Identification of Gastric Cancer Patients by Serum Protein Profiling Matthias P. A. Ebert,*,† Jo1 rn Meuer,| Jan C. Wiemer,| Hans-Ulrich Schulz,§ Marc A. Reymond,§ Ulrich Traugott,| Peter Malfertheiner,† and Christoph Ro1 cken‡ Departments of Gastroenterology, Hepatology and Infectious Diseases, Pathology, and General Surgery, Otto-von-Guericke University, D-39120 Magdeburg, Germany and Europroteome AG, D-16761 Berlin-Hennigsdorf, Germany Received August 4, 2004
Using surface-enhanced laser desorption ionization mass spectrometry (SELDI/TOF-MS) and ProteinChip technology, coupled with a pattern-matching algorithm and serum samples, we screened for protein patterns to differentiate gastric cancer patients from noncancer patients. A classifier ensemble, consisting of 50 decision trees, correctly classified all gastric cancers and all controls of a training set (100% sensitivity and 100% specificity). Eight of 9 stage I gastric cancers (88.9% sensitivity for stage I) were correctly classified. In addition, 28 sera from gastric cancer patients taken in different hospitals were correctly classified (100% sensitivity). Furthermore, all 11 control sera obtained from patients without gastric cancer (100% specificity) were classified correctly and 29 of 30 healthy blood-donors were classified as noncancerous. ProteinChip technology in conjunction with bioinformatics allows the highly sensitive and specific recognition of gastric cancer patients. Keywords: Proteomics • diagnosis • stomach • SELDI
Introduction Gastric cancer is the fourth most common malignancy worldwide and affects approximately 1 million individuals every year. The mortality from gastric cancer is second only to lung cancer. This poor prognosis is primarily related to late diagnosis and therapeutic limitations. While surgery may be curative in early stages, all other treatment modalities in advanced disease, including chemotherapy and radiation, are disappointing.1,2 The identification of early stages of gastric cancer and efficient screening of high-risk patients would enable earlier treatment, thereby greatly improving the poor prognosis. However, despite intensive studies over the past decades, no valid serum markers for gastric cancer have been identified so far.2-8 Very recently, a new technique, surface-enhanced laser desorption ionization/time-of-flight mass spectrometry (SELDI/ TOF-MS), has been developed, whereby small amounts of proteins are bound to a biochip carrying spots with different types of chromatographic material, i.e., hydrophobic, hydrophilic, cation-exchanging, and anion-exchanging characteristics.9,10 SELDI/TOF-MS can be applied to analyze differential peptide and protein expression patterns in cancer and noncancer patients in various biological fluids.11-17 The great advantages of this method are its speed, its high through-put capability, and, most importantly, the requirement of only small * To whom correspondence should be addressed. Tel: +49-391-6721520. Fax: +49-391-67190054. E-mail:
[email protected]. † Department of Gastroenterology, Hepatology and Infectious Diseases, Otto-von-Guericke University. ‡ Department of Pathology, Otto-von-Guericke University. § Department of General Surgery, Otto-von-Guericke University. | Europroteome AG. 10.1021/pr049865s CCC: $27.50
2004 American Chemical Society
amounts of material.9,10 Using SELDI/TOF-MS and ProteinChip technology coupled with a pattern-matching algorithm, we aimed to diagnose gastric cancer.
Patients and Methods Patient Populations and Samples. Sera were obtained from 50 patients with gastric cancer (group 1; Table 1) and 60 patients with dyspeptic symptoms and without gastric cancer (group 2; Table 1). Patients of group 2 denied a personal history of cancer, were otherwise healthy, and were followed-up for up to a maximum of 5 years, in which none developed gastric cancer. All patients of groups 1 and 2 underwent gastroscopy, and gastric cancer was either histologically confirmed (group 1) or excluded (group 2). In patients with gastric cancer of group 1, 12 cancers were located in the cardia region, 4 cancers in the fundus, 23 cancers in the body and in 6 cases in the antrum. In five cases, the primary location of the tumor could not be identified or was found at multiple sites throughout the stomach. H. pylori infection was present in 13 individuals with gastric cancer (group 1) and in 21 of the patients with dyspeptic symptoms (group 2). Patients with adenocarcinoma of the esophagus were excluded from this study. Sera from 28 patients with gastric cancer (group 3; Table 1) were obtained from three different hospitals (Cottbus (18 patients), Magdeburg (8), Erlangen (2)). Sera from 30 healthy blood donors (group 4; completely anonymous patient data) were provided by the German Red Cross (Berlin; Germany). Criteria of inclusion for blood donors are no serological evidence of infection with HIV, hepatitis B or C virus; liver function test, urea, electrolytes, and full blood count within normal ranges. Blood donors are generally Journal of Proteome Research 2004, 3, 1261-1266
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Table 1. Patient Characteristicsa
patients n
group 1
(training set)
(1. test set) group 2
tumor stage gender (m/f)
41
63.3 ( 11.4 [64; 26-83]
28/13
9
62.3 ( 11.5 [63; 47-79] 55.8 ( 9.0 [57; 40-70] 54.8 ( 9.1 [57; 42-68] 67.5 ( 9.2 [69; 46-84]
8/1
(training set)
49
(1. test set)
11
group 3
(2. test set)
28
group 4
(3. test set)
30
a
age (years) mean ( SD [median; range]
n.d.
histological type (Laure´n)
IA n (%)
18 diffuse 21 intestinal 1 mixed type 1 undifferentiated 1 diffuse 8 intestinal
IB n (%)
3 (33)
6 (66)
2 (7)
3 (11)
II n (%)
IIIA n (%)
IIIB n (%)
IV n (%)
3 (7)
12 (29)
4 (10)
22 (54)
3 (11)
2 (7)
2 (7)
16 (57)
15/34 6/5 18/10
12 diffuse 15 intestinal 1 mixed
n.d.
Legend: n.d., no data.
Table 2. Overview of Classifier Performance on Training and Test Data training set
classifier
characterization
group 1 n ) 41 sensitivity
single mass single tree tree ensemble
3946 Da 3946 Da + 3503 Da + 15958 Da 50 decision trees, 28 masses
85.4 92.7 100
between 18 and 68 years old and can be considered as healthy individuals, as donors are checked for diseases before each donation. All serum samples were collected before initiation of any treatment and were stored at -80 °C for later use. Patients gave informed consent prior to the study and the protocol was approved by the Ethics Committee of the University of Magdeburg (Approval no. 53/03). ProteinChip Array Analysis. Patient sera were analyzed with SAX2-(anion exchange) ProteinChip arrays (Ciphergen Biosystems, Inc.). The SAX2 arrays were placed in the Bioprocessor (Ciphergen Biosystems, Inc.) and preincubated with 200 µL binding buffer (0.1 M Tris-HCl, 0.02% Triton X-100, pH 8.5). 10 µL serum were dissolved in 40 µL denaturing buffer (7 M urea, 2 M thiourea, 4% CHAPS, 1% DTT, 2% ampholine), diluted 1:10 in binding buffer and pipetted onto the spots. Following incubation for 120 min, the unbound proteins were removed by washing with binding buffer (2 × 200 µL). Energy absorbing molecule solution (EAMS; 20 mg/mL sinapinic acid, 50% acetonitrile, 0.5% trifluoroacetic acid; 2 × 0.5 µL) was applied and air-dried for 10 min. Finally, the arrays were placed into the ProteinChip Reader (ProteinChip Biology System II, Ciphergen Biosystems, Inc.). Time-of-flight spectra were generated by laser shots collected in the positive mode. The laser intensity was set to 215, detector sensitivity to 8, and 60 laser shots per average spectrum were performed. A mixture of mass standard calibrant proteins was used for calibration of mass accuracy. 0.5 µL were applied to a single spot of a H4 ProteinChip array, and 2 × 1.0 µL EAMS were applied. Timeof-flight spectra were generated as described above. Time-offlight values were correlated to the molecular masses of the standard proteins, and calibration was performed according to manufacturer’s instructions. 1262
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1. test set
2. test set
3. test set
group 2 n ) 49 specificity
group 1 n)9 sensitivity
group 2 n ) 11 specificity
group 3 n ) 28 sensitivity
group 4 n ) 30 specificity
91.8 94.1 100
89.9 89.9 89.9
100 90.9 100
100 92.8 100
90 86.7 96.7
Peak Detection, Data Analysis and Decision Tree Classification. The data were analyzed by peak detection and alignment, selection of peaks with high discriminatory power, and classifier construction using decision trees. The first set of patients (group 1) was divided into a training set of 41 patients with and 49 patients without gastric cancer. The 1st test set (Table 2) was composed of 9 patients with stage I gastric cancer and 11 randomly selected patients without cancer. The classification algorithm, generated on the basis of the spectra of the subgroup of patients selected for the training set, was tested with this blind set and the other two test sets (group 2 and 3) in order to determine accuracy and validity of the algorithm. Peak detection was performed using the Ciphergen ProteinChip Software 3.0. Spectra ranging from 1300 to 50 000 Da were selected for the analysis. Smaller masses were not used, since artifacts with EAMS and other contaminants could not be excluded. The spectra were normalized according to the intensity of total ion current. Automatic peak detection was performed in the range of 1300 to 20 000 Da, following baseline subtraction. The following settings were chosen for automatic peak detection: (a) auto-detect peaks to cluster, (b) first pass: 3 signal/noise, (c) minimal peak threshold: 25% of all spectra, (d) cluster mass window: 0.5% of mass. Decision trees were constructed from our training set on the basis of the peak intensities of the obtained 71 signal clusters. The high-dimensional feature space spanned by 71 masses is partitioned into a set of rectangles and each rectangle is assigned to either cancer or noncancer class. We applied the “CART” decision tree approach with Gini diversity index in the form of Ciphergen’s Biomarker Patterns Software (classification and regression trees).18 Bagging of decision trees was applied to overcome typical instabilities of forward variable selection procedures such as
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decision trees and to increase overall classifier performance:18 50 bootstrap samples were generated from our training data set (sampling with replacement, maximal 3 sample redraws) and applied to construct 50 un-pruned decisions. The decision trees were combined to constitute an overall classifier in the form of a classifier ensemble predicting class membership by plurality vote.
Results Peak Reproducibility. We first analyzed the peaks in the mass range of 1300 Da to 50 000 Da of 110 serum samples taken from 50 patients with gastric cancer (group 1) and 60 patients without cancer (group 2) (Figure 1a and b). To assess reproducibility of the spectra, we determined the mass location and signal intensity of each sample on a single chip (intra-assay) and between chips (inter-assay) using all 110 pooled spectra. Inter-assay reproducibility was analyzed by selecting three peaks, which were present in all group 1 and group 2 patients, i.e., 2020, 8483, and 13 779 Da (Figure 1c). The following mean masses and standard deviations were determined for these peaks: 2020.5(0.89 Da ((0.044%), 8483.5(5.81 Da ((0.068%), and 13 779.6(5.59 Da ((0.04%). The intra-assay reproducibility was assessed by measuring a randomly selected serum sample on all eight spots of a SAX2 ProteinChip array, giving 2020.7(0.33 Da ((0.016%), 8479.5(2.56 Da ((0.03%), and 13 779.6(5.28 Da ((0.038%). For normalized intensity (peak height or relative concentration), the intra-assay standard deviations were 9.93% (2020.7 Da), 17.7% (8479.5 Da), and 12.5% (13 779.6 Da), while the inter-assay standard deviations (repeat measurement of the sample on different days using different lots of SAX2 arrays; Figure 1c) were 15.1% (2020.7 Da), 21.0% (8479.5 Da) and 21.1% (13,779.6 Da). Structure of Biomarker Patterns. We next aimed to find peaks and peak patterns, which separate gastric cancer patients from patients without gastric cancer using a training set of 41 and 49 patients of group 1 and 2, respectively (Table 1). Peaks were detected by automatic peak detection following baseline subtraction,11 and lead to the identification of 71 signal clusters. On the basis of the normalized peak intensities of these 71 signal clusters, decision trees were constructed and applied on our training set. Peaks with high discriminatory power were selected and used to create three classifiers of different complexity. Decision trees are flowchart-like tree structures, which consist of repeated splits of a data set into subsets, in accordance with the given cancer versus noncancer classification task (Figure 2a). Each split consists of a simple rule applied to each patient and uses only one mass, for example, “if the value of ‘mass 1’ is larger than ‘threshold 1’ then go left (class as, e.g., cancer) else go right (class as, e.g., noncancer)”. By random variations of the training set, 50 different decision trees were generated, which employed 28 masses out of the initial 71 signal clusters. A single decision tree consisted of up to 5 masses (6 end nodes), with 3 and 4 masses being typical (Table 3). Each decision tree was read as a specific pattern of masses valuable for classifying cancer versus noncancer. The importance of each mass could be roughly deduced by how often it appears in the classifier ensemble and showed that the mass 3946 Da is selected as the most informative mass in 28 (56%) of 50 runs and is the most promising mass on the single mass level in separating cancer from noncancer patients (Table 3;
Figure 1. SELDI/TOF-MS spectra. (A) Overlay of protein mass spectra processed on SAX2 ProteinChip surface. Protein mass spectra obtained in sera of patients with gastric cancer (blue) and noncancer individuals (red) are superimposed. Differential expression and variations in intensity indicate potential biomarkers. (B) Representative protein mass spectra from two patients without gastric cancer (upper panel) and two patients with gastric cancer (lower panel), depicted as mass spectra and respective gel views. Frames indicate the position of the three biomarkers from the single best tree. (C) Representative protein mass spectra from a patient without gastric cancer processed on different ProteinChip arrays at different days in order to demonstrate reproducibility of the protein mass spectra. Upper panel, mass spectra; lower panel, respective gel views. Frames indicate the position of the three proteins that were used to determine the variance in intensity and mass.
single mass classifier). On the level of 3-dimensional patterns, the most frequent set consists of masses 3946, 3503, and 15 958 Da and is selected three times (Table 3; decision trees Nos. 17-19). Accordingly, this constitutes the most promising decision tree of 3-masses complexity (single tree classifier). Finally, all 50 generated decision trees were combined by majority voting (tree ensemble classifier). The frequency of masses occurring in the tree ensemble classifier is summarized in Figure 2b for specific (a-e) and all hierarchical levels (f). Journal of Proteome Research • Vol. 3, No. 6, 2004 1263
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Ebert et al. Table 3. Table of Decision Treesa mass no.
Figure 2. Biostatistics. (A) Diagram of the single best tree. The numbers in each box indicate the total number of samples, together with the number of cancer and noncancer individuals. Upper panel, training set. Lower panel: 4 different test sets. Grey boxes indicate misclassified cases. (B) Variable importance. The frequency of variable selection is presented in histogram form for each hierarchical level (a-e) and for all hierarchical levels taken together (f).
Testing the Classifier. The classification performance regarding sensitivity and specificity for the differentiation between cancer and noncancer was determined for the three classifiers of different complexity: (a) the most promising single mass, (b) the most promising decision tree considering three masses, and (c) a classifier ensemble comprising 28 masses in 50 decision trees. The performance results on the training set and three different test sets are summarized in Table 2. Overall sensitivity and specificity of the three different classifiers ranged from 85.4% to 100% on the training set. However, the most fundamental property required from a serum based marker is detection of cancer in its early stage, when treatment can have the greatest impact on the prognosis. The 1st test set comprised 9 patients with stage I gastric cancer (Table 1) obtained from group 1, and 11 controls obtained from group 2. Neither of these patients had been included in the training set. Using the classifiers described above, 8 (88.9%) patients with early stage gastric cancer were correctly classified. 1264
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trees
1
2
3
4
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 frequency [n(%)]
2610 3654 3654 3654 3654 3946 3946 3946 3946 3946 3946 3946 3946 3946 3946 3946 3946 3946 3946 3946 3946 3946 3946 3946 3946 3946 3946 3946 3946 3946 3946 3946 3946 5492 5492 5492 5492 12471 12471 12471 12471 12471 12471 12471 12471 12471 12471 12471 12471 12471 0
12471 1510 1510 2610 3503 1510 1510 1510 1510 1510 1510 15958 2049 3503 3503 3503 3503 3503 3503 3503 3503 3503 4198 4198 4198 4198 4198 4198 4198 4198 5650 5650 7966 1510 1510 1510 4103 2049 2610 2610 6449 2610 3654 3946 4478 4478 5492 6647 6647 7966 0
5650 2610 5650 5650 1510 17409 2049 3503 3503 4359 5492 3654 8791 12471 12471 1510 15958 15958 15958 2610 6879 6879 12471 12471 15958 18137 18137 3503 6879 8791 18137 4478 12471 12663 2610 8791 2049 2610 11537 1510 1510 7966 1510 1510 1510 6647 11537 2610 7966 6647 20 (40)
1510 4198 5650 4158 6879 8938 8938 4103 8233
4478 6879
5
1510
7966
2022 6449 9435 4198
6449 6449 15958 15958
3654 4478
3946
3946 3503 3946 6647 1510 2049 3946 3946 6647 25 (50)
11537
3654 5 (10)
a “Mass number” expresses the stepwise and hierarchical order of mass selection, “1” denoting the first selected mass, “2” the second mass, etc. The most promising single mass, and the most promising decision tree considering 3 masses is italicized.
Specificity was calculated as 90.9% or 100% (Table 2). These results underscore the efficacy of the classifiers for the diagnosis of gastric cancer. In daily clinical practice, serum samples may be obtained from different clinics and hospitals. To ensure that the different spectra were indeed related to the presence of gastric cancer and not falsified by sampling errors, we also tested our classifiers against a 2nd test set of 28 sera taken from gastric cancer patients in different hospitals (group 3). All of these patients had histologically confirmed gastric cancer, albeit the mode of serum sampling and processing was not standardized as in the training set or the 1st test set. This collection of sera
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reflects more closely the actual clinical condition of various sera obtained at different time points in different hospitals. Nonetheless, sensitivities of 100% (tree ensemble and single mass classifier) and 92.8% (single tree classifier) were achieved and our classifier ensemble correctly classified all 28 gastric cancer samples of the 2nd test set as cancer, thus supporting the efficacy of our biomarkers. Finally, we investigated the specificity of our classifiers using a 3rd test set of 30 blood donors (group 4). Interestingly, high specificity of 96.7% was achieved by the tree ensemble classifier, and specificities of 90% and 86.7% were still reached by single mass and single tree classifiers (Table 2; worse specificity of single tree classifier was due to overfitting, see Discussion). The extent of the primary tumor, presence of lymph node or distant metastases in cancer patients was independent of the markers mentioned above. The normalized peak intensities of the best single masses, i.e., 3946, 3503, and 15 958 Da, did not correlate with patient age and gender in any of the patient groups studied.
Discussion Gastric cancer is frequently diagnosed in advanced stages and no valid serum markers for gastric cancer have been identified.1,2 The sensitivity of the most frequently used markers, such as CEA, Ca19-9 and Ca72-4, lies between 20.9% and 56%.3-8 Moreover, curatively treatable stage I cancers are detected in less than 23% of gastric cancers. Recently, new proteomic approaches, including SELDI/TOF-MS, have been developed.9,10 Coupled to a pattern-matching algorithm, this technique led to the identification of biomarker patterns in pancreatic juice, urine, or serum, which correctly classified with high sensitivity and specificity cancer and noncancer in patient populations suffering from prostate, breast, bladder, ovary, or pancreatic cancer.11-17 To the best of our knowledge, sera from gastric cancer patients have not yet been analyzed. Using SELDI/TOF-MS, we analyzed sera from a large set of well-characterized gastric cancer patients, wherein all cases with gastric cancer were histologically confirmed. Patients without gastric cancer, who all had undergone upper gastrointestinal tract endoscopy and follow-up surveillance for up to 5 years, served as control. Our bioinformatical approach finally generated an ensemble of 50 decision trees based on bootstrapping (bagging). Decision trees are classifiers that are not just based on single masses, but on more dimensional sets consisting of masses tuned to each other in order to achieve high performance classification. This is advantageous to other more basic approaches that first evaluate masses one at a time on a single mass level and then construct classifiers on the basis of a set of the determined best-performing masses.12 It is important to prevent overfitting, which may lead to construction of an overly complex single tree classifier with too many masses.19 Overfitting can be prevented by more rigorous statistical approaches such as bootstrapping13 and boosting.15,20 We applied bootstrapping in the form of bagging to generate an ensemble classifier of many decision trees.21 We challenged our algorithm with different patient groups (1st, 2nd, and 3rd test set) and demonstrated that our classifiers can detect with high sensitivity and specificity gastric cancer patients. Although patients with gastric cancer were significantly older than our control patients, the normalized peak intensities of the best three single masses did not correlate with patient age or gender, and therefore patient age and gender did not influence sensitivity or specificity of our markers. The
2nd and 3rd test set are of considerable interest since these sera were collected in different hospitals under unknown conditions, and reflect the clinical situation in the management of cancer patients. Despite different circumstances of blood sampling, sample storage and transport conditions, these variables did not compromise the sensitivity and specificity of protein profiling for the classification of gastric cancer from noncancer, further validating the quality and reliability of our classifiers. Differential serum protein expression, as detected by SELDI/ TOF-MS, probably reflects all aspects commonly found in cancer patients: the gain or loss of peaks related to altered gene expression, altered post-translational modification and/ or altered protein metabolism. In this respect, it was of particular interest to note that several peaks, including the most promising single mass (i.e., 3946 Da; Figure 1b), were decreased in gastric cancer patients. This is different from all other serum based markers currently used: the investigation of serum samples in cancer patients usually searches for tumor-derived proteins secreted into the bloodstream. Using decreased expression or loss of serum peaks as classifiers in cancer patients is a novelty that has not been described so far. SELDI/TOF-MS may detect tumor-derived secretory products, as well as the immmunologic and metabolic response to the presence of cancer as a result of tumor-host interactions. These changes appear to be an early event in the pathogenesis of cancers, since 8 out of 9 patients of the 1st test set and all patients of the 2nd test set with stage I gastric cancer were classified correctly in our series. Furthermore, these markers are less sensitive toward the tumor mass as further evidenced in this study: the extent of the primary tumor, the presence of lymph node and/or distant metastasis was independent from our classifiers. Li et al. made similar observations in breast cancer patients.13 Therefore, future approaches for the identification and development of serum markers should focus on serum proteins, which are decreased or lost in cancer patient sera. As yet, apart from the molecular mass, we know little about the nature of the protein peaks separating gastric cancer from noncancer patients. The identification of these peaks is necessary in order to develop new serum-based diagnostic tests for screening patients at risk for gastric cancer. Our study indicates that SELDI/TOF-MS is a new promising tool in clinical proteomics and may help to detect gastric cancer.
Acknowledgment. M. Ebert is supported by the Heisenberg-Programm of the Deutsche Forschungsgemeinschaft (Eb 187/5-1) and the Land Sachsen-Anhalt (3488A/0103M). References (1) Ebert, M. P.; Malfertheiner, P. Aliment. Pharmacol. Ther. 2002, 16, 1059-1066. (2) Roth, A. D. Crit. Rev. Oncol. Hematol. 2003, 46, 59-100. (3) Marrelli, D.; Rovello, F.; de Stefano, A.; Farnetani, M.; Marosi, L.; Messano, A.; Pinto, E. Oncology 1999, 57, 55-62. (4) Nakajiima, K.; Ochiai, T.; Suzuki, T.; Shimada, H.; Hayashi, H.; Yasumoto, A.; Takeda, A.; Hishikawa, E.; Isono, K. Tumor Biol. 1998, 19, 464-469. (5) Ishigami, S.; Natsugoe, S.; Hokita, S.; Che, X.; Tokuda, K.; Nakajo, A.; Iwashige, H.; Tokushige, M.; Watanabe, T.; Takao, S.; Aikou, T. J. Clin. Gastroenterol. 2001, 32, 41-44. (6) Tocchi, A.; Costa, G.; Lepre, L.; Lotta, G.; Mazzoni, G.; Cianetti, A.; Tannini, P. J. Cancer Res. Clin. Oncol. 1998, 124, 450-455. (7) Gaspar, M. J.; Arribas, I.; Coca, M. C.; Diez-Alonso, M. Tumor Biol. 2001, 22, 318-322. (8) Marrelli, D.; Pinto, E.; De Stefano, A.; Farnetani, M.; Garosi, L.; Roviello, F. Am. J. Surg. 2001, 181, 16-19. (9) Issaq, H. J.; Veenstra, T. D.; Conrads, T. P.; Felschow, D. Biochem. Biophys. Res. Comm. 2002, 292, 587-592.
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research articles (10) Issaq, H. J.; Conrads, T. P.; Prieto, D. A.; Tirumalai, R.; Veenstra, T. D. Anal. Chem. 2003, 75, 148A-155A. (11) Adam, 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, G. L. Cancer Res. 2002, 62, 3609-3614. (12) Vlahou, A.; Schellhammer, P. F.; Mandrinos, S.; Patel, K.; Kondylis, F. I.; Gong, L.; Nasim, S.; Wright, G. L. Am. J. Pathol. 2001, 158, 1491-1502. (13) Li, J.; Zhang, Z.; Rosenzweig, J.; Wang, Y. Y.; Chan, D. W. Clin. Chem. 2002, 48, 1296-1304. (14) Rosty, C.; Christa, L.; Kuzdzal, S.; Baldwin, W. M.; Zahurak, M. L.; Carnot, F.; Chan, D. W.; Canto, M.; Lillemoe, K. D.; Cameron, J. L.; Yeo, C. J.; Hruban, R. H.; Goggings, M. Cancer Res. 2002, 62, 1868-1875. (15) Qu, Y.; Adam, B. L.; Yasui, Y.; Ward, M. D.; Cazares, L. H.; Schellhammer, P. F.; Feng, Z.; Semmes, O. J.; Wright, G. L. Clin. Chem. 2002, 48, 1835-1843.
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Ebert et al. (16) Petricoin, 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.; EmmertBuck, M. R.; Steinberg, S. M.; Kohn, E. C.; Liotta, L. A. J. Natl. Cancer. Inst. 2002, 94, 1576-1578. (17) Petricoin, 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. Lancet 2002, 359, 572-577. (18) Breiman, L.; Friedman, J.; Olhsen, R.; Stone, C. Pacific Grove: Wadsworth, 1984. (19) Ransohoff, D. F. Gastroenterology 2003, 125, 290. (20) Freund, Y.; Schapire, R. J. Japan Soc. Artif. Intel. 1999, 14, 771780. (21) Breiman, L. Machine learning. 1996, 123-140.
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