MALDI Imaging Identifies Prognostic Seven-Protein Signature of Novel Tissue Markers in Intestinal-Type Gastric Cancer
2011; Elsevier BV; Volume: 179; Issue: 6 Linguagem: Inglês
10.1016/j.ajpath.2011.08.032
ISSN1525-2191
AutoresBenjamin Balluff, Sandra Rauser, Stephan Meding, Mareike Elsner, Cédrik Schöne, Annette Feuchtinger, Christoph Schuhmacher, Alexander Novotny, Uta Jütting, Giuseppina Maccarrone, Hakan Sarioglu, Marius Ueffing, Herbert Braselmann, Horst Zitzelsberger, Roland M. Schmid, Heinz Höfler, Matthias Ebert, Axel Walch,
Tópico(s)RNA modifications and cancer
ResumoProteomics-based approaches allow us to investigate the biology of cancer beyond genomic initiatives. We used histology-based matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry to identify proteins that predict disease outcome in gastric cancer after surgical resection. A total of 181 intestinal-type primary resected gastric cancer tissues from two independent patient cohorts were analyzed. Protein profiles of the discovery cohort (n = 63) were directly obtained from tumor tissue sections by MALDI imaging. A seven-protein signature was associated with an unfavorable overall survival independent of major clinical covariates. The prognostic significance of three individual proteins identified (CRIP1, HNP-1, and S100-A6) was validated immunohistochemically on tissue microarrays of an independent validation cohort (n = 118). Whereas HNP-1 and S100-A6 were found to further subdivide early-stage (Union Internationale Contre le Cancer [UICC]–I) and late-stage (UICC II and III) cancer patients into different prognostic groups, CRIP1, a protein previously unknown in gastric cancer, was confirmed as a novel and independent prognostic factor for all patients in the validation cohort. The protein pattern described here serves as a new independent indicator of patient survival complementing the previously known clinical parameters in terms of prognostic relevance. These results show that this tissue-based proteomic approach may provide clinically relevant information that might be beneficial in improving risk stratification for gastric cancer patients. Proteomics-based approaches allow us to investigate the biology of cancer beyond genomic initiatives. We used histology-based matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry to identify proteins that predict disease outcome in gastric cancer after surgical resection. A total of 181 intestinal-type primary resected gastric cancer tissues from two independent patient cohorts were analyzed. Protein profiles of the discovery cohort (n = 63) were directly obtained from tumor tissue sections by MALDI imaging. A seven-protein signature was associated with an unfavorable overall survival independent of major clinical covariates. The prognostic significance of three individual proteins identified (CRIP1, HNP-1, and S100-A6) was validated immunohistochemically on tissue microarrays of an independent validation cohort (n = 118). Whereas HNP-1 and S100-A6 were found to further subdivide early-stage (Union Internationale Contre le Cancer [UICC]–I) and late-stage (UICC II and III) cancer patients into different prognostic groups, CRIP1, a protein previously unknown in gastric cancer, was confirmed as a novel and independent prognostic factor for all patients in the validation cohort. The protein pattern described here serves as a new independent indicator of patient survival complementing the previously known clinical parameters in terms of prognostic relevance. These results show that this tissue-based proteomic approach may provide clinically relevant information that might be beneficial in improving risk stratification for gastric cancer patients. Although the incidence of gastric cancer has declined worldwide over the past 30 years, especially in Western countries, it remains the second leading cause of cancer-related death and accounts for 9.7% of cancer deaths globally.1Ferlay J. Shin H.R. Bray F. Forman D. Mathers C. Parkin D.M. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008.Int J Cancer. 2010; 127: 2893-2917Crossref PubMed Scopus (13611) Google Scholar, 2Jemal A. Siegel R. Ward E. Hao Y. Xu J. Murray T. Thun M.J. Cancer statistics, 2008.CA Cancer J Clin. 2008; 58: 71-96Crossref PubMed Scopus (10196) Google Scholar Despite complex treatment regimens and further understanding of its biology and possible causes, surgery is the only potentially curative treatment for gastric cancer.3Shi Y. Zhou Y. The role of surgery in the treatment of gastric cancer.J Surg Oncol. 2010; 101: 687-692Crossref PubMed Scopus (63) Google Scholar Patients with stage I disease have a good prognosis, whereas those with stage IV disease show a poor prognosis. Interestingly, the prognosis varies widely in patients with stage II or III disease for as-yet undetermined biological reasons.4Allgayer H. Heiss M.M. Schildberg F.W. Prognostic factors in gastric cancer.Br J Surg. 1997; 84: 1651-1664Crossref PubMed Scopus (117) Google Scholar The clinical and biological behavior of individual gastric cancer patients cannot be understood through the analysis of individual or small numbers of genes, so cDNA microarray analysis has been used with some success to simultaneously investigate thousands of RNA expression levels and attempt to identify patterns associated with biological characteristics.5Chen C.N. Lin J.J. Chen J.J. Lee P.H. Yang C.Y. Kuo M.L. Chang K.J. Hsieh F.J. Gene expression profile predicts patient survival of gastric cancer after surgical resection.J Clin Oncol. 2005; 23: 7286-7295Crossref PubMed Scopus (116) Google Scholar, 6Leung S.Y. Yuen S.T. Chu K.M. Mathy J.A. Li R. Chan A.S. Law S. Wong J. Chen X. So S. 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Proteomic applications for the early detection of cancer.Nat Rev Cancer. 2003; 3: 267-275Crossref PubMed Scopus (771) Google Scholar Accordingly, comprehensive analysis of protein expression patterns might improve our ability to understand the molecular complexities of tumor tissues. Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry, or MALDI imaging, is a powerful tool for investigating protein patterns through the direct (in situ) analysis of tissue sections.9Stoeckli M. Chaurand P. Hallahan D.E. Caprioli R.M. Imaging mass spectrometry: a new technology for the analysis of protein expression in mammalian tissues.Nat Med. 2001; 7: 493-496Crossref PubMed Scopus (1003) Google Scholar Similarly to immunohistochemistry, MALDI imaging has advantages over other assay methods (ie, those requiring homogenization) because it is morphology driven.10Walch A. Rauser S. Deininger S.O. Hofler H. MALDI imaging mass spectrometry for direct tissue analysis: a new frontier for molecular histology.Histochem Cell Biol. 2008; 130: 421-434Crossref PubMed Scopus (252) Google Scholar This characteristic allows to directly evaluate tumor cells, to determine correlations with other morphological features, and to assay smaller patient tumor tissue specimens, such as surgical or endoscopic biopsy specimens.11Kim H.K. Reyzer M.L. Choi I.J. Kim C.G. Kim H.S. Oshima A. Chertov O. Colantonio S. Fisher R.J. Allen J.L. Caprioli R.M. Green J.E. Gastric cancer-specific protein profile identified using endoscopic biopsy samples via MALDI mass spectrometry.J Proteome Res. 2010; 9: 4123-4130Crossref PubMed Scopus (71) Google Scholar These features make it an interesting tool for tissue analysis and molecular histology.12Chaurand P. Sanders M.E. Jensen R.A. Caprioli R.M. Proteomics in diagnostic pathology: profiling and imaging proteins directly in tissue sections.Am J Pathol. 2004; 165: 1057-1068Abstract Full Text Full Text PDF PubMed Scopus (233) Google Scholar In addition, MALDI imaging can determine the distribution of hundreds of compounds in a single measurement without any need for labeling.13Chaurand P. Schwartz S.A. Caprioli R.M. Profiling and imaging proteins in tissue sections by MS.Anal Chem. 2004; 76: 87A-93APubMed Google Scholar The great potential of a highly sensitive and molecularly specific technology such as MALDI imaging to the field of oncology is currently being realized. Until now, this technique has been successfully applied to various types of cancer tissues, including human non–small-cell lung cancer, gliomas, and ovarian, prostate, and breast cancer.14Cazares L.H. Troyer D. Mendrinos S. Lance R.A. Nyalwidhe J.O. Beydoun H.A. Clements M.A. Drake R.R. Semmes O.J. Imaging mass spectrometry of a specific fragment of mitogen-activated protein kinase/extracellular signal-regulated kinase kinase kinase 2 discriminates cancer from uninvolved prostate tissue.Clin Cancer Res. 2009; 15: 5541-5551Crossref PubMed Scopus (164) Google Scholar, 15Cornett D.S. Mobley J.A. Dias E.C. Andersson M. Arteaga C.L. Sanders M.E. Caprioli R.M. A novel histology-directed strategy for MALDI-MS tissue profiling that improves throughput and cellular specificity in human breast cancer.Mol Cell Proteomics. 2006; 5: 1975-1983Crossref PubMed Scopus (159) Google Scholar, 16Lemaire R. Menguellet S.A. Stauber J. Marchaudon V. Lucot J.P. Collinet P. Farine M.O. Vinatier D. Day R. Ducoroy P. Salzet M. Fournier I. Specific MALDI imaging and profiling for biomarker hunting and validation: fragment of the 11S proteasome activator complex Reg alpha fragment, is a new potential ovary cancer biomarker.J Proteome Res. 2007; 6: 4127-4134Crossref PubMed Scopus (165) Google Scholar, 17Schwamborn K. Krieg R.C. Reska M. Jakse G. Knuechel R. Wellmann A. Identifying prostate carcinoma by MALDI-imaging.Int J Mol Med. 2007; 20: 155-159PubMed Google Scholar, 18Schwartz S.A. Weil R.J. Thompson R.C. Shyr Y. Moore J.H. Toms S.A. Johnson M.D. Caprioli R.M. Proteomic-based prognosis of brain tumor patients using direct-tissue matrix-assisted laser desorption ionization mass spectrometry.Cancer Res. 2005; 65: 7674-7681Crossref PubMed Scopus (193) Google Scholar, 19Yanagisawa K. Shyr Y. Xu B.J. Massion P.P. Larsen P.H. White B.C. Roberts J.R. Edgerton M. Gonzalez A. Nadaf S. Moore J.H. Caprioli R.M. Carbone D.P. Proteomic patterns of tumour subsets in non-small-cell lung cancer.Lancet. 2003; 362: 433-439Abstract Full Text Full Text PDF PubMed Scopus (544) Google Scholar Analysis of the resulting complex mass spectrometry data sets using modern biocomputational tools has resulted in the identification of both disease state, response prediction, and patient prognosis-specific protein patterns.18Schwartz S.A. Weil R.J. Thompson R.C. Shyr Y. Moore J.H. Toms S.A. Johnson M.D. Caprioli R.M. Proteomic-based prognosis of brain tumor patients using direct-tissue matrix-assisted laser desorption ionization mass spectrometry.Cancer Res. 2005; 65: 7674-7681Crossref PubMed Scopus (193) Google Scholar, 19Yanagisawa K. Shyr Y. Xu B.J. Massion P.P. Larsen P.H. White B.C. Roberts J.R. Edgerton M. Gonzalez A. Nadaf S. Moore J.H. Caprioli R.M. Carbone D.P. Proteomic patterns of tumour subsets in non-small-cell lung cancer.Lancet. 2003; 362: 433-439Abstract Full Text Full Text PDF PubMed Scopus (544) Google Scholar, 20Bauer J.A. Chakravarthy A.B. Rosenbluth J.M. Mi D. Seeley E.H. De Matos Granja-Ingram N. Olivares M.G. Kelley M.C. Mayer I.A. Meszoely I.M. Means-Powell J.A. Johnson K.N. Tsai C.J. Ayers G.D. Sanders M.E. Schneider R.J. Formenti S.C. Caprioli R.M. Pietenpol J.A. Identification of markers of taxane sensitivity using proteomic and genomic analyses of breast tumors from patients receiving neoadjuvant paclitaxel and radiation.Clin Cancer Res. 2010; 16: 681-690Crossref PubMed Scopus (148) Google Scholar To explore the possibility of using tissue-based proteomic analysis as a predictor of outcome in resected gastric cancer, we used MALDI imaging for direct tissue analysis of protein expression to identify proteins that predict disease outcome in patients with intestinal gastric cancer. All tissues investigated in this study were obtained from patients (n = 181) who underwent gastrectomy between 1991 and 2005 at the Surgery Department at the Technische Universität München. Histological classification was performed according to the World Health Organization and the TNM classification systems designed by the Union Internationale Contre le Cancer (UICC).21Aaltonen L.A. Hamilton S.R. World Health OrganizationInternational Agency for Research on Cancer: Pathology and genetics of tumours of the digestive system. IARC Press, Lyon2000: 39-52Google Scholar, 22Sobin L.H. Wittekind C. International Union against Cancer: TNM classification of malignant tumours. (German edition). Wiley-Liss, New York2002: 59-62Google Scholar All tumors analyzed in this study were intestinal-type tumors according to the Lauren classification system.23Lauren P. The two histological main types of gastric carcinoma: diffuse and so-called intestinal-type carcinoma an attempt at a histo-clinical classification.Acta Pathol Microbiol Scand. 1965; 64: 31-49Crossref PubMed Scopus (5052) Google Scholar Follow-up data were available for all patients, and the overall survival was calculated from the date of surgical resection to the date of death or last follow-up. This study was approved by the Institutional Review Board and the Ethics Committee of the Faculty of Medicine of the Technische Universität München with informed consent from all subjects and patients. The clinicopathological data of all patients are listed in Table 1.Table 1Correlation of Spectral Features and Their Respective Identified Proteins with Clinicopathological Parameters for PatientsDiscovery cohort (n = 63)Validation cohort (n = 118)No. of patientsMALDI imaging m/z signalsNo. of patientsImmunohistochemistry antigens3445 (HNP-1)62788406 (CRIP1)845310098 (S100-A6)1135311613HNP-1 (m/z 3445)CRIP1 (m/z 8406)S100-A6 (m/z 10098)Sex⁎P value calculated by t-test.0.2570.3480.9530.3830.0020.5790.9510.4480.0920.259 Male4689 Female1729Age†P value calculated by Spearman's rank correlation.0.1140.2200.1590.5640.0390.2900.9470.0090.1520.678Primary tumor†P value calculated by Spearman's rank correlation.———————0.2480.3750.224 pT1015 pT26354 pT3044 pT405Regional lymph nodes†P value calculated by Spearman's rank correlation.0.7300.5720.0590.3960.0810.4000.3050.0160.9640.023 pN01836 pN12435 pN21635 pN359 pNx03Distant metastasis‡P value calculated by Mann-Whitney U test.0.9760.3210.0890.6870.0050.0360.6160.5170.7790.038 M05487 M1931Resection status‡P value calculated by Mann-Whitney U test.0.6750.2380.0550.1290.0110.4480.1500.1960.6240.361 R05381 R1926 Rx111Grading†P value calculated by Spearman's rank correlation.0.3890.6850.7200.3890.2270.0330.1040.1680.3880.018 G101 G21636 G34781Overall survival§P value calculated by univariate Cox proportional hazard regression.0.0750.0090.0180.0220.0130.0120.0260.0862¶Union Internationale Contre le Cancer (UICC) stage I.0.0160.0766∥UICC stages II and III.Bold print values indicate that the P value is <0.05. P value calculated by t-test.† P value calculated by Spearman's rank correlation.‡ P value calculated by Mann-Whitney U test.§ P value calculated by univariate Cox proportional hazard regression.¶ Union Internationale Contre le Cancer (UICC) stage I.∥ UICC stages II and III. Open table in a new tab Bold print values indicate that the P value is <0.05. Fresh-frozen tissue samples were obtained from 63 primary resected gastric carcinoma patients that were matched to UICC-T status (T = 2). Patients were on average 66.5 years of age (range, 33–85 years), and their median overall survival time was 33.1 months (range, 0–53.4 months). The tissues were snap-frozen and stored in liquid nitrogen. This discovery cohort was used for tissue-based proteomic analysis by MALDI imaging. The patient cohort of the validation set comprised 118 tumor samples and was provided in triplicate in formalin-fixed paraffin-embedded tissue microarrays from the Institute of Pathology of the Technische Universität München. The clinicopathological data of this independent sample set are also included in Table 1. The patients' median overall survival time was 54.7 months (range, 0–135.5 months), and their mean age was 66.4 years (range, 41–80 years). The validation of the proteins was performed in this independent patient cohort by immunohistochemical analyses. Frozen tissue sections from the discovery cohort were cut on a cryostat (CM1950, Leica Microsystems, Wetzlar, Germany) at a 12-μm thickness onto indium-tin-oxide–coated glass slides (Bruker Daltonics, Bremen, Germany). After brief washing in both 70% and 100% ethanol pro analysis solutions, slides were coated with sinapinic acid matrix solution (Sigma-Aldrich, Taufkirchen, Germany) at 10 mg/mL in water/acetonitrile 40:60 (v/v) with 0.2% trifluoroacetic acid pro analysis (TFA) by an automated spraying device (ImagePrep, Bruker Daltonics). For mass spectrometric measurements, tumor areas were defined using the FlexControl 3.0 and FlexImaging 2.1 software packages (both Bruker Daltonics). Spectra were acquired using the Ultraflex III MALDI-TOF/TOF (Bruker Daltonics) in positive linear mode, whereas ions were detected in a mass range of m/z 2500 to 25,000 with a lateral resolution of 70 μm. A ready-made protein standard was used for spectra calibration (Bruker Daltonics). After the MALDI experiments, the glass slides were incubated in 70% ethanol to elute the matrix and were then stained with hematoxylin and eosin. Finally, the stained slides were scanned with a digital slide scanning system (Mirax Desk, Carl Zeiss MicroImaging, Göttingen, Germany) and co-registered to the MALDI imaging results to align mass spectrometric data with the histological features of the very same sections. Tumor-specific spectra were selected using the FlexImaging software (Bruker Daltonics). A total of 80 spectra per case were picked randomly and were imported into the ClinProTools 2.2 software (Bruker Daltonics), on which the data underwent normalization, recalibration (both to enable comparability of measurements), and peak picking. After processing, the data were exported for further statistical analyses. Ten cryosectioned slices (25 μm each) of three different tissue specimens underwent protein extraction with aqueous 0.1% TFA and ultrasonication. The extracted proteins were separated on an mRP-C18 column (Agilent Technologies, Santa Clara, CA), and the fractionated aliquots were collected in a 96-well-plate. The HPLC fractions were manually spotted onto a PAC target (Bruker Daltonics) and analyzed by MALDI-MS (Ultraflex I, Bruker Daltonics) to locate fractions containing the m/z species of interest. Fractions of interest underwent tryptic digestion, and the resulting peptides were separated on a nano-RP-HPLC column (PepMap, LC Packings, Sunnyvale, CA), which was connected to a linear quadrupole ion trap mass spectrometer (LTQ Orbitrap XL, Thermo Scientific, Waltham, MA) equipped with a nano-ESI ion source. All obtained MS/MS spectra were searched in the NCBInr human sequence database using Mascot (v2.2.06, Matrix Science, London, UK). The final evaluation of the protein/peptide identification results was done using the Scaffold 3 software framework (Proteome Software, Portland, OR). Immunohistochemical staining of the 3-μm tissue microarray sections was performed using an automated stainer (Discovery XT) and a DAB Map kit (both, Ventana Medical Systems, Tucson, AZ). The dilutions used for primary antibodies against HNP-1 (BMA Biomedicals, Augst, Switzerland), CRIP1 (AbD Serotec, Oxford, UK), and S100-A6 (Thermo Scientific) were 1:400, 1:100, and 1:100, respectively. The analysis of the immunohistochemical staining was conducted with an image analysis platform (Definiens Enterprise Image Intelligence Suite, Definiens AG, Munich, Germany). For this purpose, all stained slides were scanned at ×20 objective magnification by a digital slide scanner (Mirax Desk, Carl Zeiss MicroImaging), and the images were imported into the image analysis software. Specific rule sets were then defined to detect and quantify the immunohistochemical staining intensities of semantic classes. Whereas the quantified parameter for CRIP1 and S100-A6 staining was the brown intensity of the tumor cells, the area of the peptide expressing granulocytes was the quantified parameter for HNP-1. Correlations between the investigated parameters and clinicopathological features were determined as outlined in Table 1. The m/z species associated with overall survival, obtained by MALDI imaging, were identified by corrected multiple testing using the Significance Analysis of Microarrays (SAM) package with a maximum false discovery rate of 0.1.24Tusher V.G. Tibshirani R. Chu G. Significance analysis of microarrays applied to the ionizing radiation response.Proc Natl Acad Sci USA. 2001; 98: 5116-5121Crossref PubMed Scopus (9751) Google Scholar To investigate the predictive power of the combined MALDI imaging signals, all patients were clustered into two groups by hierarchical clustering. The dendrogram was calculated using the Ward linkage method based on a weighted Euclidean distance. Each weight corresponded to the reciprocal of the respective m/z species' univariate P value. The correct classification rate of this protein pattern to one of the groups was tested by establishing a classification model based on a support vector machine, running with standard parameters (kernel = radial, cost = 1) and a 10-fold cross-validation. Multivariate analyses for the assessment of clinical parameter influences were done by Cox regression with P values calculated by the Wald test. Kaplan-Meier curves were calculated by defining favorable and unfavorable prognostic groups using an intensity-based threshold score, which maximized overall survival differences between both respective groups while minimizing imbalances in group sizes. Differences between the curves were assessed using the log-rank test. All statistical analyses were performed within the R statistical environment (R Foundation for Statistical Computing, Vienna, Austria), in which P values <0.05 were considered statistically significant and values between 0.05 and 0.1 were considered trends. To detect protein signals associated with overall survival in gastric cancer, we acquired the cancer protein profiles of 63 patients using MALDI imaging mass spectrometry in the discovery cohort. This strategy allowed the histology-directed acquisition of cancer cell-specific protein spectra from the measured tissue samples. On average, we could resolve between 150 and 200 peaks per case within the mass range of m/z 2500 to 25,000 and a mass accuracy of ±3 m/z. For example, a representative tumor peak (m/z species) and the morphological features of an individual patient's tissue sample are shown in Figure 1. After setting the false discovery threshold to 0.1 and excluding correlated features, we found seven m/z species at an average of m/z 3445, m/z 6278, m/z 8406, m/z 8453, m/z 10098, m/z 11353, and m/z 11613, which were associated with patient survival (see Supplemental Figures S1 at http://ajp.amjpathol.org). Correlations to clinicopathological parameters are listed in Table 1. The influence of each m/z species on survival was then studied in more detail. Univariate Cox regression showed that, with the exception of m/z 3445 (P = 0.075), which indicates a prognostic trend, all signals exhibit a strong nonfavorable effect on survival. The value m/z 6278 (P = 0.009) has the highest prognostic value, followed by m/z 11353 (P = 0.012), m/z 10098 (P = 0.013), m/z 8406 (P = 0.018), m/z 8453 (P = 0.022), and m/z 11613 (P = 0.026) (Table 1). Setting intensity thresholds for each single m/z signal resulted in poor and good prognosis groups which all differed significantly in terms of overall survival (all P < 0.05). A selection of Kaplan-Meier graphs for the long-and short-term survivor groups are depicted in Figure 2, A and B, andFigure 3A (for all Kaplan-Meier graphs, see Supplemental Figure S2 at http://ajp.amjpathol.org).Figure 3CRIP1, a previously unknown protein in gastric cancer, was found by MALDI imaging as a novel prognostic factor in the discovery cohort (A, C). Immunohistochemical validation confirmed this by showing a strong relationship between the high expression of CRIP1 (E) and poor survival (B) and vice versa (D, B), as calculated by Kaplan-Meier analysis (n = 114).View Large Image Figure ViewerDownload Hi-res image Download (PPT) Multivariate Cox regression models of each respective m/z species, with nodal and resection status as well as distant metastasis status as covariables, showed that m/z 6278, m/z 8453, m/z 10098, and m/z 11613 are independent prognostic factors (all P < 0.05), whereas m/z values of 3445 (P = 0.063) and 8406 (P = 0.07) showed slight dependencies (Table 2). In contrast, m/z 11353 does not exert an independent influence on survival (P = 0.16).Table 2Multivariate Survival AnalysesCovariableHazard ratio95% Confidence intervalP valueMALDI imaging m/z 3445 (HNP-1)1.0320.998–1.0700.063 Nodal status2.3041.382–3.8400.001 Distant metastasis0.7240.163–3.2200.670 Resection status1.3980.273–7.1600.690 m/z 62781.3321.088–1.6300.006 Nodal status2.8691.661–4.9600.000 Distant metastasis0.6610.165–2.6400.560 Resection status0.5310.092–3.0800.480 m/z 8406 (CRIP1)1.4580.970–2.1900.070 Nodal status2.4771.459–4.2100.001 Distant metastasis0.5210.109–2.4900.410 Resection status0.7720.116–5.1600.790 m/z 84533.6261.275–10.310.016 Nodal status2.5791.527–4.3600.000 Distant metastasis0.7600.185–3.1300.700 Resection status0.6430.121–3.4200.600 m/z 10098 (S100-A6)1.2191.012–1.4700.037 Nodal status2.5221.469–4.3300.001 Distant metastasis0.4070.078–2.1300.290 Resection status1.0420.171–6.3500.960 m/z 113531.1770.939–1.4800.160 Nodal status2.0911.231–3.5500.006 Distant metastasis0.5850.138–2.4800.470 Resection status1.6680.326–8.5300.540 m/z 116131.6941.082–2.6500.021 Nodal status2.5701.529–4.3200.000 Distant metastasis0.5840.121–2.8200.500 Resection status0.8670.137–5.4700.880 Seven-protein signature4.0311.691–9.6100.002 Nodal status2.5011.521–4.1100.000 Distant metastasis0.7250.183–2.8700.650 Resection status1.1650.260–5.2200.840Immunohistochemistry CRIP1 (m/z 8406)1.5701.012–2.4400.044 Primary tumor1.6600.939–2.9500.081 Nodal status1.6701.045–2.6700.032 Distant metastasis1.0900.437–2.7200.860 Resection status1.0300.363–2.9500.950 S100-A6 (m/z 10098)⁎Union Internationale Contre le Cancer (UICC) stages II and III only.3.8001.130–12.810.031 Primary tumor1.7200.611–4.8600.300 Nodal status2.1900.865–5.5700.098 Distant metastasis1.1200.310–4.0500.860 Resection status1.6700.355–7.8300.520Data are calculated by Cox proportional hazard regression. Bold print indicates that the P value is <0.05. Union Internationale Contre le Cancer (UICC) stages II and III only. Open table in a new tab Data are calculated by Cox proportional hazard regression. Bold print indicates that the P value is <0.05. Protein identification of m/z 3445 was performed by tissue extraction and fractionation followed by bottom-up tandem mass spectrometry. Human neutrophil peptide-1 (HNP-1) was identified with a Mascot Score of 109. Protein scores above 56 indicate identity or extensive homology (P < 0.05) (see Supplemental Figure S3 at http://ajp.amjpathol.org). In addition, this mass has already been reported as HNP-1 in several other studies.11Kim H.K. Reyzer M.L. Choi I.J. Kim C.G. Kim H.S. Oshima A. Chertov O. Colantonio S. Fisher R.J. Allen J.L. Caprioli R.M. Green J.E. Gastric cancer-specific protein profile identified using endoscopic biopsy samples via MALDI mass spectrometry.J Proteome Res. 2010; 9: 4123-4130Crossref PubMed Scopus (71) Google Scholar, 20Bauer J.A. Chakravarthy A.B. Rosenbluth J.M. Mi D. Seeley E.H. De Matos Granja-Ingram N. Olivares M.G. Kelley M.C. Mayer I.A. Meszoely I.M. Means-Powell J.A. Johnson K.N. Tsai C.J. Ayers G.D. Sanders M.E. Schneider R.J. Formenti S.C. Caprioli R.M. Pietenpol J.A. Identification of markers of taxane sensitivity using proteomic and genomic analyses of breast tumors from patients receiving neoadjuvant paclitaxel and radiation.Clin Cancer Res. 2010; 16: 681-690Crossref PubMed Scopus (148) Google Scholar Signal m/z 8406 (±3 m/z) has previously been identified by our group as Cysteine-rich intestinal protein 1 (CRIP1).25Rauser S. Marquardt C. Balluff B. Deininger S.O. Albers C. Belau E. Hartmer R. Suckau D. Specht K. Ebert M.P. Schmitt M. Aubele M. Hofler H. Walch A. Classification of HER2 receptor status in breast cancer tissues by MALDI imaging mass spectrometry.J Proteome Res. 2010; 9: 1854-1863Crossref PubMed Scopus (218) Google Scholar Similarly, the signal at m/z 10098 corresponds to the calcium binding protein, S100-A6, as previously shown by Schwartz et al.18Schwartz S.A. Weil R.J. Thompson R.C. Shyr Y. Moore J.H. Toms S.A. Johnson M.D. Caprioli R.M. Proteomic-based prognosis of brain tumor patients using direct-tissue matrix-assisted laser desorption ionization mass spectrometry.Cancer Res. 2005; 65: 7674-7681Crossref PubMed Scopus (193) Google Scholar The other four molecular species have remained unidentified and require further elucidation efforts. Based on the results of the discovery study, we validated the predictive power of the identified proteins CRIP1, S100-A6, and HNP-1 using an independent test cohort comprising 118 patients. Although univariate analysis indicated a significant correlation of CRIP1 (P = 0.016) on patient
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