Increased Serum Levels of Complement C3a Anaphylatoxin Indicate the Presence of Colorectal Tumors
2006; Elsevier BV; Volume: 131; Issue: 4 Linguagem: Inglês
10.1053/j.gastro.2006.07.011
ISSN1528-0012
AutoresJens K. Habermann, Uwe J. Roblick, Brian T. Luke, DaRue A. Prieto, William J.J. Finlay, Vladimir N. Podust, John M. Roman, E. Oevermann, Thomas Schiedeck, Nils Homann, M. Duchrow, Thomas P. Conrads, Timothy D. Veenstra, Stanley K. Burt, Hans–Peter Bruch, Gert Auer, Thomas Ried,
Tópico(s)Glycosylation and Glycoproteins Research
ResumoBackground & Aims: Late diagnosis of colorectal carcinoma results in a significant reduction of average survival times. Yet despite screening programs, about 70% of tumors are detected at advanced stages (International Union Against Cancer stages III/IV). We explored whether detection of malignant disease would be possible through identification of tumor-specific protein biomarkers in serum samples. Methods: A discovery set of sera from patients with colorectal malignancy (n = 58) and healthy control individuals (n = 32) were screened for potential differences using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. Candidate proteins were identified and their expression levels were validated in independent sample sets using a specific immunoassay (enzyme-linked immunosorbent assay). Results: By using class comparison and custom-developed algorithms we identified several m/z values that were expressed differentially between the malignant samples and the healthy controls of the discovery set. Characterization of the most prominent m/z values revealed a member of the complement system, the stable form of C3a anaphylatoxin (ie, C3a-desArg). Based on a specific enzyme-linked immunosorbent assay, serum levels of complement C3a-desArg predicted the presence of colorectal malignancy in a blinded validation set (n = 59) with a sensitivity of 96.8% and a specificity of 96.2%. Increased serum levels were also detected in 86.1% of independently collected sera from patients with colorectal adenomas (n = 36), whereas only 5.6% were classified as normal. Conclusions: Complement C3a-desArg is present at significantly higher levels in serum from patients with colorectal adenomas (P < .0001) and carcinomas (P < .0001) than in healthy individuals. This suggests that quantification of C3a-desArg levels could ameliorate existing screening tests for colorectal cancer. Background & Aims: Late diagnosis of colorectal carcinoma results in a significant reduction of average survival times. Yet despite screening programs, about 70% of tumors are detected at advanced stages (International Union Against Cancer stages III/IV). We explored whether detection of malignant disease would be possible through identification of tumor-specific protein biomarkers in serum samples. Methods: A discovery set of sera from patients with colorectal malignancy (n = 58) and healthy control individuals (n = 32) were screened for potential differences using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. Candidate proteins were identified and their expression levels were validated in independent sample sets using a specific immunoassay (enzyme-linked immunosorbent assay). Results: By using class comparison and custom-developed algorithms we identified several m/z values that were expressed differentially between the malignant samples and the healthy controls of the discovery set. Characterization of the most prominent m/z values revealed a member of the complement system, the stable form of C3a anaphylatoxin (ie, C3a-desArg). Based on a specific enzyme-linked immunosorbent assay, serum levels of complement C3a-desArg predicted the presence of colorectal malignancy in a blinded validation set (n = 59) with a sensitivity of 96.8% and a specificity of 96.2%. Increased serum levels were also detected in 86.1% of independently collected sera from patients with colorectal adenomas (n = 36), whereas only 5.6% were classified as normal. Conclusions: Complement C3a-desArg is present at significantly higher levels in serum from patients with colorectal adenomas (P < .0001) and carcinomas (P < .0001) than in healthy individuals. This suggests that quantification of C3a-desArg levels could ameliorate existing screening tests for colorectal cancer. See CME Quiz on page 1284. See CME Quiz on page 1284. Detection of cancer at early stages is critical for curative treatment interventions. Although the 5-year disease-free survival for International Union Against Cancer (UICC) stage I tumors exceeds 90%, this percentage is reduced to 63% in UICC stage III carcinomas.1O'Connell J.B. Maggard M.A. Ko C.Y. Colon cancer survival rates with the new American Joint Committee on Cancer sixth edition staging.J Natl Cancer Inst. 2004; 96: 1420-1425Crossref PubMed Scopus (1332) Google Scholar It should therefore be obvious that tools and methodologies for early cancer detection directly impact survival times. In present clinical practice, screening for cancer and preinvasive polyps of the colorectum is based on clinical examination, the detection of occult fecal blood,2Mak T. Lalloo F. Evans D.G. Hill J. Molecular stool screening for colorectal cancer.Br J Surg. 2004; 91: 790-800Crossref PubMed Scopus (27) Google Scholar and on sigmoidoscopy or colonoscopy. The successful implementation of these screening procedures has contributed to a reduction of disease-associated mortality of colorectal carcinomas.3Fleischer D.E. Goldberg S.B. Browning T.H. Cooper J.N. Friedman E. Goldner F.H. Keeffe E.B. Smith L.E. Detection and surveillance of colorectal cancer.JAMA. 1989; 261: 580-585Crossref PubMed Scopus (187) Google Scholar However, colorectal tumors still rank among the most common malignancies in the Western world: approximately 140,000 new cases will be diagnosed in the United States annually, and about 55,000 patients will die of the disease.1O'Connell J.B. Maggard M.A. Ko C.Y. Colon cancer survival rates with the new American Joint Committee on Cancer sixth edition staging.J Natl Cancer Inst. 2004; 96: 1420-1425Crossref PubMed Scopus (1332) Google Scholar The persistent delay in diagnosis and the associated high mortality are attributable to a low compliance to some screening tests (eg, colonoscopy) and to the low sensitivity of other tests (eg, occult fecal blood test).4Schulmann K. Reiser M. Schmiegel W. Colonic cancer and polyps.Best Pract Res Clin Gastroenterol. 2002; 16: 91-114Abstract Full Text PDF PubMed Scopus (42) Google Scholar There is reasonable hope and emerging evidence that the presence of malignant disease could be detected by specific changes in the composition of serum proteins. Comprehensive serum proteome profiling for such tumor-specific markers has therefore become a field of intensive research.5Srinivas P.R. Srivastava S. Hanash S. Wright Jr, G.L. Proteomics in early detection of cancer.Clin Chem. 2001; 47: 1901-1911PubMed Google Scholar, 6Petricoin E.F. Zoon K.C. Kohn E.C. Barrett J.C. Liotta L.A. Clinical proteomics: translating benchside promise into bedside reality.Nat Rev Drug Discov. 2002; 1: 683-695Crossref PubMed Scopus (496) Google Scholar, 7Conrads T.P. Hood B.L. Issaq H.J. Veenstra T.D. Proteomic patterns as a diagnostic tool for early-stage cancer: a review of its progress to a clinically relevant tool.Mol Diagn. 2004; 8: 77-85PubMed Google Scholar, 8Hanash S. Integrated global profiling of cancer.Nat Rev Cancer. 2004; 4: 638-644Crossref PubMed Scopus (119) Google Scholar For instance, determination of serum levels of prostate-specific antigen for the detection of prostate cancer, despite issues regarding specificity and sensitivity, has become routine clinical practice.9Albertsen P.C. Prostate-specific antigen: how to advise patients as the screening debate continues.Cleve Clin J Med. 2005; 72: 521-527Crossref PubMed Scopus (5) Google Scholar Other biomarkers indicate the presence of ovarian and prostate carcinomas.10Petricoin E.F. Ardekani A.M. Hitt B.A. Levine P.J. Fusaro V.A. Steinberg S.M. Mills G.B. Simone C. Fishman D.A. Kohn E.C. Liotta L.A. Use of proteomic patterns in serum to identify ovarian cancer.Lancet. 2002; 359: 572-577Abstract Full Text Full Text PDF PubMed Scopus (2901) Google Scholar, 11Petricoin 3rd, E.F. Ornstein D.K. Paweletz C.P. Ardekani A. Hackett P.S. Hitt B.A. Velassco A. Trucco C. Wiegand L. Wood K. Simone C.B. Levine P.J. Linehan W.M. Emmert-Buck M.R. Steinberg S.M. Kohn E.C. Liotta L.A. Serum proteomic patterns for detection of prostate cancer.J Natl Cancer Inst. 2002; 94: 1576-1578Crossref PubMed Scopus (663) Google Scholar, 12Adam B.L. Qu Y. Davis J.W. Ward M.D. Clements M.A. Cazares L.H. Semmes O.J. Schellhammer P.F. Yasui Y. Feng Z. Wright Jr, G.L. Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men.Cancer Res. 2002; 62: 3609-3614PubMed Google Scholar, 13Zhang Z. Bast Jr, R.C. Yu Y. Li J. Sokoll L.J. Rai A.J. Rosenzweig J.M. Cameron B. Wang Y.Y. Meng X.Y. Berchuck A. Van Haaften-Day C. Hacker N.F. de Bruijn H.W. van der Zee A.G. Jacobs I.J. Fung E.T. Chan D.W. Three biomarkers identified from serum proteomic analysis for the detection of early stage ovarian cancer.Cancer Res. 2004; 64: 5882-5890Crossref PubMed Scopus (849) Google Scholar However, the use of single or a combination of serum markers, including carcinoembryonic antigen, has so far failed to deliver diagnostic tests of high sensitivity and specificity for colon cancer. Several technologies are available for proteome screening. One approach is based on surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). SELDI uses chromatographic surfaces that retain proteins from a complex sample mixture according to their specific properties (eg, hydrophobicity and charge), and the molecular weights of the retained proteins then are measured by TOF MS.14Zhu H. Snyder M. Protein chip technology.Curr Opin Chem Biol. 2003; 7: 55-63Crossref PubMed Scopus (840) Google Scholar, 15Yip T.T. Lomas L. SELDI ProteinChip array in oncoproteomic research.Technol Cancer Res Treat. 2002; 1: 273-280PubMed Google Scholar We have investigated the potential of this methodology for discovery of features (proteins or protein complexes) in the serum that are characteristic for patients with colorectal malignancy. This discovery phase was followed by protein identification of prominent features at specific m/z values and independent experimental verification with an enzyme-linked immunosorbent assay (ELISA) test using an extended validation set including serum samples from patients with colorectal adenomas. A total of 149 serum samples were collected at the Department of Surgery, University Hospital Schleswig-Holstein, Campus Lübeck, Germany, consisting of a discovery set of 32 healthy controls and 58 patients with colorectal malignancy and an independently collected, nonoverlapping, blinded validation set of 59 samples. Peripheral blood samples were collected in adherence with protocols approved by the local Institutional Ethical Review Board as follows: blood from cancer patients was collected from patients during the initial presentation at the hospital, which in our clinic precedes the day of surgery by about 4–5 days. These patients were not fasting nor were they admitted to the hospital at the time of phlebotomy and therefore were not exposed to specific environmental factors. The healthy control group was composed of medical personnel who also were not fasting at the time of blood collection. Blood was drawn into serum tubes (S-Monovette; Sarstedt, Nümbrecht, Germany) and immediately was stored on ice until serum preparation was performed (within 2 hours after collection). Samples then were stored at −20°C. Clinical data are summarized in Table 1. In addition to the collection of serum samples for SELDI-TOF MS–based protein profiling, we collected a set of samples from patients with colorectal polyps (n = 36). These samples were collected at the Department of Internal Medicine at the University Hospital Schleswig-Holstein, Campus Lübeck, Germany, before an explorative colonoscopy. These samples were used for quantification of serum levels of complement C3a-desArg using an ELISA test only (see later). Therefore, 3 sets of samples were used in this investigation: a discovery set containing 32 healthy individuals and 58 patients with colorectal malignancy; a validation set containing 59 samples with unknown status (either with or without colon cancer); and a testing set containing 36 patients with colorectal polyps. Mass spectra were acquired for all individuals in the discovery and validation sets; the discovery set was used to identify a putative biomarker and its discriminating ability was tested on the validation set. Serum levels of complement C3a-desArg then were measured using an ELISA test in all 3 sets of individuals. Serum levels of the discovery set were used to establish thresholds that were applied to the validation set. We also used levels of both the discovery and validation set (whose status was now known) to determine appropriate serum concentration thresholds that then were applied to the testing set of polyp sera. The clinical data are provided in Table 2.Table 1Clinical Parameter of Samples in the Discovery Set and the Blinded Validation SetDiscovery set (n = 90)Validation set (n = 59)MalignancyControlMalignancyControlPatientsn = 58n = 32n = 38n = 21 Tumor (T), metastasis (M)(T = 38, M = 20)(T = 17, M = 21)SELDI-TOF MSn = 69n = 39n = 76n = 42 Tumor (T), metastasis (M)(T = 46, M = 23)(T = 34, M = 42)Sex Female26161112 Male3216279Average age, y63.0531.3465.0737.33Range39–8119–4342–8126–61UICC staging I85 II114 III183 IV2126TNM staging T122 T284 T32310 T451Localization Cecum11 Ascending02 Transverse10 Descending01 Sigmoid125 Rectum248Metastasis/recurrence Liver817 Lung51 Liver and lung32 Recurrence41 Open table in a new tab Table 2Clinical Parameters for 36 Serum Samples From Patients With Colorectal PolypsPatientAge, ySexPolyp size, mmPolyp locationHistologyDyplasiaSynchronous polypsELISA Adjusted Concentration, ng/mLCoefficient of variation %SD163m203HyperplasticNo028,317.85312.20.345275f163TubularLow grade050,345.4133.60.179363m122TubularLow grade044,735.14710.50.47468f30NATubulovillousLow grade033,889.2724.40.149586f43TubulovillousLow grade222,058.8164.40.098675m153TubulovillousLow grade05648.8085.30.03780f153TubularLow grade315,112.20610.70.162855f73TubularLow grade317,698.28215.20.268989f23TubularCarcinoma in situ120,257.06612.80.2591070f103TubulovillousLow grade028,649.786150.4291153f80TubulovillousLow grade036,740.4097.70.2811278m90NACarcinoma in situ027,534.23314.50.41374f63TubularLow grade019,772.25114.70.2911473m20 and 3TubularLow grade624,323.2591.90.0461571mNA0 and 3TubularLow grade511,869.2556.60.0781675m5NANANA017,289.49611.90.2071767f30HyperplasticNo025,875.3688.10.2091861f23HyperplasticNo015,661.01512.90.2011976m20 and 3TubularLow grade315,655.5951.20.022062m23HyperplasticNo321,696.21711.90.2572169m33HyperplasticNo032,292.1457.10.2282262m70 and 3TubulovillousLow grade316,996.1136.70.1142379f33TubularLow grade029,859.7188.30.2472466f60 and 3TubulovillousLow grade6024,894.4085.90.1462581m50TubulovillousLow grade323,211.3596.40.1482647f23HyperplasticNo520,885.9965.60.1172771m53HyperplasticNo218,776.9446.10.1152888f20, 2, and 3TubularLow grade79122.9042.40.0222962m150TubulovillousLow grade015,495.5834.40.0693071m103TubularLow grade418,287.728100.1833152f30 and 3HyperplasticNo311,967.3713.50.1623264m221 and 3TubulovillousLow grade417,067.895.20.0893354m151 and 3TubularLow grade213,206.7235.60.0743469m101 and 3TubularLow grade342,076.83910.90.4593567m150 and 3TubulovillousLow grade418,987.84940.0753653f73TubulovillousLow grade029,158.6524.20.122NOTE. 0 = cecum, ascending colon, right flexure; 1 = transverse colon and left flexure; 2 = descending colon; 3 = sigmoid colon and rectum.NA, not analyzed. Open table in a new tab NOTE. 0 = cecum, ascending colon, right flexure; 1 = transverse colon and left flexure; 2 = descending colon; 3 = sigmoid colon and rectum. NA, not analyzed. Nonfractionated, total serum samples were processed using 2 types of ProteinChip Arrays, immobilized metal affinity capture (IMAC3) and weak cationic exchange (WCX2) arrays, according to protocols provided by the manufacturer (Ciphergen Biosystems, Inc., Fremont, CA). All samples were randomized; duplicates were analyzed on separate ProteinChip Arrays (Ciphergen Biosystems, Inc.). Both types of ProteinChip Arrays were analyzed on the ProteinChip Biology System II SELDI-TOF mass spectrometer (Ciphergen). Mass accuracy was assessed daily through external calibration with All-in-1 Peptide and All-in-1 Protein standards (Ciphergen). The arrays were analyzed using the following ProteinChip Biology System II automated settings: laser intensities 215 (IMAC3) and 220 (WCX2), detector sensitivity 8, focus mass 5000, m/z range 0–200,000, and 130 averaged laser shots per sample spectrum. Data were collected using Ciphergen ProteinChip software version 3.0.2. The ProteinChip Array data were treated by an initial truncation of the spectra to eliminate m/z values less than 1500 daltons. After scaling each spectrum in the discovery set to a constant total ion current, the spectra were averaged into a single spectrum to identify peak regions with sufficient intensity. Each region had a total width of 0.3% of m/z and in general contained approximately 15 recorded intensities. A region was retained only if the maximum intensity did not occur in the first or last 2 recorded m/z values of this region in at least 60% of the samples of a given status (normal or cancer), thereby removing shoulder regions from consideration. This conservative approach dramatically reduced the SELDI-TOF MS data points to 305 significant regions on the IMAC3 array, and 322 significant regions on the WCX2 array, therefore reducing probability of chance fitting of data.16Ransohoff D.F. Lessons from controversy: ovarian cancer screening and serum proteomics.J Natl Cancer Inst. 2005; 97: 315-319Crossref PubMed Scopus (235) Google Scholar, 17Baggerly K.A. Morris J.S. Edmonson S.R. Coombes K.R. Signal in noise: evaluating reported reproducibility of serum proteomic tests for ovarian cancer.J Natl Cancer Inst. 2005; 97: 307-309Crossref PubMed Scopus (256) Google Scholar The same scaling was applied to each spectrum in the validation set, and the final set of 305 and 322 significant regions were examined to find the maximum intensity in each region for the IMAC3 and WCX2 spectra, respectively. The spectra of the 2 array surfaces (IMAC3 and WCX2) then were combined, such that each spectrum in the discovery and validation set presented 627 features. Because validation set spectra were not used for the identification of putative biomarkers, only discovery set spectra then were analyzed as to whether the 2 technical repeats per serum sample should be averaged or kept as duplicates. Because biomarkers are serum proteins whose blood concentration depends on whether or not an individual has a disease, it is important to distinguish these peaks from those used in a single classifier to account for variations in peak intensities caused by individual and experimental variations. The experimental variation is the difference between the duplicate spectra; if it is too small the spectra should be averaged so that they do not adversely influence the classifiers. If the experimental variation is large, the samples should be kept separate to maintain a realistic spread in peak intensities. Although we acknowledge the possibility that averaging of duplicate spectra may be problematic, we submit that our procedure did not adversely affect the qualitative results, as shown through confirmation of serum C3a levels with an independent ELISA test. The 627 peak intensities from both chip surfaces were used to determine the Euclidean distance between each spectrum and its duplicate, and this was compared with the distances between it and the spectra from other samples. If each member of a duplicate pair of spectra had, on average, 2 or more spectra from other samples that were closer to it than it was to its duplicate, there was no a priori way to associate these spectra with the same individual and they were kept separate. Otherwise, the duplicate spectra were averaged. This also has the effect of not allowing a suboptimal spectrum to contaminate its duplicate. Outlier detection identified 8 spectra that were excluded from subsequent analysis. The remaining spectra (69 cancerous, 39 controls) comprised the discovery set, which was used exclusively to identify features that distinguish malignant sera from control sera. We then applied a total of 11 independent methods with the rationale that a true biomarker will appear not only in one but several analytic algorithms as a strong discriminative feature. Five of these different methods were used to determine how malignant sera could be separated from healthy control samples based only on individual features. In addition, evolutionary programming in 6 sets of 16 runs was used to test how well features could separate in a pair-wise concerted form, using average-linkage and complete-linkage clustering algorithms and distance-dependent K-nearest neighbors.18Luke B.T. Nature-inspired methods in chemometrics: genetics algorithms and artificial neural networks. Elsevier, Amsterdam2003Google Scholar Here, the Euclidian distance metric was used with either absolute differences or relative differences in the intensities of the chosen set of features. Further information on all of these methods is available in the Supplementary Methods section (see supplementary materials online at www.gastrojournal.org). Based on all methods, a total of 21 features were selected based on scoring in the top 5 models by any of the methods that examined individual features, or when appearing in the best model or regular appearance in the top 100 models at least 5 times in a set of 16 runs (Supplementary Table 1; see supplementary materials online at www.gastrojournal.org). This set of 21 features then was used to identify representative peaks in the spectrum by finding all features whose intensities have a sufficient correlation to those listed in Supplementary Table 1 (r > 0.70) and then visually inspecting the raw spectra. This produced a set of 33 peaks (18 from the IMAC3 array and 15 from the WCX2 array) that clustered into 9 groups. The intensities of the peaks in each group are shown in Supplementary Figure 1 (see supplementary materials online at www.gastrojournal.org). The results on the IMAC array show that the peaks at 9148.7 and 8941.1 were identified by 10 and 8 of the 11 methods, respectively, and appeared to have a high discriminating value. The peak at 8941.1 has a higher intensity than the 9148.7 peak (maximum intensities, 246.3 and 78.8, respectively), suggesting that the former represents the major serum state of this protein product and the latter represents some modified form (which was confirmed after protein identification). All analytic procedures were completed before our clinical collaborators in Lübeck, Germany, decoded patient diagnoses of the validation set. Serum samples were fractionated on an anion-exchange resin (Q HyperD F; Pall Corporation, East Hill, NY). The resulting fractions were enriched further using YM-30 Microcon filtration units (Millipore Inc., Bedford, MA) or additionally purified by reverse-phase chromatography using RPC Poly-Bio beads (Polymer Laboratories Inc., Amherst, MA). The chromatographic fractions were monitored by SELDI-TOF MS. Enriched fractions were finally purified by sodium dodecyl sulfate–polyacrylamide gel electrophoresis (Invitrogen, Carlsbad, CA). Colloidal Blue–stained bands were excised from gels. Whole bands of interest were extracted from gels with 50% formic acid, 25% acetonitrile, 15% isopropanol, and 10% water,19Grus F.H. Podust V.N. Bruns K. Lackner K. Fu S. Dalmasso E.A. Wirthlin A. Pfeiffer N. SELDI-TOF-MS ProteinChip array profiling of tears from patients with dry eye.Invest Ophthalmol Vis Sci. 2005; 46: 863-876Crossref PubMed Scopus (179) Google Scholar and reanalyzed using the SELDI-TOF MS to confirm that masses of proteins from sodium dodecyl sulfate–polyacrylamide gel electrophoresis bands corresponded to masses of selected biomarkers/features. Extracts were evaporated in vacuum and in solution digested with trypsin.19Grus F.H. Podust V.N. Bruns K. Lackner K. Fu S. Dalmasso E.A. Wirthlin A. Pfeiffer N. SELDI-TOF-MS ProteinChip array profiling of tears from patients with dry eye.Invest Ophthalmol Vis Sci. 2005; 46: 863-876Crossref PubMed Scopus (179) Google Scholar Tryptic digests were analyzed using tandem mass spectrometer Q-TOF2 (Waters-Micromass Inc., Milford, MA) equipped with PCI-1000 ProteinChip Interface (Ciphergen). Spectra were collected from 1 to 3 kilodaltons in single MS mode. After reviewing the spectra, specific ions were analyzed by MS/MS. The collision-induced dissociation spectra were submitted to the database-mining tool Mascot (Matrix Science Inc., Boston, MA) for identification. The identity of biomarkers was confirmed by ProteinChip immunoassay or a beads-based immunoassay. In the first case, a specific antibody was cross-linked to the PS20 ProteinChip array. The crude serum was incubated on spots with immobilized antibody, unbound proteins were removed by multiple washes, and the specifically captured proteins were analyzed directly using the ProteinChip Reader.20Davies H. Lomas L. Austen B. Profiling of amyloid beta peptide variants using SELDI Protein Chip arrays.Biotechniques. 1999; 27: 1258-1261PubMed Google Scholar, 21Boot R.G. Verhoek M. de Fost M. Hollak C.E. Maas M. Bleijlevens B. van Breemen M.J. van Meurs M. Boven L.A. Laman J.D. Moran M.T. Cox T.M. Aerts J.M. Marked elevation of the chemokine CCL18/PARC in Gaucher disease: a novel surrogate marker for assessing therapeutic intervention.Blood. 2004; 103: 33-39Crossref PubMed Scopus (271) Google Scholar In the second approach, 2 μL of Protein A Hyper D beads (Pall Corporation) were loaded with a specific antibody. Beads were washed 3 times with phosphate-buffered saline (PBS) to remove unbound proteins. A total of 2- to 5-μL serum samples diluted to 50 μL in PBS were bound to the beads for 30 minutes at room temperature. The beads were washed 3 times with PBS and once with water. Bound proteins were eluted from the beads with 0.1 mol/L acetic acid. Eluted fractions were analyzed by SELDI-TOF MS using NP20 ProteinChip Arrays. All measurements of serum concentration for complement C3a and complement C3a-desArg were performed using the OptEIA Human C3a ELISA kit (BD Biosciences Pharmingen, San Diego, CA). In accordance with the manufacturer's recommendations, all serum samples were examined at a dilution of 1:10,000 to ensure signal in the linear range of the reference standard curve. With the use of this ELISA kit, physiologic serum levels of complement C3a-desArg are in the range of 8707.2 ± 1797.3 ng/mL. Analyses for each serum sample and reference standard in all ELISA tests were performed in triplicate. The mean coefficient of variation value for serum analyses of the complement C3a ELISA test was 5.61% ± 3.66%. All ELISA tests were performed using the Ultrawash Plus Plate Washer (Dynex, Chantilly, VA) and the VersaMax Mircoplate Reader (Molecular Devices, Sunnyvale, CA). Here we report a comprehensive evaluation of serum protein patterns in an effort to identify biomarkers for colon tumors. Figure 1A presents a summary of the experimental set-up. In the first step of the experimental procedures we screened sera from 32 healthy controls and 58 sera of patients with colorectal malignancy using SELDI-TOF MS. After truncation of spectra and normalization, SELDI-TOF MS revealed 33 m/z values that were a reflection of 9 different serum proteins and their associated adducts. The m/z values on the IMAC3 array at 8941.1 and 9148.7 daltons appeared to be the strongest discriminative features, although the discriminating ability of the proteins producing the group 8 and 9 peaks (Supplementary Figure 1) were not as convincing. These findings were corroborated by the identification of a corresponding peak from the WCX2 array surface at 8937.6 daltons (r = 0.811, P < .0001). Figure 1B exemplarily shows a SELDI-TOF (IMAC3 array) spectrum from a normal sample and a cancer sample covering the m/z values at 8941.1 and 9148.7 daltons. Because the control sera were collected from significantly younger individuals as compared with the malignant sera (Table 1) we analyzed each selected m/z value for the possibility that the observed differences might simply be a reflection of age. We could not detect any age-dependent expression of any of these m/z values in the cancer samples of the discovery set; for instance, the m/z value at 8941.1 revealed a Pearson's correlation coefficient of expression levels and age of r = 0.204, showing that there is no correlation between expression levels and age (Figure 2A). The analysis of the discovery set therefore suggested that serum profiling using SELDI-TOF MS identifies protein peaks that allow the discernment of patients with colorectal malignancy from control individuals in our collection of sera. To exclude fortuitous separation of the malignant samples from healthy controls in the discovery set, the predictive value of the 8941.1 dalton peak then was tested with an independently collected, blinded validation set consisting of 59 samples. Thirteen of the 59 samples (22.0%) received an unknown classification (ie, the peak values were between the upper and lower thresholds). Forty-five of the remaining 46 samples were classified correctly (sensitivity, 96.9%; specificity, 100%).Figure 2(A) Scatter plot of SELDI-TOF MS–based m/z intensities at 8941.1 and age of patients. A Pearson correlation coefficient of expression levels and age of r = 0.204 indicated that there is no correlation. (B) Summary of the SELDI-TOF MS–based values for the peak intensity at 8941.1 of the discovery and the validation set. Note that the peak intensities do not correlate with the U
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