A novel orthotopic metastatic model of mouse prostate cancer was developed using MHC-negative TRAMP-ClP3 (transgenic adenocarcinoma of mouse prostate) cells derived by serial passage of the parental TRAMP-C1 line in mouse prostate glands. TRAMP-ClP3 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-ClP3 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-ClP3 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-ClP3 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.

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.

2109 Background: This study examined proteomic changes in response to paclitaxel or FAC chemotherapy in the plasma of patients with stage I-III breast cancer. We also compared plasma profiles of cancer patients with profiles of healthy women to identify breast cancer 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 post-treatment, or 3 days apart in the case of healthy women. Twenty-nine patients received preoperative and 40 received postoperative chemotherapy. SELDI-MS was used to generate protein mass profiles. Results: We observed few changes in the plasma during treatment. We identified only 1 protein peak at 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 treated preoperatively but was also present with lesser intensity in about 40% of postoperative cases. There was no clear correlation between induction of m/z 2790 during one course of treatment and the final tumor response to preoperative chemotherapy. We also identified 5 other peaks that discriminate plasma of cancer patients from that of normal subjects. These same peaks were also detectable in a subset of patients who already had their cancer removed by surgery. Conclusions: We identified a single chemotherapy inducible SELD-MS peak and 5 other peaks that discriminate breast cancer from normal plasma. These peptides/proteins are candidates for markers of micrometastatic disease after surgery. No significant financial relationships to disclose.

At present, the most reliable means of diagnosis and surveillance of bladder cancer are cystoscopic examination and bladder biopsy for histologic confirmation. The invasive and labor-intensive nature of this procedure underscores the need to develop better, less costly, and nonsurgical diagnostic tools (1)(2). Use of surface-enhanced laser desorption/ionization (SELDI) time-of-flight mass spectrometry has been successful in facilitating protein profiling of complex biological mixtures. This technology uses chemical affinity platforms to capture protein molecules from various biological sources. Retained proteins are subsequently analyzed by mass spectrometry [reviewed in Ref. ((3))]. Recent reports provide evidence that analysis of SELDI data by “learning” algorithms can lead to the identification of serum protein “fingerprints” for prostate, ovarian, and breast cancers (4)(5)(6)(7)(8)(9) and urinary fingerprints for kidney cancer (10). We recently reported the application of the SELDI system for detection of potential bladder cancer-associated biomarkers in urine (11). In this earlier study, we showed that combination of five transitional cell carcinoma (TCC)-associated protein peaks by simple statistical methods provided 87% sensitivity and 66% specificity in disease detection. The objectives of the current study were ( a ) to evaluate a commercial available data-mining classification algorithm for the analysis of the SELDI mass spectral data, and ( b ) assessing the clinical utility of this assay in detecting bladder cancer from a geographically and clinically mixed population. Fresh spot-voided urine specimens from 230 individuals were included in the study. Specimens were collected from patients …

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.

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 2,778 to 20,800 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.

Summary.  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%.

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.

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 ∼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. © 2003 Wiley‐Liss, Inc.

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.

One of the major goals of the postgenomic era is understanding the structures, interactions, and functions of all cell proteins. This becomes a daunting task considering the estimation that there are between 100 000 and 200 000 individual proteins resulting from alternative splicing of the 30 000 genes encoded by the human genome. Since the cellular proteome is a dynamic profile, subject to change in response to various signals through posttranslational modification, translocation, and protein-protein and protein-nucleic acid interactions, the task becomes even more complex looming to a million or more modification events. Proteomics encompasses the study of expressed proteins, including identification and elucidation of the structure-function interrelationships which define healthy and disease conditions. Information at the level of the proteome is critical to understanding the function of cellular phenotype and its role in health and disease. Since posttranslational events and, indeed, an accurate assessment of protein expression levels cannot always be predicted by mRNA analysis, proteomics, used in concert with genomics, can provide a holistic understanding of the biology underlying the disease process. The challenge in deciphering the proteome is the development and integration of analytical instrumentation combined with bioinformatics that provide rapid, high-throughput, sensitive, and reproducible tools. This issue of the Journal of Biomedicine and Biotechnology presents the first of a two-part series consisting of ten papers that describe both technical and bioinformatic advances to define the cell proteome towards a better understanding of health and disease. The current issue consists of the first five articles beginning with papers by Bensmail and Haoudi, and Pruess and Apweiler that describe bioinformatics approaches for defining the cancer cell proteome and for in silico proteomic analyses. Because of the high dimensionality of the data generated by proteomic methodologies, such as protein microarrays and mass spectral analyses, more efficient and accurate bioinformatics tools are required to mine and analyze the data. Major advances in mass spectrometry have resulted in rapid, high-throughput technologies for protein biomarker discovery, protein identification, disease analyses, and identification of posttranslational modifications. One advance, SELDI ProteinChip mass spectrometry, is the subject of the next two papers that describe its use for biomarker discovery and its potential as a platform for development of multimarker clinical assays. The first paper by Reddy and Dalmasso presents a review describing the use of SELDI for biomarker discovery, drug discovery, protein identification, and for development of multiplex clinical assays, citing examples for cancer, neurological disorders, and infectious diseases. Feng and associates then describe an automated peak identification and calibration procedure for more precise mass analyses when attempting to differentiate disease from nondisease protein patterns. The last paper of this issue by Qoronfleh and associates describes a method for the isolation of membrane proteins for proteomic analysis. The next issue (volume 2003, issue 5) presents the remaining five papers. This issue begins with a review by Xu and Lam on protein and chemical microarray approaches being utilized for proteomic studies. Then Flower and colleagues describe bioinformatics approaches for defining the immunome for discovery of novel vaccines. This is followed by a paper by Qoronfleh and colleagues who describe improved methods for detecting protein: protein interactions. Piccoli's research team then report a method for optimizing the rolling circle application technology for generating a sensitive high-throughput multiplex protein microarray for analysis of protein expression and molecular diagnosis. The final paper in this issue is by Vlahou and associates who describe the use of SELDI protein profiling coupled with a commercial decision tree learning algorithm for biomarker discovery and diagnosis of ovarian cancer. The content of this special issue, although broad and addressing several key issues in proteomics research, still leaves many issues to be covered, especially functional and structural proteomics, in this fast evolving field of research. We anticipate addressing other new discoveries and applications in the proteomics field in future issues of the Journal of Biomedicine and Biotechnology.

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.

Development of noninvasive methods for the diagnosis of transitional cell carcinoma (TCC) of the bladder remains a challenge. A ProteinChip technology (surface enhanced laser desorption/ionization time of flight mass spectrometry) has recently been developed to facilitate protein profiling of biological mixtures. This report describes an exploratory study of this technology as a TCC diagnostic tool. Ninety-four urine samples from patients with TCC, patients with other urogenital diseases, and healthy donors were analyzed. Multiple protein changes were reproducibly detected in the TCC group, including five potential novel TCC biomarkers and seven protein clusters (mass range, 3.3 to 133 kd). One of the TCC biomarkers (3.4 kd) was also detected in bladder cancer cells procured from bladder barbotage and was identified as defensin. The TCC detection rates provided by the individual markers ranged from 43 to 70% and specificities from 70 to 86%. Combination of the protein biomarkers and clusters, increased significantly the sensitivity for detecting TCC to 87% with a specificity of 66%. Interestingly, this combinatorial approach provided sensitivity of 78% for detecting low-grade TCC compared to only 33% of voided urine or bladder-washing cytology. Collectively these results support the potential of this proteomic approach for the development of a highly sensitive urinary TCC diagnostic test.

The lack of a sensitive immunoassay for quantitating serum prostate-specific membrane antigen (PSMA) hinders its clinical utility as a diagnostic/prognostic biomarker. An innovative protein biochip immunoassay was used to quantitate and compare serum PSMA levels in healthy men and patients with either benign or malignant prostate disease. PSMA was captured from serum by anti-PSMA antibody bound to ProteinChip arrays, the captured PSMA detected by surface-enhanced laser desorption/ionization mass spectrometry, and quantitated by comparing the mass signal integrals to a standard curve established using purified recombinant PSMA. The average serum PSMA value for prostate cancer (623.1 ng/ml) was significantly different (P < 0.001) from that for benign prostate hyperplasia (117.1 ng/ml) and the normal groups (age <50, 272.9 ng/ml; age >50, 359.4 ng/ml). These initial results suggest that serum PSMA may be a more effective biomarker than prostate-specific antigen for differentiating benign from malignant prostate disease and warrants additional evaluation of the surface-enhanced laser desorption/ionization PSMA immunoassay to determine its diagnostic utility.

Proteomic technologies, including high resolution two-dimensional electrophoresis (2-DE), antibody/protein arrays, and advances in mass spectrometry (MS), are providing the tools needed to discover and identify disease associated biomarkers. Although application of these technologies to search for potential diagnostic/prognostic biomarkers asscociated with prostate and bladder cancer have been somewhat limited to date, proteins either overexpressed or underexpressed have been detected in both these urological cancers. Recent advances in mass spectrometry, especially platforms that permit rapid “fingerprint” profiling of multiple biomarkers, and tandem mass spectrometers for protein identification, will most assuredly enhance the discovery, identification, and characterization of potential cancer associated biomarkers. Furthermore, application of laser capture microdissection microscopes has provided a rapid and reproducible approach to procure pure populations of cells. This technology coupled to 2-DE and MS has significantly aided the elucidation of the differential expression profiles between disease, benign and normal prostate and bladder cell populations. Finally, development and application of learning algorithms and bioinformatics to the data generated by these proteomic technologies will be essential in determining the clinical potential of a protein biomarker. The purpose of this review is to provide the reader with an overview of the application of these technologies in the search and identification of potential diagnostic/prognostic biomarkers for prostate and bladder cancers.