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Optimizing molecular signatures for predicting prostate cancer recurrence
Author(s) -
Sun Yijun,
Goodison Steve
Publication year - 2009
Publication title -
the prostate
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.295
H-Index - 123
eISSN - 1097-0045
pISSN - 0270-4137
DOI - 10.1002/pros.20961
Subject(s) - prostate cancer , medicine , prostate , oncology , prostate disease , cancer
BACKGROUND The derivation of molecular signatures indicative of disease status and predictive of subsequent behavior could facilitate the optimal choice of treatment for prostate cancer patients. METHODS In this study, 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 advanced computational algorithms can derive more accurate prognostic signatures for prostate cancer. RESULTS At the 90% sensitivity level, a newly derived prognostic genetic signature achieved 85% specificity. This is the first reported genetic signature to outperform a clinically used postoperative nomogram. Furthermore, a hybrid prognostic signature derived by combination of the nomogram and gene expression data significantly outperformed both genetic and clinical signatures, and achieved a specificity of 95%. CONCLUSIONS Our study demonstrates the feasibility of utilizing gene expression information for highly accurate prostate cancer prognosis beyond the current clinical systems, and shows that more advanced computational modeling of tissue‐derived microarray data is warranted before clinical application of molecular signatures is considered. Prostate 69:1119–1127, 2009. © 2009 Wiley‐Liss, Inc.