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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men.

The prostate-specific antigen test has been a major factor in increasing awareness and better patient management of prostate cancer (PCA), but its lack of specificity limits its use in diagnosis and makes for poor early detection of PCA. The objective of our studies is to identify better biomarkers for early detection of PCA using protein profiling technologies that can simultaneously resolve and analyze multiple proteins. Evaluating multiple proteins will be essential to establishing signature proteomic patterns that distinguish cancer from noncancer as well as identify all genetic subtypes of the cancer and their biological activity.