Proteomic analysis of talazoparib resistance in triple‐negative breast cancer cells

Gamze Guney Eskiler1

Abstract

Talazoparib (TAL) has been effectively used for the treatment of gBRCA1/2‐mutated HER2‐negative metastatic breast cancer. However, acquired resistance to TAL remains a major challenge that impedes the clinical success of TAL treatment. Therefore, elucidation of proteins and pathways that contribute to or are affected by the TAL resistance is urgently needed to improve the treatment response and provide novel treatment strategies for advanced metastatic breast cancers. Herein, we aimed to investigate the altered protein signatures in TAL‐resistant triple‐negative breast cancer (TNBC) cells by comparing with the TNBC parental cell line via proteomic analysis. After validation of TAL‐resistance by WST‐1 and Annexin V analysis, two‐dimensional gel electrophoresis (2DE)‐based proteomic analysis coupled to matrix‐assisted laser desorption/ionization (MALDI)–time of flight (TOF) mass spectrometry was performed to identify differentially regulated proteins. The findings revealed the identities of 10 differentially regulated proteins in TAL‐resistant TNBC cells whose bioinformatic analysis predicted changes in EGF/FGF signaling pathways as well as in the AMPK signaling pathway. In addition, phosphorylation/dephosphorylation dynamics were predicted to be altered in TAL‐resistant cells. The proteins identified in this study might be the targets to overcome TAL resistance for the treatment of TNBC.

KEYWORDS
drug resistance, poly(ADP‐ribose) polymerase (PARP) inhibitors, proteomic, talazoparib, triplenegative breast cancer

1 | INTRODUCTION

Poly(ADP‐ribose) polymerase inhibitors (PARPi) have been receiving increasing attention for the treatment of breast and/or ovarian cancers with a germline BRCA1/2 mutation. The basis of the mechanism of action of PARPi is based on the concept of synthetic lethality. The base excision repair (BER) mechanism is especially crucial for BRCA1/ 2‐mutant cancer cells due to deficiency in homologous recombination (HR) repair. Inhibition of PARP by PARPi can result in a stalled replication fork in BRCA‐defective tumors and thus interferes with the cell’s ability to replicate. PARPi are the first clinically approved drugs designed to exploit synthetic lethality.[1‐5] Olaparib and talazoparib (TAL), commercially available PARPi, have been approved by the Food and Drug Administration for the treatment of gBRCA1/2‐mutated HER2‐negative advanced or metastatic breast cancers.[6‐8] However, pre‐existing and/or acquired resistance limits the success of PARPi. Therefore, elucidation of the underlying molecular mechanisms associated with PARPi resistance is urgently needed for the improved treatment response and prognosis in breast cancer.
Several mechanisms, including restoration of HR repair, mutations in PARP1, mitigation of replication stress, protection of replication fork, and the activity and abundance of PAR chains and the activation of drug efflux pumps play roles in PARPi resistance as demonstrated by preclinical and clinical studies.[9‐13] In our previous studies, we found that increased RAD51 levels as well as MDR pump activity contribute to the TAL resistance.[14,15] However, other proteins, along with RAD51 in HR repair and other signaling pathways that may play key roles in TAL resistance are still not known. Thus, identification of the proteins and their associated pathways involved in PARPi resistance is needed for the development of novel therapeutic interventions and/or combined treatment strategies in the treatment of gBRCA1/2‐mutated advanced metastatic breast cancers.
Recent studies have been revealing novel potential targets for PARPi. The study by Knezevic et al.[16] found that deoxycytidine kinase (DCK) responsible for the deoxycytidin monophosphorylation and hexose‐6‐phosphate dehydrogenase (H6PD) regulation by changing NADPH/NADP + ratio within the endoplasmic reticulum, is a novel target by both the network and SAINT analysis for derivatives of niraparib and rucaparib, respectively. Additionally, NAD + binding protein (IMPDH2) and PARP1‐binding proteins (LIG3, XRCC1/5/6) are highlighted after treatment with modified derivatives of niraparib, olaparib, rucaparib, and veliparib in PTEN‐null CAL‐51 triple‐negative breast cancer (TNBC) cells. Antolin et al.[17] showed that rucaparib and niraparib altered the intracellular levels of DYRK1A/B, CDK16, and PIM3 kinases by causing off‐target inhibition through the combination of computational (CLARITY and ChEMBL) and experimental the kinome‐wide of target methods in HEK293 human embryonic kidney cells.
A number of studies also explored the roles of proteins and signaling pathways in drug resistance (adriamycin, paclitaxel, tamoxifen, mitoxantrone, sorafenib, and gemcitabine) by proteomic analysis.[18‐22] However, there has been no study that examined the novel expression signatures of PARPi resistance in BRCA1‐mutated TNBC cells at the proteomic level. The aim of this study was to, for the first time, investigate the altered protein signatures between HCC1937 BRCA1 mutant TAL‐sensitive (TAL‐S) and HCC1937 TALresistant (TAL‐R) TNBC cells by two‐dimensional gel electrophoresis (2DE)‐based proteomic analysis. After validation of resistance level to TAL by WST‐1 and Annexin V analysis, we elucidated the identities of 10 differentially regulated proteins in TAL‐R cells. Bioinformatics analysis with the differentially regulated proteins predicted involvement of EGF/FGF and AMPK signaling pathways in TAL resistance. In addition, alterations in phosphorylation/dephosphorylation events were underlined.

2 | MATERIALS AND METHODS

2.1 | Cell culture condition and generation of TAL‐R cell

HCC1937 BRCA1 mutant parental TAL‐S and HCC1937‐R TAL‐R TNBC cells culture conditions were performed as in our previous studies.[14,15] To generate TAL‐R cells, HCC1937 cells were continuously treated with 0.1 nM TAL for 12 months.

2.2 | Cell viability analysis

To determine the cytotoxic effect of TAL on the TAL‐S and TAL‐R cells, the cells were seeded in 96‐well plates (2 × 103 per well) and exposed to different TAL concentrations (0.1 and 10 nM) for 10 days to assess relative resistance. Following incubation, 10 µl of water‐soluble tetrazolium salts (WSTs) reagent was added to each well, incubated for 30 min in the dark and then analyzed by multimicroplate reader (Allsheng) at 450 nm. The relative resistance of cells was calculated according to the viability of TAL‐R cells relative to that of the TAL‐S parental cells.

2.3 | Annexin V analysis and acridine orange (AO) staining

Annexin V and AO staining were performed to determine the apoptotic cell death in TAL‐S and TAL‐R cells upon treatment with various doses of TAL. To perform Annexin V analysis, the cells were treated with 0.1 and 10 nM TAL as the minimum and maximum concentrations for 10 days. After treatment, the cells were trypsinized and washed with phosphate‐buffered saline (PBS) and stained with Muse™ Annexin V and Dead Cell Kit (Merck Millipore) and analyzed with a Muse™ Cell Analyzer (Merck Millipore). For AO staining, TAL‐S and TAL‐R cells were seeded in 6‐well plates and incubated with 1 and 10 nM of TAL for 10 days. After the completion of incubation, 4% paraformaldehyde was used for fixation and the cells were washed with cold PBS. The cells were then stained with AO for 30 min in the dark. Finally, images were monitored by EVOS FL Cell Imaging System (Thermo Fisher Scientific).

2.4 | 2DE

Two‐dimensional gel electrophoresis was carried out, as described by Ozgul et al.,[23] except 300 µg protein was loaded to 11 cm IPG strips (pH 3–10). For the second dimension separation, precast 12% Bis‐Tris Criterion XT gels (Bio‐Rad) were used. The gels were stained with Silver and imaged using Versa Doc MP4000 gel imaging system equipped with Quantity One Software (Bio‐Rad).

2.5 | Image analysis

PDQuest Advance 2DE gel analysis software (Bio‐Rad) was used for gel‐to‐gel matching and evaluation of the differences among the protein spots. Three independent biological replicates of 2DE gels were run. The quantity of each spot was normalized by a local regression model. Gel spots that significantly differed in expression levels were selected and excised using ExQuest Spot‐cutter (BioRad). A minimum of twofold up‐ or downregulation criteria was used to determine regulated protein spots, which were cut and disposed into 96‐well plates for in‐gel tryptic digestion. The statistical significance of image analysis was determined by the Student t test (statistical level of p < .05 is significant).

2.6 | Protein identification

In‐gel tryptic digestion of the proteins was performed using an in‐gel digestion kit following the recommended protocol (Pierce). Zip‐Tip cleaning was performed for each digested sample (Millipore). For matrix‐assisted laser desorption/ionization (MALDI)‐time of flight (TOF)/TOF analysis, AB SCIEX MALDI‐TOF/TOF 5800 instrument was used. Peak data were analyzed with MASCOT using a streamlined software, Protein Pilot (AB SCIEX). The search parameters included enzyme of trypsin, one missed cleavage, fixed modifications of carbamidomethyl (C), variable modifications of oxidation (M), peptide mass tolerance: 50 ppm, fragment mass tolerance:±0.4 Da, peptide charge of 1+ and monoisotopic. Only significant hits, as defined by the MASCOT probability analysis (p < .05) were accepted.

2.7 | The PANTHER analysis

The PANTHER analysis (Protein Analysis THrough Evolutionary Relationships, http://PANTHERdb.org/) was carried out using the UniProt accession numbers of the regulated proteins. The organism was specified as Homo sapiens and functional classifications were viewed as pie charts. Selected ontologies included molecular function, biological process, cellular function, cellular component, protein class, and pathway. For each of the ontologies, a manual analysis was performed by listing the selected proteins and cross‐checking their given properties with UniProt entries.

2.8 | The STRING analysis

STRING analysis (https://string-db.org/) was carried out using the UniProt accession numbers of the regulated proteins. The search engine option was set to “multiple proteins by names/identifiers” and the organism was specified as H. sapiens. The retrieved proteins were manually checked to ensure that they are all correctly retrieved from the database. Whole‐genome analysis was the preferred choice. The setting tab was used to change the stringency of the analysis. The results were downloaded as bitmap images and images were recreated by Adobe Illustrator Version 6.

2.9 | The BioGrid analysis

The BioGrid analysis (https://thebiogrid.org/) was carried out using the UniProt accession numbers of the regulated proteins.

2.10 | Statistical analysis

SPSS 22.0 (SPSS Inc.) was used to perform statistical analysis. The obtained data were represented as the mean ± standard deviation of three independent analyses. To compare the cell viability and total apoptotic cell death with controls, a one‐way analysis of variance (ANOVA) with post‐hoc Tukey was used. p < .05 was statistically significant (*p < .05, ** p < .01).

3 | RESULTS

3.1 | Assessment of TAL‐resistance in TAL‐R cells

To assess the acquired TAL‐resistance, WST‐1, Annexin V, and AO staining were performed. We first determined the relative fold resistance degree to TAL in TAL‐R cells in comparison to TAL‐S parental cells by WST‐1 assay (Figure 1). The viability of TAL‐S cells significantly reduced to 67.10 ± 1.58%, 50.07 ± 2.39%, and (p < .01), whereas these concentrations did not affect the proliferation of TAL‐R cells for 10 days. After treatment with 0.1, 1, and 10 nM TAL, the percentage of cell viability was 132.06 ± 1.13%, 124.64 ± 3.65%, and 119.38 ± 3.08%, respectively (Figure 1A). Therefore, TAL‐R cells exhibited 1.97‐, 2.49‐ and 3.79‐fold resistance to TAL compared with TAL‐S parental cells (Figure 1B).
To further confirm our results, the apoptotic effects of TAL were evaluated as shown in Figure 2. The changes in Annexin V levels indicated that apoptotic cell death was 45.99 ± 2.23% and 68.68 ± 1.76%, following incubations with 1 and 10 nM TAL, respectively, in TAL‐S cells, while a small percentage of apoptotic cells (5.22 ± 0.18% and 4.90 ± 0.69% for 1 and 10 nM, respectively) was detected in TAL‐R cells, indicating the existence of TAL resistance (Figure 2). Moreover, we observed some vacuolar formation, chromatin condensation, nuclear blebbing, and apoptotic bodies in TAL‐S cells, particularly at 10 nM TAL treatment. However, the morphology of TAL‐R cells was similar to the control group even after 10 nM TAL treatment (Figure 3). Therefore, TAL can lead to apoptotic cell death in TAL‐S cells and TAL‐R cells were evidently more resistant to TAL compared to the TAL‐S parental cells.

3.2 | Comparative proteome analysis of TAL‐S and TAL‐R cells

To elucidate the molecular mechanisms behind TAL resistance, a comparative 2DE‐based proteomic analysis was performed. Well‐resolved and reproducible 2DE gels were produced, as depicted in Figure 4. An average of 450 ± 10 protein spots per analytical gel was detected and matched. By using PDQuest advance gel analysis software, changes in spot intensities were evaluated. Spots that were up‐ or downregulated more than twofold were selected as the differentially regulated spots. To identify the regulated protein spots, the spots were cut from a preparative gel with an automated spot cutting instrument and subjected to in‐gel tryptic digestion followed by MALDI‐TOF/TOF analysis. A total of 10 proteins were identified (Table 1 and Figure 5). The MALDI identification scores ranged from 177 to 630 with expected values of <10−14, indicating that the identifications were highly reliable. In addition, the amino acid sequence coverage for the identified proteins ranged from 18% to 81%, with an average coverage value of 39 ± 18%. The highest differentially regulated protein in TAL‐R cells was serpin B5, which displayed a 100‐fold decrease in its levels. A similar level of differential regulation with an inverse trend was observed in protein‐glutamine gamma‐glutamyltransferase‐2 levels in drug‐resistant cells in comparison to the TAL‐S cells. Overall, high levels of differential regulation were observed among the identified proteins, implying that drug treatment significantly affected the overall cell proteome.

4 | DISCUSSION

In our previous study, a TAL‐resistant cell line (HCC1937‐R) was created by 0.01 nM TAL treatment for 6 months that caused the treated cells to exhibit a 2.9‐fold increase in their resistance to two‐dimensional gel electrophoresis 10 nM TAL.[14] For the current study, the use of higher TAL concentrations and longer exposure times allowed us to create a TAL‐resistance HCC1937 cell line that was 3.69‐fold more TAL resistant than the non‐TAL‐treated cells. The TAL‐S and TAL‐R cells were then used to perform a 2DE‐based comparative proteomics study to elucidate the altered molecular pathways that were caused or affected by the TAL resistance.
Ten proteins were differentially regulated upon TAL treatment. The differentially regulated proteins were subjected to bioinformatics analysis using Panther and STRING servers to associate them with protein networks and molecular pathways. Panther analysis indicated that six of the differentially regulated proteins (Q12931, P36952, P21980, P41250, P22314, and P30153) displayed catalytic activities in various metabolic events, including protein synthesis, folding, and degradation. Panther classification of each protein to determine protein classes revealed seven different protein types, namely chaperones (Q12931), cytoskeletal proteins (Q99439), metabolite interconversion enzymes (P21980), protein modifying enzymes (P22314), protein‐binding activity modifiers (P36952, P30153), scaffold/adaptor proteins (P62258), and translational proteins (P54577, P41250). Such protein diversity implied that TAL treatment affects cells as a whole and its resistance is likely to be the result of coordinated cellular events. Moreover, pathway analysis via Panther revealed changes in signaling events, especially in EGF/FGF signaling pathways. The biological activity of these two signaling pathways is likely to be altered during the acquisition of the TAL resistance. In breast cancer cells, the overexpression of FGF2 and FGF4 leads to doxorubicin or cyclophosphamide resistance by increasing glucose metabolism and DNA‐dependent protein kinase (DNA‐PK) expression.[25] Furthermore, EGF is overexpressed in TNBC patients and a higher expression of EGF is associated with resistance to different chemotherapeutic drugs.[26] Therefore, the changes in receptor tyrosine kinases‐associated signaling pathways, including Ras/Raf/ MEK/ERK, PI3K/PTEN/Akt/GSK‐3, and Jak/STAT, could be targeted through combined treatment strategies to overcome PARPi resistance in breast cancer.
STRING analysis generated results similar but more specific than the results generated by the Panther analysis (Figure 6). Regulation of protein phosphorylation/dephosphorylation appeared to be the key altered mechanism by TAL treatment (FDR: 4.4 × 10−8). In particular, the protein serine/threonine phosphatase activities are affected (FDR: 1.1 × 10−8). STRING analysis especially directed our attention to the protein phosphatase 2A complex (FDR: 3.15 × 10−17) since serine/threonine‐protein phosphatase 2A (PP2A), one of the differentially regulated proteins whose levels were affected by TAL resistance, is part of this complex. Serine/threonine‐protein phosphatase 2A serves as a hub in activation of several important kinases, including phosphorylase B kinase casein kinase 2, mitogenstimulated S6 kinase, and MAP‐2 kinase.[24] It can also activate RAF1 by dephosphorylation.[27] Therefore, upregulation of serine/ threonine‐protein phosphatase 2A may trigger a chain of events that may lead to the development of TAL resistance. Dysregulation of PP2A induces aberrant activation of different signal cascades and thus, result in JQ1 and other bromodomain inhibitor resistance in TNBC cells.[28] Our findings could contribute to new findings to elucidate the interaction of PP2A with PARPi response. Therefore, the combination of cellular PP2A inhibitors and PARPi could show promising effects for the reversal of TAL resistance.
A hint regarding the activation of the AMPK signaling pathway was also provided by STRING analysis. This pathway is normally activated in cases where cellular energy supplies are prone to depletion. AMPK signaling pathway, when activated, can positively regulate specific pathways, for example, fatty acid oxidation and autophagy to replenish cellular energy supplies and negatively regulates energy‐consuming biosynthetic pathways, including gluconeogenesis and lipid/protein synthesis.[29,30] Additionally, aberrant activation of AMPK signaling pathways lead to drug resistance in TNBC due to the regulation of cell growth and survival. On the other hand, AMPK activation inhibits the expression of EGFR, cyclin D1, and cyclin E MAPK, Src, and signal transducer and activator of transcription 3 (STAT3).[31] We are hypothesizing that to acquire TAL resistance, a large energy demand must be met, which may ultimately create an imbalance in cellular energy homeostasis. To overcome this difficulty, the cells may then boost the elements of the AMPK signaling pathway as a master regulator to overcome the demand. However, further investigations are required to identify the association of AMPK signaling with TAL resistance and improve AMPK‐targeted anti‐TNBC treatment strategies.
Among the differentially regulated proteins, relatively high regulation ratios (more than 30‐fold) were observed for three of the BMN 673 proteins, namely protein‐glutamine gamma‐glutamyltransferase 2 (TGM2), ubiquitin‐like modifier‐activating enzyme 1 (UBA1), and Serpin B5. STRING analysis performed by these three proteins revealed seemingly no interaction among them, regardless of the stringency used. However, BioGrid interactome analysis indicated that a predicted interaction between UBA1 and Serpin B5 is possible. Therefore, future work may uncover an interactome among these three proteins that may help understanding TAL resistance.

5 | CONCLUSION

In conclusion, the proteomics analysis presented here revealed identities of differentially regulated 10 proteins in the TAL‐R cells. Bioinformatics analysis predicted changes in EGF/FGF signaling pathways as well as in the AMPK signaling pathway. Consistent with these pathways, the regulation of protein phosphorylation/dephosphorylation appeared to be altered in TAL‐resistant cells. The findings of this study paved the way for future studies by which novel protein targets can be found to overcome TAL‐resistance in gBRCA1/2‐mutated advanced metastatic breast cancers. A future comparative phosphoproteome study comparing the changes in phosphoprotein levels should shed more light on the identities of key proteins/phosphoproteins that play roles in TAL resistance. Those proteins may become drug targets to overcome TAL resistance in TNBC.

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