions in the animals increased13. These data are suggestive of a vital role for T3 in TRβ-mediated tumor suppression in basal-like breast cancer, however the underlying signaling is not well-defined.

Here we investigate the TRβ regulatory network in triple negative breast cancer cells by employing stable expression of TRβ in the basal-like breast cancer cell line MDA-MB-468 with whole transcriptome profiling. This analysis gives us a better understanding of how TRβ functions in a model of aggressive, de-differentiated cancer. Results from RNA-sequencing analysis indicates that TRβ regulates breast differentiation, invasive genes, and metabolic signaling in these cells. As we had previously evaluated TRβ signaling in ATC we wanted to better understand if there are common networks that TRβ regulates in different cell types. When this analysis was compared to our prior analysis, we were able to further refine the TRβ regulome to define a set of genes regulated in both these cell lines. Overall, this study identifies common TRβ responsive genes in these cancer cell lines and reveals new insights into the mechanisms by which TRβ represses tumorigenic signaling in breast and thyroid cancers.

Material and Methods

Cell Culture

MDA-MB-468 cells were maintained at 37°C, 5% CO2, and 100% humidity. The cells were grown in αMEM supplemented with 10% fetal bovine serum (Life Technologies), and L-glutamine (2 mM) as well as the addition of penicillin-streptomycin (200 IU/L) (Cellgro/Mediatech). Identity of the cells was confirmed by short tandem repeats via the Promega GenePrint10 System in the Advanced Genome Technologies Core at the University of Vermont (May 2019). The cells were tested for mycoplasma (May 2019) by PCR as described by Uphoff et al15.

The MDA-MB-468 cells were transduced as previously described16. Briefly, viral particles containing pCDH-MSCV-EF1-GFP-Puro and pCDH-MSCV-EF1-GFP-Puro-TRβ1 (ENST00000396671.7) were generated in human embryo kidney 293 cells and the particles were used to transduce the cells to express either the empty vector (468-EV) or overexpress TRβ (468-TRβ). Following lentiviral transduction, the media was further supplemented with 1 μg/ml puromycin (Gold Bio).

Mammosphere-forming assays

To generate mammospheres, adherent cells were dissociated with Trypsin-EDTA (Thermo Fisher Scientific) and resuspended in PBS. To obtain a single cell suspension, a syringe with a 21-gauge needle was used to resuspend cells. Viable cell counts were obtained using trypan blue (Thermo Fisher Scientific). Single cells were moved to ultra-low attachment 24-well plates (Corning Inc, Corning, NY, USA) at a density of 500 cells/well. Mammospheres were cultured in Mammocult growth media (STEMCELL Technologies) with 10−8 M T3 or vehicle for 5 days. Spheres were counted with an inverted microscope at 10X magnification.

Immunoblot

Protein was extracted from the cells by treatment with lysis buffer (20mM Tris-HCl [pH 8], 137mM NaCl, 10% glycerol, 1% Triton X-100, and 2mM EDTA) containing Protease Inhibitor Cocktail 78410 (Thermo Scientific). Following quantification by Bradford assay, the resulting lysate was resolved by polyacrylamide gel electrophoresis with 10% sodium dodecyl sulfate gels EC60752 (Life Technologies) and immobilized onto nitrocellulose membranes (GE Healthcare) by electroblot (Bio-Rad Laboratories). Membranes were probed for KRT5 with MA5–12596, KRT14 with MA5–11599 and for β-actin with MA5–15739 (Thermo Scientific). Blots were imaged using enhanced chemiluminescence (Thermo Scientific) on a ChemiDoc XRS+ (Bio-Rad Laboratories).

RNA Extraction and Library Generation

468-EV and 468-TRβ cells were plated at 80% confluency and hormone starved for 24 hours in phenol red free αMEM with charcoal-stripped fetal bovine serum. 10−8 M T3 was added and incubated for 24 hours prior to sample collection. Total RNA from three independent experiments was extracted and purified using RNeasy Plus Kit (Qiagen) according to manufacturer’s protocol. Purity of the total RNA samples was assessed via BioAnalyzer (Agilent) and samples with a RIN score >7 were used for library construction. rRNA was depleted using 1 μg of total RNA and strand-specific Illumina cDNA libraries were prepared using the NEBNext Ultra II RNA library kit with 7 cycles of PCR (New England Biolabs). Library quality was assessed by BioAnalyzer (Agilent) to ensure an average library size of 300 bp and the absence of excess adaptors in each sample. RNA-Seq libraries were pooled and sequenced with 50 bp single-end reads on the Illumina HiSeq 1500. Quality scores across sequenced reads were assessed using FASTQC. All samples were high quality. For alignment and transcript assembly, the sequencing reads were mapped to hg38 using STAR17. Sorted reads were counted using HTSeq18 and differential expression analysis was performed using DESeq219. Differentially expressed genes with an adjusted p-value of <0.05 and a fold change of >2 were considered significantly differentially expressed. The input expression data was log2 +1 transformed DESeq2 normalized count values for genes. Genes were clustered using k-means clustering (k=4) and datasets were grouped via hierarchical clustering. Pathway enrichment analysis was performed using the hypeR package in R20, Ingenuity Pathway Analysis (IPA) (Qiagen), Gene Set Enrichment Analysis (GSEA)21,22, and Chromatin Immunoprecipitation Enrichment Analysis (ChEA)23. Sequencing data was deposited to Gene Expression Omnibus under the accession GSE151479.

RNA Extraction and Quantitative Real-Time PCR (qRT-PCR)

Total RNA was extracted using RNeasy Plus Kit (Qiagen, Hilden, DE) according to manufacturer’s protocol. cDNA was then generated using 5X LunaScript RT SuperMix, and mRNA expression was quantified by qRT-PCR using 2X Luna Universal qPCR Master Mix (NEB, Ipswich, MA, USA) on a QuantStudio 3 real-time PCR system (Applied Biosystems, Foster City, CA, USA). Primers are listed in Table S1. Fold change in gene expression compared to endogenous controls was calculated using the ddCT method.

Fatty Acid Extraction and Quantitation

468-EV and 468-TRβ cells were plated at in 6-well plates at a density of 5×105 cells per well and hormone starved for 24 hours in phenol red free αMEM with charcoal-stripped fetal bovine serum. 10−8 M T3 or vehicle was added and incubated for 24 hours prior to sample collection and processing by the Vanderbilt Hormone Assay and Analytical Services Core. Briefly, fatty acids were extracted by the Folch-Lees method24. The lipids were then separated by thin layer chromatography, extracted from the plates by scraping and methylated as described by Morrison and Smith25. The methylated fatty acids were analyzed by gas chromatography with the standards dipentadecanoyl phosphatidylcholine, diheptadecanoin, trieicosenoin, and cholesteryl eicosenoate to allow for quantitation.

Statistical analysis

All statistical analyses were performed using GraphPad Prism software. Group comparisons were made by a 2-way ANOVA followed by a Tukey multiple comparisons test (p<0.05). Data are represented as mean ± standard deviation, or when stated otherwise mean ± standard error of the mean.

Results

Transcriptomic analysis basal-like breast cancer cells expressing TRβ

We first investigated the impact of TRβ expression on the global gene expression patterns in the MDA-MB-468 human basal-like breast cancer cell-line. Accordingly, MDA-MB-468 cells that stably expressed TRβ (468-TRβ) or control cells that contain an empty vector (468-EV) were hormone-starved for 24 hours and then treated with 10 nM thyroid hormone (T3) or with vehicle control for 24 hours prior to collection for RNA-sequencing analysis (Fig. 1A and Fig. S1A–B). Each treatment was performed in triplicate and was sequenced to obtain on average more than 10 million total raw reads per sample. Principal Component Analysis (PCA) demonstrated that replicates from each treatment group cluster together, and the primary source of variance in the data is in ligand treated cells expressing TRβ (468-TRβ) (Fig. 1B). Pairwise differential gene expression analysis was performed comparing each treatment condition (Fig. 1C). When compared to control 468-EV cells, cell expressing TRβ were more responsive to hormone. In total, we identified 24 and 779 significantly differentially expressed genes (DEGs; adjusted p-value < 0.05, absolute log2 fold change > 1) between 468-EV and 468-TRβ cells with and without hormone treatment, respectively (Fig. 1D, Fig. S1C, and Table S2). Clustering analysis of all 1,103 unique DEGs across conditions resulted in 4 distinct clusters (C1-C4) that change expression with either TRβ expression or hormone treatment (Fig. 1D). Cluster 1 (C1) contained 161 genes that were upregulated in 468-TRβ cells compared to control 468-EV cells, independent of hormone treatment, whereas C4 was comprised of 356 genes that were upregulated with hormone treatment only in 468-TRβ cells. Similarly, C2 contained 407 genes that were downregulated with hormone treatment only in 468-TRβ cells. Interestingly, C3 contained 179 genes that were upregulated with hormone treatment in 468-TRβ cells, but were downregulated in vehicle-treated 468-TRβ cells compared to 468-EV cells. Notably, known TRβ target genes DIO2, CDKN1A and PIK3R1 were downregulated with hormone treatment in 468-TRβ cells (C2), as well as KLF9 and TGFB3, which were upregulated with hormone (C3 and C4) (Fig. S1D). Overall, these transcriptomic studies revealed that MDA-MB-468 human basal-like breast cancer cells expressing TRβ are responsive to T3 and exhibit both T3- and TRβ-dependent gene expression patterns.

Figure 1: Global Gene Expression Analysis of Breast Cancer Cells expressing Thyroid Hormone Receptor Beta.

Figure 1:

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A) Experimental diagram for transcriptomic analysis. MDA-MB-468 cells expressing TRβ (468-TRβ) or an empty vector control (468-EV) were grown in triplicate in hormone starved for 24 h and treated with 10 nM T3 or Vehicle control and collected for RNA-sequencing analysis. B) Principal Component Analysis (PCA) was performed after Variance Stabilizing Transformation (VST) of RNA-sequencing count data. Each experimental group is indicated. C) MA-plots for each pairwise comparison between treatment groups as indicated. Red dots represent significantly differentially expressed genes (adj. p-value < 0.05, absolute log2 fold-change greater than 1) and triangles correspond to genes with a log2 fold-change value beyond the bounds of the axis on the plot. D) Clustered heatmap of all unique differentially expressed genes across for each pairwise comparison. Shown are the row-scaled values of log2 transformed normalized expression data, with k-means clustering (k=7).

We next sought to define the biological pathways of genes responsive to hormone treatment and TRβ expression. Pathway enrichment analyses using Hallmark and KEGG gene sets from the molecular signatures database was performed for each cluster of genes. Pathway enrichment revealed that the C2 genes repressed by hormone treatment in 468-TRβ cells were enriched with several canonical pathway gene sets associated with pathways in cancer, including p53 signaling, complement and coagulation, focal adhesion, and epithelial to mesenchymal transition (EMT) (Fig. 2A and 2B). The enriched pathways of C3 and C4 genes that were activated by hormone treatment in 468-TRβ cells were associated with cell adhesion, apoptosis, interferon response, and fatty acid metabolism. The putative downstream transcriptional pathways (secondary regulation) involved with DEGs for each cluster was also assessed using enrichment analysis for gene sets from transcription factor binding site profiles from ENCODE or published transcription factor functional studies23 (Fig. S2). This analysis showed enrichment of several different transcriptional regulator target gene sets, including EZH2, SUZ12, EP300, CEBPB, AR, and CTCF all which were particularly prominent in C2 and C4 genes strongly up- and down-regulated by hormone in 468-TRβ cells. These results are consistent with previous upstream regulator analysis from a thyroid cancer cell-line (SW1736) expressing TRβ12, and indicate that liganded TRβ can coordinate with a variety of regulatory factors to regulate cancer gene expression programs in breast cancer cells.

Figure 2: Pathway Analysis of TRb-responsive genes.

Figure 2:

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A) Pathway enrichment analysis was performed for each cluster from the heatmap in Figure 1D (shown on the bottom C1-C4). Shown are the top enriched A) KEGG pathways and B) Hallmark pathways for each cluster. In each plot, the color represents the significance (false discovery rate (FDR)) and the size signifies the gene set size.

TRβ Regulates Markers of Epithelial Cell Identity

To further investigate the pathways that liganded TRβ regulates in breast cancer cells, the pairwise comparison between T3-treated 468-EV and 468-TRβ cells was analyzed via gene set enrichment analysis (GSEA). Consistent with the above pathway analysis, the GSEA analysis indicated a TRβ-mediated repression for several pathways involved in skin development and cornification processes that involve cytokeratins (normalized enrichment scores (NES) = −2.48 and −2.74, adjusted p-values < 1E-07, respectively) as well as a reduction in the expression of EMT pathway genes (NES = −1.73, FDR < 0.001) (Fig. 3A). The expression changes of several cytokeratin genes were significantly downregulated by hormone-treated 468-TRβ cells (Fig. 3B and 3C). Cytokeratins are markers of breast cancer aggressiveness and serve as clinicopathological markers for breast cancer26. For example, the expression of the basal epithelial gene KRT14 correlates with tumor stage and a basal-like phenotype27. Similarly, the cytokeratins KRT5, KRT14, and KRT80 are drivers of an invasiveness in breast tumors27,28, and the cytokeratin KRT16 is a prognostic marker as expression correlates with reduced survival in triple negative breast cancer patients29. Interestingly, KRT14 was downregulated in 468-TRβ cells compared to 468-EV cells, whereas KRT5 downregulation was more prominent in the 486-TRβ treated with hormone. Importantly, the effect on gene expression was also observed at the protein level for KRT5 and KRT14 (Fig. 3D and 3E). These results indicate that liganded TRβ induced a pro-epithelial gene expression program in MDA-MB-468 basal breast cancer cells.

Figure 3: Treatment of TRb-expressing MDA-MB-468 cells with T3 represses cytokeratin genes.

Figure 3:

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A) Gene Set Enrichment Analysis performed between T3-treated 468-EV and 468-TRβ expression analysis revealed Gene Ontology Biological Processes or Hallmark gene sets involving differentially expressed cytokeratins genes (adjusted p-value < 1e-7). B) Heatmap of all expressed KRT genes in 468-EV or 468-TRβ cells following Vehicle or 10 nM T3 treatment. C) Normalized gene expression barplots of the cytokeratin genes KRT5 and KRT14 from RNA-seq data, n=3 for each condition. D-E) Protein expression of KRT5 and KRT14 were evaluated by western blot, compared to β-Actin as a loading control. The blot presented is representative of three independent experiments. ****p<0.0001.

The predicted repression of the EMT pathway prompted us to further examine breast differentiation. Strikingly, the cytokeratins KRT5, KRT14, and KRT17 repressed in 468-TRβ cells are a part of the PAM50 gene set used to determine breast cancer intrinsic subtypes30,31. We used the Principle Component Analysis-based iterative PAM50 subtyping (PCA-PAM50) algorithm32 on our expression data to measure subtype scores for the different experimental conditions. The scores for the luminal A and luminal B subtype were greatest in the 468-TRβ cells, particularly with T3 treatment (Fig. 4A). Additionally, there was a significant reduction in the basal-like breast cancer score in the 468-TRβ cells treated with T3. In addition to repression of the cytokeratins which are highly expressed in basal-like breast cancer, there was an increase in the expression of genes that are lowly expressed in basal-like breast cancer. Notably, there was a 60% increase in expression of ESR1, GPR160, and MAPT (Fig. 4B and Fig. S3). These data are suggestive that the basal-like character of the MDA-MB-468 cells was reduced upon overexpression of TRβ.

Figure 4: 468-TRβ cells treated with 10 nM T3 Exhibit Basal-like Characteristics.

Figure 4:

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A) PCA-PAM50 algorithm scores for Vehicle- and T3-treated 468-EV and 468-TRβ RNA-seq expression dataB) Barplots showing the normalized expression values for the PAM50 genes ESR1, GPR160, and MAPT induced following T3 treatment in the 468-TRβ cells. *p<0.05, **p<0.01 and ****p<0.0001.

Shared TRβ Target Genes in Breast and Thyroid Cancer Cells Include Metabolic Enzymes and Cancer Stem Cell Markers

Since the observed TRβ-mediated increase in epithelial identity in breast cancer cells resembled that of thyroid cancer cells12, we further investigated the cell signaling networks regulated by TRβ in both cell types. To do this, we compared the gene expression data in MDA-MB-468 cells to a recent analysis we performed in the anaplastic thyroid cancer cell line SW173612. A four-way comparison between significantly up- and down-regulated genes from T3-treated SW1736-EV and SW1736-TRβ cells and 468-EV and 468-TRβ was performed to determine which genes TRβ regulates in both cell types. A total of 255 genes were induced by T3 in both cell lines and 70 genes were repressed (Fig. 5A). These genes were used for pathway analysis using the Ingenuity Pathway Analysis (IPA) tool and the top predicted pathways were represented as a plot showing the activation z-scores that predict the pathway activation or inhibition states as a positive or negative z-score, respectively (Fig. 5B). This signaling pathway analysis revealed that the shared differentially expressed TRβ target genes belong to several activated signaling pathways that included stearate biosynthesis. Interestingly, the fatty acid stearic acid has been shown to slow the growth of breast tumors and induce apoptosis33–36. Four genes that encode stearate biosynthetic enzymes were induced by T3 in both SW1736 and MDA-MB-468 cells expressing TRβ compared to control cells (Fig. 5C and 5D). We therefore evaluated the levels of fatty acids within the MDA-MB-468 cells. While the stearic acid levels were not significantly altered by TRβ or T3, the levels of the unsaturated fatty acid vaccenic acid, which like stearic acid is synthesized from palmitic acid, were significantly increased in the T3-treated 468-TRβ cells after 24 hours (Fig. 5E). Vaccenic acid has been demonstrated to reduce the growth of cancer cells37 and may have a role in TRβ tumor suppression signaling. Inhibited pathways common between breast and thyroid regulated genes, included sirtuin signaling, a process that promotes EMT and is typically heightened in cancer stem cells38. Several genes in this signaling pathway encoding metabolic enzymes were differentially expressed in T3-treated 468-TRβ cells, including Acetyl-CoA Synthetase Short Chain Family Member 2 (ACSS2), glutaminase (GLS), and 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3) (Table S2).

Figure 5: A Subset of Genes are Regulated by TRβ in Breast and Thyroid Cancer Cells.

Figure 5:

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A) A 4-way venn diagram comparing the significantly up- and down-regulated genes in both T3-treated 468-EV and 468-TRβ cells and T3-treated SW1736-EV and SW1736-TRβ cells (n=3). 255 genes were upregulated and 70 genes were repressed independent of cell type. B) IPA was used to predict the canonical signaling pathways of these 325 genes. Shown is the color for the associated activation Z-score and size corresponds to the gene ratio for each enriched each pathway (p<0.01). C-D) Bar plots showing the normalized RNA-seq expression counts for genes encoding stearate biosynthetic enzymes MDA-MB-468 and SW1736 cell lines expressing empty vector or TRβ. E) Percentage of fatty acids within Vehicle or T3-treated (10 nM) in 468-EV and 468-TRβ cells after 24 and 48h. *p<0.05, **p<0.01, *****p<0.0001.

Sirtuin signaling has a known role in maintenance of cancer stem cell characteristics, TRβ is known to induce a reduction of stemness in luminal breast cancer cell lines39, and TRβ reduced cancer stem cell markers in thyroid cancer cells12. Therefore, we further examined the expression of breast stem cell markers in T3 treated 468-TRβ cells. The ALDH1A1 gene highly expressed in cancer stem cells40 was reduced in the T3-treated 468-TRβ cells (Fig. 6A). Similarly, breast cancer stem cells and basal-like breast cancers exhibit low CD24 and high CD44 expression41,42. T3-treated 468-TRβ cells show increased expression of CD24 and a reduced expression of CD44 in both 468-EV and 468-TRβ cells (Fig. 6A and Fig. S3). Consistent with the TRβ regulation of cancer stem cell genes, T3-treated 468-TRβ cells exhibited a significant impairment in their ability to form mammospheres (Fig. 6B and 6C). These results are further evidence that cells that express TRβ exhibit a decrease in the aggressive, basal-like breast cancer characteristics and further indicate that TRβ-mediated suppression of stem cell activity is an important role for of TRβ tumor suppression in cancer cells.

Figure 6: Thyroid Hormone Receptor Beta Represses the Cancer Stem Cell Expression Profile.

Figure 6:

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A) Bar plot showing the normalized RNA-seq expression counts for the stem cell marker gene ALDH1A1, CD24, a marker gene lowly expressed in stem cells, and CD44, a marker gene highly expressed in stem cells. B-C) Percent formation of mammospheres and representative images, n=3. *p<0.05, **p<0.01, ****p<0.0001.

Discussion

We chose to evaluate the activity of TRβ in breast and thyroid cancer cell lines as breast cancer is a risk factor for the development of thyroid cancer and vice versa43. Both types of cancers are derived from epithelial tissue. Thyroid cancer is primarily driven by mutations to the MAPK and PI3K pathways and these pathways are known to be highly activated in breast cancer, particularly triple negative breast cancer44,45. Additionally, TRβ is known to have profound anti-tumor activity in both types of tumors. We focused here on dedifferentiated models of breast and thyroid cancer as TRβ expression is most strongly repressed in both dedifferentiated anaplastic thyroid cancer as well as in basal-like triple negative breast cancer2,3.

The current study provides better insight into the impact of TRβ expression on different cellular signaling pathways. Our results show that stable expression of TRβ in the basal-like breast cancer cell-line MDA-MB-468 results in a substantial response to T3 hormone treatment, and results in extensive changes in gene expression programs of a variety of important cellular signaling pathways in cancer, including cytokeratin, inflammation, and cancer stem cell genes. As this is a single breast cancer cell line, our work here highlights novel pathways, but does not necessarily capture the full spectrum of diversity observed in the tumors of patients. Prior studies revealed that TRβ represses proliferation and migration in vitro and inhibits tumor growth and metastasis in vivo7,10,13. Our work here adds to that understanding by providing evidence of the signaling events that result from liganded TRβ activity. Importantly, there was a change in the expression of a set of genes that are used to define breast cancer subtypes, as well as EMT marker genes, revealing that the mesenchymal-like MDA-MB-468 cells shifted to a more epithelial character. This is further supported by the observed reduction in the expression of pro-invasive and pro-metastatic cytokeratins. TRβ overexpression has also been found to reduce expression of KRT8/18 in a hepatocellular model, consistent with what we have observed here13. Additionally, thyroid hormone response elements have been previously identified on KRT genes via reporter assays performed in HeLa, esophageal, and corneal cells46–49. This induction of a pro-epithelial gene program could also explain why loss of TRβ has been found to decrease the efficacy of chemotherapeutics in basal-like breast cancer cells50.

Interestingly, although there is support in the literature of the anti-tumor properties of TRβ, there is also evidence that thyroid hormone itself can promote a more aggressive phenotype in steroid hormone receptor positive breast cancer51. We did not observe an increase in aggressive characteristics in T3-treated cells in our experiments, consistent with the notion that the pro-tumorigenic actions were mediated by estrogen receptor. However, the study primarily examined thyroid hormone receptor alpha, not TRβ, so it is unclear how much of the pro-proliferative phenotype was mediated by TRβ.

Through comparison of a tumor suppressor in both thyroid and breast cancer cell lines we have revealed common TRβ-mediated signaling between both cell types. This signaling may represent the core tumor suppressive activity of TRβ, as cell type-specific effects were filtered out. Notably, there was a profound effect on pro-differentiation signaling in both cell lines, including alteration of the sirtuin pathway, which regulates cancer stemness, and we found that reintroduction of TRβ reduced mammosphere formation38. Interestingly, it has also been reported that TRβ overexpression in combination with T3 in an estrogen receptor positive breast cancer cell line also reduces the ability of the cells to produce mammospheres39. This is consistent with the observed change in PAM50 markers in the breast cancer cell line and the thyroid differentiation markers in the thyroid cancer line12. These data are suggestive that TRβ has an important role in epithelial maintenance and that role may explain why TRβ has been shown to inhibit tumorigenesis in multiple tissue types.

There is growing evidence of a common features between breast and thyroid cancers. International studies show that patients with breast cancer are at an increased risk of developing a future thyroid tumor and thyroid cancer increases the risk of breast cancer43,52–54. Here, we present common tumor suppressive signaling that arises from a tumor suppressor common to both of these diseases. The pathways that TRβ regulates in both breast and thyroid cancer cells may be important for the etiology of the link.

Supplementary Material

tS1
tS2
fS1-3

Acknowledgements

The research reported here was supported by grants from National Institutes of Health U54 GM115516 for the Northern New England Clinical and Translational Research Network; National Cancer Institute 1F99CA245796-01; UVM Cancer Center-Lake Champlain Cancer Research Organization (C3) 12577-21; and UVM Larner College of Medicine. The MDA-MB-468 cells were generously provided by Drs. Janet Stein, Jane Lian, and Gary Stein (University of Vermont). Human cell line authentication, NextGen sequencing, automated DNA sequencing, and molecular imaging was performed in the Vermont Integrative Genomics Resource supported by the University of Vermont Cancer Center, Lake Champlain Cancer Research Organization, UVM College of Agriculture and Life Sciences, and the UVM Larner College of Medicine. Fatty acid profiling was conducted by the Vanderbilt Hormone Assay & Analytical Services Core supported by NIH grants DK059637 (MMPC) and DK020593 (DRTC).

Funding information

The research reported here was supported by grants from National Institutes of Health U54 GM115516 for the Northern New England Clinical and Translational Research Network; National Cancer Institute 1F99CA245796-01; UVM Cancer Center-Lake Champlain Cancer Research Organization (C3) 12577-21; and UVM Larner College of Medicine. Human cell line authentication, NextGen sequencing, automated DNA sequencing, and molecular imaging was performed in the Vermont Integrative Genomics Resource supported by the University of Vermont Cancer Center, Lake Champlain Cancer Research Organization, UVM College of Agriculture and Life Sciences, and the UVM Larner College of Medicine. Fatty acid profiling was conducted by the Vanderbilt Hormone Assay & Analytical Services Core supported by NIH grants DK059637 (MMPC) and DK020593 (DRTC).

Footnotes

Conflict of Interest Statement

The authors declare that they have no conflict of interest.

Data Availability Statement

The data that support the findings of this study are openly available in the Gene Expression Omnibus (GEO) repository at https://www.ncbi.nlm.nih.gov/geo, accession number GSE151479.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

tS1
NIHMS1739932-supplement-tS1.pdf (21.7KB, pdf)
tS2
NIHMS1739932-supplement-tS2.pdf (398.9KB, pdf)
fS1-3
NIHMS1739932-supplement-fS1-3.pdf (785.7KB, pdf)

Data Availability Statement

The data that support the findings of this study are openly available in the Gene Expression Omnibus (GEO) repository at https://www.ncbi.nlm.nih.gov/geo, accession number GSE151479.

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