Cancer Diagnostics Using 1H-NMR-Based Metabonomics
For several solid human malignancies, currently available serum biomarkers are insufficiently reliable to distinguish patients from healthy individuals. Metabonomics, the study of metabolic processes in biologic systems, is based on the use of 1H-NMR spectroscopy and multivariate statistics for biochemical data generation and interpretation and may provide a characteristic fingerprint in disease. Here we review our initial experiences utilizing the metabonomic approach for discriminating sera from women with epithelial ovarian cancer (EOC) from healthy controls. 1H-NMR spectroscopic analysis was performed on preoperative serum specimens of 38 EOC patients, 12 patients with benign ovarian cysts and 53 healthy women. PCA analysis allowed correct separation of all serum specimens from 38 patients with EOC (100%) from all of the 21 premenopausal normal samples (100%) and from all the sera from patients with benign ovarian disease (100%). In addition, it was possible to correctly separate 37 of 38 (97.4%) cancer specimens from 31 of 32 (97%) postmenopausal control sera. ROC analysis indicated that the sera from patients with and without disease could be identified with 100% sensitivity and specificity at the 1H-NMR regions 2.77 parts per million (ppm) and 2.04 ppm from the origin (AUC of ROC curve = 1.0). These findings indicate that the 1H-NMR metabonomic approach deserves further evaluation as a potential novel strategy for the early detection of EOC.
KeywordsEpithelial Ovarian Cancer Principal Component Analysis Model Epithelial Ovarian Cancer Patient Receiver Operate Characteristic Curve Analysis Receiver Operate Characteristic Curve Curve
I thank Dr. Thomas Szyperski for helpful discussions. Supported by the Oshei Foundation, Roswell Park Cancer Center Support Grant P30CA16056 and R21 CA106949–01A1 from the National Institutes of Health.
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