Combining Nomogram and Microarray Data for Predicting Prostate Cancer Recurrence

The derivation of molecular signatures indicative of disease status and behavior are required to facilitate the optimal choice of treatment for prostate cancer patients. We conducted a computational analysis of gene expression profile data obtained from 79 cases, 39 of which were classified as having disease recurrence, to investigate whether an advanced computational algorithm can derive more accurate prognostic signatures for prostate cancer. At the 90% sensitivity level, a newly derived genetic signature achieved 85% specificity. This is the first reported genetic signature to outperform a clinically used postoperative nomogram. Furthermore, a hybrid signature derived by combination of the nomogram and gene expression data significantly outperformed both genetic and clinical signatures, and achieved a specificity of 95%. Our study demonstrates the possibility of utilizing both genetic and clinical information for highly accurate prostate cancer prognosis beyond the current clinical systems, and shows that more advanced computational modeling of microarray and clinical data is warranted before clinical application of predictive signatures is considered.