Prediction of Recurrence and Survival for Triple-Negative Breast Cancer (TNBC) by a Protein Signature in Tissue Samples
2015; Elsevier BV; Volume: 14; Issue: 11 Linguagem: Inglês
10.1074/mcp.m115.048967
ISSN1535-9484
AutoresMario Campone, Isabelle Valo, Pascal Jézéquel, Marie Moreau, Alice Boissard, L. Campion, Delphine Loussouarn, Véronique Verrièle, Olivier Coqueret, Catherine Guette,
Tópico(s)Ferroptosis and cancer prognosis
ResumoTo date, there is no available targeted therapy for patients who are diagnosed with triple-negative breast cancers (TNBC). The aim of this study was to identify a new specific target for specific treatments. Frozen primary tumors were collected from 83 adjuvant therapy-naive TNBC patients. These samples were used for global proteome profiling by iTRAQ-OFFGEL-LC-MS/MS approach in two series: a training cohort (n = 42) and a test set (n = 41). Patients who remains free of local or distant metastasis for a minimum of 5 years after surgery were classified in the no-relapse group; the others were in the relapse group. OPLS and Kaplan–Meier analyses were performed to select candidate markers, which were validated by immunohistochemistry. Three proteins were identified in the training set and validated in the test set by Kaplan–Meier method and immunohistochemistry (IHC): TrpRS as a good prognostic markers and DP and TSP1 as bad prognostic markers. We propose the establishment of an IHC test to calculate the score of TrpRS, DP, and TSP1 in TNBC tumors to evaluate the degree of aggressiveness of the tumors. Finally, we propose that DP and TSP1 could provide therapeutic targets for specific treatments. To date, there is no available targeted therapy for patients who are diagnosed with triple-negative breast cancers (TNBC). The aim of this study was to identify a new specific target for specific treatments. Frozen primary tumors were collected from 83 adjuvant therapy-naive TNBC patients. These samples were used for global proteome profiling by iTRAQ-OFFGEL-LC-MS/MS approach in two series: a training cohort (n = 42) and a test set (n = 41). Patients who remains free of local or distant metastasis for a minimum of 5 years after surgery were classified in the no-relapse group; the others were in the relapse group. OPLS and Kaplan–Meier analyses were performed to select candidate markers, which were validated by immunohistochemistry. Three proteins were identified in the training set and validated in the test set by Kaplan–Meier method and immunohistochemistry (IHC): TrpRS as a good prognostic markers and DP and TSP1 as bad prognostic markers. We propose the establishment of an IHC test to calculate the score of TrpRS, DP, and TSP1 in TNBC tumors to evaluate the degree of aggressiveness of the tumors. Finally, we propose that DP and TSP1 could provide therapeutic targets for specific treatments. Triple-negative breast cancers (TNBC)1 are defined by a lack of expression of estrogen (ER), progesterone (PR), and HER2/neu receptors and account for about 15% of all breast cancers. This subtype is associated with poor prognosis (1.Foulkes W.D. Smith I.E. Reis-Filho J.S. Triple-negative breast cancer.N. Engl. J. Med. 2010; 363: 1938-1948Crossref PubMed Scopus (2694) Google Scholar) in terms of distant free survival (DFS) and overall survival (OS), and to date, there is no clinically available targeted therapy for patients diagnosed with TNBC. Because of the absence of specific treatment guidelines for this group of patients, TNBC are managed with standard adjuvant chemotherapy (2.Harris L. Fritsche H. Mennel R. Norton L. Ravdin P. Taube S. Somerfield M.R. Hayes D.F. Bast Jr., R.C. American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer.J. Clin. Oncol. 2007; 25: 5287-5312Crossref PubMed Scopus (1893) Google Scholar), which, however, seems to be less effective in those cancers. In order to improve survival, it is important to determine new specific-targeted treatment. A proteomic analysis has several inherent advantages over a genomic approach in that measured mRNA levels do not necessarily correlate to corresponding protein levels. In addition, protein detection is probably also more reflective of the tumor microenvironment. Several proteomic studies have been conducted on TNBC (3.Gámez-Pozo A. Ferrer N.I. Ciruelos E. López-Vacas R. Martínez F.G. Espinosa E. Vara J.Á. Shotgun proteomics of archival triple-negative breast cancer samples.Proteomics Clin. Appl. 2013; 3–4: 283-291Crossref Scopus (23) Google Scholar, 4.Kim M.J. Kim D.H. Jung W.H. Koo J.S. Expression of metabolism-related proteins in triple-negative breast cancer.Int. J. Clin. Exp. Pathol. 2014; 7: 301-312PubMed Google Scholar, 5.Muñiz Lino M.A. Palacios-Rodríguez Y. Rodríguez-Cuevas S. Bautista-Piña V. Marchat L.A. Ruíz-García E. Astudillo de la Vega H. González-Santiago A.E. Flores-Pérez A. Díaz-Chavez J. Carlos-Reyes Á. Álvarez-Sánchez E. López-Camarillo C. Comparative proteomic profiling of triple-negative breast cancer reveals that up-regulation of RhoGDI-2 is associated to the inhibition of caspase 3 and caspase 9.J. Proteomics. 2014; 111: 198-211Crossref PubMed Scopus (18) Google Scholar), but no proteomic study was conducted on large cohorts including the clinical outcome of the patients, except a recent comparative proteome analysis that identified a 11-protein signature for aggressive TNBC in a large cohort of 93 microdissected tumors (6.Liu N.Q. Stingl C. Look M.P. Smid M. Braakman RB. De Marchi T. Sieuwerts AM. Span PN. Sweep F.C. Linderholm B.K. Mangia A. Paradiso A. Dirix LY. Van Laere SJ. Luider TM. Martens JW. Foekens JA. Umar A. Comparative proteome analysis revealing an 11-protein signature for aggressive triple-negative breast cancer.J. Natl. Cancer Inst. 2014; 106: 376Crossref Scopus (43) Google Scholar). Although microdissection was necessary to elucidate the contribution of TNBC cells, it did not reflect the tumor with its microenvironment that is increasingly described as fundamental to explain the tumor outcome. Thus, it is now recognized that carcinomas derive from phenomena that occur in tissues, not in individual cancer cells. From this perspective, the microenvironment becomes an integral, essential part of the tumor (7.Albini A. Sporn M.B. The tumour microenvironment as a target for chemoprevention.Nat. Rev. Cancer. 2007; 7: 139-147Crossref PubMed Scopus (663) Google Scholar, 8.Bizzarri M. Cucinal A. Proietti S. The tumor microenvironment as a target for anticancer treatment.Oncobiol. Targets. 2010; 1: 3-11Crossref Google Scholar). In this context, taking into account the tumor microenvironment, we investigated a cohort of 83 TNBC samples without microdissection by a quantitative proteomic approach using iTRAQ labeling. Based on clinical data, we established a protein signature of the most aggressive tumors. From these differentially expressed proteins, some of them appeared to be potential therapeutic targets. The study involved 83 patients diagnosed and treated at the Institut of Cancérologie de l'Ouest (ICO) for a TNBC, between early 1998 and 2007. The primary inclusion criterion was an adequate fresh tumor obtained from a resected tumor sample (see below). Patients were included if they fulfilled the following criteria: (a) female primary unilateral invasive ER/PR and HER2 negative–breast carcinoma without previous or concomitant malignancies; (b) T1T2, N0N1 N2 N3-, M0 staging according to UICC criteria; (c) older than 18 years old; and (d) surgical first-line treatment. All patients showed no evidence of distant metastasis at the time of diagnosis. None had received chemotherapy, endocrine therapy, or radiation therapy prior to surgery. Treatment decisions were based solely on consensus recommendations at the time of diagnosis. All the patients received the same adjuvant chemotherapy (FEC100) and radiotherapy treatments. Patients were followed up for disease evolution. The 83 tumors were divided in two cohorts: a training cohort (n = 42) corresponding to patients diagnosed at ICO Paul Papin (Angers) and a test cohort (n = 41) corresponding to patients diagnosed at ICO René Gauducheau (Nantes). The clinicopathological characteristics of these TNBC cohorts are listed in Supplemental data S1. Follow-up data were collected for all patients, including, disease-free survival (DFS; time from diagnosis to first recurrence of the disease or contralateral breast cancer or second primary other cancer) and overall survival (OS, time from diagnosis to death from any cause). Recurrences were defined as locoregional (breast, mammary region, or regional lymph nodes) or metastatic (visceral or not). Informed consent was obtained from patients to use their surgical specimens and clinicopathological data for research purposes, as required by the French Committee for the Protection of Human Subjects. This study did not need ethical approval. Pathological data were reviewed by two pathologists. Tumor size (pT) was measured on fresh resection specimens, as the maximum diameter (mm) of the tumor. Histological type was determined according to the WHO classification and histological grade according to the Elston and Ellis methods. ER and PR status were accessed by immunochemistry on representative formalin-fixed tumors blocks at a 4-μm thickness. Tumors were determined as negative when < 10% cells stained positive. All patients where HER-2 negative, that means an immunostaining 1+, score according to the HercepTest scoring system or 2+ without HER-2 gene amplification investigated by in situ fluorescence. All specimens were collected immediately after surgery, snap frozen, and stored in liquid nitrogen until the time of analysis. The time between the resection of the breast tumor and its freezing is less than 1 h. We also selected four normal macroscopic areas for our control pool. Frozen sections (12 μm thick) of either TNBC or normal areas were embedded in OCT and cut on a cryostat (Bright lnstrument Co. Ltd., St. Margarets Way, UK). Specific sections were stained with toluidine blue for visual reference, and each tissue section from all specimens was evaluated by experienced pathologists for cancer cell proportion determination. Samples containing less than 75% of tumor cells were removed. Ten frozen sections per tumor were lysed in a buffer consisting of 0.1 m Tris-HCl, pH 8.0; 0.1 m DTT; and 4% SDS at 95 °C for 90 min. Detergent was removed from the lysates, and the proteins were digested with trypsin using the FASP protocol (9.Wiśniewski J.R. Zougman A. Nagaraj N. Mann M. Universal sample preparation method for proteome analysis.Nat. Methods. 2009; 6: 359-362Crossref PubMed Scopus (5042) Google Scholar) using spin ultrafiltration units of nominal molecular weight cut of 30,000. Using YM-30 microcon filter units (Cat. No. MRCF0R030, Millipore) containing protein concentrates, 200 μl of 8 m urea in 0.1 m Tris/HCl, pH 8.5 (UA), was added, and samples were centrifuged at 14,000 g at 20 °C for 8 min. This step was repeated three times. Then 6 μl of 200 mm MMTS in 8 m urea was added to the filters, and the samples were incubated for 20 min. Filters were washed three times with 200 μl of 8 m UA followed by six washes with 100 μl 0.5 m TEAB. Finally, trypsin (AB sciex, Carlsbad, CA) was added in 100 μl 0.5 m TEAB to each filter. The protein to enzyme ratio was 100:1. Samples were incubated overnight at 37 °C, and released peptides were collected by centrifugation. Samples were then dried completely using a SpeedVac and resuspended in 100 μl of 0.5% trifluoroacetic acid (TFA) in 5% acetonitrile and were desalted via PepClean C-18 spin columns (Pierce Biotechnology, Rockford, IL). Peptide content was determined using Micro BCA Protein Assay Kit (Pierce-Thermo Scientific). Each peptide solution was labeled at room temperature for 2 h with one iTRAQ reagent vial previously reconstituted with 70 μl of ethanol for 4plex iTRAQ reagent and reconstituted with 50 μl of isopropanol for 8plex iTRAQ reagent. A mixture containing small aliquots from each labeled sample was analyzed by MS/MS to determine a proper mixing ratio to correct for unevenness in peptide yield. Labeled peptides were then mixed in 1:1:1:1 (or 1:1:1:1:1:1:1:1) ratio. Peptide mixture was then dried completely using a SpeedVac. For pI-based peptide separation, we used the 3100 OFFGEL Fractionator (Agilent Technologies, Böblingen, Germany) with a 24-well setup using our protocol (10.Ernoult E. Guette C. OFFGEL-Isoelectric Focusing Fractionation for the analysis of complex proteome.Neuroproteomics Edited by Ka Wan Li, Humana Press Inc, Springer protocols U.S. 2011; : 145-158Google Scholar). Briefly, prior to electrofocusing, samples were desalted onto a Sep-Pak C18 cartridge (Waters). For the 24-well setup, peptide samples were diluted to a final volume of, respectively, 3.6 ml using OFFGEL peptide sample solution. To start, the IPG gel strip of 24 cm-long (GE Healthcare, München, Germany) with a 3–10 linear pH range was rehydrated with the Peptide IPG Strip Rehydradation Solution according to the manufacturer protocol for 15 min. Then, 150 μl of sample was loaded in each well. Electrofocusing of the peptides was performed at 20 °C and 50 μA until the 50 kVh level was reached. After focusing, the 24 peptide fractions were withdrawn, and the wells were washed with 200 μl of a solution of water/methanol/formic acid (49/50/1). After 15 min, the washing solutions were pooled with their corresponding peptide fraction. All fractions were evaporated by centrifugation under vacuum and maintained at −20 °C. Just prior nano-LC, the fractions were resuspended in 20 μl of H2O with 0.1% (v/v) TFA. The samples were separated on an Ultimate 3,000 nano-LC system (Dionex, Sunnyvale, USA) using a C18 column (PepMap100, 3 μm, 100 A, 75 μm id × 15 cm, Dionex) at 300 nl/min flow rate. Buffer A was 2% ACN in water with 0.05% TFA, and buffer B was 80% ACN in water with 0.04% TFA. Peptides were desalted for 3 min using only buffer A on the precolumn, followed by a separation for 105 min using the following gradient: 0 to 20% B in 10 min, 20% to 45% B in 85 min, and 45% to 100% B in 10 min. Chromatograms were recorded at the wavelength of 214 nm. Peptide fractions were collected using a Probot microfraction collector (Dionex). We used CHCA (LaserBioLabs, Sophia-Antipolis, France) as MALDI matrix. The matrix (concentration of 2 mg/ml in 70% ACN in water with 0.1% TFA) was continuously added to the column effluent via a micro "T" mixing piece at 1.2 μl/min flow rate. After 12 min run, a start signal was sent to the Probot to initiate fractionation. Fractions were collected for 10 s and spotted on a MALDI sample plate (1,664 spots per plate, Applied Biosystems, Foster City, CA). MS and MS/MS analyses of offline spotted peptide samples were performed using the 5800 MALDI-TOF/TOF Analyzer (AB sciex) and 4000 Series Explorer software, version 4.0. The instrument was operated in a positive ion mode and externally calibrated using a mass calibration standard kit (AB sciex). The laser power was set between 2,800 and 3,400 for MS and between 3,600 and 4,200 for MS/MS acquisition. After screening all LC-MALDI sample positions in MS-positive reflector mode using 1,500 laser shots, the fragmentation of automatically selected precursors was performed at a collision energy of 1 kV using air as collision gas (pressure of ∼ 2 × 10−6 Torr) with an accumulation of 2,000 shots for each spectrum. MS spectra were acquired between m/z 1,000 and 4,000. For internal calibration, we used the parent ion of Glu1-fibrinopeptide at m/z 1,570.677 diluted in the matrix (30 femtomoles per spot). Up to 12 of the most intense ion signals per spot position having an S/N > 12 were selected as precursors for MS/MS acquisition. Peptide and protein identification were performed by the ProteinPilotTM Software V 4.0 (AB Sciex) using the Paragon algorithm as the search engine (11.Shilov I.V. Seymour S.L. Patel A.A. Loboda A. Tang W.H. Keating S.P. Hunter C.L. Nuwaysir L.M. Schaeffer D.A. The Paragon Algorithm, a next generation search engine that uses sequence temperature values and feature probabilities to identify peptides from tandem mass spectra.Mol. Cell. Proteomics. 2007; 6: 1638-1655Abstract Full Text Full Text PDF PubMed Scopus (1059) Google Scholar). Each MS/MS spectrum was searched for Homo sapiens species against the Uniprot/swissprot database (UniProtKB/Sprot 20,120,208 release 01, with 525,997 sequence entries). The searches were run using the fixed modification of methylmethanethiosulfate labeled cysteine parameter enabled. Other parameters such as tryptic cleavage specificity, precursor ion mass accuracy, and fragment ion mass accuracy are MALDI 5800 built-in functions of ProteinPilot software. The detected protein threshold (unused protscore (confidence) in the software was set to 1.3 to achieve 95% confidence, and identified proteins were grouped by the ProGroup algorithm (AB sciex) to minimize redundancy. The bias correction option was executed. A decoy database search strategy was also used to estimate the false discovery rate (FDR), defined as the percentage of decoy proteins identified against the total protein identification. The FDR was calculated by searching the spectral against the Uniprot H. sapiens decoy database. The estimated low FDR of 0.9% indicated a high reliability in the identified proteins. We employed a customized software package, iQuantitator (12.Schwacke J.H. Hill E.G. Krug E.L. Comte-Walters S. Schey K.L. iQuantitator: A tool for protein expression inference using iTRAQ.BMC Bioinformatics. 2009; 10: 342Crossref PubMed Scopus (46) Google Scholar, 13.Grant J.E. Bradshaw A.D. Schwacke J.H. Baicu C.F. Zile M.R. Schey K.L. Quantification of protein expression changes in the aging left ventricle of Rattus norvegicus.J. Proteome Res. 2009; 8: 4252-4263Crossref PubMed Scopus (47) Google Scholar, 14.Besson D. Pavageau A.H. Valo I. Bélanger A. Eymerit-Morin C. Moulière A. Chassevent A. Boisdron-Celle M. Morel A. Solassol J. Campone M. Gamelin E. Barré B. Coqueret O. Guette C. A quantitative proteomic approach of the different stages of colorectal cancer establishes OLFM4 as a new nonmetastatic tumor marker.Mol. Cell. Proteomics. 2011; 10M111.009712Abstract Full Text Full Text PDF PubMed Scopus (98) Google Scholar) to infer the magnitude of change in protein expression. The software infers sample-dependent changes in protein expression using Markov chain, Monte Carlo, and Bayesian statistical methods. Basically, this approach was used to generate means and 95% credible intervals (upper and lower) for each protein expression of each tumor of the training set and the test set by using peptide-level data for each component peptide. For proteins whose iTRAQ ratios were down-regulated in tissues, the extent of down-regulation was considered further significant if the higher limit of the credible interval had a value lower than 1. Conversely, for proteins whose iTRAQ ratios were up-regulated in tumors, the extent of up-regulation was considered further significant if the lower limit of the credible interval had a value greater than 1. The width of these credible intervals depends on the data available for a given protein. Since the number of peptides observed and the number of spectra used to quantify the change in expression for a given protein are taken into consideration, it is possible to detect small but significant changes in up- or down-regulation when many peptides are available. The peptide selection criteria for relative quantification were performed as follows. Only peptides unique for a given protein were considered for relative quantification, excluding those common to other proteins. In cases where a peptide could be assigned to more than one protein, it is eliminated from consideration prior to analysis. Proteins were identified on the basis of having at least two peptides with an ion score above 95% confidence. The protein sequence coverage (95%) was estimated for specific proteins by the percentage of matching amino acids from the identified peptides having confidence greater than or equal to 95% divided by the total number of amino acids in the sequence. Gene ontology (GO) terms for identified proteins were extracted, and overrepresented functional categories for differentially abundant proteins were determined by the high throughput GOMiner tool (National Cancer Institute, http://discover.nci.nih.gov.gate2.inist.fr/gominer/) (15.Zeeberg B.R. Qin H. Narasimhan S. Sunshine M. Cao H. Kane D.W. Reimers M. Stephens R.M. Bryant D. Burt S.K. Elnekave E. Hari D.M. Wynn T.A. Cunningham-Rundles C. Stewart D.M. Nelson D. Weinstein J.N. High-throughput GoMiner, an "industrial-strength" integrative gene ontology tool for interpretation of multiple-microarray experiments, with application to studies of common variable immune deficiency (CVID).BMC Bioinformatics. 2005; 6: 168Crossref PubMed Scopus (231) Google Scholar). All proteins that were subjected to iQuantitator analysis served as the background list, and GO terms with at least five proteins were used for statistical calculations. A p value for each term was calculated via the one-sided Fisher's exact test, and FDR was estimated by permutation analysis using 1,000 randomly selected sets of proteins sampled from the background list. Statistically significant (FDR<25%) GO terms were clustered based on the correlation of associated proteins to minimize potential redundancy in significant GO terms. To visualize clustering of groups, a two-way (by protein and tumor ID) hierarchal clustering was performed on log2-transformed data. Further multivariate statistics and modeling was performed with SIMCA (SIMCA 13.0, Umetrics, Sweden) (16.Eriksson L. Antti H. Gottfries J. Holmes E. Johansson E. Lindgren F. Long I. Lundstedt T. Trygg J. Wold S. Using chemometrics for navigating in the large data sets of genomics, proteomics, and metabonomics (gpm).Anal. Bioanal. Chem. 2004; 380: 419-429Crossref PubMed Scopus (221) Google Scholar). The analysis was performed on mean-centered, unit-variance-scaled data, assuming equal importance of each protein regardless of relative abundance and magnitude of variance between samples. Principal component analysis (PCA) (17.Geladi P Esbensen K. Regression on Multivariate Images -Principal Component Regression for Modeling, Prediction and Visual Diagnostic-Tools.J. Chemometr. 1991; 5: 97-111Crossref Google Scholar, 18.Wold S. Jonsson J. Sjostrom M. Sandberg M. Rännar S. DNA and peptide sequences and chemical processes multivariately modeled by principal component analysis and partial least-squares projections to latent structures.Anal. Chim. Acta. 1993; 277: 239-253Crossref Scopus (172) Google Scholar), was performed to get an overview of the data, detect clustering of the data, and pick up outliers if any. The PCA summarizes the variation of the data matrix (i.e. protein ratios) and shows the relationship between the observations. For classification and identification of proteins differentiating relapse from relapse-free tumors/patients, we used orthogonal partial least square analysis (OPLS) (19.Trygg J. Wold S. Orthogonal projections to latent structures (O-PLS).J. Chemometr. 2002; 16: 119-128Crossref Scopus (1827) Google Scholar). The OPLS analysis detects the protein expression data that covaries with the defined clinical groups. For optimization of the OPLS models, we used the variable importance in the projection value to judge protein influence (including prediction performance) on the model. The OPLS models were validated by sevenfold full cross-validation. Proteins with high variable importance in the projection throughout the cross-validation of the model (95% confidence interval) were selected for the optimized model. We used the plots of the scores predicted in the cross-validation and analysis of variance (CV-ANOVA) to evaluate the model validity. Fisher exact test were calculated for the training set versus the test set and for the relapse versus no-relapse cohorts. Survival rates were calculated using the nonparametric Kaplan–Meier method, and log-rank tests were performed to evaluate the difference in the time between recurrence and nonrecurrence groups. Multivariate Cox models were used to assess the prognostic value of each variable. The 42 tumors from the training set (ICO René Gauducheau) were studied by immunohistochemistry. The immunohistochemistry was carried out on 4-μm thick paraffin embedded sections of formalin-fixed tumor samples. Details of the antigen retrieval technique and dilution of primary antibodies (TrpRS, DP, and TPS1) are described in Supplemental data S2. The immunolabeling technique was performed by a benchmark automatized tissue staining system (Ventana Medical System, Tucson, AZ). The immunohistochemistry was evaluated semiquantitatively by the percentage of cytoplasmic staining cells, the intensity, and the presence or not of secretory granules. To exclude subjectivity, all slides were evaluated by two pathologists who had no knowledge of the patients' identities or clinical status. In discrepant cases, the two pathologists reviewed the slides together and reached a consensus. The percentage of immunopositive stained cells (A) was divided into four grades as: 70% (4). Second, the intensity of staining was scored by evaluating the average staining intensity (B) of the positive cells (0, none; 1, weak; 2, intermediate; and 3, strong). The score for each section was measured as A × B, and the result was defined as negative (-, 0), weakly positive (+, 1–3), positive (++, 4–7), and strongly positive (+++, 8–12). The immunohistochemical data were subjected to statistical analysis. All quantitative data were recorded as mean ± S.D. Comparison between multiple groups was performed by one-way ANOVA and Wilcoxon rank test (p value< .05). Individual and combined biomarker performances were investigated on the receiver operating characteristic curves with linear discriminant analysis. Linear discriminant analysis was used to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination was then used as a linear classifier. To determine how accurately the learning algorithm was able to predict data, cross-validation and bootstrapping methods were used. In leave-one-out cross-validation, one sample was removed from the dataset, and a classifier was generated using the remaining samples to predict the status of the removed sample. In 10-fold cross-validation, the data were divided into 10 subsets of approximately equal size, and 10 iterations of training and validation were performed. The 0.632+ bootstrap cross-validation uses resampling technique. The 10-fold cross-validation and bootstrapping procedures were replicated 100 times. Statistical analyses were performed using TANAGRA (v1.4.49). The training tumors were profiled by iTRAQ-LC-MS/MS approach (Supplemental data S3). The baseline clinical features of patients were similar between the ICO Paul Papin training set and the ICO René Gauducheau test cohorts, although patient tumor size were slightly bigger in the training set (Table I). The median follow-up for the good prognosis patients in the training and test sets was 168 (range = 68–279) and 203 (range = 51–413) months, respectively. In the training cohort, 14 patients experienced a relapse (13 distant metastasis and 1 contralateral), and among these patients, we recorded 11 deaths. In the test set, 17 patients experienced a relapse (15 distant metastasis and 2 contralateral), and among these patients, we recorded 13 deaths. Clinical data used for data analysis were updated until January 2013.Table IClinicopathological characteristics of patients for tissue proteomic studiesPatient characteristicsTraining set (n = 42)Test set (n = 41)Age (years) median [min-max]55[28–71]57[29–84]< 50 (%)15 (34.8)10 (24.4)> = 50 (%)28 (65.2)31 (75.6)No-recurrenceRecurrenceNo-recurrenceRecurrence28 (67.4)14 (32.6)24 (58.5)17 (41.5)Grade 10011Grade 22051Grade 326141815Lymph node statusPositive (%)4499Negative (%)2410158pT (mm)8–10111111–20221113721–504396>500013Type of surgerymastectomy45810tumorectomy249167 Open table in a new tab As we considered the microenvironment was an integral, essential part of the tumor, the samples were not microdissected, but each tumor section was validated as containing more than 75% tumor cells by pathologists. Using Protein Pilot and iQuantitator software, we identified and quantified a total of 2,784 nonredundant proteins with at least two peptides, according to the schematic workflow of the experimental design presented Fig. 1. By taking into consideration both the peptide and spectra numbers, this approach allowed us to detect small but significant expression changes, provided that several peptides are detected. Using this analysis, we were able to obtain a list of quantified proteins from the 20 iTRAQ experiments. Following Metacore analysis using the " Enrichment of protein function" function, (Supplemental data S4), we identified 690 enzymes, 58 phosphatases, 122 proteases, 105 kinases, 73 ligands, 82 transcription factors, and 83 receptors. This analysis showed that the best enrichment score and p value were assigned to the GO Process "Metabolic Process" and to the "Cytoskeleton Remodeling" pathway (Supplemental data S4). Among these 2,784 proteins, 220 proteins met our definition for differential expression in a comparison between tumor and normal tissues: 126 were overexpressed, and 93 were underexpressed (Supplemental data S3). We used the iQuantitator software to quantify protein expression between the different status "relapse" (Supplemental data S5) and "no relapse" (Supplemental data S6). For the relapse group, 295 proteins were significantly differentially expressed: 165 were overexpressed, and 130 were underexpressed. The Metacore analysis of this list of proteins indicated a cytoskeleton remodeling with a p value = 9.2 10–12 for the Process Network "Regulation of Cytoskeleton Rearrangement" and a best enrichment score and p value for "Binding" (p = 9.4 10–26) in the GO Molecular
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