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 Mills, Charles Simone, David A. Fishman, Elise C. Kohn, Lance A. Liotta

Research output: Contribution to journalArticle

2699 Citations (Scopus)

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 (US)
Pages (from-to)572-577
Number of pages6
JournalLancet
Volume359
Issue number9306
DOIs
StatePublished - Feb 16 2002
Externally publishedYes

Fingerprint

Proteomics
Ovarian Neoplasms
Serum
Technology
Neoplasms
Computational Biology
Population
Ovary
Mass Spectrometry
Spectrum Analysis
Lasers

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Petricoin, E. F., Ardekani, A. M., Hitt, B. A., Levine, P. J., Fusaro, V. A., Steinberg, S. M., ... Liotta, L. A. (2002). Use of proteomic patterns in serum to identify ovarian cancer. Lancet, 359(9306), 572-577. https://doi.org/10.1016/S0140-6736(02)07746-2

Use of proteomic patterns in serum to identify ovarian cancer. / Petricoin, Emanuel F.; Ardekani, Ali M.; Hitt, Ben A.; Levine, Peter J.; Fusaro, Vincent A.; Steinberg, Seth M.; Mills, Gordon; Simone, Charles; Fishman, David A.; Kohn, Elise C.; Liotta, Lance A.

In: Lancet, Vol. 359, No. 9306, 16.02.2002, p. 572-577.

Research output: Contribution to journalArticle

Petricoin, EF, Ardekani, AM, Hitt, BA, Levine, PJ, Fusaro, VA, Steinberg, SM, Mills, G, Simone, C, Fishman, DA, Kohn, EC & Liotta, LA 2002, 'Use of proteomic patterns in serum to identify ovarian cancer', Lancet, vol. 359, no. 9306, pp. 572-577. https://doi.org/10.1016/S0140-6736(02)07746-2
Petricoin EF, Ardekani AM, Hitt BA, Levine PJ, Fusaro VA, Steinberg SM et al. Use of proteomic patterns in serum to identify ovarian cancer. Lancet. 2002 Feb 16;359(9306):572-577. https://doi.org/10.1016/S0140-6736(02)07746-2
Petricoin, Emanuel F. ; Ardekani, Ali M. ; Hitt, Ben A. ; Levine, Peter J. ; Fusaro, Vincent A. ; Steinberg, Seth M. ; Mills, Gordon ; Simone, Charles ; Fishman, David A. ; Kohn, Elise C. ; Liotta, Lance A. / Use of proteomic patterns in serum to identify ovarian cancer. In: Lancet. 2002 ; Vol. 359, No. 9306. pp. 572-577.
@article{decbc36cbe0e40328112ab93d6b76573,
title = "Use of proteomic patterns in serum to identify ovarian cancer",
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.",
author = "Petricoin, {Emanuel F.} and Ardekani, {Ali M.} and Hitt, {Ben A.} and Levine, {Peter J.} and Fusaro, {Vincent A.} and Steinberg, {Seth M.} and Gordon Mills and Charles Simone and Fishman, {David A.} and Kohn, {Elise C.} and Liotta, {Lance A.}",
year = "2002",
month = "2",
day = "16",
doi = "10.1016/S0140-6736(02)07746-2",
language = "English (US)",
volume = "359",
pages = "572--577",
journal = "The Lancet",
issn = "0140-6736",
publisher = "Elsevier Limited",
number = "9306",

}

TY - JOUR

T1 - Use of proteomic patterns in serum to identify ovarian cancer

AU - Petricoin, Emanuel F.

AU - Ardekani, Ali M.

AU - Hitt, Ben A.

AU - Levine, Peter J.

AU - Fusaro, Vincent A.

AU - Steinberg, Seth M.

AU - Mills, Gordon

AU - Simone, Charles

AU - Fishman, David A.

AU - Kohn, Elise C.

AU - Liotta, Lance A.

PY - 2002/2/16

Y1 - 2002/2/16

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0037116832&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0037116832&partnerID=8YFLogxK

U2 - 10.1016/S0140-6736(02)07746-2

DO - 10.1016/S0140-6736(02)07746-2

M3 - Article

C2 - 11867112

AN - SCOPUS:0037116832

VL - 359

SP - 572

EP - 577

JO - The Lancet

JF - The Lancet

SN - 0140-6736

IS - 9306

ER -