Ovarian cancer detection by logical analysis of proteomic data

Gabriela Alexe, Sorin Alexe, Lance A. Liotta, Emanuel Petricoin, Michael Reiss, Peter L. Hammer*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

94 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)766-783
Number of pages18
JournalProteomics
Volume4
Issue number3
DOIs
StatePublished - Mar 2004
Externally publishedYes

Keywords

  • Logical analysis of data
  • Ovarian cancer

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