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Pathologic states within the prostate may be reflected by changes in serum proteomic patterns. To test this hypothesis, we analyzed serum proteomic mass spectra with a bioinformatics tool to reveal the most fit pattern that discriminated the training set of sera of men with a histopathologic diagnosis of prostate cancer (serum prostate-specific antigen [PSA] > or =4 ng/mL) from those men without prostate cancer (serum PSA level <1 ng/mL). Mass spectra of blinded sera (N = 266) from a test set derived from men with prostate cancer or men without prostate cancer were matched against the discriminating pattern revealed by the training set. A predicted diagnosis of benign disease or cancer was rendered based on similarity to the discriminating pattern discovered from the training set. The proteomic pattern correctly predicted 36 (95%, 95% confidence interval [CI] = 82% to 99%) of 38 patients with prostate cancer, while 177 (78%, 95% CI = 72% to 83%) of 228 patients were correctly classified as having benign conditions. For men with marginally elevated PSA levels (4-10 ng/mL; n = 137), the specificity was 71%. If validated in future series, serum proteomic pattern diagnostics may be of value in deciding whether to perform a biopsy on a man with an elevated PSA level.

The need for specific and sensitive markers of ovarian cancer is critical. Finding a sensitive and specific test for its detection has an important public health impact. Currently, there are no effective screening options available for patients with ovarian cancer. CA-125, the most widely used biomarker for ovarian cancer, does not have a high positive predictive value and it is only effective when used in combination with other diagnostic tests. However, pathologic changes taking place within the ovary may be reflected in biomarker patterns in the serum. Combination of mass spectra generated by new proteomic technologies, such as surface-enhanced laser desorption ionization time-of-flight (SELDI-TOF) and artificial-intelligence-based informatic algorithms, have been used to discover a small set of key protein values and discriminate normal from ovarian cancer patients. Serum proteomic pattern analysis might be applied ultimately in medical screening clinics, as a supplement to the diagnostic work-up and evaluation.