Proteomic profiling identifies afamin as a potential biomarker for ovarian cancer.

PURPOSE: To discover and validate serum glycoprotein biomarkers in ovarian cancer using proteomic-based approaches. EXPERIMENTAL DESIGN: Serum samples from a "discovery set" of 20 patients with ovarian cancer or benign ovarian cysts or healthy volunteers were compared by fluorescence two-dimensional differential in-gel electrophoresis and parallel lectin-based two-dimensional profiling. Validation of a candidate biomarker was carried out with Western blotting and immunoassay (n = 424). RESULTS: Twenty-six proteins that changed significantly were identified by mass spectrometric sequencing. One of these, confirmed by Western blotting, was afamin, a vitamin E binding protein, with two isoforms decreasing in patients with ovarian cancer. Validation using cross-sectional samples from 303 individuals (healthy controls and patients with benign, borderline, or malignant ovarian conditions and other cancers) assayed by ELISA showed significantly decreased total afamin concentrations in patients with ovarian cancer compared with healthy controls (P = 0.002) and patients with benign disease (P = 0.046). However, the receiver operating characteristic areas for total afamin for the comparison of ovarian cancer with healthy controls or benign controls were only 0.67 and 0.60, respectively, with comparable figures for CA-125 being 0.92 and 0.88 although corresponding figures for a subgroup of samples analyzed by isoelectric focusing for afamin isoform 2 were 0.85 and 0.79. Analysis of a further 121 samples collected prospectively from 9 patients pretreatment through to relapse indicated complementarity of afamin with CA-125, including two cases in whom CA-125 was noninformative. CONCLUSIONS: Afamin shows potential complementarity with CA-125 in longitudinal monitoring of patients with ovarian cancer, justifying prospective larger-scale investigation. Changes in specific isoforms may provide further information.

Statistically integrated metabonomic-proteomic studies on a human prostate cancer xenograft model in mice.

A novel statistically integrated proteometabonomic method has been developed and applied to a human tumor xenograft mouse model of prostate cancer. Parallel 2D-DIGE proteomic and 1H NMR metabolic profile data were collected on blood plasma from mice implanted with a prostate cancer (PC-3) xenograft and from matched control animals. To interpret the xenograft-induced differences in plasma profiles, multivariate statistical algorithms including orthogonal projection to latent structure (OPLS) were applied to generate models characterizing the disease profile. Two approaches to integrating metabonomic data matrices are presented based on OPLS algorithms to provide a framework for generating models relating to the specific and common sources of variation in the metabolite concentrations and protein abundances that can be directly related to the disease model. Multiple correlations between metabolites and proteins were found, including associations between serotransferrin precursor and both tyrosine and 3-D-hydroxybutyrate. Additionally, a correlation between decreased concentration of tyrosine and increased presence of gelsolin was also observed. This approach can provide enhanced recovery of combination candidate biomarkers across multi-omic platforms, thus, enhancing understanding of in vivo model systems studied by multiple omic technologies.