Ovarian cancer detection by logical analysis of proteomic data

A new type of efficient and accurate proteomic ovarian cancer diagnosis systems is proposed. The system is developed using the combinatorics and optimization‐based methodology of logical analysis of data (LAD) to the Ovarian Dataset 8‐7‐02 (http://clinicalproteomics.steem.com), which updates the one...

Full description

Saved in:
Bibliographic Details
Published in:Proteomics (Weinheim) 2004-03, Vol.4 (3), p.766-783
Main Authors: Alexe, Gabriela, Alexe, Sorin, Liotta, Lance A., Petricoin, Emanuel, Reiss, Michael, Hammer, Peter L.
Format: Article
Language:English
Subjects:
Algorithms
Analytical, structural and metabolic biochemistry
Biological and medical sciences
Biomarkers, Tumor
Calibration
Databases as Topic
Female
Female genital diseases
Fundamental and applied biological sciences. Psychology
Gynecology. Andrology. Obstetrics
Humans
Logical analysis of data
Mass Spectrometry
Medical sciences
Miscellaneous
Models, Theoretical
Ovarian cancer
Ovarian Neoplasms - diagnosis
Ovarian Neoplasms - metabolism
Peptides - chemistry
Proteins
Proteome
Proteomics - methods
Software
Time Factors
Tumors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A new type of efficient and accurate proteomic ovarian cancer diagnosis systems is proposed. The system is developed using the combinatorics and optimization‐based methodology of logical analysis of data (LAD) to the Ovarian Dataset 8‐7‐02 (http://clinicalproteomics.steem.com), which updates the one used by Petricoin et al. in The Lancet 2002, 359, 572–577. This mass spectroscopy‐generated dataset contains expression profiles of 15 154 peptides defined by their mass/charge ratios (m/z) in serum of 162 ovarian cancer and 91 control cases. Several fully reproducible models using only 7–9 of the 15 154 peptides were constructed, and shown in multiple cross‐validation tests (k‐folding and leave‐one‐out) to provide sensitivities and specificities of up to 100%. A special diagnostic system for stage I ovarian cancer patients is shown to have similarly high accuracy. Other results: (i) expressions of peptides with relatively low m/z values in the dataset are shown to be better at distinguishing ovarian cancer cases from controls than those with higher m/z values; (ii) two large groups of patients with a high degree of similarities among their formal (mathematical) profiles are detected; (iii) several peptides with a blocking or promoting effect on ovarian cancer are identified.
ISSN:1615-9853
1615-9861
DOI:10.1002/pmic.200300574