Treatment of cancer patients is greatly facilitated by detection of the cancer prior to metastasis. One of the obstacles to early cancer detection is the lack of availability of biomarkers with sufficient specificity. With modern differential proteomic techniques, the potential exists to identify high specificity cancer biomarkers. We have delineated a set of protocols for the isolation and identification of serum biomarkers for ovarian cancer that exist in the low molecular weight serum fraction. After isolation of the low molecular weight fraction by ultrafiltration, the potential biomarkers are separated by reversed phase nano liquid chromatography. Detection via TOF or FT-ICR yields a data set for each sample. We compared stage III/IV ovarian cancer serum with postmenopausal age-matched controls. Using bioinformatics tools developed at Mayo, we normalized each sample for intensity and chromatographic alignment. Normalized data sets are subsequently compared and potential biomarkers identified. Several candidate biomarkers were found. One of these contains the sequence of fibrinopeptide-A known to be elevated in many types of cancer including ovarian cancer. The protocols utilized will be examined and would be applicable to a wide variety of cancers or disease states.

H. Robert Bergen

George Vasmatzis

William A. Cliby

Kenneth L. Johnson

Ann L. Oberg

David C. Muddiman

Discovery of Ovarian Cancer Biomarkers in Serum Using NanoLC Electrospray Ionization TOF and FT-ICR Mass Spectrometry

The ability to identify patterns of diagnostic signatures in proteomic data generated by high throughput mass spectrometry (MS) based serum analysis has recently generated much excitement and interest from the scientific community. These data sets can be very large, with high-resolution MS instrumentation producing 1&#x2013;2 million data points per sample. Approaches to analyze mass spectral data using unsupervised and supervised data mining operations would greatly benefit from tools that effectively allow for data reduction without losing important diagnostic information. In the past, investigators have proposed approaches where data reduction is performed by a priori &#x201c;peak picking&#x201d; and alignment/warping/smoothing components using rule-based signal-to-noise measurements. Unfortunately, while this type of system has been employed for gene microarray analysis, it is unclear whether it will be effective in the analysis of mass spectral data, which unlike microarray data, is comprised of continuous measurement operations. Moreover, it is unclear where true signal begins and noise ends. Therefore, we have developed an approach to MS data analysis using new types of data visualization and mining operations in which data reduction is accomplished by culling via the intensity of the peaks themselves instead of by location. Applying this new analysis method on a large study set of high resolution mass spectra from healthy and ovarian cancer patients, shows that all of the diagnostic information is contained within the very lowest amplitude regions of the mass spectra. This region can then be selected and studied to identify the exact location and amplitude of the diagnostic biomarkers.

Donald J. Johann

Michael D. McGuigan

Stanimire Tomov

Vincent A. Fusaro

Sally Ross

Thomas P. Conrads

Timothy D. Veenstra

David A. Fishman

Gordon R. Whiteley

Emanuel F. Petricoin

Lance A. Liotta

Novel Approaches to Visualization and Data Mining Reveals Diagnostic Information in the Low Amplitude Region of Serum Mass Spectra from Ovarian Cancer Patients

Sudhir Srivastava

Proteomics in Diagnostics

Multiple studies have reported that analysis of serum and other bodily fluids using surface enhanced laser desorption/ionization time of flight mass spectroscopy (SELDI-TOF-MS) can identify a &#x201c;fingerprint&#x201d; or &#x201c;signature&#x201d; of spectral peaks that can separate patients with a specific disease from normal control patients. Ultimately, classification by SELDI-TOF-MS relies on spectral differences in position and amplitude of resolved peaks. Since the reproducibility of quantitation, resolution and mass accuracy of the SELDI-TOF-MS, or any high throughput mass spectrometric technique, has never been determined this method has come under some skepticism as to its clinical usefulness. This manuscript describes a detailed design of a three-phase study to validate the clinical usefulness of SELDI-TOF-MS in the identification of patients with prostatic adenocarcinoma (PCA). At the end of this validation study, the usefulness of the general SELDI-TOF-MS approach to identifying patients with PCA will be demonstrated and how it compares with PCA diagnosis by measuring prostate specific antigen.

William E. Grizzle

Bao-Ling Adam

William L. Bigbee

Christopher Carroll

Ziding Feng

Elzbieta Izbicka

Moncef Jendoubi

Donald Johnsey

Jacob Kagan

Robin J. Leach

Diane B. McCarthy

O. John Semmes

Shiv Srivastava

Ian M. Thompson

Mark D. Thornquist

Mukesh Verma

Zhen Zhang

Zhiqiang Zou

Serum Protein Expression Profiling for Cancer Detection: Validation of a SELDI-Based Approach for Prostate Cancer

The advent of systems biology approaches that have stemmed from the sequencing of the human genome has led to the search for new methods to diagnose diseases. While much effort has been focused on the identification of disease-specific biomarkers, recent efforts are underway toward the use of proteomic and metabonomic patterns to indicate disease. We have developed and contrasted the use of both proteomic and metabonomic patterns in urine for the detection of interstitial cystitis (IC). The methodology relies on advanced bioinformatics to scrutinize information contained within mass spectrometry (MS) and high-resolution proton nuclear magnetic resonance (1H-NMR) spectral patterns to distinguish IC-affected from non-affected individuals as well as those suffering from bacterial cystitis (BC). We have applied a novel pattern recognition tool that employs an unsupervised system (self-organizing-type cluster mapping) as a fitness test for a supervised system (a genetic algorithm). With this approach, a training set comprised of mass spectra and 1H-NMR spectra from urine derived from either unaffected individuals or patients with IC is employed so that the most fit combination of relative, normalized intensity features defined at precise m/z or chemical shift values plotted in n-space can reliably distinguish the cohorts used in training. Using this bioinformatic approach, we were able to discriminate spectral patterns associated with IC-affected, BC-affected, and unaffected patients with a success rate of approximately 84&#x25;.

Que N. Van

John R. Klose

David A. Lucas

DaRue A. Prieto

Brian Luke

Jack Collins

Stanley K. Burt

Gwendolyn N. Chmurny

Haleem J. Issaq

Susan K. Keay

The Use of Urine Proteomic and Metabonomic Patterns for the Diagnosis of Interstitial Cystitis and Bacterial Cystitis

The ability to interrogate thousands of proteins found in complex biological samples using proteomic technologies has brought the hope of discovering novel disease-specific biomarkers. While most proteomic technologies used to discover diagnostic biomarkers are quite sophisticated, &#x22;proteomic pattern analysis&#x22; has emerged as a simple, yet potentially revolutionary, method for the early diagnosis of diseases. Utilizing this technology, hundreds of clinical samples can be analyzed per day and several preliminary studies suggest proteomic pattern analysis has the potential to be a novel, highly sensitive diagnostic tool for the early detection of cancer.

Li-Rong Yu

Ming Zhou

Diagnostic Proteomics: Serum Proteomic Patterns for the Detection of Early Stage 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&#x2013;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.

Christine Laronga

Stephen Becker

Patrice Watson

Betsy Gregory

Lisa Cazares

Henry Lynch

Roger R. Perry

George L. Wright

Richard R. Drake

SELDI-TOF Serum Profiling for Prognostic and Diagnostic Classification of Breast Cancers

To determine how the repertoire of plasma membrane proteins change with disease state, specifically related to cancer, several methods for preparation of plasma membrane proteins were evaluated. Cultured cells derived from stage IV ovarian tumors were grown to 90&#x25; confluence and harvested in buffer containing CHAPS detergent. This preparation was centrifuged at low speed to remove insoluble cellular debris resulting in a crude homogenate. Glycosylated proteins in the crude homogenate were selectively enriched using lectin affinity chromatography. The crude homogenate and the lectin purified sample were prepared for mass spectrometric evaluation. The general procedure for protein identification began with trypsin digestion of protein fractions followed by separation by reversed phase liquid chromatography that was coupled directly to a conventional tandem mass spectrometer (i.e. LCQ ion trap). Mass and fragmentation data for the peptides were searched against a human proteome data base using the informatics program SEQUEST. Using this procedure 398 proteins were identified with high confidence, including receptors, membrane-associated ligands, proteases, phosphatases, as well as structural and adhesion proteins. Results indicate that lectin chromatography provides a select subset of proteins and that the number and quality of the identifications improve as does the confidence of the protein identifications for this subset. These results represent the first step in development of methods to separate and successfully identify plasma membrane proteins from advanced ovarian cancer cells. Further characterization of plasma membrane proteins will contribute to our understanding of the mechanisms underlying progression of this deadly disease and may lead to new targeted interventions as well as new biomarkers for diagnosis.

David L. Springer

Deanna L. Auberry

Mamoun Ahram

Joshua N. Adkins

Jane M. Feldhaus

Jon H. Wahl

David S. Wunschel

Karin D. Rodland

Characterization of Plasma Membrane Proteins from Ovarian Cancer Cells Using Mass Spectrometry

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Disease Markers publishes papers related to the identification of disease markers, the elucidation of their role and mechanism, as well as their application in the prognosis, diagnosis and treatment of diseases.

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Chief Editor Paola Gazzaniga is an Associate Professor in the Department of Molecular Medicine at Sapienza University of Rome, Italy. Her core research focuses on liquid biopsies in patients with solid tumors.

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Disease Markers publishes papers related to the identification of disease markers, the elucidation of their role and mechanism, as well as their application in the prognosis, diagnosis and treatment of diseases.

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Disease Markers | Hindawi

Disease Markers

This Issue is now open for submissions.
Papers are published upon acceptance, regardless of the Special Issue publication date.

Disease Markers

Proteomics in Diagnostics

Proteomics in Diagnostics

Articles

Discovery of Ovarian Cancer Biomarkers in Serum Using NanoLC Electrospray Ionization TOF and FT-ICR Mass Spectrometry

Novel Approaches to Visualization and Data Mining Reveals Diagnostic Information in the Low Amplitude Region of Serum Mass Spectra from Ovarian Cancer Patients

Proteomics in Diagnostics

Serum Protein Expression Profiling for Cancer Detection: Validation of a SELDI-Based Approach for Prostate Cancer

The Use of Urine Proteomic and Metabonomic Patterns for the Diagnosis of Interstitial Cystitis and Bacterial Cystitis

Diagnostic Proteomics: Serum Proteomic Patterns for the Detection of Early Stage Cancers

SELDI-TOF Serum Profiling for Prognostic and Diagnostic Classification of Breast Cancers

Characterization of Plasma Membrane Proteins from Ovarian Cancer Cells Using Mass Spectrometry