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1 Department of Pathology, Biomarker Discovery Center, Johns Hopkins Medical Institutions, Baltimore, Maryland; 2 M. D. Anderson Cancer Center, Houston, Texas; 3 Ciphergen Biosystems, Inc., Fremont, California; 4 Duke University Medical Center, Durham, North Carolina; 5 The Royal Hospital for Women, Randwick, New South Wales, Australia; 6 University Hospital Groningen, Groningen, the Netherlands; and 7 Bart’s and The London, Queen Mary’s School of Medicine, London, United Kingdom
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ABSTRACT |
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 Top ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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-trypsin inhibitor heavy chain H4 (up-regulated). In independent validation to detect early stage invasive epithelial ovarian cancer from healthy controls, the sensitivity of a multivariate model combining the three biomarkers and CA125 [74% (95% CI, 52–90%)] was higher than that of CA125 alone [65% (95% CI, 43–84%)] at a matched specificity of 97% (95% CI, 89–100%). When compared at a fixed sensitivity of 83% (95% CI, 61–95%), the specificity of the model [94% (95% CI, 85–98%)] was significantly better than that of CA125 alone [52% (95% CI, 39–65%)]. These biomarkers demonstrated the potential to improve the detection of early stage ovarian cancer. |
INTRODUCTION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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23,000 women are diagnosed with the disease and almost 14,000 women die from it (1)
. Given our knowledge about the steep survival gradient relative to the stage at which the disease is diagnosed, it is reasonable to suggest that early detection remains the most promising approach to improve the long-term survival of ovarian cancer patients. The relatively low prevalence (40 out of 100,000) of ovarian cancer among postmenopausal women in the general population, the lack of a clearly defined precursor lesion, and the high cost and possible complications associated with surgical confirmatory procedures have placed stringent requirements on any test intended for general population screening. Currently, none of the existing serum markers, such as CA125, CA 72–4, or macrophage colony-stimulating factor, can be used individually for screening (2) . Longitudinal studies are under way in Europe, Japan, and the United States to evaluate screening strategies using CA125 and/or transvaginal sonography (3, 4, 5) and their impact on overall cancer mortality (6) . Preliminary results have shown encouraging evidence of a survival benefit among patients diagnosed through a screening regimen (3) .
Reports from retrospective studies have shown that multivariate predictive models combining existing tumor markers improve cancer detection (7 , 8) . Recent advances in genomic and proteomic profiling technology have made it possible to apply computational methods to detect changes in protein expressions and their association to disease conditions, thereby hastening the identification of novel markers that may contribute to multimarker combinations with better diagnostic performance (9, 10, 11, 12, 13) .
In this study, we hypothesized that comparison of protein expressions of serum specimens from patients with early stage ovarian cancer with those from healthy women could lead to the discovery of candidate biomarkers for the detection of early stage ovarian cancer. To ensure that the discovered biomarkers are truly associated with ovarian cancer rather than the result of biases in samples, profiling data of specimens from multiple institutions were used for cross-comparison and independent validation. We additionally determined the protein identities of the discovered biomarkers to allow for additional validation with independent methods and as a first step toward understanding the pathways in which they may function.
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MATERIALS AND METHODS |
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Proteomic Expression Profiling.
The ProteinChip Biomarker System (Ciphergen Biosystems), a platform for surface-enhanced laser desorption/ionization time-of-flight mass spectrometry, was used for protein expression profiling (14
, 15)
. Samples from all of the centers were processed identically. To increase the total number of detectable peaks, serum samples (20 µl) were first fractionated via anion exchange chromatography by stepwise pH gradient elution (taken at pH 9/flow through, 7, 5, 4, 3, and organic solvent) using a Biomek 2000 equipped with the ProteinChip Biomarker Integration Package (Ciphergen Biosystems). A control sample of pooled human serum specimen (Intergen) was used for data calibration between experiments. Aliquots of each fraction were bound in triplicate with a randomized chip/spot allocation scheme to IMAC3-Cu, SAX2, H50, and WCX2 ProteinChip arrays. The energy absorbing molecule (crystallization matrix) for surface-enhanced laser desorption/ionization time-of-flight mass spectrometry, saturated sinapinic acid dissolved in 50% acetonitrile/0.5% trifluoracetic acid, was applied promptly. The spotted arrays were read on PBS II ProteinChip readers. Instruments were monitored weekly for performance using insulin and immunoglobulin standards.
Spectra were externally calibrated, baseline subtracted, and normalized to total ion current within m/z (mass/charge) range of 1.5–150 kDa. Qualified mass peaks (signal/noise >5; cluster mass window at 0.3%) within the m/z range of 2–50 kDa were selected automatically. Logarithmic transformation was applied to the peak intensity before analysis for biomarker discovery. After biomarker discovery, the quality and intensity readings of the selected peaks were manually reconfirmed from raw spectra.
Multicenter Study Design.
The diagram in Fig. 1
describes the design of this multicenter study and usage of samples for biomarker discovery, predictive model construction, and independent validation. To minimize the possibility of false discovery because of site-specific systematic biases from preanalytical variables, a key feature of the design is that for biomarker discovery, the stage I/II ovarian cancer samples and healthy controls from Duke University Medical Center and Groningen University Hospital were analyzed separately according to sites. Only those potential biomarkers that were deemed statistically informative and shared the same up or down dysregulation patterns in analyses of data from both sites were additionally validated using the remaining samples at these two sites and all of the samples at the Royal Hospital for Women and M. D. Anderson Cancer Center. Samples from Johns Hopkins Medical Institutions were used only for additional independent confirmation by immunoassays. Because of insufficient specimen volume, the immunoassays were not tested on the samples from the first four institutions.
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Protein Identification.
The discovered biomarkers were purified according to individual biochemical properties using a series of protein separation procedures including anion exchange, size exclusion, and reverse-phase chromatography followed by SDS-PAGE separation. To monitor the purification process, healthy control samples were processed in parallel with the ovarian cancer samples. During each of the iterations, the new fractions were profiled on the same type of ProteinChip arrays used for discovery to monitor the presence or absence of the biomarkers of interest. The purified biomarkers were excised and digested with trypsin before being spotted onto NP20 ProteinChip arrays and read in a PBSII ProteinChip reader. The masses of the proteolytic fragments were used for database searching with the ProFound algorithm. For confirmation, the NP20 arrays containing the proteolytic fragments were analyzed by collision-induced dissociation using a Q-STAR MS/MS instrument (Applied Biosystems/MDS Sciex) equipped with a PCI 1000 ProteinChip Interface (Ciphergen Biosystems).
Multivariate Predictive Models.
The profiling data from the two sites used for biomarker discovery were combined and then divided randomly into a training set and a test set for the derivation and testing of nonlinear multivariate predictive models using nonlinear unified maximum separability analysis. The derived models were then additionally evaluated, in the same way as the individual biomarkers, on the independent validation data that were not involved in biomarker discovery or model construction.
Statistical Analysis.
The performance of the identified biomarkers in detecting early stage ovarian cancer from healthy controls was evaluated by descriptive statistics, Mann-Whitney U test or Kruskal-Wallis test, and receiver operating characteristic curves (ROCKIT; University of Chicago, Chicago, IL; Ref. 20
). Performance of the nonlinear multivariate predictive models was compared with performance of the CA125 assay alone by receiver operating characteristic analysis and estimated sensitivity and specificity. All of the statistical analyses were performed using Statistica 6.1 (Statsoft).
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RESULTS |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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trypsin inhibitor, heavy chain H4 (PK-120). This result was confirmed by the analysis of pepsin digestion products of the marker.
Discriminatory Power of Individual Biomarkers.
Table 2
provides the descriptive statistics (mean ± SD, median) and results from statistical tests of these three biomarkers between healthy controls and ovarian cancer patients of different stage groups in the combined discovery sets and in the independent validation set. Within the discovery sets, the expression levels of the three biomarkers were statistically significantly different between the healthy controls and the early stage ovarian cancer patients (P < 0.000001 for all of the three markers). With the independent validation set, the two biomarkers at m/z 12,828 and m/z 28,043 retained their statistically discriminatory power in detecting stage I/II ovarian cancer (P < 0.000001 and P < 0.000008, respectively). However, the biomarker at m/z 3,272, with its large within-group variances, had only a marginal effect in separating healthy controls from epithelial ovarian cancer. In Table 3
, the distributions of the biomarkers are compared among subgroups of the cancer samples based on stages, histological subtypes, and age.
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, compares the discriminatory power of the individual biomarkers with that of CA125, using receiver operating characteristic analysis on data from patients with early stage ovarian cancer and healthy controls. CA125 and m/z 12,828 performed comparably on both the discovery and independent validation sets, whereas the other two markers had a lower area-under-curve than CA125 in one or both data sets. However, the estimated correlations among the three biomarkers and CA125 were low (data not shown), suggesting the possibility that they were complementary to each other and that a multivariate approach might outperform the single assay of CA125.
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50 years and those below 50 years (Table 3)
. Previous population-based studies have shown that levels of apolipoprotein A1 actually increase slightly with age (21
, 22)
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Multivariate Predictive Models.
The two data sets used for biomarker discovery were merged and then randomly divided into a training set and a test set. The training set had 28 ovarian cancer cases and 33 healthy controls, whereas the test set consisted of 29 cancer cases and 46 controls. The training and test data sets retained only the peak intensity values of the three discovered biomarkers and CA125 test results.
Two multivariate predictive models were constructed. The first used only the three biomarkers as its input and the second used the three biomarkers along with the CA125 level. Panels E–H in Fig. 3
compare the overall diagnostic performance of the two models with that of CA125 using receiver operating characteristic analysis, among which are the superimposed views of both the empirical and fitted receiver operating characteristic curves estimated using the healthy control samples against all of the stage I/II epithelial ovarian cancer samples (Fig. 3G)
or the invasive stage I/II epithelial ovarian cancer samples (Fig. 3H)
in the independent validation data set.
Using cutoffs that maximized the sum of sensitivity and specificity on the training data, these models were applied to the test data and the independent validation data (Table 4)
. For discrimination between healthy controls and stages I/II invasive ovarian cancer in the independent validation set, the multivariate model using the three biomarkers and CA125, at a sensitivity of 83% (95% CI, 61–95%), had a specificity of 94% (95% CI, 85–98%). This is statistically significantly better than CA125 at the same sensitivity of 83% (with a cutoff of 11 units/ml), which yielded a specificity of only 52% (95% CI, 39–65%). On the other hand, CA125 at the cutoff of 35 units/ml had a specificity of 97% (95% CI, 89–100%) and a corresponding sensitivity of 65% (95% CI, 43–84%). At the same fixed specificity, the multivariate model using the three biomarkers and CA125 resulted in a sensitivity of 74% (95% CI, 52–90%). The difference, however, is not statistically significant, partially because of the few stage I/II invasive cases in the independent validation set. Table 4
also lists in detail the estimated sensitivities or specificities for individual diagnostic groups in the training set, test set, and independent validation set.
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Evaluation Using Immunoassays.
The 142 archived specimens from the Johns Hopkins Medical Institutions were analyzed for apolipoprotein A1 using a turbidimetric immunoassay performed in a microtiter plate format (Wako Chemical USA) and for transthyretin using a particle enhanced turbidimetric immunoassay performed on the Dimension RxL Instrument (Dade-Behring; Table 5
). The serum levels of CA125 were up-regulated among the 41 patients with late-stage ovarian cancer compared with the 41 healthy controls (mean ± SD in units/ml, 2388 ± 4723 versus 18 ± 23, P = 0.000000), whereas levels of apolipoprotein A1 and transthyretin were down-regulated (mean ± SD in mg/dl, 122 ± 42 versus 153 ± 28, P = 0.0004, and 20 ± 7 versus 27 ± 6, P = 0.00005, respectively). The mean serum apolipoprotein A1 level among the healthy controls was not significantly different from that of patients with breast or colorectal cancer (P = 0.77 and P = 0.69, respectively) and only marginally different from that of patients with prostate cancer (P = 0.02). The mean serum transthyretin level was down-regulated among patients with colorectal cancer (P = 0.01) albeit to a lesser degree than that in patients with ovarian cancer. The differences in mean serum transthyretin levels between the healthy controls and patients with breast or prostate cancer were not significant (P = 0.51 and P = 0.22, respectively).
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DISCUSSION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Results from receiver operating characteristic curve analysis (Fig. 3, G and H)
show that at a fixed high sensitivity, the predictive models, with or without CA125 as one of its inputs, had a much-improved specificity over that of CA125 alone. However, when the models were compared with CA125 at a fixed high specificity, the improvement in sensitivity was more moderate and tended to diminish as specificity approached 98% and above. The level of analytical variability in peak intensity measurement relative to the few early stage ovarian cancer cases in the independent validation set made it difficult to evaluate in a statistically meaningful way the diagnostic performance of the individual biomarkers or the multivariate models at extreme specificity or sensitivity values.
The differences in area-under-curve in detecting stage I/II invasive cancer from healthy controls were not statistically significant for the independent validation set. Given the fairly large absolute differences in area-under-curve, a likely reason would be the relatively few stage I/II invasive cases available.
Serum levels of CA125 have been used widely for distinguishing benign from malignant pelvic masses and for monitoring the clinical course of patients with ovarian cancer. However, CA125 is elevated in only about half of stage I/II ovarian cancer patients (23) . Lowering the cutoff of CA125 would increase its sensitivity in detecting stage I/II cancer but result in many false positives in patients with benign conditions as well as in healthy women. The improvement in sensitivity by the multivariate models to detect early stage cancer also increased the false-positive rate among patients with benign pelvic masses, but unlike with CA125, it did not lead to appreciable concomitant loss of specificity among healthy women. At a sensitivity of 83% and specificity of 94%, the current multivariate model by itself is not suitable for general population screening. However, in a two-stage approach with a combination of a serum test followed by sonography, a high specificity might be reached at an acceptable total cost.
The identified biomarkers have been characterized generally as acute-phase reactants. The down-regulation of transthyretin and apolipoprotein A1 in ovarian cancer patients suggests the possibility that they are byproducts of the host response to the tumor. Although recent data have supported the concept that inflammation is a critical component of tumor progression (24) , such acute-phase reactants may still represent only epiphenomena because of the presence of tumor and may not be specific to a particular type of cancer (25 , 26) . In this study, with preliminary data from immunoassays analysis, we were able to verify that the levels of apolipoprotein A1 were not altered in the serum of breast or colon cancer patients and the levels of transthyretin were not altered in the serum of breast or prostate cancer patients.
Transthyretin and lipoprotein(a) have been separately reported to be decreased in epithelial ovarian cancer (27 , 28) . Transthyretin is the major carrier for serum thyroxine and tri-iodothyronine and facilitates the transport of retinol via its interaction with retinol binding protein. Transgenic mice lacking transthyretin expression have dramatically lower levels of retinol and retinol binding protein, and decreased levels of retinol binding protein as well as cellular retinol binding protein have been shown to be associated with an increased rate of malignant transformation of ovarian epithelium (29 , 30) . In addition, levels of cellular retinal binding proteins have been reported to be changed in ovarian cancer by oligonucleotide array analysis (31) and have been shown to be decreased in approximately one-third of ovarian cancers by immunohistochemistry (32) .
The carboxyl portion of inter- trypsin inhibitor, heavy chain H4, from which the m/z 3,272 biomarker is derived, has been shown to be a substrate for plasma kallikrein (33
, 34)
. The fragment that we identified as a biomarker differs from the postulated fragment derived from cleavage by plasma kallikrein, suggesting a different protease is responsible for the generation of this biomarker. Kallikrein proteases consist of plasma kallikrein and tissue kallikreins, which have overlapping substrate specificity (35)
. The tissue kallikreins are products of a large multigene family that includes prostate specific antigen (hK3), a tumor marker for prostate cancer. Gene expression of seven of the tissue kallikreins has been found to be up-regulated in ovarian cancer patients, and expression of hK6, hK10, and hK11 has been found to be increased in >50% of patients with ovarian cancer (36
, 37)
.
The identified proteins associated with the three biomarkers are all of high abundance in serum and are unlikely to be released by tumor cells. This limitation has been pointed out previously as being common among studies using mass spectrometry based proteomic profiling (25 , 26 , 38) . However, this is not purely a limitation of the mass spectrometry technological platforms, but rather reflects the limitation of high throughput sample preprocessing technologies that would allow us to examine subsets of the serum proteome without introducing additional significant analytical variability. The use of sample prefractionation in addition to multiple array surfaces in this study is a first attempt in surface-enhanced laser desorption/ionization mass spectrometry based clinical proteomics for biomarker discovery.
Two of the markers found in this study are cleavage products of precursor proteins and present in the serum at a fraction of the concentrations of their corresponding full-length proteins. Neither of these truncation products has been reported previously, indicating that they are new candidate markers. These markers may be the product of cleavage by one or more proteases, including plasma kallikrein, tissue kallikreins, matrix metalloproteases, or prostasin, a trypsin-like serine protease that was reported recently to be increased in cases of ovarian cancer (39)
. The proteases that generate these markers may themselves act as markers that can be combined with those discovered here in a predictive model. More generally, these results support the increasing body of evidence indicating alterations in the balance of protease and protease inhibitor activity in the serum and tissue of patients with cancer (40
, 41)
. Preliminary results from additional immunoprecipitation pull-down analysis suggest that the forms and relative frequencies of posttranslational modifications of inter- trypsin inhibitor, heavy chain H4 and transthyretin may be associated with the ovarian cancer disease status (data not shown).
The use of proteomics to identify potential biomarkers has been explored previously for the detection of a number of cancers including prostate (13 , 42, 43, 44) , colon (45) , bladder (46) , breast (12) , and ovarian (9 , 17) . For example, Petricoin et al. reported the use of self-organizing map coupled with genetic algorithm to search through raw mass spectrum data for informative variables and to form clusters of training samples as the basis of a predictive model. More recently, Kozak et al. (47) reported several panels of selected surface-enhanced laser desorption/ionization peaks for the detection of ovarian cancer. A number of these peaks and their expression patterns are consistent with those from our current or previous results (17) .
Biomarker discovery using clinical proteomics involves the simultaneous analysis of expression levels of many proteins measured on a relatively few clinical samples. The difficulty in statistical analysis is complicated additionally by the possibility of nondisease-related biases and variability in data from preanalytical and analytical variables and within-group heterogeneity associated with clinical samples. A major concern has always been whether the discovered biomarkers and the derived multivariate models are truly associated with the disease process. Recent reports examined and highlighted the danger of such issues (26 , 38 , 48) . The current study had a number of features that were designed to alleviate the impact of these factors. First, only the intensities of detectable peaks were used in analysis, which in general are less sensitive to mass shift in raw spectrum data, and the discovered biomarkers are more likely to have an identifiable biological identity. Second, biomarker discovery and construction of predictive models were done in two separate stages. During the discovery stage, only linear models were used to evaluate the informativeness of individual peaks. The bootstrap resampling was used to additionally ensure that the peak selection results are robust among multiple subpopulations. It was only after a small group of biomarkers had been selected and individually validated across the multiple data sets that the more complicated nonlinear unified maximum separability analysis predictive models were derived to combine the multiple markers. It would have been difficult to explain and validate the exact roles of individual inputs if we had started the peak selection directly with a complex nonlinear model using raw spectrum data. Third, in this study, the data sets from multiple sites were used separately to cross-validate discoveries from other data sets and to detect systematic biases in data such as those because of differences in preanalytical sample processing procedures. This differs significantly from the approaches in which data from multiple sites are pooled together and then divided through randomization into (artificially made) identically distributed training and test data sets. Considering the high cost associated with post discovery validation, however, we believe that the conservative approach that we used is more close to the real clinical environment and appropriate for biomarker discovery through expression profiling. Finally, the identification of the discovered biomarkers allowed additional confirmation through immunoassay tests.
Previous studies have illustrated the benefits of combining multiple markers and the longitudinal use of tumor markers in the detection of ovarian cancers (8 , 49 , 50) . New biomarkers derived from proteomic analysis of clinical samples, once validated, may provide additional choices in the selection of an optimal panel of markers that, through a multivariate approach, would be capable of detecting early stage ovarian cancer in a more general population. Studies remain to be performed to evaluate these markers individually and in combination in larger populations and, most importantly, in preclinical samples from women with ovarian cancer obtained in screening trials. It should be pointed out, however, that intensity data from mass spectra measure the relative abundance of proteins. In this study, all of the surface-enhanced laser desorption/ionization data were generated under well-controlled conditions, with which we were able to demonstrate the performance of the combined three markers and their complementary value to CA125, using samples across multiple sites. The successful application of the multivariate predictive models to new clinical samples, however, will require additional work to assure the consistency of measured peak intensities between multiple runs and across different instruments and sites. Knowledge of the identities of the biomarkers will certainly help to expedite future development of assays.
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FOOTNOTES |
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The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Requests for reprints: Zhen Zhang, Center for Biomarker Discovery, Johns Hopkins Medical Institutions, 419 N. Caroline Street, Room 200, Baltimore, MD 21231. E-mail: zzhang7{at}jhmi.edu
Received 3/ 1/04. Revised 5/17/04. Accepted 5/26/04.
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M. F. Lopez, A. Mikulskis, S. Kuzdzal, E. Golenko, E. F. Petricoin III, L. A. Liotta, W. F. Patton, G. R. Whiteley, K. Rosenblatt, P. Gurnani, et al. A Novel, High-Throughput Workflow for Discovery and Identification of Serum Carrier Protein-Bound Peptide Biomarker Candidates in Ovarian Cancer Samples Clin. Chem., June 1, 2007; 53(6): 1067 - 1074. [Abstract] [Full Text] [PDF]
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J. F. Timms, E. Arslan-Low, A. Gentry-Maharaj, Z. Luo, D. T'Jampens, V. N. Podust, J. Ford, E. T. Fung, A. Gammerman, I. Jacobs, et al. Preanalytic Influence of Sample Handling on SELDI-TOF Serum Protein Profiles Clin. Chem., April 1, 2007; 53(4): 645 - 656. [Abstract] [Full Text] [PDF]
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W. C.S. Cho, T. T.C. Yip, R. K.C. Ngan, T.-T. Yip, V. N. Podust, C. Yip, H. H.Y. Yiu, V. Yip, W.-W. Cheng, V. W.S. Ma, et al. ProteinChip Array Profiling for Identification of Disease- and Chemotherapy-Associated Biomarkers of Nasopharyngeal Carcinoma Clin. Chem., February 1, 2007; 53(2): 241 - 250. [Abstract] [Full Text] [PDF]
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H. Meistermann, J. L. Norris, H.-R. Aerni, D. S. Cornett, A. Friedlein, A. R. Erskine, A. Augustin, M. C. De Vera Mudry, S. Ruepp, L. Suter, et al. Biomarker Discovery by Imaging Mass Spectrometry: Transthyretin is a Biomarker for Gentamicin-induced Nephrotoxicity in Rat Mol. Cell. Proteomics, October 1, 2006; 5(10): 1876 - 1886. [Abstract] [Full Text] [PDF]
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F. Bertucci, D. Birnbaum, and A. Goncalves Proteomics of Breast Cancer: Principles and Potential Clinical Applications Mol. Cell. Proteomics, October 1, 2006; 5(10): 1772 - 1786. [Abstract] [Full Text] [PDF]
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D. Nedelkov, U. A. Kiernan, E. E. Niederkofler, K. A. Tubbs, and R. W. Nelson Population Proteomics: The Concept, Attributes, and Potential for Cancer Biomarker Research Mol. Cell. Proteomics, October 1, 2006; 5(10): 1811 - 1818. [Abstract] [Full Text] [PDF]
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R. R. Drake, E. E. Schwegler, G. Malik, J. Diaz, T. Block, A. Mehta, and O. J. Semmes Lectin Capture Strategies Combined with Mass Spectrometry for the Discovery of Serum Glycoprotein Biomarkers Mol. Cell. Proteomics, October 1, 2006; 5(10): 1957 - 1967. [Abstract] [Full Text] [PDF]
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L. E. Moore, E. T. Fung, M. McGuire, C. C. Rabkin, A. Molinaro, Z. Wang, F. Zhang, J. Wang, C. Yip, X.-Y. Meng, et al. Evaluation of apolipoprotein A1 and posttranslationally modified forms of transthyretin as biomarkers for ovarian cancer detection in an independent study population. Cancer Epidemiol. Biomarkers Prev., September 1, 2006; 15(9): 1641 - 1646. [Abstract] [Full Text] [PDF]
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G. L. Hortin The MALDI-TOF Mass Spectrometric View of the Plasma Proteome and Peptidome Clin. Chem., July 1, 2006; 52(7): 1223 - 1237. [Abstract] [Full Text] [PDF]
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U. Muller, G. Ernst, C. Melle, R. Guthke, and F. von Eggeling Convergence of the proteomic pattern in cancer Bioinformatics, June 1, 2006; 22(11): 1293 - 1296. [Abstract] [Full Text] [PDF]
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J. Song, M. Patel, C. N. Rosenzweig, Y. Chan-Li, L. J. Sokoll, E. T. Fung, N.-H. Choi-Miura, M. Goggins, D. W. Chan, and Z. Zhang Quantification of Fragments of Human Serum Inter-{alpha}-Trypsin Inhibitor Heavy Chain 4 by a Surface-Enhanced Laser Desorption/Ionization-Based Immunoassay Clin. Chem., June 1, 2006; 52(6): 1045 - 1053. [Abstract] [Full Text] [PDF]
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J. Li, Z. Zhang, and D. W. Chan Validation of Breast Cancer Biomarkers Identified by Mass Spectrometry: Some of the authors of the article cited above respond: Clin. Chem., April 1, 2006; 52(4): 772 - 772. [Full Text] [PDF]
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T. E. Werbowetski-Ogilvie, N. Y.R. Agar, R. M. Waldkircher de Oliveira, D. Faury, J. P. Antel, N. Jabado, and R. F. Del Maestro Isolation of a Natural Inhibitor of Human Malignant Glial Cell Invasion: Inter {alpha}-Trypsin Inhibitor Heavy Chain 2 Cancer Res., February 1, 2006; 66(3): 1464 - 1472. [Abstract] [Full Text] [PDF]
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B. Ye, S. Skates, S. C. Mok, N. K. Horick, H. F. Rosenberg, A. Vitonis, D. Edwards, P. Sluss, W. K. Han, R. S. Berkowitz, et al. Proteomic-Based Discovery and Characterization of Glycosylated Eosinophil-Derived Neurotoxin and COOH-Terminal Osteopontin Fragments for Ovarian Cancer in Urine Clin. Cancer Res., January 15, 2006; 12(2): 432 - 441. [Abstract] [Full Text] [PDF]
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J. Li, J. Zhao, X. Yu, J. Lange, H. Kuerer, S. Krishnamurthy, E. Schilling, S. A. Khan, S. Sukumar, and D. W. Chan Identification of Biomarkers for Breast Cancer in Nipple Aspiration and Ductal Lavage Fluid Clin. Cancer Res., December 1, 2005; 11(23): 8312 - 8320. [Abstract] [Full Text] [PDF]
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K. Honda, Y. Hayashida, T. Umaki, T. Okusaka, T. Kosuge, S. Kikuchi, M. Endo, A. Tsuchida, T. Aoki, T. Itoi, et al. Possible Detection of Pancreatic Cancer by Plasma Protein Profiling Cancer Res., November 15, 2005; 65(22): 10613 - 10622. [Abstract] [Full Text] [PDF]
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Y. Hayashida, K. Honda, Y. Osaka, T. Hara, T. Umaki, A. Tsuchida, T. Aoki, S. Hirohashi, and T. Yamada Possible Prediction of Chemoradiosensitivity of Esophageal Cancer by Serum Protein Profiling Clin. Cancer Res., November 15, 2005; 11(22): 8042 - 8047. [Abstract] [Full Text] [PDF]
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H. W. Ressom, R. S. Varghese, M. Abdel-Hamid, S. A.-L. Eissa, D. Saha, L. Goldman, E. F. Petricoin, T. P. Conrads, T. D. Veenstra, C. A. Loffredo, et al. Analysis of mass spectral serum profiles for biomarker selection Bioinformatics, November 1, 2005; 21(21): 4039 - 4045. [Abstract] [Full Text] [PDF]
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T. D. Veenstra, T. P. Conrads, B. L. Hood, A. M. Avellino, R. G. Ellenbogen, and R. S. Morrison Biomarkers: Mining the Biofluid Proteome Mol. Cell. Proteomics, April 1, 2005; 4(4): 409 - 418. [Abstract] [Full Text] [PDF]
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L. A. Liotta, M. Lowenthal, A. Mehta, T. P. Conrads, T. D. Veenstra, D. A. Fishman, and E. F. Petricoin III Importance of Communication Between Producers and Consumers of Publicly Available Experimental Data J Natl Cancer Inst, February 16, 2005; 97(4): 310 - 314. [Abstract] [Full Text] [PDF]
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D. F. Ransohoff Lessons from Controversy: Ovarian Cancer Screening and Serum Proteomics J Natl Cancer Inst, February 16, 2005; 97(4): 315 - 319. [Abstract] [Full Text] [PDF]
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F. J. Schweigert, J. Sehouli, Z. Zhang, and D. W. Chan Transthyretin, a Biomarker for Nutritional Status and Ovarian Cancer Cancer Res., February 1, 2005; 65(3): 1114 - 1114. [Full Text] [PDF]
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E. P. Diamandis and D.-E. van der Merwe Plasma Protein Profiling by Mass Spectrometry for Cancer Diagnosis: Opportunities and Limitations Clin. Cancer Res., February 1, 2005; 11(3): 963 - 965. [Full Text] [PDF]
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K. R. Coombes Analysis of Mass Spectrometry Profiles of the Serum Proteome Clin. Chem., January 1, 2005; 51(1): 1 - 2. [Full Text] [PDF]
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O. J. Semmes Defining the Role of Mass Spectrometry in Cancer Diagnostics Cancer Epidemiol. Biomarkers Prev., October 1, 2004; 13(10): 1555 - 1557. [Full Text] [PDF]
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, CA125; 
, multivariate model using the three biomarkers; and 
, multivariate model using the three biomarkers and CA125. G–H, the fitted receiver operating characteristic curves are superimposed by the empirical receiver operating characteristic curves. *, Part of biomarker discovery set. 
, Epithelial ovarian cancer included both invasive and borderline tumors. Panels G and H: vertical and horizontal dashed lines correspond to 90% specificity and 83% sensitivity, respectively.