ARTICLE pubs.acs.org/jpr
Proteomic Approach Reveals FKBP4 and S100A9 as Potential Prediction Markers of Therapeutic Response to Neoadjuvant Chemotherapy in Patients with Breast Cancer Won Suk Yang,†,|| Hyeong-Gon Moon,‡ Hee Sung Kim,^ Eui-Ju Choi,|| Myeong-Hee Yu,§ Dong-Young Noh,*,‡ and Cheolju Lee*,† †
BRI, Korea Institute of Science and Technology, 39-1 Hawolgok, Seongbuk, Seoul 136-791, Republic of Korea Department of Surgery and Cancer Research Institute, College of Medicine, Seoul National University College of Medicine, Seoul, 28 Yeongeon-dong, Jongno-gu, Seoul 110-744, Republic of Korea § Functional Proteomics Center, Korea Institute of Science and Technology, Seoul, Republic of Korea School of Life Sciences and Biotechnology, Korea University, Seoul 136-701, Republic of Korea ^ Department of Pathology, KEPCO Medical Foundation, Hanil General Hospital, Seoul 132-703, Republic of Korea
)
‡
bS Supporting Information ABSTRACT: Although doxorubicin (Doxo) and docetaxel (Docet) in combination are widely used in treatment regimens for a broad spectrum of breast cancer patients, a major obstacle has emerged in that some patients are intrinsically resistant to these chemotherapeutics. Our study aimed to discover potential prediction markers of drug resistance in needle-biopsied tissues of breast cancer patients prior to neoadjuvant chemotherapy. Tissues collected before chemotherapy were analyzed by mass spectrometry. A total of 2,331 proteins were identified and comparatively quantified between drug sensitive (DS) and drug resistant (DR) patient groups by spectral count. Of them, 298 proteins were differentially expressed by more than 1.5-fold. Some of the differentially expressed proteins (DEPs) were further confirmed by Western blotting. Bioinformatic analysis revealed that the DEPs were largely associated with drug metabolism, acute phase response signaling, and fatty acid elongation in mitochondria. Clinical validation of two selected proteins by immunohistochemistry found that FKBP4 and S100A9 might be putative prediction markers in discriminating the DR group from the DS group of breast cancer patients. The results demonstrate that a quantitative proteomics/bioinformatics approach is useful for discovering prediction markers of drug resistance, and possibly for the development of a new therapeutic strategy. KEYWORDS: neoadjuvant chemotherapy, drug resistance, quantitative proteomics, FKBP4, S100A9
’ INTRODUCTION Breast cancer is the most commonly diagnosed cancer in women in the United States, with 182,460 new cases in 2008 (representing 26% of all cancers in women).1 The goals of systemic therapy, including chemotherapy, in advanced and metastatic settings, are to maximize the control of symptoms, prevent serious complications, and prolong survival while maintaining quality of life.2 Chemotherapy plays an important role in the treatment of breast cancer at various stages.3,4 There have been a number of reports of attempts to identify prediction markers of resistance to doxorubicin (Doxo), docetaxel (Docet), and/or their combination and to elucidate the underlying molecular mechanisms using genetic and proteomic approaches.57 However, most of the studies were carried out on cell culture models instead of clinical samples. These approaches using transformed drug-resistant cell models have yielded only limited r 2011 American Chemical Society
information. While very useful for the study of specific targets, homogeneous in vitro cultured cells are devoid of contributions of the host-tumor microenvironment and no single cell line can recapitulate the heterogeneity of human tumors.8 Relevant markers of drug-resistance that are reliable enough to guide appropriate treatment and provide a comprehensive understanding of the mechanisms of drug resistance in breast cancer are still largely unknown. Here, we report the identification of putative prediction markers of drug resistance using a combination of quantitative proteomics and bioinformatics. Instead of in vitro cultured cells, we directly used needle biopsy tissues of breast cancer patients prior to neoadjuvant chemotherapy. Since our study aimed to Received: August 24, 2011 Published: November 10, 2011 1078
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Table 1. Continued
Table 1. Histopathological Characteristics of Breast Cancer Tissues Used for Discovery and Validation of Chemoresistance Prediction Markers
discovery characteristic
discovery N (= 15)
characteristic
%
validation N (= 68)
N (= 15)
%
N (= 68)
%
1 0
6.7 0.0
0 7
0.0 10.3
5
33.3
18
26.5
V VI
%
validation
Anamnesisa
Initial Clinical T Stage cT1
0
0
2
2.9
cT2
10
66.7
46
67.6
cT3
5
33.3
19
27.9
cT4
0
0
1
1.5
Initial Clinical N Stage cN0
0
0.0
7
cN1
6
33.3
34
50
10.3
cN2
9
53.3
25
36.8
cN3
2
13.3
2
2.9
(DS: 1/DR: 4)
(DS: 13/DR: 5)
a
Anamnesis includes hypertension, diabetic, tuberculosis, chronic liver disease, heart disease, allergy (D, discovery; V, validation).
identify prediction markers of drug resistance in biopsy tissues, it was our expectation that the results would be clinically more relevant. On the basis of follow-up validation by immunohistochemistry (IHC), we suggest FKBP4 and S100A9 as prediction markers for drug resistance in neoadjuvant chemotherapy using Doxo in combination with Docet.
Initial Clinical AJCC Stage
’ MATERIALS AND METHODS
IB
0
0
1
1.5
IIA
0
0
7
10.3
IIB
0
0
25
36.8
Patients and Tumor Tissues
IIIA IIIB
4 8
26.7 53.3
32 1
47.1 1.5
IIIC
2
13.3
2
2.9
IV
1
6.7
0
0
Breast cancer tissues from locally advanced breast cancer patients (15 cases for discovery study; 38 cases and other cases for validation study) were obtained before the initiation of cytotoxic chemotherapy at the Seoul National University Hospital Breast Care Center (Table 1 and Supporting Information Table 1). Clinical stages were evaluated by physical examination, breast imaging (mammography, breast ultrasonography, and breast magnetic resonance imaging), and distant metastasis-workup. Patients with locally advanced breast cancer received preoperative chemotherapy after having discussions with treating surgeons and medical oncologists. Tissues were obtained during diagnostic ultrasonography-guided core needle biopsy procedures and stored at 80 C. Informed consent was obtained from all patients. Chemotherapy regimens were chosen from standard neoadjuvant regimens at Seoul National University Hospital. During the study period, trastuzumab was not approved for neoadjuvant use in Korea. Most patients received Doxo and Docet-based chemotherapy. Doxo (5060 mg/m2) and Docet (6075 mg/m2) were administered by intravenous infusion every 3 weeks for three cycles, with granulocyte colony stimulating factor as a primary prophylaxis. Clinical response was determined by serial breast magnetic resonance imaging performed before and after the neoadjuvant chemotherapy based on RECIST criteria.9 The patients who showed reduction in tumor size by more than 30% along the longest axis (partial response, PR) after the chemotherapy or complete remission of tumor (complete response, CR) based on final pathologic examination after the operation are grouped as drug sensitive (DS). In contrast, drug resistant (DR) patients were defined as those who showed reduction of tumor sizes by less than 30% (stable disease, SD) or rather increase of tumor size (progressive disease, PD). Our study included no patients who belonged to the group of PD.
Clinical Response complete response (CR)
3
20.0
2
2.9
partial response (PR)
5
33.3
40
58.8
stable disease (SD)
7
46.7
26
38.2
14 1
93.3 6.7
66 1
97.1 1.5
0
0.0
1
1.5
Histologic Subtype invasive ductal carcinoma invasive lobular carcinoma mixed ductal and lobular carcinoma
Estrogen Receptor negative
8
53.3
28
41.2
positive
7
46.7
40
58.8
11 4
73.3 26.7
40 28
58.8 41.2
progesterone receptor negative positive
HER2 Overexpression negative
2
13.3
38
55.9
positive
9
60.0
19
27.9
unknown
4
26.7
11
16.2
chemotherapy regimen doxorubicin + docetaxel
13
86.7
62
91.1
doxorubicin + docetaxel + cyclophosphamide
1
6.7
0
0.0
paclitaxel + doxorubicin + trastuzumab
1
6.7
0
0.0
doxorubicin + cyclophosphamide
0
0.0
2
2.9
paclitaxel + trastuzumab
0
0.0
1
1.5
paclitaxel + capecitabine
0
0.0
1
1.5
docetaxel
0
0.0
2
3
Chemotherapy Cycle III
14
93.3
58
85.3
IV
0
0.0
3
4.4
Preparation of Protein Extract
Biopsy tissues were washed with ice cold 1 PBS to reduce blood contamination, followed by lysis in 200 μL of lysis buffer (6 M urea, 50 mM Tris-Cl, pH 8.3, 5 mM EDTA, 0.05% SDS, and 1 Protease inhibitor cocktail) by sonication at 4 C for 1 min with an intermittent cooling period. After centrifugation at 3,000 rpm 1079
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Journal of Proteome Research for 10 min at 4 C, supernatants were collected. For removal of lipid from the lysate, 480 μL of methanol and 160 μL of CHCl3 were added to 200 μL of the tissue lysate. Samples were mixed by vortexing, after which 640 μL of water was added and the samples were subjected to centrifugation at 12,000 rpm for 5 min at 4 C. Proteins were present in the middle layer between the MeOH/ H2O (top) and CHCl3 (bottom) layers. The top layer was aspirated, and then 300 μL of MeOH was added and the samples were vortexed. After centrifugation at 12,000 rpm for 5 min at 4 C, the upper layer was removed and the protein pellet was dried in air. The protein pellet was then resuspended in lysis buffer. The concentration of total protein was determined by Bradford assay. The tissue lysates were stored at 80 C until use. Protein Digestion for Mass Spectrometry
Eleven protein samples (six DS and five DR; 150 μg each) were adjusted to the same protein concentration, reduced with 10 mM DTT for 30 min at 37 C, and then alkylated with 40 mM iodoacetamide for 1 h in the dark at 25 C. After the samples were diluted 10-fold with 50 mM NH4HCO3, trypsin was added at a ratio of 1:40 (w:w), followed by incubation overnight at 37 C. Tryptic digests were loaded on a Polysulfethyl A strong cation exchange (SCX) column (4.6 mm 200 mm) equilibrated with buffer A (10 mM KH2PO4, pH 3.0, and 25% acetonitrile). Peptides were eluted with a gradient of 040% buffer B (buffer A + 1 M KCl) for 20 min, followed by 100% buffer B for 10 min. Fifteen fractions were collected for each sample. Two or more sequential column chromatography elution fractions with relatively low absorbance at 280 nm were combined in order to balance the protein amount and reduce the number of fractions to 11. Finally, the 11 subfractions were applied to a C18 cartridge. Purified peptide samples were dried and stored at 4 C until LCMS/MS analysis.
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validated with Trans-Proteome Pipeline provided by the Institute for Systems Biology (TPP, version 4.0, http://www.proteomecenter. org). The peptides that had a Peptideprophet score greater than 0.05 were passed through to the Proteinprophet.10,11 All reported data were based on the peptides and proteins with probability scores greater than 0.5 (Peptideprophet) and 0.9 (Proteinprophet), respectively. Quantitation and Statistical Analysis
The relative abundance for identified proteins was estimated on the basis of spectral count (the number of MS/MS spectra matched to the protein concerned). Spectral counts data were extracted using the Proteinprophet program and exported to MS-EXCEL. Afterward, the normalized spectral abundance factor (NSAF) for each protein was obtained considering the total numbers of spectra for the DS and DR groups (29,661 and 30,184, respectively).12 We calculated an NSAF for each protein as follows: ðNSAFÞk ¼
∑ ðSpC=LÞi
i¼1
in which the total number of tandem MS spectra matching peptides from protein k (SpC) was divided by the protein’s length (L) and then divided by the sum of SpC/L for all N proteins. The abundance ratio for each protein between DR and DS was calculated as protein ratio = (average NSAF of the protein in DR group)/(average NSAF of the protein in DS group). In order to filter out differentially expressed proteins (DEPs) that were statistically significant, G-test which is adjusted by the William’s correction was performed. The spectral counts for each protein were first normalized as
∑ ðSpCÞj þ 0:5 nðDSÞi ¼ ðSpCÞi ∈ DS ðSpCÞk ∑ k ∈ DS
LC-MS/MS Analysis
An Agilent nanoflow-1200 series HPLC system was connected to a linear ion trap mass spectrometer (LTQ, Thermo Electron, San Jose, CA, USA). The dried peptide samples were resuspended in 20 μL of 0.4% acetic acid, and an aliquot (3 μL) was injected to a reverse-phase Magic C18AQ column (12 cm 75 μm) equilibrated with 95% buffer A (0.1% formic acid in H2O) + 5% buffer B (0.1% formic acid in acetonitrile). The peptides were eluted in a linear gradient of 1040% buffer B over 40 min. A total of 363 LC-MS/MS runs (11 tissues 11 SCX fractions per tissue triplicated runs per fraction) were performed. An LTQ ion-trap mass spectrometer (ThermoFinnigan, San Jose, CA) equipped with an in-house built microspray device was used for all analyses. The MS survey was scanned from 300 to 2000 m/z with a 1 μ scan, followed by three data-dependent MS/MS scans (isolation width, 1.5 m/z; normalized collision energy, 28%; dynamic exclusion duration, 3 min). Automated database searching using SEQUEST software (SEQUEST algorithm), part of the BioWorks (version 3.3.1) data analysis package (Thermo Fisher, San Jose, CA), was performed to identify peptide and protein sequence matches for each recorded MS/MS spectrum. A human database (ipi. HUMAN.v3.44) with typical contaminants such as bovine trypsin was used for the search. SEQUEST search parameters were set as follows. Cleavage site specificity, no enzyme; precursor mass tolerance, 3.0 Da; fragment mass tolerance, 0.5 Da. A static modification of +57 Da for cysteine carboxamidomethylation and a variable modification of +16 Da for methionine oxidation were allowed. Protein identification was statistically
ðSpC=LÞk N
j ∈ DS
nðDRÞi ¼ ðSpCÞi ∈ DR þ 0:5 where (SpC)i∈DS and (SpC)i∈DR denote spectral counts of the i-th protein from the DS group and the DR group, respectively. ∑j∈DS(SpC)j and ∑k∈DS(SpC)k are the total numbers of spectra. The G-value for the i-th protein was then calculated as follows: ! 2nðDSÞi Gi ¼ 2 ln nðDSÞi nðDSÞi þ nðDRÞi ! 2nðDRÞi þ 2 ln nðDRÞi nðDSÞi þ nðDRÞi A G-value higher than 3.841 is considered significant, with P < 0.05 according to the χ2-distribution.13 In addition, DEPs were subjected to a MannWhitney Willcoxon rank sum test by using the MedCalc program (version 11.6). To show how the samples and DEPs were grouped on the basis of their expression patterns, we performed a hierarchical clustering using Gene Cluster (open source clustering software, version 3.0) and Maple tree (version 0.2.3.2 BETA). Principal component analysis (PCA) was used to represent the contribution of proteins that discriminate between DS and DR groups. PCA was performed with NSAF values on an 1080
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Journal of Proteome Research XLSTAT MS-EXCEL add-in program (version 2011.2.08: http://www.xlstat.com).
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DEPs quantified by spectral count were analyzed using the Ingenuity Pathway Analysis (IPA) software (http://www.ingenuity. com). The IPA software uses proteins and their identifiers to navigate curated literature databases and extract an overlapping network between the candidate proteins. The biological function assigned to each network was ranked according to the significance of that biological function to the network. A score higher than 2 is usually attributed to a valid network (the score represents the log probability that the network was found by random chance).
resistance, cancer tissues biopsied before chemotherapy were used for proteomic profiling (Supporting Information Figure 1). To assess eligibility, the clinical criteria for each breast cancer patient were verified before and after chemotherapy. On the basis of drug responsiveness, the patients were classified into two major groups. The patients whose tumor sizes were reduced to less than 30% compared to the original sizes were classified as the DS group (n = 8), whereas all others were classified as the DR group (n = 7). Among the 15 samples, only 11 (six DS and five DR) were used for LCMS/MS analysis, due to limited sample amount. For confirmation of the results, 14 biopsy samples were analyzed by Western blot. Two other independent sets of FFPE biopsy samples (set I, 38 cases; set II, 30 cases) were used for IHC validation.
Immunoblot Analysis
Proteomic Profiles of DS and DR Groups
Proteins were separated by SDS-PAGE and then transferred to a PVDF membrane (Bio-Rad, Hercules, CA) using a Bio-Rad Transblot Cell system (Bio-Rad, Hercules, CA). Electrophoretic transfer to the PVDF membrane was performed at 400 mA for 3 h. Nonspecific binding sites on the membrane were blocked by incubation with 5% skim milk for 30 min at RT. After washing three times with 1 TBS-T buffer (10 mM Tris-Cl, pH 7.8, 15 mM NaCl, and 0.05% Tween 20), the membrane was incubated with primary antibody at 4 C overnight. The membrane was washed three times with TBS-T buffer and then incubated with secondary antibody at RT for 1 h. Immunoreactive proteins were detected using ECL plus (GE Healthcare, Piscataway, NJ). The primary antibodies used in the current study were directed against the following proteins: FKBP4 (ab54991, Abcam), RUVBL2 (BD612482, BD Biosciences), POSTN (sc-49479, Santa Cruz), ASAH1 (sc-28486, Santa Cruz), HSPA4 (HPA010023, SIGMA), S100A8 (sc-48352, Santa Cruz), S100A9 (sc-58706, Santa Cruz), and FABP4 (sb23693, Abcam). Band intensities of Western blot images were quantified using ImageQuant version 5.2. (GE Healthcare Biosciences), and a Wilcoxon rank-sum test was performed by using MedCalc (version 11.6.1.0) to calculate p values.
A summary of the proteomic profiling results is presented in Figure 1A. LC-MS/MS and SEQUEST searches of the mass spectra yielded 7,642 peptides, corresponding to 1,608 proteins in the DS group, and 8,134 peptides, corresponding to 1,948 proteins in the DR group. A total of 11,390 peptides and 2,331 proteins were identified by LC-MS/MS; and 1,225 proteins (52.6%) and 4,386 (38.5%) peptides were overlapped by both groups (Figure 1A, Supporting Information Figures 2 and 3 and Table 2). On the basis of the number of MS/MS spectra assigned to individual proteins, the G-value was calculated. The G-value of each protein under comparison was used to assess whether or not the protein was differentially expressed according to a χ2 distribution. From the G-test, 320 proteins were classified as differentially expressed between the two groups (G-value > 3.841; p < 0.05). Of these DEPs, 298 showed more than 1.5fold difference: 210 were upregulated and 88 were downregulated in the DR group compared to the DS group. To test the predictive power of the putative profile of 298 DEPs within the two groups, unsupervised hierarchical clustering was performed on the NSAF values of 298 DEPs, and the resultant data were represented as a dendrogram (Figure 1B, Supporting Information Tables 3 and 4A). The 298 DEPs were subsequently subjected to a MannWhitney test, which narrowed the list down to 88 proteins with p-values < 0.05. Of these DEPs, 72 were upregulated and 16 were downregulated in the DR group compared to the DS group. A clustering analysis of these DEPs was performed as well (Figure 1C, Supporting Information Table 4B). On the basis of the NSAF values of 88 DEPs, DS and DR samples were effectively separated from each other as illustrated by the two main clusters in the dendrogram. To evaluate the relative contribution of the 88 DEPs to the classification between DS and DR patients, PCA was performed. We obtained a similar result as that of the clustering analysis. The DS group was well separated from the DR group along the first principal axis, as revealed in the PCA score plot (Figure 1D, Supporting Information Figure 4). The percentage of variance explained by the PC1 and PC2 was 52.14% and 10.62%, respectively. From the clustering analysis and PCA, it is clear that the DS and DR groups were completely separated from each other on the basis of their DEPs.
Ingenuity Pathway Analysis (IPA)
Immunohistochemistry
Serial sections from formalin-fixed, paraffin-embedded (FFPE) blocks were applied to 3-aminopropyltriethoxysilane-coated slides. Deparaffinization and rehydration were performed using xylene and alcohol. The slides were pretreated in a microwave oven for antigen retrieval. Sections were incubated for 30 min at room temperature with antibodies against FKBP4 (ab54991, Abcam), S100A9 (sc-58706, Santa Cruz). To block endogenous peroxidase activity, treatment with blocking reagent (DAKO, Glostrup, Denmark) for 5 min was carried out before incubation with primary antibody for 30 min at 25 C. Enzyme-conjugated polymer (DAKO) and diaminobenzidine (DAKO) were used as a visualization system and chromogen, respectively. The expression of each of the two proteins was categorized as either positive or negative. In the evaluation of each protein, cases with definite epithelial staining in more than 1% of the tumor cells were categorized as positive, whereas additional cases with definite epithelial staining of the tumor cells were categorized as negative.
’ RESULTS Collection of Biopsy Tissue Samples from Breast Cancer Patients before Therapy
Fifteen cancer patients diagnosed at various TNM stages and prescribed neoadjuvant chemotherapy were selected. Since the aim of this study was to identity putative prediction markers of drug
Correlation between Quantitative Proteomic Data and Immunoblot Analysis
To test the reliability of our quantitation data, several proteins were randomly selected, on the basis of their cancer-relatedness as viewed in the UniProt protein database and the availability of commercial antibodies, and then analyzed by Western blot analysis (Figure 2A). As estimated from the densitiometric analysis of immunoblot images, RUVBL2, HSPA4, S100A9, and FABP4 1081
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Figure 1. Experimental summary. (A) Comparison of proteins and peptides identified by mass spectrometric analysis. (B) Hierarchical clustering of patient samples using DEPs. Quantitation was performed on the basis of spectral count analysis. Red and green colors indicate upregulated and downregulated protein expression levels, respectively. Presented are 298 DEPs (>1.5-fold, G-test passed). (C) Heatmap of 88 DEPs (>1.5-fold, G-test passed, MannWhitney rank-sum test passed). The length of the tree arms is inversely correlated with similarity. Patient samples are listed horizontally, and DEPs (IPI accession number) are listed vertically in the same order as in Supporting Information Table 4. (D) Score plot of PCA is presented.
showed 1.5-, 1.8-, 1.7-, and 3.0-fold difference in the DR group (Figure 2B). The immunoblot data coincided with the mass spectrometry data. In the spectral count analysis, RUVBL2 and HSPA4 levels were increased 2.68- and 3.35-fold, whereas S100A9 and FABP4 were decreased 2.37- and 55.63-fold in the DR group. We also tested S100A8, since the protein is known to form a heterodimer with S100A9. Like S100A9, S100A8 decreased 1.8fold in the DR group. Overall, the results of the Western blot analysis highly coincided with the MS data, although the measured ratios were slightly different. The results indicate that our proteomic methods are quite reliable as a measure of protein quantity. Bioinformatic Analysis of DEPs
To identify putative prediction markers associated with drug resistance and to elucidate the dynamic molecular mechanisms of drug resistance, the DEPs were subjected to IPA. Fourteen networks (more than two proteins, score >2) were annotated from 281 DEPs (94% of total DEPs). Representative functions of the networks, protein description (gene symbol), and significance scores for all 14 constructed networks are presented in Supporting Information Table 5.
Drug Metabolism-Related Proteins Are Intimately Involved in Drug Resistance. To obtain more relevant information about DR processes, we first focused on DEPs in networks related to drug metabolism (rank 1 and rank 3 networks). The expression levels of eight proteins associated with drug metabolism in these networks were significantly altered: GLO1, NQO1, PTGES3, and FKBP4 were increased 21.79-, 12.64-, 4.87-, and 3.50-fold, respectively, whereas SOD2, ERBB2, STMN1, and GSTP1 were decreased 4.22-, 3.39-, 3.04-, and 1.57-fold, respectively, in the DR group (Supporting Information Table 6). The proteins are related to glutathione metabolism, progesteronebinding, docetaxel-binding, or activation of tamoxifen. The eight proteins were shaped into two functional groups when integrated by the STRING program (Figure 3A). One group included NQO1, GSTP1, SOD2, ERBB2, and GLO1; and the other included PTGES3, STMN1, and FKBP4. We selected FKBP4 (FK506-binding protein 4) for Western blot analysis, since it showed the highest G-value (26.255) and a significant p-value (0.004) from the MannWhitney test among the eight proteins. The immunoblot data (2.3-fold increase in DR) were consistent with the mass spectrometric data (3.50-fold 1082
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Figure 2. Comparison between mass spectrometric data and Western blot analysis. (A) Upregulation of RUVBL2 and HSPA4 and downregulation of S100A9, FABP4, and S100A8 were confirmed by Western blotting. Western blot images were quantified by densitometric scanning. (B) The intensities of the bands are shown after normalization with respect to β-actin. Dots represent medians, and bars represent 95% confidence intervals for the median values calculated by using the MannWhitney test.
increase; Figure 3B). Figure 3C shows the sequence coverage of FKBP4 and the MS/MS spectra of two representative peptides. FEIGEGENLDLPYGLER (975.98 m/z, +2) was identified from both groups while ALELDSNNEKGLFR (803.41 m/z, +2) was identified only in the DR group. In the DR group, 15 peptides matched FKBP4, whereas only 3 peptides did in the DS group. Expression of FKBP4 was also tested in breast cancer cell lines such as Hs578T, SK-BR-3, MDA-MB-231, ZR-75-1, and MCF7. It has been reported that SK-BR-3 is resistant to paclitaxel, MDAMB-231 to doxorubicin, and ZR-75-1 and MCF7 to both drugs, whereas Hs578T is sensitive to both drugs.14 In comparison with Hs578T, SK-BR-3, MDA-MB-231, ZR-75-1, and MCF7 showed 2.4-, 1.5-, 1.7-, and 2.5-fold higher expression of FKBP4, respectively (Supporting Information Figure 5), supporting that overexpression of FKBP4 is correlated with drug resistance. Regulation of Drug Resistance by Cell Death and Cellular Assembly Related Proteins. Many of the DEPs were grouped into the networks of cell death (102 DEPs) and cellular assembly and organization (56 DEPs; Supporting Information Table 7). For confirmation of our proteomic data, we selected two representative proteins: one from the cell death network (ASAH1) and the other from the cellular assembly network (POSTN). The proteins were upregulated in the DR group, showed relatively high G-values, had similar spectral numbers for each of the DR samples, and had previously been reported due to their cancer-relatedness.15,16 The expression levels of ASAH1 and POSTN (increased 2.0- and 5.0-fold, respectively) as determined by Western blot were similar to those derived by the spectral count analysis (increased 31.37- and 1.69-fold, respectively) (Figure 3D). Immunoblot analysis of individual samples produced similar fold changes. Figure 3E shows the representative MS/MS spectra of ASAH1 and POSTN. Identification of Canonical Pathways Associated with Drug Resistance. We next moved to known canonical pathways populated with our DEPs. Of the 298 DEPs, 40 were enriched in the top five canonical pathways (Supporting Information Table 8). An interaction map of the 40 DEPs was constructed using the STRING program (http://string-db.org/) as shown in Figure 4A. In particular, 17 proteins were involved in the acute phase response signaling pathway and mostly exhibited downregulation (with the exception of RAC1 and C4B) in the DR group. From the IPA knowledge base, 108 proteins were included
in the network of the acute phase response signaling pathway (Supporting Information Table 9). The 17 DEPs were not evenly distributed in the whole network; rather they were localized with single edges, suggesting direct interaction between the DEPs (Figure 4B). The remaining 23 DEPs, which were involved in fatty acid elongation in mitochondria, glycolysis/gluconeogenesis, granzyme B signaling, and glutamate metabolism, for the most part exhibited upregulation in the DR group (Figure 4A). Immunohistochemistry of FKBP4 and S100A9
In order to validate the usefulness of the protein markers for predicting drug responsiveness, we chose two proteins and performed IHC on a different set of needle biopsy tissues. FKBP4 was selected due to the significant statistical values (G-test G-value, 26.26; MannWhitney p value = 0.004) and its relatedness to drug metabolism. S100A9 was selected due to its known cancerrelatedness. We have previously discovered S100A9 as a serological marker of colon cancer. S100A9 was upregulated in several cancers and increased in the chemoradiotherapy-sensitive group of human cervical carcinoma (see Discussion). First, we tested FKBP4 on 38 tumor blocks (Validation 1: DS 23 and DR 15 blocks). FKBP4 was expressed more in the cytoplasm of the DR group tissues than in the DS group. Since the amount of biopsy tissues was quite limited, IHC of S100A9 was performed on a completely different set of tumor blocks (Validation 2: 19 DS and 11 DR blocks) compared to the first IHC sample set. Nine DS samples exhibited strong S100A9 staining in the nucleus, whereas only two DR samples were stained for S100A9. Representative IHC images are presented in Figure 5. The abilities of the protein markers to predict drug responsiveness were assessed by calculating diagnostic values on the basis of the IHC data. The sensitivity, specificity, and positive predictive values (DR vs DS) were 80.0%, 39.1%, and 46.2% for FKBP4 and 47.4%, 81.8%, and 81.8% for S100A9, respectively (Table 2).
’ DISCUSSION We performed a comprehensive quantitative proteomic analysis on needle biopsy tissues obtained before neoadjuvant chemotherapy in an effort to identify clinically relevant prediction markers for drug responsiveness in breast cancer. Our multilateral approach consisted of the following: (1) proteomic 1083
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Figure 3. Confirmation of representative proteins, FKBP4, ASAH1, and POSTN, distinguished between DR and DS groups. (A) Eight proteins associated with drug-metabolism were mapped using the STRING program. A triangle shows increased proteins in the DR group while an inverted triangle shows decreased proteins. The functions of proteins related to drug metabolism were provided by IPA. (B) Western blot analysis of FKBP4. The protein was increased 2.3-fold in the DR group. (C) Peptides identified from each group are indicated in the amino acid sequence of FKBP4. The MS/MS spectrum (ALELDSNNEKGLFR, 803.41 m/z, +2) at the top was identified only in the DR group (* in the sequence), while the MS/MS spectrum (FEIGEGENLDLPYGLER, 975.98 m/z, +2) at the bottom was commonly identified in both groups (∧). (D) ASAH1 and POSTN were confirmed by Western blotting. ASAH1 and POSTN were increased 2.0-fold and 5.0-fold in the DR group, respectively. (E) Representative peptides of ASAH1 (DRKESLDVYELDAK, 840.91 m/z, +2) and POSTN (TLLAPVNNAFSDDTLSM*DQR, 1121.61 m/z +2). M* indicates oxidized methionine. The intensities of the bands are shown after normalization with respect to β-actin. Dots represent median values, and bars represent the 95% confidence intervals of the MannWhitney test.
profiling of biopsy tissue samples from DS and DR patients using MS-based quantitative methods; (2) network analysis to explore the
characteristics of the identified DEPs; (3) identification of the specific molecular biofunctions and canonical pathways to which 1084
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Figure 5. Immunohistochemical staining of FKBP4 and S100A9. Representative immunohistochemical images of FKBP4 (left) and S100A9 (right) are shown. Immunohistochemical staining was performed on the tumor blocks of DS and DR groups. For evaluation of protein expression, a score corresponding to staining intensity (0 = negative; 1 = weak; 2 = intermediate; 3 = strong) was established. Group 0 was categorized as “negative”, and the other groups (13) were categorized as “positive”. Note that an IHC-positive image for FKBP4 was taken from the DR group, and that for S100A9 was from the DS group.
Figure 4. Canonical pathways populated with DEPs. (A) The top five canonical pathways populated with the identified DEPs were integrated using the STRING program. Each edge represents proteinprotein interaction of high confidence (String probability > 0.7). The increased proteins are marked by triangles while the decreased proteins are marked by inverted triangles. The asterisks represent potential prediction markers discovered from other studies. (B) The 108 proteins belonging to acute phase response signaling in the IPA knowledge base are presented using the STRING program. DEPs identified in the current study are circled with black solid lines. The increased proteins are marked by up-arrows while the decreased proteins are marked by downarrows. The 108 proteins are listed in Supporting Information Table 9.
the DEPs are mapped; and (4) confirmation of the results by Western blot analysis. In this study, we discovered potential prediction markers, especially FKBP4 and S100A9, which could distinguish the patient group resistant to neoadjuvant chemotherapy. In our study, LC-MS/MS analyses were performed in triplicate, which amounted to about 121 LC-MS/MS runs for each tissue sample. The number of identified proteins and peptides showed similar values for each analysis. The 298 DEPs identified by spectral count analysis occupied 12.8% of the total 2,331 proteins. Particularly, 88 DEPs which passed a MannWhitney rank sum test divided the DS group from the DR group as shown
in a PCA plot. With the limited quantity of clinical samples, our quantitation strategy allowed for in-depth analysis with sufficient sensitivity and wide coverage of the tissue proteome. Our strategy could maximize the advantage of spectral count analysis, which was able to identify more proteins than other previous studies.17,18 The DEPs detected in our study are involved in a variety of biological processes. Several of them (RUVBL2, HSPA4, ASAH1, and POSTN) further confirmed by Western blot analysis have been reported to be associated with cancer development and progression, as well as chemotherapy-resistance in vitro,15,16,19,20 suggesting that our quantitation methods were quite robust and reproducible. Although we confirmed only a subset of DEPs, the results suggest that the identified DEPs are involved in multiple dynamic processes that initiate or promote DR processes in breast cancer patients. Validation study using IHC focused on two DEPs associated with drug metabolism and cancer. FKBP4 and S100A9 were proven to be putative prediction markers in discriminating DR breast cancer patients from DS patients in a combined chemotherapy using Doxo and Docet. Although we used different tissue sets for validation of FKBP4 and S100A9, the IHC data showed similar values of sensitivity, specificity, and PPV. It is natural that proteins involved in drug metabolism are closely linked to drug resistance in single or combination treatment of breast cancer patients.21,22 FKBP4 belongs to a subclass of FK506-binding proteins.23 FKBP4 and its family member FKBP5 are regulated during steroid receptor function, including by progesterone, androgen, and glucocorticoid receptors (GR), by forming a complex with the heat shock proteins Hsp90/ Hsp70.24 Several studies have reported that FKBP5 could also be a biomarker for tumorigenesis and provide chemoresistance through several different signaling pathways.2527 Ward et al. has recently reported that FKBP4 is ubiquitously, although variably, expressed in the breast cancer cell lines, especially significantly higher in ER-positive compared to ER-negative cell lines.28 Our 1085
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Table 2. Diagnostic Values of FKBP4 and S100A9 Determined by Immunohistochemistrya protein FKBP4
S100A9
DR group
DS group
total
IHC (+) staining
12 (TP)
14 (FP)
IHC () staining
3 (FN)
9 (TN)
12
total
15
23
38
IHC () staining
9 (TP)
2 (FP)
11
IHC (+) staining
10 (FN)
9 (TN)
19
total
19
11
30
sensitivity (%)
specificity (%)
PPV (%)
80.0
39.1
46.2
47.4
81.8
81.8
26
TP, true positive; TN, true negative; FP, false positive; FN, false negative; PPV, positive predictive value. Sensitivity (%) = (TP)/(TP + FN) 100, Specificity (%) = (TN)/(FP + TN) 100, PPV (%) = (TP)/(TP + FP) 100. a
results showed that FKBP4 was particularly upregulated in drugresistant cell lines such as SK-BR-3 (ER-negative) and MCF7 (ER-positive) compared to the case in other cell lines (Supporting Information Figure 5A), suggesting that FKBP4 expression was rather correlated with drug responsiveness. This becomes more obvious if we put the report by Ward et al. together with a recent study about drug-responsiveness of various breast cancer cell lines against paclitaxel and doxorubicin (Supporting Information Figure 5B).14 We have previously discovered S100A9 and S100A8 as selorogical markers of colon cancer.29 S100A9 is robustly upregulated in several cancers and increased in the chemoradiotherapy-sensitive group in human cervical carcinoma,30,31 as it was in the DS group of breast cancer revealed in this study. S100 proteins are markedly overexpressed under various cell stress conditions, such as wounding and inflammation-associated disorders, and they are often associated with altered or abnormal pathways of epithelial cell differentiation.32 In the current IHC data, S100A9 very well distinguished the DS group from the DR group. Therefore, we consider that S100 proteins (S100A9 and S100A8) can also be used as prediction markers for the resistance of several anticancer drugs. The acute phase response signaling pathway was ranked first and included 17 proteins when the DEPs were mapped on known canonical pathways using the Ingenuity Pathway Analysis program. The acute phase response signaling pathway is a wellknown rapid inflammatory response that provides protection against microorganisms via nonspecific defense mechanisms.33 Acute phase-related proteins are a class of proteins whose plasma concentrations increase (positive acute phase proteins) or decrease (negative acute phase proteins) in response to inflammation. Especially, albumin, transthyretin, and transferrin, which are well-known negative regulators of the acute phase response,34 were downregulated in the DR group in our study. Transthyretin has been reported to also be associated with drug resistance to colon cancer.35 A recent study expanded the concept that inflammation is a critical component of tumor progression.36 Our data show that breast cancer patients with decreased levels of negative acute phase proteins are less sensitive to neoadjuvant chemotherapy. Particularly, some proteins in the canonical pathway mapped by our DEPs were previously reported on the basis of the proteomic approach as potentially prediction markers related to the resistance to several anticancer drugs.35,37,38 Pathways involved in energy metabolism (glycolysis/gluconeogenesis, glutamate metabolism) and granzyme B signaling are likely to be closely connected with drug resistance in cancer cells.3941 Another interesting point is the expression pattern of mitochondrial proteins between the DS and DR groups. Mitochondria have been previously linked to drug resistance in cancer in several in vitro model systems.4245 About 12% of DEPs (37 proteins among 298 total DEPs) could be classified as mitochondrial
proteins according to the Human MitoCarta database.46 The percentage value was similar to the value calculated using the whole identified proteome (11%, 255 mitochondrial proteins out of 2,331 total proteins). However, the expression levels of 33 out of the 37 mitochondrial DEPs, including the DEPs related to fatty acid elongation in mitochondria signaling, were elevated in the DR group. Of note, FASN was significantly upregulated in the DR group. The upregulation of FASN is known to cause drug resistance in breast cancer cells.47 Thus, mitochondrial proteins involved in fatty-acid elongation could also potentially serve as prediction markers of DR breast cancer. In conclusion, we identified putative prediction markers of drug resistance in breast cancer biopsy tissues using a strategic quantitative proteomicsbioinformatics approach. Two proteins, FKBP4 and S100A9, were found to represent possible prediction markers of drug resistance. Our study may help predict the response to chemotherapy in breast cancer patients.
’ ASSOCIATED CONTENT
bS
Supporting Information Summary of the experimental workflow; comparison of identified peptides and proteins between LC-MS/MS replicates; comparison of identified proteins between patient samples; principal component analysis; comparison of FKBP4 expression levels among breast cancer cell lines; detailed information of breast cancer patients for quantitative proteome analysis; number of identified peptides and proteins from LC-MS analysis; list of the 298 differentially expressed proteins identified by spectral count analysis; hierarchical clustering results; network analysis of DEPs; DEPs known to be associated with drug-metabolism; molecular biofunctions of DEPs; canonical pathway populated with DEPs; the 108 representative proteins of the acute phase response signaling. This material is available free of charge via the Internet at http://pubs.acs.org.
’ AUTHOR INFORMATION Corresponding Author
*D.-Y.N.: e-mail,
[email protected]. C.L.: e-mail, clee270@ kist.re.kr; phone, +82-2-958-6788; fax, +82-2-958-6919.
’ ACKNOWLEDGMENT This work was supported by grants from the 21C Frontier Functional Proteomics Project funded by the Korean Ministry of Education, Science and Technology. 1086
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’ REFERENCES (1) Jemal, A.; Siegel, R.; Ward, E.; Hao, Y.; Xu, J.; Murray, T.; Thun, M. J. Cancer statistics, 2008. Ca—Cancer J. Clin. 2008, 58 (2), 71–96. (2) Mayer, E. L.; Burstein, H. J. Chemotherapy for metastatic breast cancer. Hematol. Oncol. Clin. North Am. 2007, 21 (2), 257–72. (3) Miller, K. D.; Chap, L. I.; Holmes, F. A.; Cobleigh, M. A.; Marcom, P. K.; Fehrenbacher, L.; Dickler, M.; Overmoyer, B. A.; Reimann, J. D.; Sing, A. P.; Langmuir, V.; Rugo, H. S. Randomized phase III trial of capecitabine compared with bevacizumab plus capecitabine in patients with previously treated metastatic breast cancer. J. Clin. Oncol. 2005, 23 (4), 792–9. (4) Bartsch, R.; Wenzel, C.; Gampenrieder, S. P.; Pluschnig, U.; Altorjai, G.; Rudas, M.; Mader, R. M.; Dubsky, P.; Rottenfusser, A.; Gnant, M.; Zielinski, C. C.; Steger, G. G. Trastuzumab and gemcitabine as salvage therapy in heavily pre-treated patients with metastatic breast cancer. Cancer Chemother. Pharmacol. 2008, 62 (5), 903–10. (5) Gyorffy, B.; Serra, V.; Jurchott, K.; Abdul-Ghani, R.; Garber, M.; Stein, U.; Petersen, I.; Lage, H.; Dietel, M.; Schafer, R. Prediction of doxorubicin sensitivity in breast tumors based on gene expression profiles of drug-resistant cell lines correlates with patient survival. Oncogene 2005, 24 (51), 7542–51. (6) Rottenberg, S.; Nygren, A. O.; Pajic, M.; van Leeuwen, F. W.; van der Heijden, I.; van de Wetering, K.; Liu, X.; de Visser, K. E.; Gilhuijs, K. G.; van Tellingen, O.; Schouten, J. P.; Jonkers, J.; Borst, P. Selective induction of chemotherapy resistance of mammary tumors in a conditional mouse model for hereditary breast cancer. Proc. Natl. Acad. Sci. U. S. A. 2007, 104 (29), 12117–22. (7) Lee, S. C.; Xu, X.; Lim, Y. W.; Iau, P.; Sukri, N.; Lim, S. E.; Yap, H. L.; Yeo, W. L.; Tan, P.; Tan, S. H.; McLeod, H.; Goh, B. C. Chemotherapyinduced tumor gene expression changes in human breast cancers. Pharmacogenet. Genomics 2009, 19 (3), 181–92. (8) Pan, C.; Kumar, C.; Bohl, S.; Klingmueller, U.; Mann, M. Comparative proteomic phenotyping of cell lines and primary cells to assess preservation of cell type-specific functions. Mol. Cell Proteomics 2009, 8 (3), 443–50. (9) Therasse, P.; Arbuck, S. G.; Eisenhauer, E. A.; Wanders, J.; Kaplan, R. S.; Rubinstein, L.; Verweij, J.; Van Glabbeke, M.; van Oosterom, A. T.; Christian, M. C.; Gwyther, S. G. New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J. Natl. Cancer Inst. 2000, 92 (3), 205–16. (10) Keller, A.; Nesvizhskii, A. I.; Kolker, E.; Aebersold, R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 2002, 74 (20), 5383–92. (11) Nesvizhskii, A. I.; Keller, A.; Kolker, E.; Aebersold, R. A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 2003, 75 (17), 4646–58. (12) Paoletti, A. C.; Parmely, T. J.; Tomomori-Sato, C.; Sato, S.; Zhu, D.; Conaway, R. C.; Conaway, J. W.; Florens, L.; Washburn, M. P. Quantitative proteomic analysis of distinct mammalian Mediator complexes using normalized spectral abundance factors. Proc. Natl. Acad. Sci. U. S. A. 2006, 103 (50), 18928–33. (13) Zhang, B.; VerBerkmoes, N. C.; Langston, M. A.; Uberbacher, E.; Hettich, R. L.; Samatova, N. F. Detecting differential and correlated protein expression in label-free shotgun proteomics. J. Proteome Res. 2006, 5 (11), 2909–18. (14) Liedtke, C.; Wang, J.; Tordai, A.; Symmans, W. F.; Hortobagyi, G. N.; Kiesel, L.; Hess, K.; Baggerly, K. A.; Coombes, K. R.; Pusztai, L. Clinical evaluation of chemotherapy response predictors developed from breast cancer cell lines. Breast Cancer Res. Treat. 2010, 121 (2), 301–9. (15) Lucci, A.; Han, T. Y.; Liu, Y. Y.; Giuliano, A. E.; Cabot, M. C. Multidrug resistance modulators and doxorubicin synergize to elevate ceramide levels and elicit apoptosis in drug-resistant cancer cells. Cancer 1999, 86 (2), 300–11. (16) Zhang, Y.; Zhang, G.; Li, J.; Tao, Q.; Tang, W. The expression analysis of periostin in human breast cancer. J. Surg. Res. 2010, 160 (1), 102–6.
ARTICLE
(17) Zhao, L.; Lee, B. Y.; Brown, D. A.; Molloy, M. P.; Marx, G. M.; Pavlakis, N.; Boyer, M. J.; Stockler, M. R.; Kaplan, W.; Breit, S. N.; Sutherland, R. L.; Henshall, S. M.; Horvath, L. G. Identification of candidate biomarkers of therapeutic response to docetaxel by proteomic profiling. Cancer Res. 2009, 69 (19), 7696–703. (18) Han, C. L.; Chen, J. S.; Chan, E. C.; Wu, C. P.; Yu, K. H.; Chen, K. T.; Tsou, C. C.; Tsai, C. F.; Chien, C. W.; Kuo, Y. B.; Lin, P. Y.; Yu, J. S.; Hsueh, C.; Chen, M. C.; Chan, C. C.; Chang, Y. S.; Chen, Y. J. An informatics-assisted label-free approach for personalized tissue membrane proteomics: case study on colorectal cancer. Mol. Cell. Proteomics 2011, 10 (4), M110 003087. (19) Rousseau, B.; Menard, L.; Haurie, V.; Taras, D.; Blanc, J. F.; Moreau-Gaudry, F.; Metzler, P.; Hugues, M.; Boyault, S.; Lemiere, S.; Canron, X.; Costet, P.; Cole, M.; Balabaud, C.; Bioulac-Sage, P.; ZucmanRossi, J.; Rosenbaum, J. Overexpression and role of the ATPase and putative DNA helicase RuvB-like 2 in human hepatocellular carcinoma. Hepatology 2007, 46 (4), 1108–18. (20) Jaattela, M.; Wissing, D.; Kokholm, K.; Kallunki, T.; Egeblad, M. Hsp70 exerts its anti-apoptotic function downstream of caspase-3like proteases. EMBO J. 1998, 17 (21), 6124–34. (21) Jain, D. Cardiotoxicity of doxorubicin and other anthracycline derivatives. J. Nucl. Cardiol. 2000, 7 (1), 53–62. (22) Riddick, D. S.; Lee, C.; Ramji, S.; Chinje, E. C.; Cowen, R. L.; Williams, K. J.; Patterson, A. V.; Stratford, I. J.; Morrow, C. S.; Townsend, A. J.; Jounaidi, Y.; Chen, C. S.; Su, T.; Lu, H.; Schwartz, P. S.; Waxman, D. J. Cancer chemotherapy and drug metabolism. Drug Metab. Dispos. 2005, 33 (8), 1083–96. (23) Davies, T. H.; Sanchez, E. R. Fkbp52. Int. J. Biochem. Cell Biol. 2005, 37 (1), 42–7. (24) Davies, T. H.; Ning, Y. M.; Sanchez, E. R. A new first step in activation of steroid receptors: hormone-induced switching of FKBP51 and FKBP52 immunophilins. J. Biol. Chem. 2002, 277 (7), 4597–600. (25) Rees-Unwin, K. S.; Craven, R. A.; Davenport, E.; Hanrahan, S.; Totty, N. F.; Dring, A. M.; Banks, R. E.; Morgan, G. J.; Davies, F. E. Proteomic evaluation of pathways associated with dexamethasonemediated apoptosis and resistance in multiple myeloma. Br. J. Haematol. 2007, 139 (4), 559–67. (26) Pei, H.; Li, L.; Fridley, B. L.; Jenkins, G. D.; Kalari, K. R.; Lingle, W.; Petersen, G.; Lou, Z.; Wang, L. FKBP51 affects cancer cell response to chemotherapy by negatively regulating Akt. Cancer Cell 2009, 16 (3), 259–66. (27) Li, L.; Lou, Z.; Wang, L. The role of FKBP5 in cancer aetiology and chemoresistance. Br. J. Cancer 2010. (28) Ward, B. K.; Mark, P. J.; Ingram, D. M.; Minchin, R. F.; Ratajczak, T. Expression of the estrogen receptor-associated immunophilins, cyclophilin 40 and FKBP52, in breast cancer. Breast Cancer Res. Treat. 1999, 58 (3), 267–80. (29) Kim, H. J.; Kang, H. J.; Lee, H.; Lee, S. T.; Yu, M. H.; Kim, H.; Lee, C. Identification of S100A8 and S100A9 as serological markers for colorectal cancer. J. Proteome Res. 2009, 8 (3), 1368–79. (30) Cross, S. S.; Hamdy, F. C.; Deloulme, J. C.; Rehman, I. Expression of S100 proteins in normal human tissues and common cancers using tissue microarrays: S100A6, S100A8, S100A9 and S100A11 are all overexpressed in common cancers. Histopathology 2005, 46 (3), 256–69. (31) Zhu, H.; Pei, H. P.; Zeng, S.; Chen, J.; Shen, L. F.; Zhong, M. Z.; Yao, R. J.; Shen, H. Profiling protein markers associated with the sensitivity to concurrent chemoradiotherapy in human cervical carcinoma. J. Proteome Res. 2009, 8 (8), 3969–76. (32) Thorey, I. S.; Roth, J.; Regenbogen, J.; Halle, J. P.; Bittner, M.; Vogl, T.; Kaesler, S.; Bugnon, P.; Reitmaier, B.; Durka, S.; Graf, A.; Wockner, M.; Rieger, N.; Konstantinow, A.; Wolf, E.; Goppelt, A.; Werner, S. The Ca2+-binding proteins S100A8 and S100A9 are encoded by novel injury-regulated genes. J. Biol. Chem. 2001, 276 (38), 35818–25. (33) Baumann, H.; Gauldie, J. The acute phase response. Immunol. Today 1994, 15 (2), 74–80. (34) Ritchie, R. F.; Palomaki, G. E.; Neveux, L. M.; Navolotskaia, O. Reference distributions for the negative acute-phase proteins, albumin, transferrin, and transthyretin: a comparison of a large cohort to the world’s literature. J. Clin. Lab. Anal. 1999, 13 (6), 280–6. 1087
dx.doi.org/10.1021/pr2008187 |J. Proteome Res. 2012, 11, 1078–1088
Journal of Proteome Research
ARTICLE
(35) Dessein, A. F.; Stechly, L.; Jonckheere, N.; Dumont, P.; Monte, D.; Leteurtre, E.; Truant, S.; Pruvot, F. R.; Figeac, M.; Hebbar, M.; Lecellier, C. H.; Lesuffleur, T.; Dessein, R.; Grard, G.; Dejonghe, M. J.; de Launoit, Y.; Furuichi, Y.; Prevost, G.; Porchet, N.; Gespach, C.; Huet, G. Autocrine induction of invasive and metastatic phenotypes by the MIF-CXCR4 axis in drug-resistant human colon cancer cells. Cancer Res. 2010, 70 (11), 4644–54. (36) Coussens, L. M.; Werb, Z. Inflammation and cancer. Nature 2002, 420 (6917), 860–7. (37) Cicchillitti, L.; Di Michele, M.; Urbani, A.; Ferlini, C.; Donat, M. B.; Scambia, G.; Rotilio, D. Comparative proteomic analysis of paclitaxel sensitive A2780 epithelial ovarian cancer cell line and its resistant counterpart A2780TC1 by 2D-DIGE: the role of ERp57. J. Proteome Res. 2009, 8 (4), 1902–12. (38) Gong, F.; Peng, X.; Zeng, Z.; Yu, M.; Zhao, Y.; Tong, A. Proteomic analysis of cisplatin resistance in human ovarian cancer using 2-DE method. Mol. Cell. Biochem. 2011, 348 (12), 141–7. (39) Thornburg, J. M.; Nelson, K. K.; Clem, B. F.; Lane, A. N.; Arumugam, S.; Simmons, A.; Eaton, J. W.; Telang, S.; Chesney, J. Targeting aspartate aminotransferase in breast cancer. Breast Cancer Res. 2008, 10 (5), R84. (40) Zhou, M.; Zhao, Y.; Ding, Y.; Liu, H.; Liu, Z.; Fodstad, O.; Riker, A. I.; Kamarajugadda, S.; Lu, J.; Owen, L. B.; Ledoux, S. P.; Tan, M. Warburg effect in chemosensitivity: targeting lactate dehydrogenaseA re-sensitizes taxol-resistant cancer cells to taxol. Mol. Cancer 2010, 9, 33. (41) Heitz, F.; Harter, P.; Ewald-Riegler, N.; Papsdorf, M.; Kommoss, S.; du Bois, A. Poly(ADP-ribosyl)ation polymerases: mechanism and new target of anticancer therapy. Expert Rev. Anticancer Ther. 2010, 10 (7), 1125–36. (42) Munteanu, E.; Verdier, M.; Grandjean-Forestier, F.; Stenger, C.; Jayat-Vignoles, C.; Huet, S.; Robert, J.; Ratinaud, M. H. Mitochondrial localization and activity of P-glycoprotein in doxorubicin-resistant K562 cells. Biochem. Pharmacol. 2006, 71 (8), 1162–74. (43) Kutuk, O.; Letai, A. Alteration of the mitochondrial apoptotic pathway is key to acquired paclitaxel resistance and can be reversed by ABT-737. Cancer Res. 2008, 68 (19), 7985–94. (44) Naresh, A.; Thor, A. D.; Edgerton, S. M.; Torkko, K. C.; Kumar, R.; Jones, F. E. The HER4/4ICD estrogen receptor coactivator and BH3-only protein is an effector of tamoxifen-induced apoptosis. Cancer Res. 2008, 68 (15), 6387–95. (45) Solazzo, M.; Fantappie, O.; D’Amico, M.; Sassoli, C.; Tani, A.; Cipriani, G.; Bogani, C.; Formigli, L.; Mazzanti, R. Mitochondrial expression and functional activity of breast cancer resistance protein in different multiple drug-resistant cell lines. Cancer Res. 2009, 69 (18), 7235–42. (46) Pagliarini, D. J.; Calvo, S. E.; Chang, B.; Sheth, S. A.; Vafai, S. B.; Ong, S. E.; Walford, G. A.; Sugiana, C.; Boneh, A.; Chen, W. K.; Hill, D. E.; Vidal, M.; Evans, J. G.; Thorburn, D. R.; Carr, S. A.; Mootha, V. K. A mitochondrial protein compendium elucidates complex I disease biology. Cell 2008, 134 (1), 112–23. (47) Liu, H.; Liu, Y.; Zhang, J. T. A new mechanism of drug resistance in breast cancer cells: fatty acid synthase overexpression-mediated palmitate overproduction. Mol. Cancer Ther. 2008, 7 (2), 263–70.
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