Genomic classifier identifies men with adverse pathology after radical prostatectomy who benefit from adjuvant radiation therapy

Abstract

Purpose: The optimal timing of postoperative radiotherapy (RT) after radical prostatectomy (RP) is unclear. We hypothesized that a genomic classifier (GC) would provide prognostic and predictive insight into the development of clinical metastases in men receiving post-RP RT and inform decision making.

Patients and methods: GC scores were calculated from 188 patients with pT3 or margin-positive prostate cancer, who received post-RP RT at Thomas Jefferson University and Mayo Clinic between 1990 and 2009. The primary end point was clinical metastasis. Prognostic accuracy of the models was tested using the concordance index for censored data and decision curve analysis. Cox regression analysis tested the relationship between GC and metastasis.

Results: The cumulative incidence of metastasis at 5 years after RT was 0%, 9%, and 29% for low, average, and high GC scores, respectively (P = .002). In multivariable analysis, GC and pre-RP prostate-specific antigen were independent predictors of metastasis (both P < .01). Within the low GC score (< 0.4), there were no differences in the cumulative incidence of metastasis comparing patients who received adjuvant or salvage RT (P = .79). However, for patients with higher GC scores (≥ 0.4), cumulative incidence of metastasis at 5 years was 6% for patients treated with adjuvant RT compared with 23% for patients treated with salvage RT (P < .01).

Conclusion: In patients treated with post-RP RT, GC is prognostic for the development of clinical metastasis beyond routine clinical and pathologic features. Although preliminary, patients with low GC scores are best treated with salvage RT, whereas those with high GC scores benefit from adjuvant therapy. These findings provide the first rational selection of timing for post-RP RT.

Conflict of interest statement

Authors' disclosures of potential conflicts of interest are found in the article online at www.jco.org. Author contributions are found at the end of this article.

© 2015 by American Society of Clinical Oncology.

Figures

Fig 1.

Discriminatory performance of the genomic classifier (GC) compared with clinical risk factors and Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) score using different metrics. (A) GC has the highest survival concordance index compared with clinical models at 5 years after radiotherapy (RT). (B) Decision curve analysis for 5-year post-RT metastasis prediction shows that models including GC have the highest net benefit across 0% to 25% threshold probabilities. (C) Lasso hazards coefficient path of GC and clinical risk factors showing that GC is the first variable to enter the model as the penalty parameter is decreased. EPE, extraprostatic extension; GS, Gleason score; PSA, prostate-specific antigen; RP, radical prostatectomy; SMS, surgical margin status; SVI, seminal vesicle invasion.

Fig 2.

Cumulative incidence curves stratified by (A) Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) score and (B) genomic classifier (GC) to evaluate their prognosis for postradiotherapy metastasis. RT, radiotherapy.

Fig 3.

Cumulative incidence curves to evaluate benefit from adjuvant radiotherapy (RT) versus salvage RT stratified by (A and B) Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) score and (C and D) genomic classifier (GC).

Fig 4.

Cumulative incidence curves to evaluate benefit for three preradiotherapy prostate-specific antigen (PSA) levels ( 0.5 ng/mL) stratified by (A) low genomic classifier (GC) score (

Fig A1.

Risk score distribution of study patients based on (A) Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) score and (B) genomic classifier (GC).

Fig A2.

Discriminatory performance of genomic classifier (GC) compared with Stephenson nomogram. (A) GC has the highest survival concordance index compared with clinical models at 5 years after radiotherapy (RT). (B) Decision curve analysis for 5-year post-RT metastasis prediction shows that models including GC have the highest net benefit across 0% to 25% threshold probabilities.