Genomic classifier augments the role of pathological features in identifying optimal candidates for adjuvant radiation therapy in patients with prostate cancer: Development and internal validation of a multivariable prognostic model

Deepansh Dalela, María Santiago-Jiménez, Kasra Yousefi, Robert Jeffrey Karnes, Ashley E. Ross, Robert B. Den, Stephen J. Freedland, Edward M. Schaeffer, Adam P. Dicker, Mani Menon, Alberto Briganti, Elai Davicioni, Firas Abdollah

Research output: Contribution to journalArticle

35 Scopus citations

Abstract

Purpose: Despite documented oncologic benefit, use of postoperative adjuvant radiotherapy (aRT) in patients with prostate cancer is still limited in the United States. We aimed to develop and internally validate a risk-stratification tool incorporating the Decipher score, along with routinely available clinicopathologic features, to identify patients who would benefit the most from aRT. Patient and Methods: Our cohort included 512 patients with prostate cancer treated with radical prostatectomy at one of four US academic centers between 1990 and 2010. All patients had ≥ pT3a disease, positive surgical margins, and/or pathologic lymph node invasion. Multivariable Cox regression analysis tested the relationship between available predictors (including Decipher score) and clinical recurrence (CR), which were then used to develop a novel risk-stratification tool. Our study adhered to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines for development of prognostic models. Results: Overall, 21.9% of patients received aRT. Median follow-up in censored patients was 8.3 years. The 10-year CR rate was 4.9% vs. 17.4% in patients treated with aRT versus initial observation (P, .001). Pathologic T3b/T4 stage, Gleason score 8-10, lymph node invasion, and Decipher score . 0.6 were independent predictors of CR (all P, .01). The cumulative number of risk factors was 0, 1, 2, and 3 to 4 in 46.5%, 28.9%, 17.2%, and 7.4% of patients, respectively. aRT was associated with decreased CR rate in patients with two or more risk factors (10-year CR rate 10.1% in aRT v 42.1% in initial observation; P = .012), but not in those with fewer than two risk factors (P = .18). Conclusion: Using the new model to indicate aRT might reduce overtreatment, decrease unnecessary adverse effects, and reduce risk of CR in the subset of patients (approximately 25% of all patients with aggressive pathologic disease in our cohort) who benefit from this therapy.

Original languageEnglish (US)
Pages (from-to)1982-1990
Number of pages9
JournalJournal of Clinical Oncology
Volume35
Issue number18
DOIs
StatePublished - Jun 20 2017

    Fingerprint

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

Cite this

Dalela, D., Santiago-Jiménez, M., Yousefi, K., Karnes, R. J., Ross, A. E., Den, R. B., Freedland, S. J., Schaeffer, E. M., Dicker, A. P., Menon, M., Briganti, A., Davicioni, E., & Abdollah, F. (2017). Genomic classifier augments the role of pathological features in identifying optimal candidates for adjuvant radiation therapy in patients with prostate cancer: Development and internal validation of a multivariable prognostic model. Journal of Clinical Oncology, 35(18), 1982-1990. https://doi.org/10.1200/JCO.2016.69.9918