Novel approaches to visualization and data mining reveals diagnostic information in the low amplitude region of serum mass spectra from ovarian cancer patients

Issue title: PROTEOMICS IN DIAGNOSTICS

Article type: Research Article

Authors: Johann, Jr., Donald J. | McGuigan, Michael D. | Tomov, Stanimire | Fusaro, Vincent A. | Ross, Sally | Conrads, Thomas P. | Veenstra, Timothy D. | Fishman, David A. | Whiteley, Gordon R. | Petricoin, Emanuel F. | Liotta, Lance A.

Affiliations: NCI-FDA Clinical Proteomics Program, Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA | Brookhaven National Laboratory, Information Technology Division, Upton, NY, USA | Laboratory of Proteomics and Analytical Technologies, SAIC-Frederick, Inc., National Cancer Institute at Frederick, Frederick, MD, USA | National Ovarian Cancer Early Detection Program, Northwestern University Medical School, Chicago, IL, USA | NCI-FDA Clinical Proteomics Program, Clinical Proteomics Reference Laboratory, SAIC Frederick, Gaithersburg, MD, USA | NCI-FDA Clinical Proteomics Program, Office of Cell and Gene Therapy, Center for Biologics Evaluation and Research, Food and Drug Administration, Bethesda, MD, USA

Note: [] Corresponding author: Dr. Donald J. Johann, Jr., NCI-FDA Clinical Proteomics Program, 8800 Rockville Pike, Building 29A, Room 2A21, Bethesda, MD 20892, USA. Tel.: +1 301 827 5194; Fax: +1 301 480 3256; E-mail: dj151o@nih.gov

Abstract: 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 1–2 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 "peak picking" 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.

Keywords: ovarian cancer, SELDI-TOF MS, data visualization, diagnosis

Journal: Disease Markers, vol. 19, no. 4-5, pp. 197-207, 2003,2004