In:
Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 31, No. 6_suppl ( 2013-02-20), p. 36-36
Abstract:
36 Background: Following radical prostatectomy (RP), 30-40% of patients have adverse pathology and are deemed at high risk for metastatic progression. The objective of this study was to validate the ability of Decipher, a genomic classifier (GC), to improve prediction of metastatic disease progression compared with clinical variables in order to better identify candidates for therapy intensification. Methods: A previously developed 22-feature GC model was validated in a prospectively designed case-cohort study of a clinically high-risk population (i.e., with one or more adverse pathological features) of 1,010 RP patients treated at Mayo Clinic between 2000-2006. A random sample of 20% of the cohort was subjected to microarray analysis and GC scores were generated for 219 patients. The primary endpoint, the c-index for predicting metastatic disease progression (i.e., positive bone or CT scans) was evaluated in a blinded analysis. Cox modeling and decision curve analyses were used to compare the performance of GC to individual clinical variables and prediction models. Results: GC had a c-index 0.79 (95% CI 0.71-0.86) that was significantly better than any single clinical variable. Cumulative incidence curves in the cohort showed that 72% of patients had low GC scores with only 3% and 6% incidence of metastatic disease at 5 and 10 years post RP. In contrast, for the 28% of patients with high GC scores, the cumulative incidence was 17% and 25% at 5 and 10 years post RP. Decision curve analysis showed that the GC model had higher overall net benefit compared to clinical variables over a wide range of ‘decision-to-treat’ thresholds for risk of metastasis. In multivariable modeling with clinicopathologic variables, GC remained the only significant independent predictor of metastasis (HR=1.51, for each 0.1 unit increment, p 〈 0.001). Conclusions: GC can better predict metastatic disease progression compared with clinical variables and may select among patients with adverse pathology a majority that is in fact at low risk for metastasis.
Type of Medium:
Online Resource
ISSN:
0732-183X
,
1527-7755
DOI:
10.1200/jco.2013.31.6_suppl.36
Language:
English
Publisher:
American Society of Clinical Oncology (ASCO)
Publication Date:
2013
detail.hit.zdb_id:
2005181-5
