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, R. 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

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

Prostatic Neoplasms
Radiotherapy
Adjuvant Radiotherapy
Recurrence
Lymph Nodes
Observation
Neoplasm Grading
Prostatectomy
Regression Analysis
Guidelines

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

Cite this

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

In: Journal of Clinical Oncology, Vol. 35, No. 18, 20.06.2017, p. 1982-1990.

Research output: Contribution to journalArticle

Dalela, D, Santiago-Jiménez, M, Yousefi, K, Karnes, RJ, Ross, AE, Den, RB, Freedland, SJ, Schaeffer, EM, Dicker, AP, 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, vol. 35, no. 18, pp. 1982-1990. https://doi.org/10.1200/JCO.2016.69.9918
Dalela, Deepansh ; Santiago-Jiménez, María ; Yousefi, Kasra ; Karnes, R. Jeffrey ; Ross, Ashley E. ; Den, Robert B. ; Freedland, Stephen J. ; Schaeffer, Edward M. ; Dicker, Adam P. ; Menon, Mani ; Briganti, Alberto ; Davicioni, Elai ; Abdollah, Firas. / 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. In: Journal of Clinical Oncology. 2017 ; Vol. 35, No. 18. pp. 1982-1990.
@article{39e385f293fd4a17901d4bdad42f7da3,
title = "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",
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.",
author = "Deepansh Dalela and Mar{\'i}a Santiago-Jim{\'e}nez and Kasra Yousefi and Karnes, {R. Jeffrey} and Ross, {Ashley E.} and Den, {Robert B.} and Freedland, {Stephen J.} and Schaeffer, {Edward M.} and Dicker, {Adam P.} and Mani Menon and Alberto Briganti and Elai Davicioni and Firas Abdollah",
year = "2017",
month = "6",
day = "20",
doi = "10.1200/JCO.2016.69.9918",
language = "English (US)",
volume = "35",
pages = "1982--1990",
journal = "Journal of Clinical Oncology",
issn = "0732-183X",
publisher = "American Society of Clinical Oncology",
number = "18",

}

TY - JOUR

T1 - Genomic classifier augments the role of pathological features in identifying optimal candidates for adjuvant radiation therapy in patients with prostate cancer

T2 - Development and internal validation of a multivariable prognostic model

AU - Dalela, Deepansh

AU - Santiago-Jiménez, María

AU - Yousefi, Kasra

AU - Karnes, R. Jeffrey

AU - Ross, Ashley E.

AU - Den, Robert B.

AU - Freedland, Stephen J.

AU - Schaeffer, Edward M.

AU - Dicker, Adam P.

AU - Menon, Mani

AU - Briganti, Alberto

AU - Davicioni, Elai

AU - Abdollah, Firas

PY - 2017/6/20

Y1 - 2017/6/20

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85021696495&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85021696495&partnerID=8YFLogxK

U2 - 10.1200/JCO.2016.69.9918

DO - 10.1200/JCO.2016.69.9918

M3 - Article

VL - 35

SP - 1982

EP - 1990

JO - Journal of Clinical Oncology

JF - Journal of Clinical Oncology

SN - 0732-183X

IS - 18

ER -