Skip to main content
NCBI home page
Endocrinology logoLink to Endocrinology
. 2020 Dec 8;162(2):bqaa226. doi: 10.1210/endocr/bqaa226

A Positive Feedback Loop Between TGFβ and Androgen Receptor Supports Triple-negative Breast Cancer Anoikis Resistance

Emmanuel Rosas 1, Justin T Roberts 2, Kathleen I O’Neill 1, Jessica L Christenson 1, Michelle M Williams 1, Toru Hanamura 1, Nicole S Spoelstra 1, Jeffery M Vahrenkamp 3, Jason Gertz 3, Jennifer K Richer 1,✉
PMCID: PMC7806239  PMID: 33294922

Abstract

Triple-negative breast cancer (TNBC) is an aggressive subtype with peak recurrence as metastatic disease within the first few years of diagnosis. Androgen receptor (AR) expression is increased in anchorage-independent cells in TNBC preclinical models. Both AR knockdown and inhibition lead to reduced TNBC invasion in vitro, reduced tumorigenicity, and less recurrence in vivo in preclinical models. Transforming growth factor β (TGFβ) pathway gene signatures also increased during anchorage-independent survival both in vitro and in vivo in preclinical models and in circulating tumor cells (CTCs) from patients during emergence of chemo resistant disease. We hypothesized that a positive loop between AR and TGFβ signaling facilitates TNBC anchorage-independent survival. We find that multiple components of the TGFβ pathway, including TGFβ1 and 3, as well as pathway activity measured by nuclear localization and transcriptional activity of phosphorylated Smad3, are enhanced in anchorage-independent conditions. Further, exogenous TGFβ increased AR protein while TGFβ inhibition decreased AR and TNBC viability, particularly under anchorage-independent culture conditions. ChIP-seq experiments revealed AR binding to TGFB1 and SMAD3 regulatory regions in MDA-MB-453 cells. In clinical datasets, TGFB3 and AR positively correlate and high expression of both genes together corresponded to significantly worse recurrence-free and overall survival in both ER-negative and basal-like breast cancer. Finally, inhibiting both AR and TGFβ decreased cell survival, particularly under anchorage-independent conditions. These findings warrant further investigations into whether combined inhibition of AR and TGFβ pathways might decrease metastatic recurrence rates and mortality from TNBC.

Keywords: triple negative breast cancer, TGFβ, androgen receptor, enzalutamide, LY2109761, LY2157299, anoikis resistance

Triple-negative breast cancer (TNBC), characterized by the lack of 3 important biomarkers, estrogen receptor (ER) alpha, progesterone receptor (PR), and amplification of human epidermal growth factor receptor 2 (HER2), is an aggressive breast cancer (BC) subtype with few Food and Drug Administration–approved targeted therapies compared with other subtypes (1-3); and cytotoxic chemotherapeutic agents and radiation remain the standard of care (4). The peak risk of recurrence for TNBC is between 1 and 3 years following surgery, and 70% of deaths occur within the first 5 years postdiagnosis (5), demonstrating a persistent need for novel therapeutic interventions or new combinations of targeted therapeutics.

Although TNBC lack ER and PR, between 20% and 50% of primary tumors express nuclear androgen receptor (AR) protein, depending on the study, antibody used, and the criteria for positivity (6-9). Studies investigating AR action in TNBC point to a role in tumor cell survival, growth, cancer stem cell (CSC) properties, and specific steps in the metastatic cascade, such as anchorage-independent survival or “anoikis resistance” (10, 11). In 2005, Farmer et al. coined the term “molecular apocrine” to describe an ER negative (ER–) subtype of BC with high AR and an increased AR gene signature, laying the groundwork for establishing AR as a potential driver and target in a subset of TNBC (12). Likewise, Doane et al. identified an ER–/PR– BC subset with a gene expression profile similar to that of ER+ BC that responded to androgens (13). TNBC with high expression of AR, later termed the “luminal AR (LAR) subtype” (14, 15), have a gene expression profile more similar to ER+ BC than the other TNBC subtypes, because AR regulates many of the same genes as ER that are associated with the “luminal” gene signature (9, 14, 15). Other non-LAR TNBC subtypes often express AR, albeit at lower levels, and also rely on AR for cell survival, including cell lines representative of the TNBC mesenchymal (M) or mesenchymal stem-like/claudin-low (MSL/CL) subtypes (10, 11, 14-17). Further, when these TNBC cells are cultured under anchorage-independent conditions, AR transcripts, protein levels, and transcriptional activity increase, and inhibition or knockdown of AR dramatically decreases growth on soft agar, mammosphere formation, invasion, and tumorgenicity (10, 11). AR protein is mutually exclusive with cleaved caspase 3 in TNBC in anchorage-independent cells, indicating that AR protects against cell death due to detachment, a process called anoikis (10).

Transforming growth factor β (TGFβ) signaling can promote epithelial-to-mesenchymal transition (EMT) and the early stages of metastasis (18). TNBC M or MSL/CL cell lines have high TGFβ gene signatures and high enrichment scores for TGFβ signaling (14, 15, 17) and TGFβ promotes proliferation of M and MSL/CL TNBC cell lines (17). Patient circulating tumor cells (CTCs) isolated by microfluidic antibody capture have high characteristics of EMT and the most significantly upregulated gene signature in the more M CTCs was the TGFβ pathway (19). Both AR and TGFβ pathway transcripts increased in CTCs from TNBC patient-derived xenografts (PDXs) and in micrometastases compared with primary tumors (20).

AR and the TGFβ pathway have been linked in prostate cancer. In chromatin immunoprecipitation (ChIP) assays in prostate cancer cells, following Activin A (a TGFβ superfamily ligand) treatment, phosphorylated Smad3 (pSmad3) bound to the AR promoter at Smad-binding elements. pSmad3 has also been described as an AR coregulator (21-23). Furthermore, multiple functional androgen response elements (AREs) upstream of the TGFB1 start site established AR as a direct regulator of TGFB1 in prostate cancer cell lines (24, 25). Collectively, these studies suggested a positive feedback loop between the AR and TGFβ pathway in prostate cancer, but this relationship, or its role in supporting anchorage-independent survival, has not been investigated in TNBC.

We hypothesized that a positive feedback loop between AR and TGFβ signaling supports anoikis resistance in AR+ TNBC and the ability to rapidly deploy this feed-forward loop may contribute to the early risk of metastatic recurrence in TNBC.

Materials and Methods

Cell lines

Only cells under 10 passages were used in this study. MDA-MB-453 (LAR) cells (purchased from ATCC in 2012) (26) were grown in Dulbecco’s modified Eagle’s medium with 10% fetal bovine serum (FBS). SUM159PT (MSL/CL) cells (purchased from the University of Colorado Cancer Center Tissue Culture Core in 2013) (27) were grown in Ham’s F-12 with 5% FBS, hydrocortisone, insulin, and HEPES. BT549 (M) cells (ATCC in 2008) (28) were grown in RPMI 1640 with 10% FBS and insulin. All cell lines were authenticated by short tandem repeat analysis (Promega) at the University of Colorado Cancer Center Tissue Culture Core and tested negative for Mycoplasma in 2019. Cells were maintained at 37°C in a humidified incubator containing 95% air and 5% CO2.

Reagents

Cells were treated with TGFβ receptor 1 inhibitors, 10 µM LY2109761 or 10 µM LY2197299 (Selleckchem #S2704 and #S2230, respectively), which came predissolved in dimethyl sulfoxide (DMSO), 10 nM of dihydrotestosterone (DHT) in ethanol, 40 µM enzalutamide (Medivation) in DMSO.

Gene expression array analysis

BT549 cells were grown in attached or forced suspended culture on poly-2-hydroxyethyl methacrylate (poly-hema) coated plates in quadruplicate for 24 hours and harvested for RNA using TRIzol as previously reported (10). The array data are available in the Gene Expression Omnibus (GEO) database as GSE95472. Gene expression values from this study were subjected to Gene Set Enrichment Analysis (GSEA) and changes in certain pathways were examined using the Kyoto Encyclopedia of Genes and Genomes (KEGG) gene list. Normalized enrichment scores (NESs) and P values were calculated using GSEA software (29). Microarray and MetaCore Pathway Analysis (Thomson Reuters) were performed as previously described (10).

Forced suspension (anchorage-independent) culture

Poly-hema (Sigma) was reconstituted in 95% ethanol to a final concertation of 11 mg/mL and tissue culture plates were coated and incubated overnight to allow ethanol evaporation. Plates were washed with PBS prior to use.

Quantitative RT-PCR

RNA was isolated by TRIzol (Invitrogen), and cDNA was synthesized from 1 µg of total RNA using qScript cDNA Supermix (QuantaBio). SYBR green quantitative gene expression analysis was performed on an ABI 7500 Fast Real Time PCR System using primer sequences in Table S1 (30). Reported values are the means and SD of 3 technical replicates and 2 biological replicates (n = 6).

Reverse phase protein array

Reverse phase protein array (RPPA) printing and analysis was performed in the laboratory of Dr. Emmanuel Petricoin as previously described with validated antibodies (31-33). BT549 cells were grown in technical triplicate in attached or suspended (on poly-hema coated plates) culture for 24, 48, and 72 hours and cell pellets were lysed as described previously (34).

Enzyme-linked immunosorbent assays

TGFβ1 Human DuoSet enzyme-linked immunosorbent assay (ELISA) Kits (R&D Systems) were used to measure secreted TGFβ1 (35). Results are representative of 3 technical triplicates from conditioned medium from MDA-MB-453, SUM159PT, and BT549 cell lines grown in attached or suspended (on poly-hema coated plates) culture for 24 and 48 hours.

Immunohistochemistry

Cells were fixed in 10% formalin and pelleted in histogel (Thermo Fisher) and formalin fixed and paraffin embedded (FFPE). Slides were deparaffinized using xylenes and ethanols, and antigens were heat retrieved in 10 mM citrate buffer pH 6.0. Antibody pSmad3 (ab52903; Abcam) (36) was used. ImmPRESS® HRP antirabbit immunoglobulin G (Peroxidase) Polymer Detection Kit (Vector Laboratories Inc.) (37) was used for detection, and Tris-buffered saline with 0.05% Tween 20 was used for all washes. Using a BX40 microscope (Olympus) with a SPOT Insight Mosaic 4.2 camera and software (Diagnostic Instruments, Inc.), representative images were taken and quantifications for 3 fields at 40× magnification performed using IHC Profiler (38).

Luciferase assays

Lipofectamine 3000 (Thermo-Fisher) was used for transfections and firefly luciferase activity from the p3TP-lux plasmid (as described in [39]) was normalized to renilla luciferase expressed from pRL-SV40 plasmid. Cells were harvested and assayed using the Dual Luciferase Assay System (Promega # E1910) and measured on a Biotek Synergy 2 multimode microplate reader.

Western blot assays

Whole-cell protein extracts were prepared using radioimmunoprecipitation (RIPA) lysis buffer. Whole cell lysates (10-50 µg total protein) were combined with sodium dodecyl sulfate-polyacrylamide gel electrophoresis loading buffer containing 0.6 M dithiothreitol and resolved by gel electrophoresis, transferred to Immobilon-FL PVDF transfer membranes (Millipore) or nitrocellulose membranes (Fisher Scientific) and blocked with either 5% BSA, 5% milk in TBS-Tween, or Odyssey Blocking Buffer (Li-Cor) for 1 hour. Blots were probed with primary antibodies shown in Table S2 (30, 36, 40-46). Secondary antibodies used were goat antirabbit IRDye® 800CW (47) and goat antimouse IRDye® 680RD (Li-Cor) (48). Following secondary antibody incubation, results were detected using the Odyssey® CLx Imaging System and analyzed using the Image Studio Ver 5.2 software (Li-Cor) and ImageJ (NIH). Cells were treated with 10 ng/mL of recombinant human TGFβ1 (R&D Systems). Experiments were repeated at least twice and representative blots shown.

Trypan blue exclusion assays

Cells were treated with either 10 µM LY2109761 or 10 µM LY2197299. Cell counts were assessed using trypan blue exclusion. Cell pellets were harvested and washed in PBS and 10 μL of 0.4% trypan blue was mixed with 10 μL of cell suspension. Trypan blue–positive and –negative cells were counted using the Countess™ II Automated Cell Counter. Reported values are the means and standard deviation of three technical replicates repeated in three biological replicates (n = 9).

Kaplan–Meier plots

Kaplan–Meier plotter (KM plotter) (49) was used to generate a series of Kaplan–Meier plots to interrogate relapse-free survival (RFS), overall survival (OS), and distant metastasis–free survival (DMFS) in basal and ER-BC (49). Using the multiple genes option, TGFB3 (Affymetrix ID: 209747_at) was entered into the database and patient data above or below the median was designated as high versus low, then filtered using high or low median expression of AR (Affymetrix ID: 201272_at) in either basal or ER- cases using auto best cut-off.

Chromatin immunoprecipitation

MDA-MB-453 cells were grown in charcoal-stripped serum media for a total of 72 hours before treatment. Twenty-four hours prior to treatment, cells were trypsinized and equal cell numbers were plated on control tissue culture dishes (attached) or poly-hema coated dishes (suspended). Cells were treated with DMSO (vehicle control), DHT (10 nM), or DHT+Enza (10 µM) for 4 hours, followed by fixation in 1% formaldehyde. ChIP-seq was performed as previously described (50). Chromatin was sonicated using an Epishear Probe Sonicator (Active Motif) for 4 minutes (cycles of 30 seconds with 30 seconds of rest in between) at 40% power. AR antibody H-280 (Santa Cruz) was utilized for immunoprecipitation (51). Libraries were sequenced on an Illumina HiSeq 2500 as single-end 50 bp reads to a minimum depth of 18 million reads per sample. Reads were aligned to the hg19 build of the human genome with bowtie (52) using the following parameters: -m 1 -t --best -q -S -l 32 -e 80 -n 2. Peaks were called with MACS2 (53) using a P value cutoff of 1 × 10–10, the mfold parameter bounded between 15 and 100, and the -SPMR option for normalization. Read depth at each peak was calculated by BEDTools (54) using the coverage subcommand. Visualization was performed on the IGV browser (55) using the normalized bedgraphs output by MACS2 (GEO record GSE157862).

Crystal violet assays

SUM159PT cells were treated with 10 µM of LY2109761 or 40 µM of Enza (Medivation). Enza (40 μM) approximates the IC50 of the SUM159PT cell line as described previously (56). LY2109761 (10 µM) has been used previously as an effective dose in SUM159PT cells (57). Cells were fixed in 10% formalin, rinsed in PBS, and stained with 1% crystal violet. Crystal violet was dissolved in 10% acetic acid and measured at 540 nm using a Biotek Synergy 2 multimode microplate reader (n = 3).

Statistical significance

Statistical significance was evaluated using GraphPad Prism software to assess either two-tailed unpaired Student t tests, or 1-way analysis of variance followed by post hoc tests. Spearman’s rank correlation coefficient (Spearman r) was used for correlation analysis. P ≤ .05 was considered statistically significant with P values indicated in figures as *P ≤ .05, **P ≤ .01, ***P ≤ .001, ****P ≤ .0001. Error bars represent SD unless otherwise noted.

Results

TGFβ signature is enhanced in anchorage-independent TNBC cells

MetaCore pathway analysis of gene expression profiling of BT549 TNBC gene changes after 24 hours of culture in attached versus anchorage-independent culture (10) indicated that SMAD3 was one of the top predicted upstream regulators (Fig. 1A). GSEA analysis confirmed that the TGFβ pathway was increased in TNBC cells surviving in anchorage-independent versus attached conditions (NES = –1.60, nominal P ≤ .001) (Fig. 1B). TGFβ-associated genes were significantly differentially expressed between attached and suspended conditions (Fig. 1C), including 5 involved in the canonical TGFβ signaling pathway (red stars). Changes in canonical TGFβ signaling genes (TGFB1, TGFB3, SMAD3, TGFBR1) were independently verified by qPCR (Fig. 1D), where the majority of TGFβ pathway genes in SUM159PT and BT549 TNBC were significantly increased by 48 hours in anchorage-independent culture compared with attached culture, indicating that multiple transcripts encoding components of the TGFβ pathway are significantly upregulated in response to anchorage-independent conditions.

Figure 1.

Figure 1.

Open in a new tab

TGFβ pathway signature increases in TNBC cells upon anchorage-independent culture and androgen receptor and SMAD3 are predicted upstream regulators. (A) Metacore pathway analysis of BT549 microarray data showing SMAD3 connecting with other genes altered under anchorage-independent culture for 24 hours. Original data set from Barton et al (2015) (GEO record GSE95472). (B) GSEA pathway enrichment analysis showing changes in the TGFβ pathway in BT549 cells cultured in attached or under anchorage-independent culture for 24 hours. (C) Heatmap of significantly altered genes associated with the TGFβ pathway in BT549 cells grown in attached versus suspended culture conditions for 24 hours (n = 4). Gene list from KEGG database on TGFβ Signaling Pathway. Red asterisk: genes associated with the canonical TGFβ signaling pathway. (D) qRT-PCR for canonical TGFβ signaling pathway gene expression in attached versus under anchorage-independent culture conditions at 48 hours in SUM159PT and BT549 cell lines (n = 6). Mean ± SD; *P < .05; **P < .01; ***P < .001. ****P < .0001.

Anchorage-independent culture increases TGFβ ligand secretion and pathway activation

Since our transcriptome profiling and qPCR showed increased expression of genes encoding TGFβ ligands by TNBC cells cultured in anchorage-independent conditions, we hypothesized that anchorage-independent culture would increase ligand-dependent TGFβ pathway activation. To investigate this hypothesis, we performed RPPA analysis on BT549 lysates from attached and anchorage-independent culture for 24 to 72 hours. These results confirmed our prior finding that anchorage-independent culture increased AR protein (10, 11) and also indicated a significant increase in TGFβ1, TGFβ3, and pSmad2 by RPPA within the first 24 hours in anchorage-independent culture, indicating pathway activation, possibly via enhanced ligand production and/or secretion (Fig. 2A and Fig. S1A) (30). To confirm this, we performed an ELISA for TGFβ1 in conditioned medium from TNBC cells cultured in attached or suspended conditions for 24 or 48 hours. Secreted TGFβ1 significantly increased in conditioned medium from all cell lines following 48 hours in anchorage-independent culture (Fig. 2B). TGFβ pathway activation was confirmed by staining for pSmad3 by immunohistochemistry, which demonstrated increased expression and nuclear translocation of pSmad3 in TNBC cells under anchorage-independent conditions (Fig. 2C). pSmad3 transcriptional activity was further examined using the p3TP-lux reporter, a luciferase reporter under the control of a portion of the pSmad3-regulated PAI1 promoter, where luciferase activity was significantly increased in TNBC cells in anchorage-independent culture when compared with cells cultured in attached conditions (Fig. 2D and Fig. S1B) (30). Together, these results demonstrate that TGFβ ligand production and secretion is increased by TNBC cells under anchorage-independent conditions leading to TGFβ pathway activation.

Figure 2.

Figure 2.

Open in a new tab

Anchorage-independent conditions increase TGFβ1 expression and secretion and nuclear pSmad3 localization and activity. (A) RPPA data showing changes in protein in BT549 cells grown in attached versus under anchorage-independent culture conditions for 24, 48, and 72 hours with biological triplicates at each condition and timepoint shown. Red, high relative expression levels; black, intermediate relative expression levels; green, low relative expression levels. Full panel of RPPA data in (30). (B) ELISA measuring changes in TGFβ1 in conditioned medium from three TNBC lines cultured in attached versus suspended conditions for 24 and 48 hours normalized to total live cell count as determined by trypan blue. (C) IHC and quantification of percentage of pixels corresponding to nuclear DAB staining for pSmad3 in MDA-MB-453, SUM159PT, and BT549 cells in attached versus suspended culture conditions for 24 hours. (D) 3TP-lux TGFβ reporter-linked luciferase activity normalized to renilla in SUM159PT cells grown in attached versus suspended culture. Mean ± SD; *P < .05; **P < .01; ***P < .001. ****P < .0001.

Manipulating the TGFβ pathway affects AR expression and cell viability in TNBC cell lines

Since TNBC cells in anchorage-independent culture upregulated production of TGFβ ligand and AR protein, and AR increases via binding of pSmad3 to the AR promoter in prostate cancer, we hypothesized that recombinant TGFβ would increase AR protein levels even in attached TNBC cells. We conducted a time-course to monitor AR protein levels over time post-treatment with exogenous TGFβ1. pSmad2 increased within the first 30 minutes, indicating that the TGFβ pathway was activated (Fig. 3A). ID1, a known TGFβ target gene, peaked at 2 hours and decreased by 6 hours. Interestingly, AR protein levels also increased and peaked between 2 and 4 hours post-treatment. To examine whether TGFβ1 affected other hormone receptor levels we also examined GR, but its levels remained constant. Since AR and pSmad3 were upregulated in anchorage-independent conditions, and recombinant TGFβ increased AR protein (Fig. 3A), we next tested whether TGFβ inhibitor (TGFβi) would decrease AR protein that might be induced by TGFβ ligand secreted by anchorage-independent tumor cells. LY2109761 (LY1) treatment caused a significant downregulation of AR gene expression (Fig. 3B). In fact, TGFβi LY1 blocked the anchorage-independent induction of AR protein in 3 TNBC lines, MDA-MB-453, SUM159PT, and BT549 (Fig. 3C). To account for potential LY1 off-target effects, we repeated and confirmed our results using an additional inhibitor LY2157299 (Galunisertib) (LY2) that also targets the kinase domain of TGFβ receptor 1 (TβRI) (58, 59). LY1 and LY2 decreased AR in all 3 cell lines in anchorage-independent culture for 48 hours relative to vehicle control (Fig. S2A) (30). Since TGFβi decreased AR levels, and TNBC cells in anchorage-independent conditions rely on AR for survival (10), we hypothesized that TGFβi would increase cell death in anchorage-independent culture. Indeed, SUM159PT and BT549 cell lines displayed increased apoptosis in anchorage-independent culture when treated with TGFβi, as measured by cleaved poly (ADP-ribose) polymerase (PARP) (Fig. 3D). Both the SUM159PT and BT549 lines, which dramatically upregulate AR in anchorage-independent culture (Fig. 3C and (10)), showed increased cell death in the presence of LY1 or LY2 (Fig. 3E). These results demonstrate that the TGFβ signaling pathway affects AR expression and anoikis resistance in TNBC cells. In contrast, in the MDA-MB-453 cell line, which has high baseline levels of AR (10), LY1 or LY2 treatment did not affect cleaved PARP levels (Fig. S2B) (30) and trypan blue exclusion assays on cells grown in anchorage-independent culture conditions in the presence of TGFβi showed that cell viability in this cell line was not affected by LY1 nor LY2 in either attached or suspended culture (Fig. S2C) (30).

Figure 3.

Figure 3.

Open in a new tab

Exogenous TGFβ increases AR protein levels and TGFβ inhibition decreases AR and cell viability in anchorage-independent conditions. (A) Western blot for pSmad2, Smad2, Smad3, ID1, AR, GR, and GAPDH in the SUM159PT cell line treated with recombinant TGFβ1 (10 ng/mL) over a time course of 24 hours. (B) qRT-PCR for AR in SUM159 and BT549 cells grown in attached conditions for 48 hours ± LY2109761 (10 µM). (C) Western blot for AR was performed on MDA-MB-453, SUM159PT, and BT549 cell lines grown in attached or suspended conditions for 48 hours ± LY2109761 (10 µM). (D) Western Blot to analyze the apoptosis marker cleaved-PARP in SUM159PT and BT549 cells in attached or suspended conditions for 48 hours ± LY2109761 or LY2157299 (10 µM). Densitometry values calculated as (cleaved-PARP/PARP)/GAPDH. (E) Trypan blue assay for percentage of dead cells in SUM159PT and BT549 cells in attached or suspended conditions for 48 hours ± LY2109761 or LY2157299 (10 µM). Western blot quantifications performed by ratio of protein of interest to loading control and normalized to control. Mean ± SD; *P < .05; **P < .01; ***P < .001. ****P < .0001.

High TGFβ levels positively correlate with AR in patients and is associated with poor outcomes

Due to the observed relationship between AR and TGFβ in vitro, we examined how AR correlates with TGFβ ligands and receptors in publicly available BC clinical data. We hypothesized that TGFβ pathway-associated gene expression would correlate with AR expression in TNBC patients. To determine if AR expression correlated with TGFB1, TGFB3, TGFBR1, or TGFBR2, we examined RNA expression data from TNBC patients in The Cancer Genome Atlas (TCGA)-BRCA and the Sweden Cancerome Analysis Network - Breast (SCAN-B) cohorts (60, 61). Although all TGFβ genes were significantly correlated (P ≤ .05; Fig. S3) (30), TGFB3 showed the highest correlation with AR in both the TCGA and SCAN-B cohorts (Fig. 4A). Next, we examined how the different TGFβ pathway genes correlate with a known AR-response gene signature. GSEA analysis was performed, using an androgen-responsive gene set generated from AR+ TNBC HCI-009 PDX tumors in mice treated with or without DHT (GSE152246), and we observed correlation between androgen-responsive genes and TGFβ signaling genes (Fig. 4B). In the TCGA cohort, TGFB1, TGFB3, and TGFBR1 were significantly correlated with an AR gene signature (Fig. 4B and Fig. S4A) (30). In the SCAN-B cohort, only TGFB3 and TGFBR1 were significantly correlated with an active AR gene signature (Fig. 4B and Fig. S4B) (30).

Figure 4.

Figure 4.

Open in a new tab

Clinical data suggests a high AR and TGFB3 is associated with worse outcome. (A) Correlation analysis on the co-expression of AR and the TGFβ ligand TGFB3 in TNBC patients in the TCGA-BRCA (n = 123) and SCAN-B (n = 143) cohorts. (B) GSEA analysis on TGFB3 relating to the androgen response gene set from the HCI-009 PDX. (C) KMplotter was used to stratify basal BC patients stratified into ARhigh (top) or ARlow (bottom) populations. TGFB3 expression was then interrogated based on overall survival (OS, n = 241) and distant metastasis free survival (DMFS, n = 232)

Using KMplotter (49), we generated a series of Kaplan-Meier curves investigating how TGFB3 levels correlate with survival in basal BC (classified as ER-, HER2-) after first separating patients with AR expression (high: ARhigh or low: ARlow) as defined by above or below the median. Survival outcomes examined included RFS, OS, and DMFS. In ARhigh basal BC, high TGFB3 was associated overall shorter RFS, OS, and DMFS compared to tumors with low TGFB3 (Fig. 4C and Fig. S5) (30). However, in ARlow basal BC, high TGFB3 showed the opposite result and did not correlate with OS or DMFS (Fig. 4C and Fig. S5) (30). Similar results were obtained in ER- BC. ARhigh ER- BC cases with high TGFB3 expression were associated with shorter RFS and DMFS compared to cases with low TGFB3 (Fig. S6) (30), and there was no correlation with OS. In ARlow, ER– BC, TGFB3 expression did not correlate with RFS, OS, or DMFS. Overall, these results demonstrate that TGFB3 correlates with AR expression and pathway activation, and basal and ER- BCs that exhibit both high AR and high TGFB3 have overall worse outcome than those where either or both are low.

AR binds to genomic regions proximal to TGFB1 and SMAD3

Since AR has been reported to bind to multiple AREs in the TGFB1 promoter to increase its expression in prostate cancer (25, 62) and was a predicted upstream regulator of genes increased in anchorage-independent culture of TNBC lines (10), we investigated what other genomic regions AR binds to in anchorage-independent culture compared with attached conditions. We performed ChIP-seq for AR using the MDA-MB-453 cell line because of its robust AR expression in both the attached and anchorage-independent condition. Cells were treated with either vehicle control, DHT, or DHT+Enza. DHT-induced AR binding in attached and suspended conditions, and Enza decreased AR binding to levels comparable with vehicle control (Fig. 5A). Many DHT-induced AR binding sites show 2-fold or greater changes in the attached versus suspended condition (Fig. 5B). GREAT analysis software (63) was used to identify genes near AR bound sites more than 2-fold different in the attached versus suspension culture. Gene ontology biological processes revealed regulation of cell junction assembly and focal adhesion assembly as the 2 enriched biological processes upregulated by DHT-induced AR activation (Fig. 5C). Thirty-one genes involved in focal adhesion assembly were identified as more than 2-fold enriched for AR binding in the anchorage-independent condition, including SMAD3 (Fig. 5D). For SMAD3, we identified 6 AR binding regions (4 upstream of the canonical transcriptional start site (TSS) and 2 within the SMAD3 gene itself) that exhibited 2-fold enrichment of AR binding in suspended compared to attached culture (Fig. 5E). Two peaks located ~300 kb upstream of the SMAD3 TSS are within the gene locus of another SMAD family member, SMAD6. Interestingly, we also found differential peak calls in 2 annotated lncRNAs, LINC02206 (~134 kb upstream) and LOC102723493 (~40 kb upstream) that could putatively play additional roles in AR-mediated SMAD3 regulation. The 2 peaks following the TSS are within the SMAD3 loci itself and occur in the intronic region of the canonical isoform that also serves as the promoter region of multiple alternate SMAD3 isoforms. For TGFB1, an AR ChIP-seq signal was found to encompass a 2000 bp window, consisting of 500 bp upstream of the transcription start site, and 1500 bp into the 5′ untranslated region and first exon (Fig. 5F). Together, these results may explain the increases in transcript levels detected by qRT-PCR in under anchorage-independent cultured cells (Fig. 2B). Future functional analyses will determine the relative functional contributions of these regions.

Figure 5.

Figure 5.

Figure 5.

Open in a new tab

AR ChIP-seq peaks are identified near SMAD3 and TGFB1. (A) Heatmaps show that differential AR genome binding in attached and suspended conditions with vehicle, DHT, and DHT+Enza treatments. (B) Heatmaps display the signal at differential DHT-induced AR binding sites that show a 2-fold or greater enrichment in the suspended condition. (C) Gene ontology biological processes found nearby AR bound sites that are more than 2-fold enriched in suspended cells using GREAT (63). (D) Genes involved in focal adhesion assembly that were identified as being 2-fold enriched in under anchorage-independent culture by AR ChIP-seq. (E) AR ChIP-seq peaks identified near SMAD3 are shown. The scale bar at the top is labeled for each loci to indicate the relative distance from the canonical SMAD3 TSS along with the respective gene annotations. For each region, the AR ChIP-seq signal for each treatment condition in attached (black) and suspended (red) are shown. The peaks at each loci are scaled to the same height for accurate comparison but are not directly comparable between loci due to differences in ChIP-seq signal intensity. The chromosomal coordinates of the area covered by each peak are shown below each region. (F) Browser tracks show AR binding near the TGFβ1 promoter. AR ChIP-seq signal within a 2000 bp window covering 500 bp upstream of the TGFβ1 TSS and 1500 bp into the 5′ UTR and first exon is shown. ChIP-seq signal is scaled to the same height for comparison and represents each treatment condition in attached (black) and suspended (red), with the chromosomal coordinates included at the bottom for reference.

Combined inhibition of TGFβ and AR inhibit TNBC cell growth

Our in vitro and clinical data suggest TGFβ and AR play an important role in TNBC progression. Therefore, we hypothesized that inhibiting both pathways together might inhibit tumor cell survival, particularly in the anchorage-independent condition where evidence of signaling from both pathways increases. Crystal violet assays on TNBC cells in attached conditions with either vehicle control, 10 μM LY1, 40 μM Enza, or both for 72 hours, showed significantly less growth than vehicle control (P = .0375, P = .0197, P = .0005, respectively), with the combination treatment demonstrating an additive effect significantly different from LY1 (P = .0260) or Enza (P = .0498) (Fig. 6A).

Figure 6.

Figure 6.

Open in a new tab

SUM159PT cells are more sensitive to a combined treatment with Enza and TGFβ inhibitor than either drug alone, particularly after anchorage-independent culture. (A) SUM159PT cells were grown in triplicate in 6-well plates in attached conditions for 72 hours with either vehicle, LY1 (10 μM), Enza (40 μM), or both, followed by crystal violet assay. (B) SUM159PT cells were grown in triplicate in 6-well plates in attached conditions for 72 hours with the same treatments, followed by washout of drug and an additional 72 hours in culture, then crystal violet assay was performed. (C) SUM159PT cells were grown in under anchorage-independent culture on poly-hema coated plates for 48 hours with the treatments mentioned above, followed by replating of cells in attached conditions for an additional 48 hours. Crystal violet assay was performed after. Mean ± SD; *P < .05; **P < .01; ***P < .001. ****P < .0001.

To assess the capacity for regrowth following treatment, cells were treated with the same drugs for 72 hours, followed by washout of the drug and an additional 72 hours of growth in regular media. LY1-treated cells grew significantly less than vehicle-treated cells (P = .0230) (Fig. 6B). As before, cells treated with the combination grew significantly less than vehicle-treated cells (P = .0003). Compared with LY1- or Enza-treated cells, the combination-treated cells grew significantly less than either treatment (P = .0272; P = .0023, respectively), recapitulating the additive effect on cell growth (Fig. 6A). Lastly, to test the effects of combination drug treatment on the capacity for cell regrowth following treatment in anchorage-independent conditions, SUM159PT cells were grown for 48 hours with treatments in anchorage-independent culture and were then replated with regular media in attached conditions for an additional 48 hours. While cells treated with LY1 or Enza in anchorage-independent culture showed modest, but significantly, reduced growth in attached conditions (P < .0001), cells treated with the combination in anchorage-independent culture showed a significantly reduced regrowth capability (P < .0001) (Fig. 6C). Together, these results indicate that inhibiting AR and TGFβ signaling simultaneously has a greater effect on cell viability than either treatment alone.

Discussion

Our data demonstrate the importance of AR and TGFβ signaling in supporting anoikis resistance in TNBC. The findings support a novel role for a positive feedback loop between AR and TGFβ that supports anchorage-independent survival. TGFβ gene signature, pathway components, and activation increased in anchorage-independent conditions. Further, TGFβi prevented the upregulation of AR protein in the anchorage-independent condition that supports anoikis resistance. TGFβ inhibition decreased AR protein levels and cell viability in anchorage-independent culture; however, further studies are needed to investigate whether TGFβi-induced cell death is solely caused by decreased AR protein levels. In support of the previously unexplored role of this positive feedback loop during anchorage-independent survival, we find that exogenous TGFβ increased AR levels and AR binds to regulatory regions of genes involved in the canonical TGFβ signaling pathway. Studies in prostate cancer also show that AR binds to multiple functional AREs within the TGFB1 promoter (25). The increased TGFβ ligand production that we observed under anchorage-independent conditions stimulates pSmad3 nuclear localization, supporting the reciprocal relationship (Fig. 7). This connection has been previously demonstrated in normal dermal papilla cells, where recombinant TGFβ increases AR transcriptional activity (64), and in developing testis, AR correlates with Smad3 (65), further supporting an AR-TGFβ connection.

Figure 7.

Figure 7.

Open in a new tab

Proposed model of a positive feedback loop whereby TGFβ signaling increases AR expression, and AR increases core TGFβ pathway components.

Our previous findings show that even TNBC with low AR expression may benefit from AR-targeted therapies (10, 11). AR mRNA, protein, and transcriptional activity in TNBC increased in anchorage-independent culture in vitro (10, 11) and in vivo (19, 20), and Enza significantly decreased growth on soft agar (10). Results from a Phase II clinical study assessing the efficacy of the AR antagonist Enzalutamide in metastatic, heavily pretreated AR+ TNBC demonstrated clinical benefit, as did an earlier study with bicalutamide (66, 67).

Early TGFβi monotherapies were not as promising in clinical studies (68-71), but TGFβi combined with chemotherapeutics produced favorable results (3, 72-75). TGFβi has several major drawbacks including (1) TGFβ has paradoxical roles as both tumor suppressor and tumor promoter; generally, TGFβ causes cell cycle arrest in normal and premalignant cells, but has tumor promoting properties in certain cells and late-stage metastases (76-80), (2) tumors can develop resistance to long-term TGFβi and eventual progression (81), and (3) clinical trials with TGFβi monotherapy yielded mixed results (68, 69, 71, 82). However, short-term TGFβi and combination with chemotherapeutics can address these issues (2) and numerous studies have effectively attenuated metastasis in preclinical models using TGFβi (76, 83-88). LY2109761 (LY), a small-molecule dual inhibitor of TGFβ receptor I (TβRI) (88), reduces breast, pancreatic, and liver metastasis, prolonging survival in preclinical models (79, 84, 85). Clinical trials for various forms of TGFβi (neutralizing antibodies, soluble receptors, small molecules, etc.) in BC have shown benefit (89) with more underway (NCT02672475, NCT03620201, NCT03834662). While clinical trials have investigated the efficacy of TGFβi and AR inhibitors separately in BC, our study suggests that inhibiting both pathways might be a logical therapeutic strategy for TNBC even in subtypes with low AR, since increased AR levels are critical for anchorage independence, an early step in the metastatic cascade (10, 11, 20).

The TCGA-BRCA and SCAN-B cohorts both indicate that TGFB3 positively correlates with both AR expression and an AR-regulated gene signature, and data from KmPlot also suggest that ARhigh/TGFB3high basal BC cases have an overall worse outcome. The same trend of ARhigh/TGFB3high cases having a worse outcome was also observed in all ER– BC.

Our results showed that TGFβi combined with Enza had greater effect on cell viability, particularly under anchorage-independent culture (modeling circulating tumor cells), than either treatment alone. While the combination treatment of LY1+Enza in anchorage-independent culture severely impacted regrowth in attached conditions, future tests for true synergy and dose reduction in vitro and in vivo in metastatic models, as completed previously for Enza and the mTOR inhibitor everolimus (34) and paclitaxel (10), are needed. The latter preclinical results have led to trials such as the Artemis trial at MD Anderson treating patients with AR+ TNBC with Enza plus paclitaxel (NCT02276443).

In summary, this study indicates that a positive feedback loop between AR and TGFβ is present in TNBC that supports anoikis resistance and provides rationale for a combinatorial therapeutic strategy using both AR and TGFβ inhibitors to slow TNBC progression. Future studies are needed to evaluate the efficacy of AR/TGFβ dual inhibition therapy in vivo through use of metastatic TNBC PDXs (20) and transgenic mouse models of mammary cancer that retain AR during metastatic progression (56). While clinical trials studying the efficacy of AR and TGFβ inhibitors have produced favorable results, the proposed combinatorial strategy may further decrease TNBC metastasis and reduce patient mortality.

Acknowledgments

We would like to thank Dr. Xiao-Jing Wang’s laboratory, especially Christian Young and Dongyan Wang in her laboratory, and Dr. Phillip Owens for advice and reagents, as well as Drs. Emanuel F. Petricoin and Julia Wulfkuhle at George Mason University for the RPPA experiments and analysis. We gratefully acknowledge new funding from National Institute of General Medical Sciences (NIGMS) K12 GM08802 to ER. The authors acknowledge use of the shared resources of the University of Colorado Cancer Center National Cancer Institute (NCI) Support Grant (P30CA046934).

Glossary

Abbreviations

AR

androgen receptor

ARE

androgen response element

BC

breast cancer

ChIP

chromatin immunoprecipitation

CTC

circulating tumor cell

DHT

dihydrotestosterone

DMSO

dimethyl sulfoxide

EMT

epithelial-to-mesenchymal transition

ER

estrogen receptor

FFPE

formalin fixed and paraffin embedded

FB

fetal bovine serum

GEO

Gene Expression Omnibus

HER2

human epidermal growth factor receptor 2

KEGG

Kyoto Encyclopedia of Genes and Genomes

LAR

luminal androgen receptor

M

mesenchymal

MSL/CL

mesenchymal stem-like/claudin-low

NES

Normalized enrichment score

PDX

patient-derived xenograft

PR

progesterone receptor

RPPA

reverse phase protein array

TNBC

triple-negative breast cancer

TSS

transcriptional start site

Additional Information

Financial Support: National Cancer Institute (NCI) grants R01CA187733 and R01CA187733-02S1 to J.K.R. National Institute of General Medical Sciences (NIGMS) grant T32GM008730 and NCI grant F31CA247343 to J.T.R.

Disclosures: The authors declare no competing interests.

Data Availability

All data generated or analyzed during this study are included in this published article or in the data repositories listed in References.

References

  • 1. Bauer  KR, Brown  M, Cress  RD, Parise  CA, Caggiano  V. Descriptive analysis of estrogen receptor (ER)-negative, progesterone receptor (PR)-negative, and HER2-negative invasive breast cancer, the so-called triple-negative phenotype: a population-based study from the California cancer Registry. Cancer.  2007;109(9):1721-1728. [DOI] [PubMed] [Google Scholar]
  • 2. Liedtke  C, Mazouni  C, Hess  KR, et al.  Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer. J Clin Oncol.  2008;26(8):1275-1281. [DOI] [PubMed] [Google Scholar]
  • 3. Livraghi  L, Garber  JE. PARP inhibitors in the management of breast cancer: current data and future prospects. BMC Med.  2015;13(1):188. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. Park  JH, Ahn  JH, Kim  SB. How shall we treat early triple-negative breast cancer (TNBC): from the current standard to upcoming immuno-molecular strategies. ESMO Open.  2018;3(Suppl 1):e000357. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Dent  R, Trudeau  M, Pritchard  KI, et al.  Triple-negative breast cancer: clinical features and patterns of recurrence. Clin Cancer Res.  2007;13(15 Pt 1):4429-4434. [DOI] [PubMed] [Google Scholar]
  • 6. Collins  LC, Cole  KS, Marotti  JD, Hu  R, Schnitt  SJ, Tamimi  RM. Androgen receptor expression in breast cancer in relation to molecular phenotype: results from the Nurses’ Health Study. Mod Pathol.  2011;24(7):924-931. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Groner  AC, Brown  M. Role of steroid receptor and coregulator mutations in hormone-dependent cancers. J Clin Invest.  2017;127(4):1126-1135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Pakula  H, Xiang  D, Li  Z. A tale of two signals: AR and WNT in development and tumorigenesis of prostate and mammary gland. Cancers.  2017;9(2):14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Robinson  JL, Macarthur  S, Ross-Innes  CS, et al.  Androgen receptor driven transcription in molecular apocrine breast cancer is mediated by FoxA1. EMBO J.  2011;30(15):3019-3027. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Barton  VN, Christenson  JL, Gordon  MA, et al.  Androgen receptor supports an anchorage-independent, cancer stem cell-like population in triple-negative breast cancer. Cancer Res.  2017;77(13):3455-3466. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Barton  VN, D’Amato  NC, Gordon  MA, et al.  Multiple molecular subtypes of triple-negative breast cancer critically rely on androgen receptor and respond to enzalutamide in vivo. Mol Cancer Ther.  2015;14(3):769-778. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Farmer  P, Bonnefoi  H, Becette  V, et al.  Identification of molecular apocrine breast tumours by microarray analysis. Oncogene.  2005;24(29):4660-4671. [DOI] [PubMed] [Google Scholar]
  • 13. Doane  AS, Danso  M, Lal  P, et al.  An estrogen receptor-negative breast cancer subset characterized by a hormonally regulated transcriptional program and response to androgen. Oncogene.  2006;25(28):3994-4008. [DOI] [PubMed] [Google Scholar]
  • 14. Lehmann  BD, Bauer  JA, Chen  X, et al.  Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest.  2011;121(7):2750-2767. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Lehmann  BD, Jovanović  B, Chen  X, et al.  Refinement of triple-negative breast cancer molecular subtypes: implications for neoadjuvant chemotherapy selection. PLoS One.  2016;11(6):e0157368. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Barton  VN, D’Amato  NC, Gordon  MA, Christenson  JL, Elias  A, Richer  JK. Androgen receptor biology in triple negative breast cancer: a case for classification as AR+ or quadruple negative disease. Horm Cancer.  2015;6(5-6):206-213. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Wahdan-Alaswad  R, Harrell  JC, Fan  Z, Edgerton  SM, Liu  B, Thor  AD. Metformin attenuates transforming growth factor beta (TGF-β) mediated oncogenesis in mesenchymal stem-like/claudin-low triple negative breast cancer. Cell Cycle.  2016;15(8):1046-1059. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Xu  J, Lamouille  S, Derynck  R. TGF-beta-induced epithelial to mesenchymal transition. Cell Res.  2009;19(2):156-172. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Yu  M, Bardia  A, Wittner  BS, et al.  Circulating breast tumor cells exhibit dynamic changes in epithelial and mesenchymal composition. Science.  2013;339(6119):580-584. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Lawson  DA, Bhakta  NR, Kessenbrock  K, et al.  Single-cell analysis reveals a stem-cell program in human metastatic breast cancer cells. Nature.  2015;526(7571):131-135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Kang  HY, Huang  HY, Hsieh  CY, et al.  Activin A enhances prostate cancer cell migration through activation of androgen receptor and is overexpressed in metastatic prostate cancer. J Bone Miner Res.  2009;24(7):1180-1193. [DOI] [PubMed] [Google Scholar]
  • 22. Kang  H-Y, Lin  H-K, Hu  Y-C, Yeh  S, Huang  K-E, Chang  C. From transforming growth factor-β signaling to androgen action: identification of Smad3 as an androgen receptor coregulator in prostate cancer cells. Proc Natl Acad Sci U S A.  2001;98(6):3018-3023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Wang  H, Song  K, Sponseller  TL, Danielpour  D. Novel function of androgen receptor-associated protein 55/Hic-5 as a negative regulator of Smad3 signaling. J Biol Chem.  2005;280(7):5154-5162. [DOI] [PubMed] [Google Scholar]
  • 24. Lonergan  PE, Tindall  DJ. Androgen receptor signaling in prostate cancer development and progression. J Carcinog.  2011;10(20):20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. Qi  W, Gao  S, Wang  Z. Transcriptional regulation of the TGF-β1 promoter by androgen receptor. Biochem J.  2008;416(3):453-462. [DOI] [PubMed] [Google Scholar]
  • 26.RRID:CVCL_0418.
  • 27.RRID:CVCL_5423.
  • 28.RRID:CVCL_1092.
  • 29. Subramanian  A, Tamayo  P, Mootha  VK, et al.  Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A.  2005;102(43):15545-15550. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Rosas  E, Roberts  JT, O’Neill  K, et al.  Data from: a positive feedback loop between TGFβ and androgen receptor supports triple-negative breast cancer anoikis resistance. figshare  2020. Deposited December 2, 2020. doi: 10.6084/m9.figshare.13323140.v1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Sheehan  KM, Calvert  VS, Kay  EW, et al.  Use of reverse phase protein microarrays and reference standard development for molecular network analysis of metastatic ovarian carcinoma. Mol Cell Proteomics.  2005;4(4):346-355. [DOI] [PubMed] [Google Scholar]
  • 32. Wulfkuhle  JD, Berg  D, Wolff  C, et al. ; I-SPY 1 TRIAL Investigators . Molecular analysis of HER2 signaling in human breast cancer by functional protein pathway activation mapping. Clin Cancer Res.  2012;18(23):6426-6435. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Wulfkuhle  JD, Speer  R, Pierobon  M, et al.  Multiplexed cell signaling analysis of human breast cancer applications for personalized therapy. J Proteome Res.  2008;7(4):1508-1517. [DOI] [PubMed] [Google Scholar]
  • 34. Gordon  MA, D’Amato  NC, Gu  H, et al.  Synergy between androgen receptor antagonism and inhibition of mTOR and HER2 in breast cancer. Mol Cancer Ther.  2017;16(7):1389-1400. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35. RRID:AB_2877059. [Google Scholar]
  • 36. RRID:AB_882596. [Google Scholar]
  • 37. RRID:AB_2336529. [Google Scholar]
  • 38. Varghese  F, Bukhari  AB, Malhotra  R, De  A. IHC profiler: an open source plugin for the quantitative evaluation and automated scoring of immunohistochemistry images of human tissue samples. PLoS One.  2014;9(5):e96801. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Wrana  JL, Attisano  L, Cárcamo  J, et al.  TGF beta signals through a heteromeric protein kinase receptor complex. Cell.  1992;71(6):1003-1014. [DOI] [PubMed] [Google Scholar]
  • 40. RRID:AB_310214. [Google Scholar]
  • 41. RRID:AB_490941. [Google Scholar]
  • 42. RRID:AB_631701. [Google Scholar]
  • 43. RRID:AB_1078991. [Google Scholar]
  • 44. RRID:AB_2160739. [Google Scholar]
  • 45. RRID:AB_10889933. [Google Scholar]
  • 46. RRID:AB_11179215. [Google Scholar]
  • 47. RRID:AB_621843. [Google Scholar]
  • 48. RRID:AB_10956588. [Google Scholar]
  • 49. Györffy  B, Lanczky  A, Eklund  AC, et al.  An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients. Breast Cancer Res Treat.  2010;123(3):725-731. [DOI] [PubMed] [Google Scholar]
  • 50. Reddy  TE, Pauli  F, Sprouse  RO, et al.  Genomic determination of the glucocorticoid response reveals unexpected mechanisms of gene regulation. Genome Res.  2009;19(12):2163-2171. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51. RRID:AB_633881. [Google Scholar]
  • 52. Langmead  B, Trapnell  C, Pop  M, Salzberg  SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol.  2009;10(3):R25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53. Zhang  Y, Liu  T, Meyer  CA, et al.  Model-based analysis of ChIP-Seq (MACS). Genome Biol.  2008;9(9):R137. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54. Quinlan  AR, Hall  IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics.  2010;26(6):841-842. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55. Robinson  JT, Thorvaldsdóttir  H, Winckler  W, et al.  Integrative genomics viewer. Nat Biotechnol.  2011;29(1):24-26. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56. Christenson  JL, Butterfield  KT, Spoelstra  NS, et al.  MMTV-PyMT and derived Met-1 mouse mammary tumor cells as models for studying the role of the androgen receptor in triple-negative breast cancer progression. Horm Cancer.  2017;8(2):69-77. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57. Kim  S, Lee  J, Jeon  M, Lee  JE, Nam  SJ. Zerumbone suppresses the motility and tumorigenecity of triple negative breast cancer cells via the inhibition of TGF-β1 signaling pathway. Oncotarget.  2016;7(2):1544-1558. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58. Li  HY, McMillen  WT, Heap  CR, et al.  Optimization of a dihydropyrrolopyrazole series of transforming growth factor-beta type I receptor kinase domain inhibitors: discovery of an orally bioavailable transforming growth factor-beta receptor type I inhibitor as antitumor agent. J Med Chem.  2008;51(7):2302-2306. [DOI] [PubMed] [Google Scholar]
  • 59. Yingling  J. Targeting the TGF-β RI kinase with LY2157299: A PK/PD-driven drug discovery and clinical development program. Cancer Res. 2005;65(9 Supplement):1463. [Google Scholar]
  • 60. Koboldt  DC, Fulton  RS, McLellan  MD, et al.  Comprehensive molecular portraits of human breast tumours. Nature.  2012;490(7418):61-70. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61. Saal  LH, Vallon-Christersson  J, Häkkinen  J, et al.  The Sweden Cancerome Analysis Network – Breast (SCAN-B) initiative: a large-scale multicenter infrastructure towards implementation of breast cancer genomic analyses in the clinical routine. Genome Med.  2015;7(1):20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62. Qi  W, Gao  S, Chu  J, Zhou  L, Wang  Z. Negative androgen-response elements mediate androgen-dependent transcriptional inhibition of TGF-β1 and CDK2 promoters in the prostate gland. J Androl.  2012;33(1):27-36. [DOI] [PubMed] [Google Scholar]
  • 63. McLean  CY, Bristor  D, Hiller  M, et al.  GREAT improves functional interpretation of cis-regulatory regions. Nat Biotechnol.  2010;28(5):495-501. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64. Inui  S, Itami  S. Androgen receptor transactivity is potentiated by TGF-β1 through Smad3 but checked by its coactivator Hic-5/ARA55 in balding dermal papilla cells. J Dermatol Sci.  2011;64(2):149-151. [DOI] [PubMed] [Google Scholar]
  • 65. Itman  C, Wong  C, Hunyadi  B, Ernst  M, Jans  DA, Loveland  KL. Smad3 dosage determines androgen responsiveness and sets the pace of postnatal testis development. Endocrinology.  2011;152(5):2076-2089. [DOI] [PubMed] [Google Scholar]
  • 66. Gucalp  A, Tolaney  S, Isakoff  SJ, et al. ; Translational Breast Cancer Research Consortium (TBCRC 011) . Phase II trial of bicalutamide in patients with androgen receptor-positive, estrogen receptor-negative metastatic breast cancer. Clin Cancer Res.  2013;19(19):5505-5512. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67. Traina  TA, Miller  K, Yardley  DA, et al.  Enzalutamide for the treatment of androgen receptor-expressing triple-negative breast cancer. J Clin Oncol.  2018;36(9):884-890. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68. Connolly  EC, Freimuth  J, Akhurst  RJ. Complexities of TGF-β targeted cancer therapy. Int J Biol Sci.  2012;8(7):964-978. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69. Giaccone  G, Bazhenova  LA, Nemunaitis  J, et al.  A phase III study of belagenpumatucel-L, an allogeneic tumour cell vaccine, as maintenance therapy for non-small cell lung cancer. Eur J Cancer.  2015;51(16):2321-2329. [DOI] [PubMed] [Google Scholar]
  • 70. Guo  Q, Betts  C, Pennock  N, Mitchell  E, Schedin  P. Mammary gland involution provides a unique model to study the TGF-β cancer paradox. J Clin Med.  2017;6(1):10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71. Neuzillet  C, Tijeras-Raballand  A, Cohen  R, et al.  Targeting the TGFβ pathway for cancer therapy. Pharmacol Ther.  2015;147:22-31. [DOI] [PubMed] [Google Scholar]
  • 72. Gao  Y, Shan  N, Zhao  C, et al.  LY2109761 enhances cisplatin antitumor activity in ovarian cancer cells. Int J Clin Exp Pathol.  2015;8(5):4923-4932. [PMC free article] [PubMed] [Google Scholar]
  • 73. Herbertz  S, Sawyer  JS, Stauber  AJ, et al.  Clinical development of galunisertib (LY2157299 monohydrate), a small molecule inhibitor of transforming growth factor-beta signaling pathway. Drug Des Devel Ther.  2015;9:4479-4499. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74. Melisi  D, Ishiyama  S, Sclabas  GM, et al.  LY2109761, a novel transforming growth factor beta receptor type I and type II dual inhibitor, as a therapeutic approach to suppressing pancreatic cancer metastasis. Mol Cancer Ther.  2008;7(4):829-840. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75. Zhang  M, Herion  TW, Timke  C, et al.  Trimodal glioblastoma treatment consisting of concurrent radiotherapy, temozolomide, and the novel TGF-β receptor I kinase inhibitor LY2109761. Neoplasia.  2011;13(6):537-549. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76. Barcellos-Hoff  MH, Akhurst  RJ. Transforming growth factor-beta in breast cancer: too much, too late. Breast Cancer Res.  2009;11(1):202. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77. Forrester  E, Chytil  A, Bierie  B, et al.  Effect of conditional knockout of the type II TGF-beta receptor gene in mammary epithelia on mammary gland development and polyomavirus middle T antigen induced tumor formation and metastasis. Cancer Res.  2005;65(6):2296-2302. [DOI] [PubMed] [Google Scholar]
  • 78. Muraoka-Cook  RS, Kurokawa  H, Koh  Y, et al.  Conditional overexpression of active transforming growth factor beta1 in vivo accelerates metastases of transgenic mammary tumors. Cancer Res.  2004;64(24):9002-9011. [DOI] [PubMed] [Google Scholar]
  • 79. Sheen  YY, Kim  MJ, Park  SA, Park  SY, Nam  JS. Targeting the transforming growth factor-β signaling in cancer therapy. Biomol Ther (Seoul).  2013;21(5):323-331. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 80. Yuan  JH, Yang  F, Wang  F, et al.  A long noncoding RNA activated by TGF-β promotes the invasion-metastasis cascade in hepatocellular carcinoma. Cancer Cell.  2014;25(5): 666-681. [DOI] [PubMed] [Google Scholar]
  • 81. Connolly  EC, Saunier  EF, Quigley  D, et al.  Outgrowth of drug-resistant carcinomas expressing markers of tumor aggression after long-term TβRI/II kinase inhibition with LY2109761. Cancer Res.  2011;71(6):2339-2349. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 82. Guo  Q, Betts  C, Pennock  N, Mitchell  E, Schedin  P. Mammary gland involution provides a unique model to study the TGF-β cancer paradox. J Clin Med.  2017;6(1):10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 83. Chiechi  A, Waning  DL, Stayrook  KR, Buijs  JT, Guise  TA, Mohammad  KS. Role of TGF-β in breast cancer bone metastases. Adv Biosci Biotechnol.  2013;4(10C):15-30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 84. Fang  Y, Chen  Y, Yu  L, et al.  Inhibition of breast cancer metastases by a novel inhibitor of TGFβ receptor 1. J Natl Cancer Inst.  2013;105(1):47-58. [DOI] [PubMed] [Google Scholar]
  • 85. Melisi  D, Ishiyama  S, Sclabas  GM, et al.  LY2109761, a novel transforming growth factor beta receptor type I and type II dual inhibitor, as a therapeutic approach to suppressing pancreatic cancer metastasis. Mol Cancer Ther.  2008;7(4):829-840. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 86. Yang  YA, Dukhanina  O, Tang  B, et al.  Lifetime exposure to a soluble TGF-beta antagonist protects mice against metastasis without adverse side effects. J Clin Invest.  2002;109(12):1607-1615. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 87. Zarzynska  JM. Two faces of TGF-beta1 in breast cancer. Mediators Inflamm.  2014;2014:141747. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 88. Zhang  B, Halder  SK, Zhang  S, Datta  PK. Targeting transforming growth factor-beta signaling in liver metastasis of colon cancer. Cancer Lett.  2009;277(1):114-120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 89. Huynh  LK, Hipolito  CJ, ten Dijke  P. A perspective on the development of TGF-β inhibitors for cancer treatment. Biomolecules.  2019;9(11):743. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Data Availability Statement

All data generated or analyzed during this study are included in this published article or in the data repositories listed in References.

Articles from Endocrinology are provided here courtesy of The Endocrine Society

RESOURCES