PURPOSE: Accurate estimates of recurrence risk are needed for optimal treatment of patients with clinically localized prostate cancer. We combined an established nomogram and what to our knowledge are novel molecular predictors into a new prognostic model of prostate specific antigen recurrence. MATERIALS AND METHODS: We analyzed gene expression profiles from formalin fixed, paraffin embedded, localized prostate cancer tissues to identify genes associated with prostate specific antigen recurrence. Profiles of the identified markers were reproduced by reverse transcriptase-polymerase chain reaction. We used the profiles of 3 of these genes along with output from the Kattan postoperative nomogram to produce a predictive model of prostate specific antigen recurrence. RESULTS: After variable selection we built a model of prostate specific antigen recurrence combining expression values of 3 genes and the postoperative nomogram. The 3-gene plus nomogram model predicted 5-year prostate specific antigen recurrence with a concordance index of 0.77 in a validation set compared to a concordance index of 0.67 for the nomogram. This model identified a subgroup of patients at high risk for recurrence that was not identified by the nomogram. CONCLUSIONS: This new gene based classifier has superior predictive power compared to that of the 5-year nomogram to assess the risk of prostate specific antigen recurrence in patients with organ confined prostate cancer. Our classifier should provide more accurate stratification of patients into high and low risk groups for treatment decisions and adjuvant clinical trials.
PURPOSE: Accurate estimates of recurrence risk are needed for optimal treatment of patients with clinically localized prostate cancer. We combined an established nomogram and what to our knowledge are novel molecular predictors into a new prognostic model of prostate specific antigen recurrence. MATERIALS AND METHODS: We analyzed gene expression profiles from formalin fixed, paraffin embedded, localized prostate cancer tissues to identify genes associated with prostate specific antigen recurrence. Profiles of the identified markers were reproduced by reverse transcriptase-polymerase chain reaction. We used the profiles of 3 of these genes along with output from the Kattan postoperative nomogram to produce a predictive model of prostate specific antigen recurrence. RESULTS: After variable selection we built a model of prostate specific antigen recurrence combining expression values of 3 genes and the postoperative nomogram. The 3-gene plus nomogram model predicted 5-year prostate specific antigen recurrence with a concordance index of 0.77 in a validation set compared to a concordance index of 0.67 for the nomogram. This model identified a subgroup of patients at high risk for recurrence that was not identified by the nomogram. CONCLUSIONS: This new gene based classifier has superior predictive power compared to that of the 5-year nomogram to assess the risk of prostate specific antigen recurrence in patients with organ confined prostate cancer. Our classifier should provide more accurate stratification of patients into high and low risk groups for treatment decisions and adjuvant clinical trials.
Authors: Chin-Lee Wu; Brock E Schroeder; Xiao-Jun Ma; Christopher J Cutie; Shulin Wu; Ranelle Salunga; Yi Zhang; Michael W Kattan; Catherine A Schnabel; Mark G Erlander; W Scott McDougal Journal: Proc Natl Acad Sci U S A Date: 2013-03-26 Impact factor: 11.205
Authors: Nicholas Erho; Anamaria Crisan; Ismael A Vergara; Anirban P Mitra; Mercedeh Ghadessi; Christine Buerki; Eric J Bergstralh; Thomas Kollmeyer; Stephanie Fink; Zaid Haddad; Benedikt Zimmermann; Thomas Sierocinski; Karla V Ballman; Timothy J Triche; Peter C Black; R Jeffrey Karnes; George Klee; Elai Davicioni; Robert B Jenkins Journal: PLoS One Date: 2013-06-24 Impact factor: 3.240
Authors: Ahva Shahabi; Juan Pablo Lewinger; Jie Ren; Craig April; Andy E Sherrod; Joseph G Hacia; Siamak Daneshmand; Inderbir Gill; Jacek K Pinski; Jian-Bing Fan; Mariana C Stern Journal: Prostate Date: 2016-06-08 Impact factor: 4.012