Serum proteomics using mass spectrometry.

The identification and eventual application of tumor markers in cancer screening, early detection, diagnosis, and prognosis is a continuing focus of significant translational cancer research. While many new candidate markers have been discovered and at least partly characterized, very few have found widespread clinical application limited presently to the use of CA-125 in ovarian cancer, CEA, primarily in colon cancer, and PSA in prostate cancer screening and patient monitoring. The rapidly emerging field of cancer genomics and proteomics, and their clinical translation as "molecular diagnosis" and "molecular medicine" are already beginning to transform the field, and the accelerating growth of information and technology in this research area will undoubtedly transform the field of tumor markers and their application in the near future leading to improved molecular tools for cancer diagnosis, prognosis, and treatment and ultimately, to the emergence of novel and more effective cancer therapies, including improved approaches for immunotherapy and cancer prevention strategies.

Analytical validation of serum proteomic profiling for diagnosis of prostate cancer: sources of sample bias.

Background: This report and a companion report describe a validation of the ability of serum proteomic profiling via SELDI-TOF mass spectrometry to detect prostatic cancer. Details of this 3-stage process have been described. This report describes the development of the algorithm and results of the blinded test for stage 1.

Serum proteomic patterns as a predictor of severity in acute pancreatitis.

Background: Approximately 20% of patients with acute pancreatitis (AP) develop a severe and potentially life-threatening course. Serum proteomic pattern analysis for disease diagnosis is a promising novel and rapidly expanding field based on the hypothesis that serum patterns of low molecular mass biomarkers can specifically reflect an underlying organ-specific pathologic state.

Aim: To evaluate the potential differences in proteomic profiles between patients with mild and severe AP.

Multiple biomarker panels for early detection of ovarian cancer.

Ovarian cancer is the eighth most common cause of cancer mortality in women. It is diagnosed in more than 20,000 women in the USA each year and approximately 15,000 women die of the disease annually. The majority of patients are diagnosed with advanced-stage ovarian cancer, as this deadly disease causes minimal and nonspecific symptoms until late in the course of the disease.

Feature Selection for Classification of SELDI-TOF-MS Proteomic Profiles.

Background: Proteomic peptide profiling is an emerging technology harbouring great expectations to enable early detection, enhance diagnosis and more clearly define prognosis of many diseases. Although previous research work has illustrated the ability of proteomic data to discriminate between cases and controls, significantly less attention has been paid to the analysis of feature selection strategies that enable learning of such predictive models. Feature selection, in addition to classification, plays an important role in successful identification of proteomic biomarker panels.

Serum protein profile in systemic-onset juvenile idiopathic arthritis differentiates response versus nonresponse to therapy.

Systemic-onset juvenile idiopathic arthritis (SJIA) is a disease of unknown etiology with an unpredictable response to treatment. We examined two groups of patients to determine whether there are serum protein profiles reflective of active disease and predictive of response to therapy. The first group (n = 8) responded to conventional therapy.

Assessing the statistical significance of the achieved classification error of classifiers constructed using serum peptide profiles, and a prescription for random sampling repeated studies for massive high-throughput genomic and proteomic studies.

Peptide profiles generated using SELDI/MALDI time of flight mass spectrometry provide a promising source of patient-specific information with high potential impact on the early detection and classification of cancer and other diseases. The new profiling technology comes, however, with numerous challenges and concerns. Particularly important are concerns of reproducibility of classification results and their significance.

Detection of cystic fibrosis mutations by peptide mass signature genotyping.

Background: The diversity of genetic mutations and polymorphisms calls for the development of practical detection methods capable of assessing more than one patient/one nucleotide position per analysis.

Methods: We developed a new method, based on peptide mass signature genotyping (PMSG), for the detection of DNA mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. Exons of the gene were amplified, cloned, and expressed in Escherichia coli as peptide fusions, in natural as well as unnatural reading frames.

Detection and assignment of TP53 mutations in tumor DNA using peptide mass signature genotyping.

This report describes the application of a new approach to tumor genotyping called peptide mass signature genotyping (PMSG) that is particularly suited to detecting minority sequences in a DNA sample. Detecting minority sequences is essential for accurate tumor genotyping because tumor resections are generally a mixture of malignant and non-malignant cells, with the mutations of interest often outnumbered by the corresponding wild-type alleles. To explore the suitability of PMSG for tumor genotyping, 25 human squamous cell carcinomas of the head and neck, as well as a set of cell lines derived from those tumors, were analyzed for mutations in exons 5 to 8 of the TP53 gene, the exons that encode the DNA-binding domains of the p53 protein.