Early Detection of Recurrent Breast Cancer Using Metabolite Profiling

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Home OnlineFirst Current Issue Past Issues Subscriptions Alerts Feedback AACR Publications AACR Home Search GO Advanced Search User Name Password Sign In Early Detection of Recurrent Breast Cancer Using Metabolite Profiling Vincent M. Asiago1, Leiddy Z. Alvarado1, Narasimhamurthy Shanaiah2, G.A. Nagana Gowda1, Kwadwo Owusu-Sarfo1, Robert A. Ballas3, and Daniel Raftery1,2

Authors' Affiliations:1Department of Chemistry, Purdue University; 2Matrix-Bio, Inc., West Lafayette, Indiana; and 3Biomarker Associates, Inc., Newark, Delaware Corresponding Author:
Daniel Raftery, Department of Chemistry, Purdue University, West Lafayette, IN 47907. Phone: 765-494-6070; Fax: 765-494-0239; E-mail: raftery{at}purdue.edu. Abstract We report on the development of a monitoring test for recurrent breast cancer, using metabolite-profiling methods. Using a combination of nuclear magnetic resonance (NMR) and two-dimensional gas chromatography–mass spectrometry (GC×GC-MS) methods, we analyzed the metabolite profiles of 257 retrospective serial serum samples from 56 previously diagnosed and surgically treated breast cancer patients. One hundred sixteen of the serial samples were from 20 patients with recurrent breast cancer, and 141 samples were from 36 patients with no clinical evidence of the disease during ~6 years of sample collection. NMR and GC×GC-MS data were analyzed by multivariate statistical methods to compare identified metabolite signals between the recurrence samples and those with no evidence of disease. Eleven metabolite markers (seven from NMR and four from GC×GC-MS) were shortlisted from an analysis of all patient samples by using logistic regression and 5-fold cross-validation. A partial least squares discriminant analysis model built using these markers with leave-one-out cross-validation provided a sensitivity of 86% and a specificity of 84% (area under the receiver operating characteristic curve = 0.88). Strikingly, 55% of the patients could be correctly predicted to have recurrence 13 months (on average) before the recurrence was clinically diagnosed, representing a large improvement over the current breast cancer–monitoring assay CA 27.29. To the best of our knowledge, this is the first study to develop and prevalidate a prediction model for early detection of recurrent breast cancer based on metabolic profiles. In particular, the combination of two advanced analytical methods, NMR and MS, provides a powerful approach for the early detection of recurrent breast cancer. Cancer Res; 70(21); 8309–18. ©2010 AACR.

Footnotes Note: Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org/).

Received April 23, 2010. Revision received August 11, 2010. Accepted August 16, 2010. ©2010 American Association for Cancer Research. CiteULike Complore Connotea Del.icio.us Digg Facebook Reddit Technorati Twitter What's this?

« Previous | Next Article »Table of Contents This Article Published OnlineFirst October 19, 2010; doi: 10.1158/0008-5472.CAN-10-1319 Cancer Res November 1, 2010 70; 8309 » Abstract Full Text Full Text (PDF) Supplementary Data All Versions of this Article: 0008-5472.CAN-10-1319v1 70/21/8309 most recent Classifications Integrated Systems and Technologies Services Email this article to a colleague Alert me when this article is cited Alert me if a correction is posted Similar articles in this journal Similar articles in PubMed Download to citation manager Citing Articles Load citing article information Citing articles via Scopus Google Scholar Articles by Asiago, V. M. Articles by Raftery, D. PubMed PubMed citation Articles by Asiago, V. M. Articles by Raftery, D. Social Bookmarking CiteULike Complore Connotea Del.icio.us Digg Facebook Reddit Technorati Twitter What's this?

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Online ISSN: 1538-7445 Print ISSN: 0008-5472

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