Use of proteomic patterns in serum to identify ovarian cancer

Emanuel F. Petricoin*, Ali M. Ardekani, Ben A. Hitt, Peter J. Levine, Vincent A. Fusaro, Seth M. Steinberg, Gordon B. Mills, Charles Simone, David A. Fishman, Elise C. Kohn, Lance A. Liotta

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2914 Scopus citations

Abstract

Background: New technologies for the detection of early-stage ovarian cancer are urgently needed. Pathological changes within an organ might be reflected in proteomic patterns in serum. We developed a bioinformatics tool and used it to identify proteomic patterns in serum that distinguish neoplastic from non-neoplastic disease within the ovary. Methods: Proteomic spectra were generated by mass spectroscopy (surface-enhanced laser desorption and ionisation). A preliminary "training" set of spectra derived from analysis of serum from 50 unaffected women and 50 patients with ovarian cancer were analysed by an iterative searching algorithm that identified a proteomic pattern that completely discriminated cancer from non-cancer. The discovered pattern was then used to classify an independent set of 116 masked serum samples: 50 from women with ovarian cancer, and 66 from unaffected women or those with non-malignant disorders. Findings The algorithm identified a cluster pattern that, in the training set, completely segregated cancer from non-cancer. The discriminatory pattern correctly identified all 50 ovarian cancer cases in the masked set, including all 18 stage I cases. Of the 66 cases of non-malignant disease, 63 were recognised as not cancer. This result yielded a sensitivity of 100% (95% CI 93-100), specificity of 95% (87-99), and positive predictive value of 94% (84-99). Interpretation: These findings justify a prospective population-based assessment of proteomic pattern technology as a screening tool for all stages of ovarian cancer in high-risk and general populations.

Original languageEnglish
Pages (from-to)572-577
Number of pages6
JournalThe Lancet
Volume359
Issue number9306
DOIs
StatePublished - 16 Feb 2002
Externally publishedYes

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