Detection of Recurrent Breast Cancer R g Metabolite Profiling

ownload report on the development of a monitoring test for recurrent breast cancer, using metaboliteng methods. Using a combination of nuclear magnetic resonance (NMR) and two-dimensional gas atography–mass spectrometry (GC×GC-MS) methods, we analyzed the metabolite profiles of 257 retrove serial serum samples from 56 previously diagnosed and surgically treated breast cancer patients. One ed sixteen of the serial samples were from 20 patients with recurrent breast cancer, and 141 samples rom 36 patients with no clinical evidence of the disease during ∼6 years of sample collection. NMR and C-MS data were analyzed by multivariate statistical methods to compare identified metabolite signals en the recurrence samples and those with no evidence of disease. Eleven metabolite markers (seven MR and four from GC×GC-MS) were shortlisted from an analysis of all patient samples by using logistic sion and 5-fold cross-validation. A partial least squares discriminant analysis model built using these rs with leave-one-out cross-validation provided a sensitivity of 86% and a specificity of 84% (area under eiver operating characteristic curve = 0.88). Strikingly, 55% of the patients could be correctly predicted to ecurrence 13 months (on average) before the recurrence was clinically diagnosed, representing a large vement over the current breast cancer–monitoring assay CA 27.29. To the best of our knowledge, this is st study to develop and prevalidate a prediction model for early detection of recurrent breast cancer based the fir on metabolic profiles. In particular, the combination of two advanced analytical methods, NMR andMS, provides a powerful approach for the early detection of recurrent breast cancer. Cancer Res; 70(21); 8309–18. ©2010 AACR.