Abstract
Currently available serum biomarkers are insufficiently reliable to distinguish patients with epithelial ovarian cancer (EOC) from healthy individuals. Metabonomics, the study of metabolic processes in biologic systems, is based on the use of ^1^H‐NMR spectroscopy and multivariate statistics for biochemical data generation and interpretation and may provide a characteristic fingerprint in disease. In an effort to examine the utility of the metabonomic approach for discriminating sera from women with EOC from healthy controls, we performed ^1^H‐NMR spectroscopic analysis on preoperative serum specimens obtained from 38 patients with EOC, 12 patients with benign ovarian cysts and 53 healthy women. After data reduction, we applied both unsupervised Principal Component Analysis (PCA) and supervised Soft Independent Modeling of Class Analogy (SIMCA) for pattern recognition. The sensitivity and specificity tradeoffs were summarized for each variable using the area under the receiver‐operating characteristic (ROC) curve. In addition, we analyzed the regions of NMR spectra that most strongly influence separation of sera of EOC patients from healthy controls. 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. SIMCA analysis using the Cooman's plot demonstrated that sera classes from patients with EOC, benign ovarian cysts and the postmenopausal healthy controls did not share multivariate space, providing validation for the class separation. ROC analysis indicated that the sera from patients with and without disease could be identified with 100% sensitivity and specificity at the ^1^H‐NMR regions 2.77 parts per million (ppm) and 2.04 ppm from the origin (AUC of ROC curve = 1.0). In addition, the regression coefficients most influential for the EOC samples compared to postmenopausal controls lie around δ3.7 ppm (due mainly to sugar hydrogens). Other loadings most influential for the EOC samples lie around δ2.25 ppm and δ1.18 ppm. These findings indicate that ^1^H‐NMR metabonomic analysis of serum achieves complete separation of EOC patients from healthy controls. The metabonomic approach deserves further evaluation as a potential novel strategy for the early detection of epithelial ovarian cancer. © 2004 Wiley‐Liss, Inc.