Improved prediction of disease relapse after radical prostatectomy through a panel of preoperative blood-based biomarkers

A nomogram based on these biomarkers improves the accuracy of standard predictive models and could help counsel patients about their risk of biochemical recurrence following radical prostatectomy.

The preoperative blood levels of biomarkers may allow accurate identification of patients who are likely to fail radical prostatectomy as a first-line therapy for localized prostate cancer, thereby allowing more efficient delivery of neoadjuvant and adjuvant therapy. The aim of this study was to determine the added value of biomarkers relative to established predictors of biochemical recurrence, such as clinical stage, biopsy Gleason sum, and preoperative prostate-specific antigen. Experimental design: The preoperative plasma levels of transforming growth factor-beta1 (TGF-beta1), interleukin-6 (IL-6), soluble IL-6 receptor (sIL-6R), vascular endothelial growth factor (VEGF), vascular cell adhesion molecule-1 (VCAM-1), endoglin, urokinase-type plasminogen activator (uPA), plasminogen activator inhibitor-1, and uPA receptor were measured with the use of commercially available enzyme immunoassays in 423 consecutive patients treated with radical prostatectomy and bilateral lymphadenectomy for clinically localized prostate cancer. Multivariable models were used to explore the gain in the predictive accuracy of the models. This predictive accuracy was quantified by the concordance index statistic and was validated with 200 bootstrap resamples.

In standard multivariable analyses, TGF-beta1 (P < 0.001), sIL-6R (P < 0.001), IL-6 (P < 0.001), VCAM-1 (P < 0.001), VEGF (P = 0.008), endoglin (P = 0.002), and uPA (P < 0.001) were associated with biochemical recurrence. The multivariable model containing standard clinical variables alone had an accuracy of 71.6%. The addition of TGF-beta1, sIL-6R, IL-6, VCAM-1, VEGF, endoglin, and uPA increased the predictive accuracy by 15% to 86.6% (P < 0.001) and showed excellent calibration. Conclusions: A nomogram based on these biomarkers improves the accuracy of standard predictive models and could help counsel patients about their risk of biochemical recurrence following radical prostatectomy.