Two-dimensional electrophoretic comparison of metastatic and non-metastatic human breast tumors using in vitrocultured epithelial cells derived from the cancer tissues

The samples were obtained in the years 1999 – 2002 from women who underwent partial breast resection or radical mastectomy at the General Faculty Hospital in Prague. Patients were chosen unselectively at the time of operation. The patient's written informed consent approved by the Ethical committee of the General Faculty Hospital in Prague was obtained prior to surgery. The morphology of tumors was determined and immunocytochemical staining for hormonal receptors (ER, PR), HER 2/neu and antigen Ki67 was performed. The patients were treated according to the stage-adjusted therapeutic standards. We estimated the clinical outcome of the patients. The patients with follow-up at least three years were chosen for further analysis. The patients were divided into two groups: distant metastase-free after three years and patients with proven distant metastases.

Paraffin sections 5 μm from formalin-fixed tissues were used. The tissue sections were incubated with primary antibodies ER, Dako (Glostrup, Denmark), clone 1D5, dilution 1 : 100; PR, Novocastra (Newcastle, UK) clone 16, dilution 1 : 100; Ki67, Novocastra (Newcastle, UK) clone MIB-1, dilution 1 : 50. Immunodetection was performed with the universal immuno-peroxidase polymer Histofine, Nichirei Biosciences INC (Tokyo, Japan). Detection of HER 2/neu was performed using HercepTest TM assay detection system, Dako (Glostrup, Denmark). Five percent 3,3'-diaminobenzidine tetrahydrochloride chromogen solution was used for visualization. Positive and negative controls were included in each run of slides.

Primary cell cultures were isolated from biopsies of human breast carcinomas. The cells were cultured by the 3T3 feeder-layer technique [12, 17].

The cells in the second or third passage were grown to confluence, harvested and stored in liquid nitrogen in the culture medium containing 10% dimethylsulfoxid and 20% of bovine serum. The cells designated for 2-DE analyses were thawed, seeded, cultivated to confluent layers and harvested as described in Selicharova et al. [15]. Out of 120 cultivated samples, primary cultures from tumor tissue of 23 patients were suitable for further 2-DE based analysis because of a sufficient amount of cultivated cells (about five millions).

The cell lysate (70 μg of proteins) in rehydration buffer composed of 7 M urea, 2 M thiourea, 4% (w/v) CHAPS, 50 mM DTT, 0.8% (v/v) ampholytes (pH 3–10) was applied to 18 cm linear IPG strips pH 4–7, GE Healthcare (Uppsala, Sweden). 2-DE was performed exactly as described [15]. Briefly, the IEF of rehydrated strips was performed with stepwise increasing voltage as follows: 250 V for 1 h, 500 V for 1 h, 1000 V for 2 h and 10,000 V for the time period necessary to reach 70,000 Vh in total. The focused strips were equilibrated for 30 min in the solution containing 6 M urea, 20% (v/v) glycerol, 2% (w/v) SDS, 0.05 M Tris/HCl pH 8.8 and 2% (v/v) DTT with traces of Bromphenol Blue. Then free thiol groups were alkylated for 30 min in the same solution containing 2.5% (w/v) iodoacetamide instead of DTT. The SDS-PAGE on gradient gels (8–16%, 4% stacking gel, 19 × 22 cm) was performed in 0.025 M Tris/0.192 M Glycine with 0.1% (w/v) SDS running buffer for 1 h at 16 mA and for about 9 h at 24 mA per gel till the Bromphenol Blue line has reached the bottom of the gel. Three silver-stained analytical gels were prepared from each sample. All the common chemicals were from Sigma (St. Louis, USA) and Fluka (Buchs, Switzerland).

Gels were scanned by a GS-800 Calibrated Densitometer, Bio-Rad (Hercules, CA) at 700 dpi resolution. The images were further processed by PDQuest Advaced 8.0.1 2D Gel Analysis Software, Bio-Rad (Hercules, CA). For computational purposes the file size was reduced to 50% and the images were cropped to frame the same clusters of spots. One or two representative gels per each cell population were used to create a match-set. Spots were detected and matched automatically to a master gel selected by the software. The spot detection and matching were edited manually. The spot boundary tool was applied to detect large spots. The patterns in sections of the gels in appropriate magnification were checked and spots were added manually to the master gel to allow matching unique spots present in the individual gels. The spot quantity table containing all matched spots was generated. The quantity of missing spots was estimated by the software. The means of logarithmic ratios method was used for normalization. The mean of log ratios method of normalization calculates the normalization factor of a gel by calculating the mean of all log ratios (log spot quantity of gel/log spot quantity of master gel) of all matched spots (master gel – gel). The quantity table was exported to a spreadsheet .xls file and submitted to statistical analyses (Additional data file 1).

Independent statistical tests were performed using R/computing environment in version 2.6.0 [18] and by adapting R/maanova package version 1.8.0. [19] which has been designed for processing microarray data. It implements sample shuffling. R/maanova provides a permutation method to calculate the nominal permutation p-values for each gene (i.e. spot intensity) using Fs test statistics. Because of multiple testing, the p-values were adjusted to false discovery rate [21].

The relations among spot intensities and clinical image were analyzed on the basis of data-mining technique GUHA [20]. The analyzed data were stored in a source database in the form of a table of n rows (objects = 2-DE gels) and m columns (variables = spot intensities). The variables were dichotomized. Each variable was categorized. Categories were actually subsets of ranges of variables given by cut points. The category was evaluated as 1 if the value of a variable dropped within a subset given by the respective cut point otherwise it was evaluated as 0. The settings of cut points were based on the specification and behavior of an impurity function [22]. In our application we employed the entropy function as impurity function for each intensity variable classified with respect to the clinical variable metastases. The impurity function takes its minimum if all objects are classified as 0 or 1. The maximum is reached if roughly a 50/50 mixture of classes is present in the group. The idea is to split the original group into two sub-groups in such a way that impurity decreases in a maximal way, i.e., that the sum of impurities of sub-groups is minimized. We identified optimal splits and corresponding optimal cut points by a script in MATLAB [23]. The cut points then enabled us to categorize spot intensities. A GUHA hypothesis is determined by the ordered pair of cedents (antecedent (A) and succedent (S)) and by a quantifier. Cedents are Boolean conjunctions formed from individual categories. The length of a cedent is given by the number of categories forming the conjunction. A cedent of length = 1 corresponds to a single category (simple cedent). Cedents of length > 1 are called compound cedents. For a given object a cedent can be evaluated as 1 (true) or 0 (false). The evaluation stems from the evaluation of single categories forming the cedent and rules for Boolean conjunction. For a given pair of cedents, we can construct a corresponding contingency table by evaluating cedents for all objects in the database and then perform statistical tests on this table. The employed quantifier determines the type of test. We used the Fisher quantifier corresponding to Fisher's exact test. A hypothesis is formally written A ~S, and if it is valid (statistically significant), it is revealed in the GUHA output. Q-values were calculated by q-val package [21]. Statistical tests were two-sided at the 5% level of significance.

The spots generated from the statistical analyses as significantly changed were researched by their spot ID in the match set created by the PDQuest software. The relative molecular masses (Mr) and isoelectric points (pI) were estimated for each protein from their positions in the gels. The statistically important spots were considered for identification. The preparative 2-DE gels were prepared from cells with a relatively high content of the protein of interest using 400 μg of the cell lysate. They were stained with colloidal Coomassie stain [24].

Selected spots on the preparative gels were excised and destained using 50% acetonitrile in 25 mM ammonium bicarbonate, dehydrated with 200 μl of acetonitrile for 5 min at 30°C using thermomixer comfort, Eppendorf AG (Hamburg, Germany) at 30°C and then vacuum-dried in SpeedVac, Thermo Scientific (Waltham, Ma). Gel pieces were rehydrated and proteins were digested for 8 hours at 37°C in the thermomixer with 30 ng/μl trypsin (Trypsin Gold Mass Spectrometry Grade, Promega, Madison, WI) in 25 mM ammonium bicarbonate. After digestion, peptides were extracted from gel pieces using step by step extraction with acetonitrile gradient (15%–60% acetonitrile with 1% trifluoroacetic acid). The extraction was performed in sonicator, Elma (Singen, Germany) with ice cubes.

Extracted peptides were concentrated in SpeedVac, Thermo Scientific (Waltham, Ma). Tandem electrospray ionization mass spectrometry (ESI-MS/MS) was used to characterize the digests. The ESI-MS/MS was performed in a quadrupole-time of flight (Q-TOF) tandem Micro mass spectrometer (Waters-Micromass) equipped with nanoelectrospray source and coupled to 2-D capillary chromatography CapLC (Waters). Chromatographic separation was achieved using the symmetry 300 Å OPTI-PAC (1 cm × 5 μm) trap column (Waters) and Atlantis dC18 (75 μm × 10 cm × 3 μm) capillary column (Waters). Data were processed by proteomic software Proteinlynx global server 2.1 (Waters) (LC-MS/MS).

Primary cultures of breast epithelial cells available for 2-DE analysis were cultivated from biopsy specimens of 23 breast cancer patients. An example of ER, PR and HER 2/neu positive immunocytochemical staining of tumor tissue of patient L116 together with in vitro outgrowths of the cells are shown in Figure 1(A–E). The example of positive staining for Ki67 with MIB-1 in Figure 1(F) is shown for sample L122. Dominating histology of tumors were invasive ductal carcinomas (16 patients), mostly high grade, five lobular carcinomas, one of them pleomorphic, one mucinous and one medulary carcinoma. Sixteen tumors were ER-positive, seven were ER-negative including three carcinomas, which were triple-negative (ER-negative, PR-negative, HER 2/neu negative). Four tumors were stained as HER 2/neu 3+ and five other were HER 2/neu 2+. The main tumor characteristics are given in Table 1. Metastatic spreading was proven in seven patients during postoperative follow-up. The pattern of metastases and time to relapse are given in Table 2.

We intended to find differences in protein expression between the samples from metastase-positive and metastase-negative patient groups.

2-DE protein maps in the pH range 4–7 were prepared in triplicate. The 2-DE protein maps were very similar in all the cases and contained all the major spots that were identified in the normal mammary epithelial (NME) cell sample as described in [15]. A typical 2-DE gel is shown in Figure 2. The main protein spots are marked. The gels were analyzed by PDQuest Advanced 8.0.1 2D Gel Analysis Software. The software is not designed to compare multiple groups of samples that arise from our experimental setting (23 samples in triplicate). We compromised between the quantity of data and our ability to process them. The spot patterns in the area of the gels above Mr 50 kDa were not homogeneous and were not suitable for spot detection and matching by the PDQuest software due to huge protein clusters formed by cytokeratins and inconsistent streaks. Only spots in the pH range from 4.2 to 6.8 and Mr range from 15 kDa to 40 kDa were analyzed, matched and quantified. The area of the gel analyzed by PDQuest is outlined in Figure 2. A match-set containing 44 gels was created. Twenty-one samples were represented by two gels. Two samples were represented for capacity and gel quality reasons by one gel only. The correlation coefficients between technical replicates of gels ranged from 0.76 to 0.88. Matching of the most typical spots was checked and corrected manually. The spot patterns in distinct areas of gels were studied and spots unique to individual gels were added to the master gel to allow their analysis. Finally, 245 spots were matched and 70 of them were matched to all gels. The log-normalized spot quantity table containing all matched spots was generated and submitted to statistical analyses (Additional file 1).

We searched for protein spots changed between the seven samples of metastase-positive and sixteen samples of metastase-negative groups.

For the GUHA analysis we averaged the intensity values for each sample. Categorization of 245 spot variables was determined on the basis of specification of maximal impurity decrease generating cut points. The basic task was to search for statistically significant relations between the presence/absence of spots and their classifications (metastatic/non-metastatic). In GUHA task settings, the antecedent variables were spot variables, succedent variables clinic variables. The lengths of antecedent and succedent were set to 1 and Fisher quantifier was employed. We searched for hypotheses rejecting the null hypothesis on independence between the presence/absence of spots and classification of 2-DE gels. Tests were taken at the 0.05 probability level. However, because we performed many simultaneous tests we had to adjust the significance level using the q-value. The q-value was calculated from the value of corresponding Fisher statistics. Q-values represent the approximation of maximal false-positive rate among the proteins supposed to be significant. Spots 7305 and 1606 fulfilled the criteria of q-value ≤ 0.05. The data from the GUHA analysis are presented in Table 3. We are showing 10 spots with the lowest q-values. The complete data can be found in Additional file 2.

The false discovery rate adjusted p-value and the fold change were calculated using the R/maanova package [19]. All spots are presented in volcano plot (Huy) with log 2 fold change on x-axis and -log 10 (adjusted P-value) as y-axis (Figure 3). Spots 7305 and 5104 fulfilled statistical criteria of adjusted p-value ≤ 0.05 and log2 fold change ≥ 2 estimated by maanova. The data from maanova analysis are presented in Table 4. We are showing 10 spots with the lowest p-values and log2 fold change ≥ 1. The complete data can be found in Additional file 3.

We considered the spots 7305, 5104 and 1606 that have been found by either type of computing as significantly changed in correlation with the metastases. The spots 5104 and 7305 were negatively correlated with the metastatic phenotype, the spot 1606 was correlated positively.

The spots found by the statistical analyses were re-searched in the match-set created by the PDQuest software. The spots are marked in Figure 2 and enlarged sections of representative gels showing the respective spots in the metastatic and non-metastatic groups are presented in Figure 4. The data from protein identification are presented in Table 5. The identity of all peptides has been confirmed at least by three fragment ions in the MS/MS spectrum.

Spot 1606 had a diffused pattern in the 2-DE gel. It was identified as nucleophosmin, [Swiss-Prot:P06748]. Spots 7305 and 5104 were of very low intensity as could be estimated from the cut-off values and judged from Figures 2 and 4. Spot 7305 was identified as 2,3-trans-enoyl-CoA isomerase, [Swiss-Prot:P42126]. Spot 5104 was identified as glutathione peroxidase 1, [Swiss-Prot:P07203].