SELDI-TOF MS whole serum proteomic profiling with IMAC surface does not reliably detect prostate cancer.

BACKGROUND: The analysis of bodily fluids using SELDI-TOF MS has been reported to identify signatures of spectral peaks that can be used to differentiate patients with a specific disease from normal or control patients. This report is the 2nd of 2 companion articles describing a validation study of a SELDI-TOF MS approach with IMAC surface sample processing to identify prostatic adenocarcinoma. METHODS: We sought to derive a decision algorithm for classification of prostate cancer from SELDI-TOF MS spectral data from a new retrospective sample cohort of 400 specimens. This new cohort was selected to minimize possible confounders identified in the previous study described in the companion paper. RESULTS: The resulting new classifier failed to separate patients with prostate cancer from biopsy-negative controls; nor did it separate patients with prostate cancer with Gleason scores <7 from those with Gleason scores > or =7. CONCLUSIONS: In this, the 2nd stage of our planned validation process, the SELDI-TOF MS-based protein expression profiling approach did not perform well enough to advance to the 3rd (prospective study) stage. We conclude that the results from our previous studies-in which differentiation between prostate cancer and noncancer was demonstrated-are not generalizable. Earlier study samples likely had biases in sample selection that upon removal, as in the present study, resulted in inability of the technique to discriminate cancer from noncancer cases.

Prostein expression is highly restricted to normal and malignant prostate tissues.

BACKGROUND: Prostein is a recently described molecule expressed at the mRNA level in a prostate-specific manner. A murine monoclonal antibody was developed, characterized, and used to evaluate the expression of prostein protein in prostatic, other normal tissue and tumor samples. METHODS: The murine anti-prostein monoclonal antibody 10E3-G4-D3 was generated using recombinant prostein. ELISA, FACS, and Western analyzes were used to characterize 10E3-G4-D3. Immunohistochemistry was used to characterize the expression of prostein in tissues. RESULTS: 10E3-G4-D3 specifically recognizes a linear intracellular epitope of prostein. IHC analysis demonstrates that prostein is expressed in the vast majority of normal and malignant prostatic tissues, regardless of grade and metastatic status. No protein expression is detected in a panel of approximately 4,700 normal and malignant tissue samples representing the great majority of essential tissues and tumors. CONCLUSIONS: Prostein is exquisitely specific for prostate tissues, indicating a potential clinical utility of 10E3-G4-D3 as a diagnostic biomarker, and support the use of prostein as a novel target for development of prostein-specific antibody and T-cell based therapeutic strategies for prostate cancer.

Pharmacoproteomic analysis of prechemotherapy and postchemotherapy plasma samples from patients receiving neoadjuvant or adjuvant chemotherapy for breast carcinoma.

BACKGROUND: In this study, proteomic changes were examined in response to paclitaxel chemotherapy or 5-fluorouracil, doxorubicin, and cyclophosphamide (FAC) chemotherapy in plasma from patients with Stage I-III breast carcinoma. The authors also compared the plasma profiles of patients with cancer with the plasma profiles of healthy women to identify breast carcinoma-associated protein markers. METHODS: Sixty-nine patients and 15 healthy volunteers participated in the study. Plasma was sampled on Day 0 before chemotherapy and on Day 3 posttreatment in the 69 patients or 3 days apart in the 15 healthy women. Twenty-nine patients received preoperative chemotherapy, and 40 received postoperative chemotherapy. Surface-enhanced laser desorption/ionization mass spectrometry was used to generate protein mass profiles. RESULTS: Few changes were observed in plasma during treatment. Only 1 protein peak was identified (mass/charge ratio [m/z], 2790) that was induced by paclitaxel and, to a lesser extent, by FAC chemotherapy. This proteomic response was detectable in 80% of patients who were treated preoperatively but also was present with lesser intensity in approximately 40% of patients treated postoperatively. There was no clear correlation between induction of m/z 2790 during a single course of treatment and final tumor response to preoperative chemotherapy. Five other peaks also were identified that discriminated between plasma from patients with breast carcinoma and plasma from normal women. These same peaks also were detectable in a subset of patients who already had undergone surgery to remove their tumors. CONCLUSIONS: A single chemotherapy-inducible SELDI-MS peak and five other peaks that distinguished plasma obtained from patients with breast carcinoma from plasma obtained from normal, healthy women were identified. The (as yet unsequenced) proteins represented by these peaks are candidate markers of micrometastatic disease after surgery.

Computational protein biomarker prediction: a case study for prostate cancer.

BACKGROUND: Recent technological advances in mass spectrometry pose challenges in computational mathematics and statistics to process the mass spectral data into predictive models with clinical and biological significance. We discuss several classification-based approaches to finding protein biomarker candidates using protein profiles obtained via mass spectrometry, and we assess their statistical significance. Our overall goal is to implicate peaks that have a high likelihood of being biologically linked to a given disease state, and thus to narrow the search for biomarker candidates. RESULTS: Thorough cross-validation studies and randomization tests are performed on a prostate cancer dataset with over 300 patients, obtained at the Eastern Virginia Medical School using SELDI-TOF mass spectrometry. We obtain average classification accuracies of 87% on a four-group classification problem using a two-stage linear SVM-based procedure and just 13 peaks, with other methods performing comparably. CONCLUSIONS: Modern feature selection and classification methods are powerful techniques for both the identification of biomarker candidates and the related problem of building predictive models from protein mass spectrometric profiles. Cross-validation and randomization are essential tools that must be performed carefully in order not to bias the results unfairly. However, only a biological validation and identification of the underlying proteins will ultimately confirm the actual value and power of any computational predictions.

Serum protein profiles to identify head and neck cancer.

PURPOSE: New and more consistent biomarkers of head and neck squamous cell carcinoma (HNSCC) are needed to improve early detection of disease and to monitor successful patient management. The purpose of this study was to determine whether a new proteomic technology could correctly identify protein expression profiles for cancer in patient serum samples. EXPERIMENTAL DESIGN: Surface-enhanced laser desorption/ionization-time of flight-mass spectrometry ProteinChip system was used to screen for differentially expressed proteins in serum from 99 patients with HNSCC and 102 normal controls. Protein peak clustering and classification analyses of the surface-enhanced laser desorption/ionization spectral data were performed using the Biomarker Wizard and Biomarker Patterns software (version 3.0), respectively (Ciphergen Biosystems, Fremont, CA). RESULTS: Several proteins, with masses ranging from 2778 to 20800 Da, were differentially expressed between HNSCC and the healthy controls. The serum protein expression profiles were used to develop and train a classification and regression tree algorithm, which reliably achieved a sensitivity of 83.3% and a specificity of 100% in discriminating HNSCC from normal controls. CONCLUSIONS: We propose that this technique has potential for the development of a screening test for the detection of HNSCC.

Identification of patients with head and neck cancer using serum protein profiles.

BACKGROUND: New and more consistent biomarkers of head and neck squamous cell carcinoma (HNSCC) are needed to improve early detection of disease and to monitor successful patient management. OBJECTIVE: To determine if a new proteomic technology can correctly identify protein expression profiles for cancer in patient serum samples as well as detect the presence of a known tumor marker. DESIGN: Direct proteomic analysis and comparison. METHODS: The surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF) ProteinChip system was used to screen for differentially expressed proteins in serum samples from 99 patients with HNSCC, 25 "healthy" smokers, and 102 healthy (normal) controls. Protein peak clustering and classification analyses of the SELDI spectral data were performed. RESULTS: Several proteins, with masses ranging from 2778 to 20,800 Da, were differentially expressed between patients with HNSCC and the normal controls. The serum protein expression profiles were used to develop a classification tree algorithm, which achieved a sensitivity of 83.3% and a specificity of 90% in discriminating HNSCC from normal and healthy smoker controls. The positive and negative predictive values were 80% and 92%, respectively. A peak with an average mass of 10,068 Da was detected in sera from HNSCC patients and identified as the known biomarker metallopanstimulin-1 (MPS-1), based on mass. Peak relative intensity of the 10,068-Da protein correlated consistently with MPS-1 levels detected by radioimmunoassay in serum samples of HNSCC patients and controls. The 10,068-Da peak was provisionally identified as MPS-1 by SELDI immunoassay. CONCLUSION: We propose that this technique may allow for the development of a reliable screening test for the early detection and diagnosis of HNSCC, as well as the potential identification of tumor biomarkers.

Prostate tumor microenvironment alters immune cells and prevents long-term survival in an orthotopic mouse model following flt3-ligand/CD40-ligand immunotherapy.

A novel orthotopic metastatic model of mouse prostate cancer was developed using MHC-negative TRAMP-C1P3 (transgenic adenocarcinoma of mouse prostate) cells derived by serial passage of the parental TRAMP-C1 line in mouse prostate glands. TRAMP-C1P3 cells grew efficiently in mouse prostate glands and reproducibly metastasized to draining lymph nodes. Using this model, we show that Fms-like tyrosine kinase-3 ligand (flt3-L) dramatically inhibited growth of preexisting orthotopic TRAMP-C1P3 tumors and the development of metastatic disease. Mice remained in remission for several months following termination of flt3-L treatment but eventually relapsed and died of progressive disease. flt3-ligand treatment induced a pronounced mixed inflammatory cell infiltrate that consisted of CD8alpha-CD4- dendritic cells (CD11c+), macrophages, granulocytes (Gr-1+) and to a lesser extent T cells (CD4+ and CD8+). Dendritic cells isolated from TRAMP-C1P3 tumors were phenotypically immature (CD11c+ B7.2-I-A-CD40-), and this phenotype was also predominant in peripheral organs of mice treated with flt3-L alone or in combination with the DC maturation factor, CD40-L. Diminished expression of TCR-beta, CD3-epsilon, and CD3-zeta was also observed on intratumoral T cells, although these signaling proteins were reexpressed following in vitro culture with IL-2. The TCR/CD3 complex remained intact on peripheral T cells except in mice treated with flt3-L where CD3-zeta loss was observed. In contrast to alphabeta-T cells, tumor-infiltrating gammadelta-T cells maintained expression of their antigen receptors but not CD3epsilon. Thus, TRAMP-C1P3 tumors quickly establish a microenvironment that profoundly diminishes expression of molecules critical for normal dendritic cell and T cell function, thus limiting the efficacy of flt3-L and CD40-L immunotherapy. Overall, these data suggest that long-term cures of established MHC-negative tumors may not be achieved until therapeutic interventions are engineered to overcome this immunosuppressive microenvironment.

Impact of the tumor microenvironment on host infiltrating cells and the efficacy of flt3-ligand combination immunotherapy evaluated in a treatment model of mouse prostate cancer.

We have previously reported that Fms-like tyrosine kinase-3 ligand (flt3-L) induced tumor stabilization and regression of palpable ectopic prostate tumors (TRAMP-C1). Although some mice remained "tumor free" for several months following termination of therapy, tumors invariably reappeared and grew progressively in all animals. The lack of a curative response suggests that TRAMP-C1 tumors may inhibit the development of a flt3-L-induced anti-tumor immune response. Consistent with this view, we demonstrate herein that TRAMP-C1 tumors isolated from flt3-L treated animals contained a marked dendritic cell (DC) infiltrate that was temporally correlated with tumor regression. However, tumor-associated DCs, especially in a flt3-L setting, progressively lost MHC class II antigen expression during tumor growth. Treatment with the DC maturation factor trimeric CD40 ligand (CD40-L) either alone or in combination with fl3-L neither prevented loss of DC class II antigens nor disease relapse. Because loss of class II antigens would prevent CD4+ helper T (Th) cell development, we treated tumor-bearing mice with agonistic anti-4-1BB antibody (Ab), which can promote cytotoxic T lymphocyte (CTL) development independent of Th cell function. However, anti-4-1BB Ab alone did not alter TRAMP-C1 growth kinetics, and, when used in combination, was no more effective than flt3-L alone. The inability of the 4-1BB co-stimulatory signal to promote tumor regression may have been related to two additional features of TRAMP-C1 tumors. First, tumor-associated T cells, but not splenic T cells from tumor-bearing animals, were profoundly deficient in expression of CD3-epsilon (CD3epsilon) and T cell receptor-beta chain (TCRbeta). Second, CTLs required 24 h to efficiently kill TRAMP-C1 target cells even after up-regulation of MHC class I antigens by interferon-gamma. This rate of tumor cell destruction by CTLs may not be sufficient to prevent tumor progression. Taken together, these data reveal several important immunosuppressive characteristics of the prostate tumor microenvironment (TME) that immunotherapeutic interventions must first overcome to achieve longterm cures. These data also highlight the importance of utilizing treatment versus vaccination models in the evaluation of immunotherapeutic modalities.

Orthotopic treatment model of prostate cancer and metastasis in the immunocompetent mouse: efficacy of flt3 ligand immunotherapy.

We established an orthotopic treatment model of prostate cancer to generate reproducible primary and metastatic carcinoma in immunocompetent C57BL/6 mice. Using an in vivo selection scheme of intraprostatic implantation of TRAMP-C1 cells, primary prostate tumors were cultured and recycled three times by intraprostatic injection resulting in the selection and establishment of the recycled cell line TRAMP-C1P3. Prostate tumors were detected approximately 30 days post-implantation with periaortic lymph node metastasis in 19/20 (95%) of mice. Tissue culture amplification, DNA ploidy and PCR amplification of the SV40 transgene were used to detect metastatic TRAMP-C1P3 in lymph node specimens. Tissue culture amplification and DNA ploidy were as sensitive as SV40 transgene amplification by PCR in detection of early metastatic disease in draining lymph nodes. To establish the use of the orthotopic model of prostate cancer for immunotherapy, mice were injected orthotopically with TRAMP-C1P3 cells and 7 days post-implantation treated daily for 28 days with either flt3L or carrier control. Carrier-treated mice had clinically detectable prostate tumors, lymph node metastasis and were moribund at 29-35 days, whereas flt3L therapy markedly suppressed primary TRAMP-C1P3 growth and lymph node metastasis, and prolonged survival. In summary, we have established a reproducible and clinically relevant orthotopic treatment model of prostate cancer in immunocompetent mice with application to a variety of therapeutic strategies. We demonstrate that flt3L treatment suppressed orthotopic prostate tumor growth and lymph node metastasis reinforcing a role for flt3L as an immunotherapeutic strategy for prostate cancer.

Recombinant glutamate carboxypeptidase II (prostate specific membrane antigen--PSMA)--cellular localization and bioactivity analyses.

Glutamate Carboxypeptidase II (also known as Prostate Specific Membrane Antigen-PSMA) is an important marker in the diagnosis of prostate cancer, however, relatively little is known about its biochemical and structure-function characteristics. We have expressed mutant forms of PSMA and have started to address the roles of three putative domains of PSMA in its cellular localization and peptidase activity. Three mutants, a full-length recombinant PSMA (rPSMA-FL), one expressing only the proposed extracellular domain of PSMA (rPSMA-ECD) and one form omitting the proposed transmembrane domain (rPSMA-deltaTMD) have been produced in human cells via a mammalian expression vector system. We show that rPSMA-FL is associated with the cell surface membrane; so too is rPSMA-deltaTMD even though it lacks the proposed transmembrane domain, whereas rPSMA-ECD has a cytosolic localization. Only rPSMA-FL retains functional hydrolytic activity and is similarly glycosylated to PSMA found in the cultured prostate cancer cell line LNCaP.

A novel approach toward development of a rapid blood test for breast cancer.

Mammography remains the diagnostic test of choice for breast cancer, but 20% of cancers still go undetected. Many serum biomarkers have been reported for breast cancer but none have proven to represent effective diagnostic strategies. ProteinChip mass spectrometry is an innovative technology that searches the proteome for differentially expressed proteins, allowing for the creation of a panel or profile of biomarkers. The objective of this study was to construct unique cancer-associated serum profiles that, combined with a classification algorithm, would enhance the detection of breast cancer Pretreatment serum samples from 134 female patients (45 with cancer, 42 with benign disease, 47 normal) were procured prospectively following institutional review board-approved protocols. Proteins were denatured, applied onto ProteinChip affinity surfaces, and subjected to surface enhanced laser desorption/ionization (SELDI) time-of-flight mass spectrometry. The SELDI output was analyzed using Biomarker Pattern Software to develop a classification tree based on group-specific protein profiles. The cross-validation analysis of cancer versus normal revealed sensitivity and specificity rates of 80% and 79%, and for cancer versus benign disease, 78% and 83%, respectively. When 2 different chip surfaces were combined the sensitivity and specificity increased to 90% and 93%, respectively. The sensitivity and specificity of this technique are comparable to those of mammography and, if confirmed in a larger study, this technique could provide the means toward development of a simple blood test to aid in the early detection of breast cancer. The combination of SELDI ProteinChip mass spectrometry and a classification- and regression-tree algorithm has the potential to use serum protein expression profiles for detection and diagnosis of breast cancer.

A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection.

With recent advances in mass spectrometry techniques, it is now possible to investigate proteins over a wide range of molecular weights in small biological specimens. This advance has generated data-analytic challenges in proteomics, similar to those created by microarray technologies in genetics, namely, discovery of 'signature' protein profiles specific to each pathologic state (e.g. normal vs. cancer) or differential profiles between experimental conditions (e.g. treated by a drug of interest vs. untreated) from high-dimensional data. We propose a data-analytic strategy for discovering protein biomarkers based on such high-dimensional mass spectrometry data. A real biomarker-discovery project on prostate cancer is taken as a concrete example throughout the paper: the project aims to identify proteins in serum that distinguish cancer, benign hyperplasia, and normal states of prostate using the Surface Enhanced Laser Desorption/Ionization (SELDI) technology, a recently developed mass spectrometry technique. Our data-analytic strategy takes properties of the SELDI mass spectrometer into account: the SELDI output of a specimen contains about 48,000 (x, y) points where x is the protein mass divided by the number of charges introduced by ionization and y is the protein intensity of the corresponding mass per charge value, x, in that specimen. Given high coefficients of variation and other characteristics of protein intensity measures (y values), we reduce the measures of protein intensities to a set of binary variables that indicate peaks in the y-axis direction in the nearest neighborhoods of each mass per charge point in the x-axis direction. We then account for a shifting (measurement error) problem of the x-axis in SELDI output. After this pre-analysis processing of data, we combine the binary predictors to generate classification rules for cancer, benign hyperplasia, and normal states of prostate. Our approach is to apply the boosting algorithm to select binary predictors and construct a summary classifier. We empirically evaluate sensitivity and specificity of the resulting summary classifiers with a test dataset that is independent from the training dataset used to construct the summary classifiers. The proposed method performed nearly perfectly in distinguishing cancer and benign hyperplasia from normal. In the classification of cancer vs. benign hyperplasia, however, an appreciable proportion of the benign specimens were classified incorrectly as cancer. We discuss practical issues associated with our proposed approach to the analysis of SELDI output and its application in cancer biomarker discovery.

Data reduction using a discrete wavelet transform in discriminant analysis of very high dimensionality data.

We present a method of data reduction using a wavelet transform in discriminant analysis when the number of variables is much greater than the number of observations. The method is illustrated with a prostate cancer study, where the sample size is 248, and the number of variables is 48,538 (generated using the ProteinChip technology). Using a discrete wavelet transform, the 48,538 data points are represented by 1271 wavelet coefficients. Information criteria identified 11 of the 1271 wavelet coefficients with the highest discriminatory power. The linear classifier with the 11 wavelet coefficients detected prostate cancer in a separate test set with a sensitivity of 97% and specificity of 100%.

SELDI-TOF serum profiling for prognostic and diagnostic classification of breast cancers.

Surface enhanced laser desorption/ionization (SELDI) time-of-flight mass spectrometry has emerged as a successful tool for serum based detection and differentiation of many cancer types, including breast cancers. In this study, we have applied the SELDI technology to evaluate three potential applications that could extend the effectiveness of established procedures and biomarkers used for prognostication of breast cancers. Paired serum samples obtained from women with breast cancers prior to surgery and post-surgery (6-9 mos.) were examined. In 14/16 post-treatment patients, serum protein profiles could be used to distinguish these samples from the pre-treatment cancer samples. When compared to serum samples from normal healthy women, 11 of these post-treatment samples retained global protein profiles not found in healthy women, including five low-mass proteins that remained elevated in both pre-treatment and post-treatment serum groups. In another pilot study, serum profiles were compared for a group of 30 women who were known BRCA-1 mutation carriers, half of whom subsequently developed breast cancer within three years of the sample procurement. SELDI protein profiling accurately classified 13/15 women with BRCA-1 breast cancers from the 15 non-cancer BRCA-1 carriers. Additionally, the ability of SELDI to distinguish between the serum profiles from sentinel lymph node positive and sentinel lymph node negative patients was evaluated. In sentinel lymph node positive samples, 22/27 samples were correctly classified, in comparison to the correct classification of 55/71 sentinel lymph node negative samples. These initial results indicate the utility of protein profiling approaches for developing new diagnostic and prognostic assays for breast cancers.

SELDI proteinchip MS: a platform for biomarker discovery and cancer diagnosis.

Identification and understanding the structures, interactions and functions of all of a cell's proteins is one of the major goals of the postgenome era. The genome project has produced a wealth of information that is greatly expounding the genetic basis of cancer. However, it falls short in not allowing for accurate prediction of what is happening at the protein level in a cancer cell or a body fluid proteome. It is the hope that, by deciphering the alterations in the cancer proteome, biomarkers and patterns of biomarkers will be found that will lead to improvements in early detection, diagnosis and treatment monitoring. To achieve this goal, rapid high-throughput proteomic technologies will be required. The SELDI ProteinChip Biomarker mass spectrometry system appears to have potential in this effort, both for biomarker discovery and as a potential clinical diagnostic assay platform.

Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients.

BACKGROUND: The low specificity of the prostate-specific antigen (PSA) test makes it a poor biomarker for early detection of prostate cancer (PCA). Because single biomarkers most likely will not be found that are expressed by all genetic forms of PCA, we evaluated and developed a proteomic approach for the simultaneous detection and analysis of multiple proteins for the differentiation of PCA from noncancer patients. METHODS: Serum samples from 386 men [197 with PCA, 92 with benign prostatic hyperplasia (BPH), and 96 healthy individuals], randomly divided into training (n = 326) and test (n = 60) sets, were analyzed by surface-enhanced laser desorption/ionization (SELDI) mass spectrometry. The 124 peaks detected by computer analyses were analyzed in the training set by a boosting tree algorithm to develop a classifier for separating PCA from the noncancer groups. The classifier was then challenged with the test set (30 PCA samples, 15 BPH samples, 15 samples from healthy men) to determine the validity and accuracy of the classification system. RESULTS: Two classifiers were developed. The AdaBoost classifier completely separated the PCA from the noncancer samples, achieving 100% sensitivity and specificity. The second classifier, the Boosted Decision Stump Feature Selection classifier, was easier to interpret and used only 21 (compared with 74) peaks and a combination of 21 (vs 500) base classifiers to achieve a sensitivity and specificity of 97% for the test set. CONCLUSIONS: The high sensitivity and specificity achieved in this study provides support of the potential for SELDI, coupled with a bioinformatics learning algorithm, to improve the early detection/diagnosis of PCA.

Normal, benign, preneoplastic, and malignant prostate cells have distinct protein expression profiles resolved by surface enhanced laser desorption/ionization mass spectrometry.

PURPOSE: The objective of this study was to discover protein biomarkers that differentiate malignant from nonmalignant cell populations, especially early protein alterations that signal the initiation of a developing cancer. We hypothesized that Surface Enhanced Laser Desorption/Ionization-time of flight-mass spectrometry-assisted protein profiling could detect these protein alterations. EXPERIMENTAL DESIGN: Epithelial cell populations [benign prostatic hyperplasia (BPH), prostate intraepithelial neoplasia (PIN), and prostate cancer (PCA)] were procured from nine prostatectomy specimens using laser capture microdissection. Surface Enhanced Laser Desorption/Ionization-time of flight-mass spectrometry analysis was performed on cell lysates, and the relative intensity levels of each protein or peptide in the mass spectra was calculated and compared for each cell type. RESULTS: Several small molecular mass peptides or proteins (3000-5000 Da) were found in greater abundance in PIN and PCA cell lysates. Another peak, with an average mass of 5666 Da, was observed to be up-regulated in 86% of the BPH cell lysates. Higher levels of this same peak were found in only 22% of the PIN lysates and none of the PCA lysates. Expression differences were also found for intracellular levels of prostate-specific antigen, which were reduced in PIN and PCA cells when compared with matched normals. Although no single protein alteration was observed in all PIN/PCA samples, combining two or more of the markers was effective in distinguishing the benign cell types (normal/BPH) from diseased cell types (PIN/PCA). Logistic regression analysis using seven differentially expressed proteins resulted in a predictive equation that correctly distinguished the diseased lysates with a sensitivity and specificity of 93.3 and 93.8%, respectively. CONCLUSIONS: We have shown that the protein profiles from prostate cells with different disease states have discriminating differences. These differentially regulated proteins are potential markers for early detection and/or risk factors for development of prostate cancer. Studies are under way to identify these protein/peptides, with the goal of developing a diagnostic test for the early detection of prostate cancer.