Use of proteomic patterns in serum to identify ovarian cancer.

@article{citeulike:921616, 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.}, address = {Food and Drug Administration/National Institutes of Health Clinical Proteomics Program, Department of Therapeutic Proteins/Center for Biologics Evaluation and Research, Food and Drug Administration, Bethesda, MD, USA. petricoin@cber.fda.gov}, author = {Petricoin, E. F. and Ardekani, A. M. and Hitt, B. A. and Levine, P. J. and Fusaro, V. A. and Steinberg, S. M. and Mills, G. B. and Simone, C. and Fishman, D. A. and Kohn, E. C. and Liotta, L. A. }, citeulike-article-id = {921616}, issn = {0140-6736}, journal = {Lancet}, keywords = {cancer, ovarian, proteomics, seldi}, month = {February}, number = {9306}, pages = {572--577}, posted-at = {2008-05-27 16:17:43}, priority = {2}, title = {Use of proteomic patterns in serum to identify ovarian cancer.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/11867112}, volume = {359}, year = {2002} }

TY - JOUR ID - citeulike:921616 L3 - citeulike-article-id:921616 TI - Use of proteomic patterns in serum to identify ovarian cancer. JF - Lancet VL - 359 IS - 9306 SP - 572 EP - 577 AD - Food and Drug Administration/National Institutes of Health Clinical Proteomics Program, Department of Therapeutic Proteins/Center for Biologics Evaluation and Research, Food and Drug Administration, Bethesda, MD, USA. petricoin@cber.fda.gov SN - 0140-6736 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. KW - cancer KW - ovarian KW - proteomics KW - seldi AU - Petricoin, EF AU - Ardekani, AM AU - Hitt, BA AU - Levine, PJ AU - Fusaro, VA AU - Steinberg, SM AU - Mills, GB AU - Simone, C AU - Fishman, DA AU - Kohn, EC AU - Liotta, LA PY - 2002/02/16/ UR - http://view.ncbi.nlm.nih.gov/pubmed/11867112 ER -