Metastatic breast cancer is still incurable so far; new specifically targeted and more effective therapies for triple-negative breast cancer (TNBC) are required in the clinic. In this study, our clinical data have established that basal and claudin-low subtypes of breast cancer (TNBC types) express significantly higher levels of Annexin A1 (ANXA1) with poor survival outcomes. Using human cancer cell lines that model the TNBC subtype, we observed a strong positive correlation between expression of ANXA1 and PPARγ. A similar correlation between these two markers was also established in our clinical breast cancer patients' specimens. To establish a link between these two markers in TNBC, we show de novo expression of ANXA1 is induced by activation of PPARγ both in vitro and in vivo and it has a predictive value in determining chemosensitivity to PPARγ ligands. Mechanistically, we show for the first time PPARγ-induced ANXA1 protein directly interacts with receptor interacting protein-1 (RIP1), promoting its deubiquitination and thereby activating the caspase-8–dependent death pathway. We further identified this underlying mechanism also involved a PPARγ-induced ANXA1-dependent autoubiquitination of cIAP1, the direct E3 ligase of RIP1, shifting cIAP1 toward proteosomal degradation. Collectively, our study provides first insight for the suitability of using drug-induced expression of ANXA1 as a new player in RIP1-induced death machinery in TNBCs, presenting itself both as an inclusion criterion for patient selection and surrogate marker for drug response in future PPARγ chemotherapy trials. Mol Cancer Ther; 16(11); 2528–42. ©2017 AACR.

In the past two decades, the risk of breast cancer has been increasing worldwide, as well in Asia (Japan, Korea and Taiwan; refs. 1–3). The rate of breast cancer incidence in Singapore has also become one of the highest in the world with 5.7% per year increase in premenopausal and 3.9% per year of postmenopausal women (4).

Breast cancers can be classified into several classes, with each representing unique molecular or genetic characteristics. Triple-negative breast cancer (TNBC), which is estrogen receptor (ER), progesterone receptor (PR), and C-erb B2 receptor (ERBB2R)-negative (5), is known to be heterogeneous in nature of disease. Anthracycline–taxane regimens remain the current standard for TNBC patients who tend to have a higher risk of relapse and worse overall survival rates with mixed outcomes (6, 7). However, the natural heterogeneity of TNBC has made it difficult to evaluate the true clinical value of the tested drugs, making the cohorts' selection based on specific biomarkers associated with distinct TNBC subgroups a prerequisite (8, 9). A “Genome-first Approach” concept has been introduced where patients will be prestratified and assigned to clinical trials designed to address the therapeutic hypotheses, based upon analysis of individual tumor profiles (8, 9).

PPARγ belongs to the nuclear hormone receptor superfamily of ligand-activated transcription factors. PPARγ ligands, 15-deoxy-prostaglandin J2 (15d-PGJ2) and the synthetic thiazolidinedione (TZD) family, are currently available in the market as therapeutic agents targeting type II diabetes mellitus (10). The role of PPARγ ligands as potential antitumor agents has been reported by many, including breast cancer (11–13). However, results from clinical trials have not been consistent among the various breast cancer subgroups, making novel biomarkers for patient selections and/or drug response for PPARγ ligand trials, a necessity.

Annexin-A1 (ANXA1), a 37-kDa calcium-dependent phospholipid binding protein, has been implicated in various biological processes including tumorigenesis and anti-inflammatory activity of glucocorticoids (14, 15). ANXA1 expression has been suggested to play roles in the progression and development of many human cancers including breast cancer (16, 17).

NFκB complex is sequestered by inhibitor of kappa B (IκB) in the cytoplasm of normal cells. Breast cancer cells with invasive and metastatic characteristics, express constitutively activated NFκB (18, 19). We have reported baseline ANXA1 expressed in the MDA-MB-231 breast cancer cells interacts with NFκB essential modulator (NEMO, also known as IKKγ) and receptor interacting Protein 1 (RIP1), driving activation of NFκB in cancer cells (20).

As our previous observations have shown that baseline ANXA1 interacts with NEMO and RIP1 to drive activation of NFκB signaling in TNBC lines (20) and having identified a putative PPARγ binding consensus within the promoter of ANXA1, we asked whether activation of PPARγ would have an effect on this multiprotein complex. Herein, we show, using a series of cell-based assays, antitumor activity of PPARγ ligands is regulated via disruption of the ANXA1–NEMO–RIP1ubiqutinated complex “switching to” an increased ANXA1-RIP1deubiqutinated interaction, activating a caspase-8–dependent death pathway.

Cell lines and cell culture conditions

MDA-MB-231, BT549, MCF-7, and T47D breast cancer cell lines were purchased from ATCC within one year of the study in 2016. Authentication was based on growth rate, morphology, and absence of mycoplasma. MCF-7 EV and MCF-7 V5 cells were generated in our laboratory via stable transfection, followed by antibiotic selection of the stably transfected clones. All the cell lines were maintained in RPMI1640 medium containing 10% FBS, 2 mmol/L l-glutamine, and 0.05 mg/mL gentamicin sulphate (Hyclone) in a 37°C incubator with 5% CO2.

PPARγ agonists and inhibitors

A stock of 15d-PGJ2 (Alexis) in solution form, with solubility of 1 mg/mL, was prepared and further diluted with treatment medium to obtain final concentrations of 5 μmol/L and 10 μmol/L. A stock concentration of 80 mmol/L of rosiglitazone (Cayman Chemical) was prepared in DMSO and further diluted with treatment medium to obtain final treatment concentrations of 50 μmol/L and 80 μmol/L. A stock concentration of 20 mmol/L GW9662 (Cayman Chemical) was further diluted using growth medium to a treatment concentration of 10 μmol/L.

siRNA oligonucleotides, plasmid/DNA transfection, cell viability assay, luciferase assay, RNA isolation, reverse transcription (RT) and real-time PCR, Western blot analysis, immunoprecipitation, and colony-forming assay

Procedures for these assays are conducted as described previously (21, 22). Detailed procedures are described in Supplementary Methods.

APC-conjugated Annexin-V/propidium iodide staining for apoptosis detection

For the measurement of percentage of cells that undergo apoptosis, an APC-conjugated Annexin-V/propidium iodide (PI) Staining Assay kit was used (Life Technologies). Cell pellets were resuspended in 100 μL of 1× binding buffer containing APC-conjugated anti-Annexin V antibody. The samples were incubated at 4°C for minimum of 30 minutes in dark conditions. At the end of antibody incubation, each sample was diluted with 400 μL cold 1× PBS. Cells were forced through the filter caps of polystyrene tubes and then analyzed using a flow cytometry (BD FACSCalibur, BD Biosciences). Two microliters of 1× PI dye were added to each sample and gently shaken for 15 seconds before the tube was placed onto the flow cytometer.

Establishment of MDA-MB-231 xenograft in nude mice and experimental design

Details for the in vivo mice xenograft work is described in ref. 23.

IHC

Use of clinical material from Canada was approved by the Institutional Review Board, Centre Hospitalier de l'Université de Montréal (CHUM) in 2005 with a renewal in 2010. Archived formalin-fixed paraffin-embedded (FFPE) tumor tissues from 164 breast cancer patients were selected from the archives of the Department of pathology, CHUM, University of Montreal at IRIC. Normal breast tissue samples were collected from healthy women undergoing plastic surgery. IHC was performed as described previously (22) and details outlined in Supplementary Methods.

Single-sample gene set enrichment analysis, identification of breast cancer subtypes, and data preprocessing of Affymetrix microarray gene expression

These analysis were performed as described in our previous study (22). Details of these analysis are provided in Supplementary Methods.

Estimation of epithelial–mesenchymal transition score

An epithelial–mesenchymal transition (EMT) scoring method was developed using ovarian carcinoma cell lines expression profiling as described previously by our group (21, 24). A higher or lower EMT score indicates that the cell line exhibits a more mesenchymal or epithelial phenotype, respectively.

Statistical analysis

Statistical analyses for all in vitro work were performed using paired Student t test. A P value of less than 0.05 was considered significant. Statistical significance evaluation for microarray analysis were performed using Mann–Whitney test and Spearman correlation test, which were computed using Matlab R2012a (MathWorks). Dot plots were done using GraphPad Prism version 5.0 (GraphPad Software). Statistical analyses for IHC work were carried out using different packages of R language (http://www.R-project.org/). The distribution of ANXA1 among different histologic grades and molecular subtypes is depicted using bar charts. Nonparametric tests are used due to the nature of ordinal and categorical data. The overall relationship between ANXA1 scores and other characteristics (i.e., histologic grades, and molecular subtypes) was evaluated by χ2 test. Correlation analysis for IHC expression levels was carried out using the Spearman ρ correlation coefficient. Statistical significance was considered, with a P value < 0.05.

ANXA1 gene expression positively correlates to PPARγ gene expression in patients' specimens and breast cancer models

Twenty-six cohorts containing 3,992 breast cancer tumors, 22 normal breast tissues collected and compiled from the public database Gene Omnibus (GEO) and ArrayExpress were analyzed for mRNA expression levels of ANXA1. ANXA1 expression is highest in Normal-like and claudin-low TNBC subtypes, followed by ERBB2+, luminal-A, and luminal-B subtype, while TNBC has significantly higher basal ANXA1 expression compared with luminal-A, luminal-B, and ERBB2+ breast cancer subtypes (Fig. 1A). ANXA1-high patients had a markedly reduced percentage of patient survival than ANXA1-low patients (log-rank test, P = 0.015; HR = 0.4815; Fig. 1B). In addition, the epithelial–mesenchymal transition (EMT) scoring system shows ANXA1 was strong and positively correlated with EMT score (Spearman correlation test, ρ = 0.6367, P < 1e−128; Supplementary Fig. S1A). To validate our ANXA1 mRNA analysis from Fig. 1A, we obtained patients' specimens from Centre hospitalier de l'Université de Montréal (CHUM) for IHC analysis. Normal breast tissue samples were collected from healthy women who underwent plastic surgery. Clinicopathologic features of these patients are shown in Table 1. IHC analyses showed higher levels of ANXA1 expression were associated with the more aggressive tumor subtypes such as TNBC and ERBB2+compared with the luminal subtypes (Fig. 1C). Higher ANXA1 expression was also detected in grade III than the lower grade breast tumors (Fig. 1D) with positive correlation observed between ANXA1 expression and cellular proliferation marker, Ki-67. Notably, the TNBC subtype expressed both high levels of ANXA1 and Ki-67 (Fig. 1E). Interestingly, clinical data analyzed using Spearman correlation test results showed ANXA1 gene expression is indeed positively correlated with PPARγ gene expression in patient samples (ρ = 0.29, P < 1e−128; Fig. 1F). In agreement with our mRNA correlation in Fig. 1F, ANXA1 protein levels also positively correlate to PPARγ protein expression in patient breast tumors by IHC scoring (Fig. 1G) as well as in breast cancer cell lines by relative mRNA quantification (Supplementary Fig. S1B). In vitro, baseline gene and protein expression levels of ANXA1 and PPARγ were performed in luminal subtype lines (MCF-7and T47D) and claudin-low TNBC subtype lines (MDA-MB-231 and BT549). Baseline ANXA1 mRNA and protein levels were significantly higher by 2.5 times in TNBC than luminal breast cancer cell lines (Fig. 1H). Importantly, we observed a similar corresponding pattern of baseline levels of PPARγ mRNA and protein levels were higher in the two TNBC cell lines as well (Fig. 1I). Concomittant to the differences in baseline PPARγ expression levels (Fig. 1I), lower PPARγ activity is observed in normal MCF10A and luminal type T47D compared with that in TNBC type MDA-MB-231 cells (Fig. 1J).

Figure 1.

High level of expression of ANXA1 and PPARγ positively correlates with the invasive, high-grade phenotype. A, ANXA1 gene expression values in various breast cancer subtypes collected from public database Gene Omnibus (GEO). B, Correlation of ANXA1 expression to breast cancer survival. Kaplan–Meier plot shows ANXA1-high (red color) and ANXA1-low groups (blue color) defined by median of ANXA1 expression in basal and claudin-low breast cancer samples (n = 172). Log-rank test was used to compute the P value. HR, hazard ratio. C and D, Clinicopathogic examination of patient tissue samples with ANXA1 positive expression in IHC staining. Bar chart (left panels) indicate percentage of patient breast tumor tissues stained positively/negatively for ANXA1, categorized according to molecular classification of breast cancer subtypes. Corresponding anti-ANXA1 IHC staining (right) shows ANXA1 expression detected across different molecular subtypes of breast cancer (BC). E, Clinicopathogic examination of patient tissue samples with ANXA1 and Ki-67–positive expression in IHC staining. Bar chart indicates the percentage (%) of patient's breast tumor tissue stained for either positively/negatively of ANXA1 or Ki-67 and categorized according to the molecular classification of breast cancer subtypes. Corresponding anti-ANXA1 and anti-Ki-67 IHC staining was detected the expression of ANXA1/Ki-67 across in different molecular subtypes of breast cancer. F, Correlation between PPARγ and ANXA1 gene expression in breast cancer patients. The red dotted line is fitted with the linear regression curve. G, Correlation between PPARγ and ANXA1 expression in breast cancer tissues based on IHC scoring. The red dotted line is fitted with the linear regression curve. H, Relative basal ANXA1 levels of the same panel of cell lines. Data are the relative percentage (%) of changes ± SEM of three independent experiments (n = 3). Western blot analysis showed the basal ANXA1 expression levels in the same panel of cells. I, Relative basal PPARγ expression levels of the same panel of cells. Data are the relative percentage (%) of changes ± SEM of three independent experiments (n = 3). Western blot analysis showed the basal PPARγ protein levels in the same panel of cell lines. β-Actin was used as a protein loading control. J, Baseline PPARγ activity measured by using a PPRE luciferase read-out in normal breast cell line, MCF10A compared with that in a luminal type cell line, T47D, and a TNBC cell line, MDA-MB-231.

Figure 1.

High level of expression of ANXA1 and PPARγ positively correlates with the invasive, high-grade phenotype. A, ANXA1 gene expression values in various breast cancer subtypes collected from public database Gene Omnibus (GEO). B, Correlation of ANXA1 expression to breast cancer survival. Kaplan–Meier plot shows ANXA1-high (red color) and ANXA1-low groups (blue color) defined by median of ANXA1 expression in basal and claudin-low breast cancer samples (n = 172). Log-rank test was used to compute the P value. HR, hazard ratio. C and D, Clinicopathogic examination of patient tissue samples with ANXA1 positive expression in IHC staining. Bar chart (left panels) indicate percentage of patient breast tumor tissues stained positively/negatively for ANXA1, categorized according to molecular classification of breast cancer subtypes. Corresponding anti-ANXA1 IHC staining (right) shows ANXA1 expression detected across different molecular subtypes of breast cancer (BC). E, Clinicopathogic examination of patient tissue samples with ANXA1 and Ki-67–positive expression in IHC staining. Bar chart indicates the percentage (%) of patient's breast tumor tissue stained for either positively/negatively of ANXA1 or Ki-67 and categorized according to the molecular classification of breast cancer subtypes. Corresponding anti-ANXA1 and anti-Ki-67 IHC staining was detected the expression of ANXA1/Ki-67 across in different molecular subtypes of breast cancer. F, Correlation between PPARγ and ANXA1 gene expression in breast cancer patients. The red dotted line is fitted with the linear regression curve. G, Correlation between PPARγ and ANXA1 expression in breast cancer tissues based on IHC scoring. The red dotted line is fitted with the linear regression curve. H, Relative basal ANXA1 levels of the same panel of cell lines. Data are the relative percentage (%) of changes ± SEM of three independent experiments (n = 3). Western blot analysis showed the basal ANXA1 expression levels in the same panel of cells. I, Relative basal PPARγ expression levels of the same panel of cells. Data are the relative percentage (%) of changes ± SEM of three independent experiments (n = 3). Western blot analysis showed the basal PPARγ protein levels in the same panel of cell lines. β-Actin was used as a protein loading control. J, Baseline PPARγ activity measured by using a PPRE luciferase read-out in normal breast cell line, MCF10A compared with that in a luminal type cell line, T47D, and a TNBC cell line, MDA-MB-231.

Close modal
Table 1.

Clinicopathologic data of the 315 patients' specimen from Centre hospitalier de l'Universit de Montreal (CHUM)

Clinicopathologic dataNumber of replicates
Age (35–92 years) 315 
Grade 
 I 26 
 II 47 
 III 242 
Histologic subtypes 
 Invasive ductal carcinoma 239 
 Invasive lobular carcinoma 
 Typical medullary carcinoma 46 
 Atypical medullary carcinoma 17 
 Colloid carcinoma 
Estrogen receptors 
 Positive 121 
 Negative 194 
Progesterone receptors 
 Positive 107 
 Negative 208 
HER-2 receptors 
 Positive 51 
 Negative 264 
Metastasis 
 Yes 120 
 No 195 
Clinicopathologic dataNumber of replicates
Age (35–92 years) 315 
Grade 
 I 26 
 II 47 
 III 242 
Histologic subtypes 
 Invasive ductal carcinoma 239 
 Invasive lobular carcinoma 
 Typical medullary carcinoma 46 
 Atypical medullary carcinoma 17 
 Colloid carcinoma 
Estrogen receptors 
 Positive 121 
 Negative 194 
Progesterone receptors 
 Positive 107 
 Negative 208 
HER-2 receptors 
 Positive 51 
 Negative 264 
Metastasis 
 Yes 120 
 No 195 

Activation of PPARγ ligands leads to an ANXA1-dependent cell death in TNBC lines

Crystal violet staining assay was performed on the breast cancer cells using two PPARγ ligands, 15d-PGJ2, or rosiglitazone. Dose-dependent decrease in cell viability was observed in MDA-MB-231 and BT549 cells after 48-hour treatments (Fig. 2A), while marginal decrease in viability of MCF-7 and T47D cells was observed (Fig. 2B). These findings were further validated by microscopic examinations in TNBC (Supplementary Fig. S1C and S1D) versus luminal breast cancer (Supplementary Fig. S1E and S1F). Colony-forming abilities of MDA-MB-231 and BT549 cells were also greatly affected by PPARγ ligand treatment (Fig. 2C). In contrast, MCF-7 and T47D cells were relatively resistant to PPARγ ligand treatment (Fig. 2D). Interestingly, in both MDA-MB-231 (Fig. 2E) and BT549 (Supplementary Fig. S2A and S2B), the significant decrease in cell viability after exposure to PPARγ ligands was abrogated when ANXA1 depleted in these cells. MDA-MB-231 cells transfected with scrambled siRNA showed 15.3% and 21.8% increase in total apoptosis after exposure of 15d-PGJ2 (10 μmol/L) and rosiglitazone (80 μmol/L), respectively (Fig. 2F and G). However, less than 5% increase in apoptosis was observed in cells with depleted ANXA1 after PPARγ ligand treatments in MDA-MB-231 cells (Fig. 2H). Conversely, in the MCF-7-V5 cells (an MCF-7 cell line stably overexpressing ANXA1 ectopically), treated with the same concentrations of 15d-PGJ2 or rosiglitazone resulted in up to 40% reduction in cell viability compared with MCF-7-EV cells (stably transfected with empty vector; Fig. 2I), with a 17.7% and 33.8% increase in total apoptosis when treated with 15d-PGJ2 or rosiglitazone, respectively. However, only 5% increase in total apoptosis was observed in MCF-7-EV cells after PPARγ ligand treatments (Fig. 2J).

Figure 2.

High ANXA1–expressing TNBC cell lines display greater sensitivity to PPARγ ligands compared with low ANXA1–expressing luminal-like cell lines. A–D, MDA-MB231 cells (basal) were treated with indicated concentrations of 15d-PGJ2 and rosiglitazone for indicated time points. Cell viability and colony-forming assay was measured using crystal violet assay after 48-hour treatment with (A and C) 15d-PGJ2 or (B and D) rosiglitazone, respectively. E, Western blot assay after knockdown with siANXA1 F and G, MDA-MB231 cells (Control or ANXA1 siRNA) were treated with indicated concentrations of 15d-PGJ2 and rosiglitazone for indicated time points. Cell viability was measured using crystal violet assay after 48-hour treatment with (F) 15d-PGJ2 or (G) rosiglitazone, respectively. H, ANXA-V PI staining measured after 48-hour treatment with 15d-PGJ2 or rosiglitazone. Percentage (%) indicates cells that underwent late and early apoptosis (total apoptosis). I and J, Viability of MCF-7 EV and MCF-7 V5 (ANXA1 overexpressing) stable cells were measured using crystal violet assay (I) or ANXV-PI staining after 48 hours of indicated PPARγ drug treatments (J). Data are all average ± SEM; n = 3 experiments; *, P < 0.05.

Figure 2.

High ANXA1–expressing TNBC cell lines display greater sensitivity to PPARγ ligands compared with low ANXA1–expressing luminal-like cell lines. A–D, MDA-MB231 cells (basal) were treated with indicated concentrations of 15d-PGJ2 and rosiglitazone for indicated time points. Cell viability and colony-forming assay was measured using crystal violet assay after 48-hour treatment with (A and C) 15d-PGJ2 or (B and D) rosiglitazone, respectively. E, Western blot assay after knockdown with siANXA1 F and G, MDA-MB231 cells (Control or ANXA1 siRNA) were treated with indicated concentrations of 15d-PGJ2 and rosiglitazone for indicated time points. Cell viability was measured using crystal violet assay after 48-hour treatment with (F) 15d-PGJ2 or (G) rosiglitazone, respectively. H, ANXA-V PI staining measured after 48-hour treatment with 15d-PGJ2 or rosiglitazone. Percentage (%) indicates cells that underwent late and early apoptosis (total apoptosis). I and J, Viability of MCF-7 EV and MCF-7 V5 (ANXA1 overexpressing) stable cells were measured using crystal violet assay (I) or ANXV-PI staining after 48 hours of indicated PPARγ drug treatments (J). Data are all average ± SEM; n = 3 experiments; *, P < 0.05.

Close modal

Activation of PPARγ induces ANXA1 gene activity and expression in TNBC cells

As we observed manipulation of ANXA1 expression influences the effect of PPARγ ligands, we next investigated whether PPARγ ligand activity could influence ANXA1 expression. ANXA1 promoter activity increased 2.5-fold and 1.5-fold after treatment with 15d-PGJ2 and rosiglitazone, respectively (Fig. 3A). A time-dependent increase in ANXA1 mRNA (Fig. 3B and C) and corresponding protein expression (Fig. 3D and E) were observed in MDA-MB-231 cells after treatment with 10 μmol/L of 15d-PGJ2 or 80 μmol/L of rosiglitazone. These in vitro observations were further confirmed in vivo using a MDA-MB-231 xenograft mouse model previously reported by our group. Mice injected with 15d-PGJ2 showed no significant increase in tumor volume throughout the experimental period, while mice treated with vehicle control exhibited an increase in tumor volume with time (23). Importantly, in agreement with our cell line data, tissue collected from these 15d-PGJ2–treated mice exhibited more positive staining for ANXA1 than the PBS-treated mice (Fig. 3F). On the other hand, the luminal MCF-7 cells that are insensitive to PPARγ ligand (Fig. 2A and B), did not show an increase in ANXA1 gene expression (Supplementary Fig. S2C).

Figure 3.

Activation of PPARγ induces expression of ANXA1 gene expression in vitro and in vivo. MDA-MB-231 cells (basal or ANXA1 siRNA silenced) were treated with indicated concentrations of 15d-PGJ2 and rosiglitazone for indicated time points. A, ANXA1 promoter activity was measured in MDA-MB-231 cells after treatment with the PPARγ drugs. Data are average relative light units/Renilla/μg protein ± SEM of three independent experiments. B and C, ANXA1 mRNA was quantified using qPCR in MDA-MB-231 cells after increasing time points of treatment with 10 μmol/L of 15d-PGJ2 (B) or 80 μmol/L rosiglitazone (C). D and E, ANXA1 protein expression was determined by Western blot analysis in MDA-MB-231 cells treated with different doses of 15d-PGJ2 (D) or rosiglitazone (E) for 48 hours. β-Actin was used as a protein-loading control. F, Tumor tissues from mice were subjected to IHC using antibodies against ANXA1. In all experiments, data are mean ± SEM of at least three experiments. P < 0.05 was considered as significant. For Western blots, a representative blot was chosen from at least three biological replicates.

Figure 3.

Activation of PPARγ induces expression of ANXA1 gene expression in vitro and in vivo. MDA-MB-231 cells (basal or ANXA1 siRNA silenced) were treated with indicated concentrations of 15d-PGJ2 and rosiglitazone for indicated time points. A, ANXA1 promoter activity was measured in MDA-MB-231 cells after treatment with the PPARγ drugs. Data are average relative light units/Renilla/μg protein ± SEM of three independent experiments. B and C, ANXA1 mRNA was quantified using qPCR in MDA-MB-231 cells after increasing time points of treatment with 10 μmol/L of 15d-PGJ2 (B) or 80 μmol/L rosiglitazone (C). D and E, ANXA1 protein expression was determined by Western blot analysis in MDA-MB-231 cells treated with different doses of 15d-PGJ2 (D) or rosiglitazone (E) for 48 hours. β-Actin was used as a protein-loading control. F, Tumor tissues from mice were subjected to IHC using antibodies against ANXA1. In all experiments, data are mean ± SEM of at least three experiments. P < 0.05 was considered as significant. For Western blots, a representative blot was chosen from at least three biological replicates.

Close modal

PPARγ ligand–induced ANXA1 gene activity and expression is receptor dependent

Next, we utilized GW9662, an irreversible and selective PPARγ antagonist (25) to ascertain whether the induction of ANXA1 expression was dependent on PPARγ activity. ANXA1 promoter activity induced by PPARγ activation was decreased in MDA-MB-231 cells preincubated with GW9662 (Fig. 4A and B). Preincubation with GW9662 abrogated the induction of ANXA1 mRNA (Fig. 4C and D) and protein expression (Fig. 4E and F) after treatment with PPARγ ligands. Consistent with these observations, preincubation with GW9662 also exerted its inhibitory effect on PPARγ-induced upregulation of ANXA1 gene expression in BT549 cells (Supplementary Fig. S3A–S3C). In addition, inhibition of PPARγ activity by GW9662 effectively blocked PPARγ-induced apoptosis in both MDA-MB-231 and BT549 cells (Supplementary Fig. S3D and S3E).

Figure 4.

Induction of ANXA1 gene expression by PPARγ ligands is receptor-dependent. MDA-MB-231 cells were preincubated with 10 μmol/L of PPARγ-inhibitor GW9662 for 4 hours, prior to treatment with 15d-PGJ2 orrosiglitazone for 18 hours. A and B, ANXA1 promoter activity was measured, using untreated set as a control (dashed line). Data are average relative light units/Renilla/μg protein. C and D, ANXA1 expression of mRNA levels was analyzed in MDA-MB-231 cells after 24-hour treatment with 15d-PGJ2 (C) or rosiglitazone (D). E and F, Alterations of ANXA1 protein levels were detected by Western blot analysis in MDA-MB-231 cells after treatment with 15d-PGJ2 (E) and rosiglitazone (F) for 48 hours. β-Actin was used as a protein-loading control. G and H, After transfection of dominant-negative PPARγ (PPARγ-DN) or empty vector control, cells were treated with 15d-PGJ2 (G) or rosiglitazone (H) for 18 hours and ANXA1 promoter activity was measured. I–L, Cells were treated as above and expression of ANXA1 mRNA and protein were measured. In all experiments, data are mean± SEM of at least three experiments. P < 0.05 was considered as significant. For Western blots, a representative blot was chosen from at least three biological replicates.

Figure 4.

Induction of ANXA1 gene expression by PPARγ ligands is receptor-dependent. MDA-MB-231 cells were preincubated with 10 μmol/L of PPARγ-inhibitor GW9662 for 4 hours, prior to treatment with 15d-PGJ2 orrosiglitazone for 18 hours. A and B, ANXA1 promoter activity was measured, using untreated set as a control (dashed line). Data are average relative light units/Renilla/μg protein. C and D, ANXA1 expression of mRNA levels was analyzed in MDA-MB-231 cells after 24-hour treatment with 15d-PGJ2 (C) or rosiglitazone (D). E and F, Alterations of ANXA1 protein levels were detected by Western blot analysis in MDA-MB-231 cells after treatment with 15d-PGJ2 (E) and rosiglitazone (F) for 48 hours. β-Actin was used as a protein-loading control. G and H, After transfection of dominant-negative PPARγ (PPARγ-DN) or empty vector control, cells were treated with 15d-PGJ2 (G) or rosiglitazone (H) for 18 hours and ANXA1 promoter activity was measured. I–L, Cells were treated as above and expression of ANXA1 mRNA and protein were measured. In all experiments, data are mean± SEM of at least three experiments. P < 0.05 was considered as significant. For Western blots, a representative blot was chosen from at least three biological replicates.

Close modal

Alternatively, the dependence of ANXA1 gene induction on PPARγ activity was examined by using a dominant-negative (DN) mutant of PPARγ (Supplementary Fig. S3F). Overexpression of DN-PPARγ significantly inhibited the induction of peroxisome proliferator response element (PPRE) luciferase activity by PPARγ ligands (Supplementary Fig. S3G). Importantly, DN-PPARγ effectively prevented the drug-induced increase in ANXA1 promoter activity (Fig. 4G and H). In addition, DN-PPARγ also significantly suppressed the PPARγ-induced ANXA1 mRNA (Fig. 4I and J) and protein expression (Fig. 4K and L).

Exposure to PPARγ ligands promotes a “switch” from ANXA1-NEMO-RIP1ubiquitinated interaction (non-death) to ANXA-RIP1deubiquitinated interaction (death)

We next assessed whether activation of PPARγ influences protein–protein interactions within ANXA1–NEMO–RIP1 complex, as we previously reported (20). Treatment of MDA-MB-231 cells with 10 μmol/L of 15d-PGJ2 leads to induction of both ANXA1 and RIP1 protein expression, but reduction in NEMO protein expression (Supplementary Fig. S4A). A 4-fold increase in RIP1 mRNA and a 30% decrease in NEMO mRNA was observed after treatment with 15d-PGJ2 (Supplementary Fig. S4B and S4C). Moreover, these changes in RIP1 and NEMO mRNA were found to be dependent on PPARγ activity, as preincubation with GW9662 attenuated these changes (Supplementary Fig. S4B and S4C). Activation of PPARγ also resulted in a receptor-dependent reduction of NFκB activation (Supplementary Fig. S4D and S4E).

To gain deeper insight into the mechanism for our observed results in Supplementary Fig. S4A–S4E, immunoprecipitation (IP) was performed for each of the proteins in the tricomplex upon PPARγ activation. MDA-MB-231 cells treated with 10 μmol/L of 15d-PGJ2, exhibited an increase in ANXA1–RIP1 interaction (Fig. 5A), together with a decrease in NEMO–RIP1 interaction (Fig. 5A) and ANXA1–NEMO interaction (Fig. 5A). The role of RIP1 on the cell viability after treatment with PPARγ ligands was also investigated. A 30% to 40% decrease in cell viability was observed in control MDA-MB-231 cells transfected with scrambled siRNA after exposure to 15d-PGJ2 or rosiglitazone (Fig. 5C). Importantly, this reduction in cell viability was attenuated upon RIP1 depletion. Moreover, silencing RIP1 expression also blocked the increase in apoptotic cells upon treatments with PPARγ ligands (Supplementary Fig. S5A). These results suggest that the loss of RIP1 expression prevented cell death stimulated by PPARγ activation. Interestingly, the loss of RIP1 not only reduced the baseline expression of ANXA1, but also prevented the induction of ANXA1 protein expression to the same level of expression by PPARγ activation. However, NEMO protein expression was not downregulated upon PPARγ activation in RIP1-depleted cells (Fig. 5D). In addition to changes in ANXA1 and NEMO protein expression levels, depletion of RIP1 did not cause significant changes in ANXA1–NEMO protein interactions in the presence of 10 μmol/L of 15d-PGJ2 (Supplementary Fig. S5B–S5D).

Figure 5.

Increased expression of ANXA1 interacts with RIP1 blocking RIP1–NEMO interaction and mediates deubiquitination of RIP1. A, Immunoprecipitation (IP) analysis was performed to confirm the expression levels of ANXA1 and NEMO interaction with RIP1; ANXA1 and RIP1 interaction with NEMO; and RIP1 and NEMO interaction with ANXA1 after 24-hour treatment with 10 μmol/L 15d-PGJ2. B, Expression of all three proteins after treatment with 10 μmol/L 15d-PGJ2.C, Cell viability of MDA-MB-231 cells measured after 48 hours of PPARγ drug treatment posttransfection with siRIP1 or siCtrl. Data are mean ± SEM of three biological replicates; *, P < 0.05. D, ANXA1, NEMO, and RIP1 protein levels determined after depletion of RIP1, followed by treatment with 15d-PGJ2 or rosiglitazone for 48 hours. E, Global polyubiquitin levels were measured after treatment with 15d-PGJ2 at different time points. F, IP analysis of ubiquitination level of RIP1 protein upon treatment with 15d-PGJ2 for various time points. G, Global protein polyubiquitin expression levels upon depletion of ANXA1, followed by treatment of 15d-PGJ2 for 24 hours. H, Intact and cleaved caspase-8 protein levels upon depletion of ANXA1, followed by treatment with 15d-PGJ2 for 48 hours. I, Caspase-3, Bcl-2, and survivin protein levels upon depletion of ANXA1, followed by treatment with 15d-PGJ2 for 48 hours.

Figure 5.

Increased expression of ANXA1 interacts with RIP1 blocking RIP1–NEMO interaction and mediates deubiquitination of RIP1. A, Immunoprecipitation (IP) analysis was performed to confirm the expression levels of ANXA1 and NEMO interaction with RIP1; ANXA1 and RIP1 interaction with NEMO; and RIP1 and NEMO interaction with ANXA1 after 24-hour treatment with 10 μmol/L 15d-PGJ2. B, Expression of all three proteins after treatment with 10 μmol/L 15d-PGJ2.C, Cell viability of MDA-MB-231 cells measured after 48 hours of PPARγ drug treatment posttransfection with siRIP1 or siCtrl. Data are mean ± SEM of three biological replicates; *, P < 0.05. D, ANXA1, NEMO, and RIP1 protein levels determined after depletion of RIP1, followed by treatment with 15d-PGJ2 or rosiglitazone for 48 hours. E, Global polyubiquitin levels were measured after treatment with 15d-PGJ2 at different time points. F, IP analysis of ubiquitination level of RIP1 protein upon treatment with 15d-PGJ2 for various time points. G, Global protein polyubiquitin expression levels upon depletion of ANXA1, followed by treatment of 15d-PGJ2 for 24 hours. H, Intact and cleaved caspase-8 protein levels upon depletion of ANXA1, followed by treatment with 15d-PGJ2 for 48 hours. I, Caspase-3, Bcl-2, and survivin protein levels upon depletion of ANXA1, followed by treatment with 15d-PGJ2 for 48 hours.

Close modal

The ubiquitination status of RIP1 determines its role as either a prosurvival or a prodeath agent (26). Interestingly, reduction in global polyubiquitination was observed in both MDA-MB-231 and BT549 cells treated with 10 μmol/L of 15d-PGJ2 in a time-dependent manner (Fig. 5E; Supplementary Fig. S5E). More importantly, treatment with the PPARγ ligands also led to a time-dependent reduction in RIP1-specific ubiquitination in TNBC cell lines (Fig. 5F). To investigate the role of ANXA1 in RIP1 ubiquitination, endogenous ANXA1 was silenced in MDA-MB-231, followed by treatment with 10 μmol/L of 15d-PGJ2. Depletion of ANXA1 prevented a decrease in global protein polyubiquitination levels in MDA-MB-231 cells (Fig. 5G).

RIP1 needs to be in a deubiquitinated state to enable its interaction with caspase-8 to promote apoptosis (27). A moderate decrease in procaspase-8, together with a marked increase in caspase-8 cleavage after treatment with 15d-PGJ2 was observed in MDA-MB-231 cells. However, these observations were not seen in ANXA1-depleted cells (Fig. 5H). Moreover, knocking down expression of ANXA1 in MDA-MB-231 cells also blocked the ligand-induced increase in caspase-3 cleavage as well as inhibiting the downregulation of prosurvival proteins such as Bcl-2 and survivin (Fig. 5I).

Activation of PPARγ induces proteosomal degradation of RIP1-specific E3 ligase, cIAP1

cIAP1 has been defined as the direct E3 ligase that promotes RIP1 ubiquitination in cancer cells (28, 29). On the basis of our findings in Fig. 5G, we were motivated to explore whether treatment with PPARγ ligands had an effect on expression of cIAP1. Exposure of MDA-MB-231 cells to 10 μmol/L of 15d-PGJ2 not only induced expression of ANXA1 and RIP1 but also downregulated cIAP1 protein expression in a time-dependent manner while preincubation with GW9662 attenuated these effects (Fig. 6A). As activation of PPARγ is known to induce proteasomal degradation of various proteins (30, 31), we proceeded to demonstrate that preincubation of MDA-MB-231 cells with MG132 a proteasomal inhibitor, followed by exposure to 10 μmol/L of 15d-PGJ2 for 24 hours, prevented the degradation of cIAP1 protein (Fig. 6B). On the other hand, we observed 15d-PGJ2 reduced the degradation of cIAP1 with a much delayed and weaker when the endogenous ANXA1 was silenced (Fig. 6C). Importantly, we also observed a marked increase in ubiquitinated cIAP in MDA-MB-231 cells after exposure to 15d-PGJ2, which was not observed when ANXA1 was silenced (Fig. 6D). Exposure to 15d-PGJ2 also reduced NEMO-specific ubiquitin levels, which was also not observed when ANXA1 was silenced (Supplementary Fig. S5F). Indeed, we observed an interaction between RIP1 and cIAP1, which was followed by a gradual time-dependent decrease in cIAP1–RIP1 interaction upon treatment with 10 μmol/L of 15d-PGJ2. However, when ANXA1 was depleted, the time-dependent dissociation of cIAP1 from RIP1 after exposure to 15d-PGJ2 was not observed (Fig. 6E). Importantly, the RIP1-specific ubiquitin level remained unchanged with 15d-PGJ2 treatment in cells silenced with ANXA1 (Fig. 6F). Finally, PPARγ ligand treatment induced an increase in RIP1-procaspase-8 association in a time-dependent manner (Fig. 6G).

Figure 6.

Activation of PPARγ leads to proteosomal degradation of cIAP1 dissociating itself from RIP1 complex to initiating a caspase-8–dependent cell death pathway. A and B, MDA-MB-231 cells were preincubated with/without 10 μmol/L of GW9662 (A) for 18 hours, or 1 μmol/L of MG132 (B) for 2 hours, followed by treatment with 10 μmol/L of 15d-PGJ2 for various time points up to 48 hours. Protein levels of RIP1, ANXA1, and cIAP1 were measured. C, Protein level of cIAP1 was measured after depletion of ANXA1 followed by treatment with 15d-PGJ2 for various time points up to 24 hours. D, Ubiquitinated cIAP1 protein levels were assessed by IP analysis before and after depletion of ANXA1, followed by treatment with 15d-PGJ2 for 24 hours. E, ANXA1 and cIAP1 proteins interaction with RIP1 was assessed by IP analysis after depletion of ANXA1, followed by treatment with 15d-PGJ2 at different time points up to 24 hours. F, Expression level of RIP1 protein was analyzed after depletion of ANXA1, followed by treatment with 15d-PGJ2. G, Caspase-8 interacting with RIP1 protein expression levels was measured after treatment with 15d-PGJ2 of different time points up to 30 hours.

Figure 6.

Activation of PPARγ leads to proteosomal degradation of cIAP1 dissociating itself from RIP1 complex to initiating a caspase-8–dependent cell death pathway. A and B, MDA-MB-231 cells were preincubated with/without 10 μmol/L of GW9662 (A) for 18 hours, or 1 μmol/L of MG132 (B) for 2 hours, followed by treatment with 10 μmol/L of 15d-PGJ2 for various time points up to 48 hours. Protein levels of RIP1, ANXA1, and cIAP1 were measured. C, Protein level of cIAP1 was measured after depletion of ANXA1 followed by treatment with 15d-PGJ2 for various time points up to 24 hours. D, Ubiquitinated cIAP1 protein levels were assessed by IP analysis before and after depletion of ANXA1, followed by treatment with 15d-PGJ2 for 24 hours. E, ANXA1 and cIAP1 proteins interaction with RIP1 was assessed by IP analysis after depletion of ANXA1, followed by treatment with 15d-PGJ2 at different time points up to 24 hours. F, Expression level of RIP1 protein was analyzed after depletion of ANXA1, followed by treatment with 15d-PGJ2. G, Caspase-8 interacting with RIP1 protein expression levels was measured after treatment with 15d-PGJ2 of different time points up to 30 hours.

Close modal

To date, several in vitro studies have shown that PPARγ activation causes cell death and induces apoptosis in various cancer cell lines, including breast cancer. Nevertheless, applications of PPARγ activators as anticancer options in clinical trials have not yet shown promising results. Particularly, in the context of breast cancer, inconsistent outcomes of PPARγ activators have been reported across different subgroups of breast cancer tissues or cells (32–34).In parallel, the antiproliferative role of ANXA1 has been reported (35, 36); however, there has not been a suggested a role for ANXA1 as a biomarker in any cancer types (17, 37). Our study aimed to address the question of whether the presence of ANXA1 could influence the effectiveness of PPARγ activators in breast cancer.

Preliminary analysis across 3,992 breast cancer tumors showed ANXA1 mRNA expression was higher in basal and claudin-low breast cancer (grouped as TNBC) subtypes correlating to poor patient survival. In addition, higher level of ANXA1expression was positively correlated with mesenchymal phenotype. In agreement with our mRNA analysis, IHC staining of various breast cancer tissues showed that TNBC subtype exhibited high level of ANXA1 expression that was correlated with very high histologic grading scores. These findings strongly support the observation of in vitro screening of breast cancer cells, that the highly invasive TNBC cells express more baseline ANXA1 than the poorly invasive ER+ breast cancer cells. Baseline PPARγ expression and therefore activity also showed a similar trend across the panel. To our understanding, this is the first report on the positive correlation between ANXA1 and PPARγ in breast cancer.

Despite the continuous reporting of ANXA1 expression in many human cancers, the results have been conflicting across different studies and in different types of cancers; thus, the exact role of ANXA1 in cancer development and progression remains undefined. Our IHC staining for ANXA1 expression in tumors of various breast cancer subtypes shows that ANXA1 expression is not evenly distributed in all breast tumor tissues. High ANXA1 expression is detected only in TNBC tumors and histologic grade III tumors. However, most of the earlier studies on ANXA1 expression in breast cancer did not include tumors of all the different molecular or histologic subtypes or histologic grade of breast cancer (16, 17, 38). This may be the reason for the inconsistency in reporting ANXA1 expression in breast cancers conducted in the earlier studies.

IHC staining results demonstrated that TNBC tumors, which express a high level of ANXA1, also demonstrate a higher level of Ki-67 expression. Additional supporting evidence on high ANXA1 expression as an indicator of cancer metastasis and invasion is found in more recent literature (37, 39, 40). Particularly, de Graauw and colleagues reported that ANXA1 expression is functionally related to the progression of breast cancer, and is associated with the highly invasive BLBC subtype (41). Linking back to our in vitro result on the basal gene expression of ANXA1 among breast cancer cell lines, these observations suggest that the ANXA1 expression level is potentially associated with the degree of malignancy of breast cancer tumors.

Importantly, depletion of endogenous ANXA1 (or rather not allowing for PPARγ-induced increased expression) conferred resistance in two TNBC cell lines with exposure to PPARγ agonists. On the other hand, stable clones of MCF-7 cells with ectopic overexpression of ANXA1 showed enhanced responsiveness to PPARγ drugs as compared with the unresponsive parental empty vector control clones. These results proved our hypothesis that high ANXA1 expression in breast cancer cells acts as an indicator of PPARγ agonist response. Interestingly, activation of PPARγ failed to induce baseline ANXA1 expression in these PPARγ-unresponsive cells. It has been reported that ERα could bind to PPRE elements and represses PPARγ transactivation in MCF-7 cells (42). Our group has previously reported ANXA1 as a putative ERα target (43). It is therefore probable that an active ERα may have negated PPARγ activity at the promoter of ANXA1 in MCF-7 cells.

PPARγ activation has been reported to be involved in suppression of tumor proliferation and growth in various types of cancer tissues (13, 23, 32, 44). The functional role of PPARγ was firstly elucidated by us when MDA-MB-231 cells were injected subcutaneously into mice mammary fat pad, followed by treatment with 15d-PGJ2. Mice exposed to 15d-PGJ2 showed inhibited growth in tumor volume as compared with mice treated with control vehicle (23). More interestingly, ANXA1 expression in tumor tissues obtained from 15d-PGJ2–treated mice was significantly higher than that obtained from the untreated animals. Using our published PPRE search engine (45), we located a putative PPRE in the promoter of ANXA1. To verify our hypothesis that perhaps ANXA1 is a novel target of PPARγ activity, we performed in vitro evaluation on changes in ANXA1 gene expression levels upon PPARγ ligand exposure in two TNBC models. We observed induction of ANXA1 expression by PPARγ ligands, from promoter activity to protein expression, suggesting that ANXA1 expression is indeed regulated by PPARγ.

The irreversible PPARγ inhibitor, GW9662, and DN-PPARγ, which has no transcriptional activity, was able to block the induction of ANXA1 expression and attenuated the apoptotic effects of PPARγ ligands. These findings further confirm that the upregulation of ANXA1 is mediated by the activity of ligand-activated PPARγ, and the apoptotic effects observed upon incubation with PPARγ ligands are also due to the intrinsic activity of the receptor.

RIP1 exists as a double-edged sword in controlling the cellular fate. Together with NEMO, they cooperate to promote cell survival in response to excessive DNA damage (46). Our earlier study reported that basal ANXA1 in MDA-MB-231 cells interacts with NEMO and RIP1, and contributes to the constitutively active NFκB in these cells (20). To take these findings further, we propose a new mechanism by which activated PPARγ promotes apoptosis in breast cancer cells in ANXA1-dependent manner. MDA-MB-231 cells treated with 15d-PGJ2 exhibit increased ANXA1 and RIP1 expression after treatment, while NEMO expression was downregulated. We also found increases in levels of cleaved caspase-8 and caspase-3 after treatment, suggesting the drug treatment leads to apoptosis via activation of caspase-8. These observations indicate that 15d-PGJ2 treatments have shifted the balance of RIP1 activity toward proapoptosis. Interestingly, 15d-PGJ2 exposure for 24 hours led to stronger interaction between RIP1 and ANXA1 but important dissociation of NEMO from both RIP1 and ANXA1.

A previous study suggested the proapoptotic activity of RIP1 requires its dissociation from NEMO (47). This is in line with our expectation of decreased RIP1–NEMO interactions upon drug treatment, due to the fact that PPARγ drugs are able to induce apoptosis. One possible explanation to the inhibitory effect of NEMO on RIP1-dependent cell death is that ubiquitination of RIP1 at lysine 377 recruits ubiquitin-binding proteins, such as NEMO, and this binding sterically hinders RIP1 from associating with caspase-8 and other downstream death mediators (29). On the other hand, ubiquitination of RIP1 may also prevent RIP1 from oligomerization. The oligomerization of the death domain of RIP1 has been implicated to contribute to its cell death–triggering ability.

The increased RIP1–ANXA1 association attracted our attention and led us to investigate the role of ANXA1 on RIP1-mediated cell death. Depletion of ANXA1 reduced apoptosis upon PPARγ ligand treatment and leads to a reduction in caspase-8 and caspase-3 activation and inhibition in the downregulation of prosurvival molecules such as Bcl-2 and survivin. More importantly, the loss of ANXA1 caused a reduction in RIP1 protein and mRNA expression in MDA-MB-231 cells. This indicates the importance of ANXA1 in maintaining both mRNA and protein expression of RIP1 and its role in the activation of caspase-8 and the initiation of apoptosis. Furthermore, activation of PPARγ failed in suppressing NEMO transcription when ANXA1 was absent in MDA-MB-231 cells. This provides possible alternative mechanism for the higher resistance to PPARγ ligands found in TNBC cells with low ANXA1 expression.

A time-kinetic study with treatment of 15d-PGJ2 showed deubiquitination of RIP1 in MDA-MB-231 cells. Without hindrance of the polyubiquitin chain, RIP1 is free to interact with the pro-caspase-8 and causes the subsequent initiation of apoptotic events. This provides a possible mechanism where activation of PPARγ induces apoptosis in MDA-MB-231 cells via the action of deubiquitinated RIP1. In agreement with our findings, activation of caspase-8 is known to be dependent on deubiquitination of RIP1 (48). The essential role of RIP1 in PPARγ-induced apoptosis was granted with the observation that depleting RIP1 was able to block the apoptosis rate in treated MDA-MB-231 cells. Furthermore, the absence of RIP1 restrains the drug-induced dissociation of NEMO from ANXA1. Interestingly, the expression of ANXA1 exerts influence on the ubiquitination status of RIP1. We observed that depletion of ANXA1 has an inhibitory effect on the reduction of total protein ubiquitin expression after the treatment, while the RIP1-specific deubiquitination is also hindered in MDA-MB-231 cells with depleted ANXA1. Linking back to the earlier observations that knocking down ANXA1 promotes resistance to PPARγ drugs in TNBC cells, this confirms that ANXA1 is an important intermediate molecule that ensures the prodeath activity of ligand-activated PPARγ.

We have demonstrated a highly possible mechanism by which PPARγ activation lead to caspase-8–initiated apoptosis in TNBC cells, through a process of ANXA1-dependent deubiquitination of RIP1. Several earlier studies reported that cIAP1 functioning as an E3 ligase is involved in catalyzing the polyubiquitination of RIP1, while loss of cIAP1/2 blocks ubiquitination of RIP1 in response to TNF (49, 50). More recently, cIAP1 has been identified as the direct E3 ligase that conjugates ubiquitin chains to RIP1 (28). Treatment with 15d-PGJ2 showed a time-dependent decrease in cIAP1 protein expression level and preincubation with GW9662 was able to block the drug-induced reduction in cIAP1 gene expression. It suggests that cIAP1 expression could be PPARγ regulated. However, the downregulation of cIAP1 in MDA-MB-231 cells may not be due to transcriptional regulation by PPARγ. The activity of PPARγ to cause degradation of cIAP1 was uncovered; we also demonstrated that inhibiting proteasome activity by MG132 was able to attenuate PPARγ-induced degradation of cIAP1 protein expression. Interestingly, silencing of ANXA1 in MDA-MB-231 cells slows down this degradation process on cIAP1 protein upon exposure to the PPARγ ligands. Moreover, the PPARγ ligands caused increased autoubiquitination of cIAP1 and turn its degradation in ANXA1-dependent manner. These findings suggest that drug-induced ANXA1 expression is crucial for the promotion of proteasome-dependent degradation of cIAP1 by PPARγ ligands.

In summary, our study proposes a novel schematic model to describe the mechanism of how PPARγ activation leads to apoptosis in TNBC through the actions of ANXA1 and RIP1 (Fig. 7). Under basal conditions, NEMO binds to polyubiquitinated RIP1 and ANXA1, inducing constitutive activation of NFκB to promote cancer cell survival (20). Agonists that activate PPARγ induce expression of RIP1 and ANXA1. Meanwhile, deubiquitinated RIP1 dissociates from NEMO, while it remains associated with ANXA1. RIP1 then binds procaspase-8 and leads to initiation of caspase-8–dependent apoptosis. ANXA1 expression required for the stabilization of RIP1 protein to ensure function as a mediator of the proapoptotic action of PPARγ ligands. Taken together, we show that drug-induced ANXA1 expression mediates actions of PPARγ ligands in promoting cell apoptosis and that ANXA1 could serve as a clinically useful surrogate biomarker for such therapies in the future trials.

Figure 7.

Schematic model summarizing the novel role of ANXA1 in mediating PPARγ-induced cell death in TNBC. Under basal conditions, NEMO binds to polyubiquitinated RIP1 and ANXA1 inducing constitutive activation of NFκB to promote cancer cell survival. Activation of PPARγ leads to increased expression of ANXA1 and RIP1 with decreased expression of NEMO. Increased interaction between ANXA1 as well as RIP1 (with decreased interaction of NEMO) dissociates E3 ligase, cIAP1 from RIP1 leading to its deubiqutination and recruitment of caspase-8 to mediate downstream cell death pathway.

Figure 7.

Schematic model summarizing the novel role of ANXA1 in mediating PPARγ-induced cell death in TNBC. Under basal conditions, NEMO binds to polyubiquitinated RIP1 and ANXA1 inducing constitutive activation of NFκB to promote cancer cell survival. Activation of PPARγ leads to increased expression of ANXA1 and RIP1 with decreased expression of NEMO. Increased interaction between ANXA1 as well as RIP1 (with decreased interaction of NEMO) dissociates E3 ligase, cIAP1 from RIP1 leading to its deubiqutination and recruitment of caspase-8 to mediate downstream cell death pathway.

Close modal

No potential conflicts of interest were disclosed.

Conception and design: L. Chen, P.F. Koh, L. Lim, A.P. Kumar

Development of methodology: L. Chen, Y. Yuan, S. Arora, P.F. Koh, H.-M. Shen, G. Sethi, L. Lim

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): L. Chen, Y. Yuan, P.F. Koh, E. Yousef, M.K. Shanmugam, S.-W. Shin, J.-I. Park, L. Gaboury, A.P. Kumar

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): L. Chen, S. Kar, P.F. Koh, E. Yousef, T.Z. Tan, H. Yang, L. Gaboury, P.E. Lobie, G. Sethi, L. Lim, A.P. Kumar

Writing, review, and/or revision of the manuscript: L. Chen, Y. Yuan, S. Kar, J. Kim, P.F. Koh, F. Arfuso, J.-I. Park, L. Gaboury, A.P. Kumar

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): L. Chen, M. Kanchi, E. Yousef, B.C. Goh, P.E. Lobie, A.P. Kumar

Study supervision: H.-M. Shen, P.E. Lobie, L. Lim, A.P. Kumar

Other (fine tuning and revision of the manuscript): R. Samy

This work was supported by grants from the National Medical Research Council of Singapore and by the NCIS Yong Siew Yoon Research Grant through donations from the Yong Loo Lin Trust (to A.P. Kumar). A.P. Kumar and P.E. Lobie were supported by grants from the Singapore NMRC Clinician Scientist IRG and the National Research Foundation Singapore and the Singapore Ministry of Education under its Research Centers of Excellence Initiative to Cancer Science Institute of Singapore, National University of Singapore. This work was also supported by the NUHS Basic Seed Funding and Ministry of Education Tier 1 grant (to G. Sethi). J.I. Park was supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government (MEST). L.H.K. Lim was supported by a grant from Singapore Ministry of Education Tier 2. This work is supported in part by National Medical Research Council Singapore (NMRC) grants (to H.M. Shen).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
DeSantis
C
,
Siegel
R
,
Bandi
P
,
Jemal
A
. 
Breast cancer statistics, 2011
.
CA Cancer J Clin
2011
;
61
:
409
18
.
2.
Ferlay
J
,
Parkin
DM
,
Steliarova-Foucher
E
. 
Estimates of cancer incidence and mortality in Europe in 2008
.
Eur J Cancer
2010
;
46
:
765
81
.
3.
Shin
H-R
,
Boniol
M
,
Joubert
C
,
Hery
C
,
Haukka
J
,
Autier
P
, et al
Secular trends in breast cancer mortality in five East Asian populations: Hong Kong, Japan, Korea, Singapore and Taiwan
.
Cancer Sci
2010
;
101
:
1241
6
.
4.
Jara-Lazaro
AR
,
Thilagaratnam
S
,
Tan
PH
. 
Breast cancer in Singapore: some perspectives
.
Breast Cancer
2010
;
17
:
23
8
.
5.
Bertucci
F
,
Finetti
P
,
Birnbaum
D
. 
Basal breast cancer: a complex and deadly molecular subtype
.
Curr Mol Med
2012
;
12
:
96
110
.
6.
Dent
R
,
Trudeau
M
,
Pritchard
KI
,
Hanna
WM
,
Kahn
HK
,
Sawka
CA
, et al
Triple-negative breast cancer: clinical features and patterns of recurrence
.
Clin Cancer Res
2007
;
13
:
4429
34
.
7.
Kassam
F
,
Enright
K
,
Dent
R
,
Dranitsaris
G
,
Myers
J
,
Flynn
C
, et al
Survival outcomes for patients with metastatic triple-negative breast cancer: implications for clinical practice and trial design
.
Clin Breast Cancer
2009
;
9
:
29
33
.
8.
Lehmann
BD
,
Bauer
JA
,
Chen
X
,
Sanders
ME
,
Chakravarthy
AB
,
Shyr
Y
, et al
Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies
.
J Clin Invest
2011
;
121
:
2750
67
.
9.
Peddi
PF
,
Ellis
MJ
,
Ma
C
. 
Molecular basis of triple negative breast cancer and implications for therapy
.
Int J Breast Cancer
2012
;
2012
:
217185
.
10.
Kliewer
SA
,
Lenhard
JM
,
Willson
TM
,
Patel
I
,
Morris
DC
,
Lehmann
JM
. 
A prostaglandin J2 metabolite binds peroxisome proliferator-activated receptor gamma and promotes adipocyte differentiation
.
Cell
1995
;
83
:
813
9
.
11.
Bonofiglio
D
,
Aquila
S
,
Catalano
S
,
Gabriele
S
,
Belmonte
M
,
Middea
E
, et al
Peroxisome proliferator-activated receptor-gamma activates p53 gene promoter binding to the nuclear factor-kappaB sequence in human MCF7 breast cancer cells
.
Mol Endocrinol
2006
;
20
:
3083
92
.
12.
Elstner
E
,
Müller
C
,
Koshizuka
K
,
Williamson
EA
,
Park
D
,
Asou
H
, et al
Ligands for peroxisome proliferator-activated receptorgamma and retinoic acid receptor inhibit growth and induce apoptosis of human breast cancer cells in vitro and in BNX mice
.
Proc Natl Acad Sci U S A
1998
;
95
:
8806
11
.
13.
Kumar
AP
,
Quake
AL
,
Chang
MK
,
Zhou
T
,
Lim
KS
,
Singh
R
, et al
Repression of NHE1 expression by PPARgamma activation is a potential new approach for specific inhibition of the growth of tumor cells in vitro and in vivo
.
Cancer Res
2009
;
69
:
8636
44
.
14.
Lim
LH
,
Pervaiz
S
. 
Annexin 1: the new face of an old molecule
.
FASEB J
2007
;
21
:
968
75
.
15.
Perretti
M
,
D'Acquisto
F
. 
Annexin A1 and glucocorticoids as effectors of the resolution of inflammation
.
Nat Rev Immunol
2009
;
9
:
62
70
.
16.
Shen
D
,
Nooraie
F
,
Elshimali
Y
,
Lonsberry
V
,
He
J
,
Bose
S
, et al
Decreased expression of annexin A1 is correlated with breast cancer development and progression as determined by a tissue microarray analysis
.
Hum Pathol
2006
;
37
:
1583
91
.
17.
Wang
LP
,
Bi
J
,
Yao
C
,
Xu
XD
,
Li
XX
,
Wang
SM
, et al
Annexin A1 expression and its prognostic significance in human breast cancer
.
Neoplasma
2010
;
57
:
253
9
.
18.
Nakshatri
H
,
Bhat-Nakshatri
P
,
Martin
DA
,
Goulet
RJ
,
Sledge
GW
. 
Constitutive activation of NF-kappaB during progression of breast cancer to hormone-independent growth
.
Mol Cell Biol
1997
;
17
:
3629
39
.
19.
Pacifico
F
,
Leonardi
A
. 
NF-kappaB in solid tumors
.
Biochem Pharmacol
2006
;
72
:
1142
52
.
20.
Bist
P
,
Leow
SC
,
Phua
QH
,
Shu
S
,
Zhuang
Q
,
Loh
WT
, et al
Annexin-1 interacts with NEMO and RIP1 to constitutively activate IKK complex and NF-κB: implication in breast cancer metastasis
.
Oncogene
2011
;
30
:
3174
85
.
21.
Loo
SY
,
Hirpara
JL
,
Pandey
V
,
Tan
TZ
,
Yap
CT
,
Lobie
PE
, et al
Manganese superoxide dismutase expression regulates the switch between an epithelial and a mesenchymal-like phenotype in breast carcinoma
.
Antioxid Redox Signal
2016
;
25
:
283
99
.
22.
Shin
EM
,
Hay
HS
,
Lee
MH
,
Goh
JN
,
Tan
TZ
,
Sen
YP
, et al
DEAD-box helicase DP103 defines metastatic potential of human breast cancers
.
J Clin Invest
2014
;
124
:
3807
24
.
23.
Kumar
AP
,
Loo
SY
,
Shin
SW
,
Tan
TZ
,
Eng
CB
,
Singh
R
, et al
Manganese superoxide dismutase is a promising target for enhancing chemosensitivity of basal-like breast carcinoma
.
Antioxid Redox Signal
2014
;
20
:
2326
46
.
24.
Tan
TZ
,
Miow
QH
,
Miki
Y
,
Noda
T
,
Mori
S
,
Huang
RY
, et al
Epithelial-mesenchymal transition spectrum quantification and its efficacy in deciphering survival and drug responses of cancer patients
.
EMBO Mol Med
2014
;
6
:
1279
93
.
25.
Leesnitzer
LM
,
Parks
DJ
,
Bledsoe
RK
,
Cobb
JE
,
Collins
JL
,
Consler
TG
, et al
Functional consequences of cysteine modification in the ligand binding sites of peroxisome proliferator activated receptors by GW9662
.
Biochemistry
2002
;
41
:
6640
50
.
26.
O'Donnell
MA
,
Legarda-Addison
D
,
Skountzos
P
,
Yeh
WC
,
Ting
AT
. 
Ubiquitination of RIP1 regulates an NF-kappaB-independent cell-death switch in TNF signaling
.
Curr Biol
2007
;
17
:
418
24
.
27.
O'Donnell
MA
,
Hase
H
,
Legarda
D
,
Ting
AT
. 
NEMO Inhibits Programmed Necrosis in an NFκB-Independent Manner by Restraining RIP1
.
PLoS One
2012
;
7
:
e41238
.
28.
Bertrand
MJM
,
Lippens
S
,
Staes
A
,
Gilbert
B
,
Roelandt
R
,
De Medts
J
, et al
cIAP1/2 are direct E3 ligases conjugating diverse types of ubiquitin chains to receptor interacting proteins kinases 1 to 4 (RIP1-4)
.
PLoS One
2011
;
6
:
e22356
.
29.
Bertrand
MJM
,
Milutinovic
S
,
Dickson
KM
,
Ho
WC
,
Boudreault
A
,
Durkin
J
, et al
cIAP1 and cIAP2 facilitate cancer cell survival by functioning as E3 ligases that promote RIP1 ubiquitination
.
Mol Cell
2008
;
30
:
689
700
.
30.
Qin
C
,
Burghardt
R
,
Smith
R
,
Wormke
M
,
Stewart
J
,
Safe
S
. 
Peroxisome proliferator-activated receptor gamma agonists induce proteasome-dependent degradation of cyclin D1 and estrogen receptor alpha in MCF-7 breast cancer cells
.
Cancer Res
2003
;
63
:
958
64
.
31.
Liu
J
,
Wang
H
,
Zuo
Y
,
Farmer
SR
. 
Functional interaction between peroxisome proliferator-activated receptor gamma and beta-catenin
.
Mol Cell Biol
2006
;
26
:
5827
37
.
32.
Saez
E
,
Rosenfeld
J
,
Livolsi
A
,
Olson
P
,
Lombardo
E
,
Nelson
M
, et al
PPAR gamma signaling exacerbates mammary gland tumor development
.
Genes Dev
2004
;
18
:
528
40
.
33.
Burstein
HJ
,
Demetri
GD
,
Mueller
E
,
Sarraf
P
,
Spiegelman
BM
,
Winer
EP
. 
Use of the peroxisome proliferator-activated receptor (PPAR) gamma ligand troglitazone as treatment for refractory breast cancer: a phase II study
.
Breast Cancer Res Treat
2003
;
79
:
391
7
.
34.
Yin
F
,
Wakino
S
,
Liu
Z
,
Kim
S
,
Hsueh
WA
,
Collins
AR
, et al
Troglitazone inhibits growth of MCF-7 breast carcinoma cells by targeting G1 cell cycle regulators
.
Biochem Biophys Res Commun
2001
;
286
:
916
22
.
35.
Silistino-Souza
R
,
Rodrigues-Lisoni
FC
,
Cury
PM
,
Maniglia
JV
,
Raposo
LS
,
Tajara
EH
, et al
Annexin 1: differential expression in tumor and mast cells in human larynx cancer
.
Int J Cancer
2007
;
120
:
2582
9
.
36.
Alldridge
LC
,
Bryant
CE
. 
Annexin 1 regulates cell proliferation by disruption of cell morphology and inhibition of cyclin D1 expression through sustained activation of the ERK1/2 MAPK signal
.
Exp Cell Res
2003
;
290
:
93
107
.
37.
Sato
Y
,
Kumamoto
K
,
Saito
K
,
Okayama
H
,
Hayase
S
,
Kofunato
Y
, et al
Up-regulated Annexin A1 expression in gastrointestinal cancer is associated with cancer invasion and lymph node metastasis
.
Exp Ther Med
2011
;
2
:
239
43
.
38.
Cao
Y
,
Li
Y
,
Edelweiss
M
,
Arun
B
,
Rosen
D
,
Resetkova
E
, et al
Loss of annexin A1 expression in breast cancer progression
.
Appl Immunohistochem Mol Morphol
2008
;
16
:
530
4
.
39.
Kang
H
,
Ko
J
,
Jang
S-W
. 
The role of annexin A1 in expression of matrix metalloproteinase-9 and invasion of breast cancer cells
.
Biochem Biophys Res Commun
2012
;
423
:
188
94
.
40.
Cheng
T-Y
,
Wu
M-S
,
Lin
J-T
,
Lin
M-T
,
Shun
C-T
,
Huang
H-Y
, et al
Annexin A1 is associated with gastric cancer survival and promotes gastric cancer cell invasiveness through the formyl peptide receptor/extracellular signal-regulated kinase/integrin beta-1-binding protein 1 pathway
.
Cancer
2012
;
118
:
5757
67
.
41.
de Graauw
M
,
van Miltenburg
MH
,
Schmidt
MK
,
Pont
C
,
Lalai
R
,
Kartopawiro
J
, et al
Annexin A1 regulates TGF-beta signaling and promotes metastasis formation of basal-like breast cancer cells
.
Proc Natl Acad Sci U S A
2010
;
107
:
6340
5
.
42.
Bonofiglio
D
,
Gabriele
S
,
Aquila
S
,
Catalano
S
,
Gentile
M
,
Middea
E
, et al
Estrogen receptor alpha binds to peroxisome proliferator-activated receptor response element and negatively interferes with peroxisome proliferator-activated receptor gamma signaling in breast cancer cells
.
Clin Cancer Res
2005
;
11
:
6139
47
.
43.
Ang
EZ-F
,
Nguyen
HT
,
Sim
H-L
,
Putti
TC
,
Lim
LHK
. 
Annexin-1 regulates growth arrest induced by high levels of estrogen in MCF-7 breast cancer cells
.
Mol Cancer Res
2009
;
7
:
266
74
.
44.
Li
S
,
Zhou
Q
,
He
H
,
Zhao
Y
,
Liu
Z
. 
Peroxisome proliferator-activated receptor gamma agonists induce cell cycle arrest through transcriptional regulation of Kruppel-like factor 4 (KLF4)
.
J Biol Chem
2013
;
288
:
4076
84
.
45.
Venkatachalam
G
,
Kumar
AP
,
Yue
LS
,
Pervaiz
S
,
Clement
MV
,
Sakharkar
MK
. 
Computational identification and experimental validation of PPRE motifs in NHE1 and MnSOD genes of human
.
BMC Genomics
2009
;
10
Suppl 3
:
S5
.
46.
Biton
S
,
Ashkenazi
A
. 
NEMO and RIP1 control cell fate in response to extensive DNA damage via TNF-α feedforward signaling
.
Cell
2011
;
145
:
92
103
.
47.
Legarda-Addison
D
,
Hase
H
,
O'Donnell
MA
,
Ting
AT
. 
NEMO/IKKgamma regulates an early NF-kappaB-independent cell-death checkpoint during TNF signaling
.
Cell Death Differ
2009
;
16
:
1279
88
.
48.
Wang
L
,
Du
F
,
Wang
X
. 
TNF-alpha induces two distinct caspase-8 activation pathways
.
Cell
2008
;
133
:
693
703
.
49.
Dynek
JN
,
Goncharov
T
,
Dueber
EC
,
Fedorova
AV
,
Izrael-Tomasevic
A
,
Phu
L
, et al
c-IAP1 and UbcH5 promote K11-linked polyubiquitination of RIP1 in TNF signalling
.
EMBO J
2010
;
29
:
4198
209
.
50.
Varfolomeev
E
,
Goncharov
T
,
Fedorova
AV
,
Dynek
JN
,
Zobel
K
,
Deshayes
K
, et al
c-IAP1 and c-IAP2 are critical mediators of tumor necrosis factor alpha (TNFalpha)-induced NF-kappaB activation
.
J Biol Chem
2008
;
283
:
24295
9
.