Evaluating the clinical impact of a genomic classifier in prostate cancer using individualized decision analysis

Jennifer Mason Lobo, Adam P. Dicker, Christine Buerki, Elai Daviconi, Robert Jeffrey Karnes, Robert Brian Jenkins, Nirav Patel, Robert B. Den, Timothy N. Showalter

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Background: Currently there is controversy surrounding the optimal way to treat patients with prostate cancer in the post-prostatectomy setting. Adjuvant therapies carry possible benefits of improved curative results, but there is uncertainty in which patients should receive adjuvant therapy. There are concerns about giving toxicity to a whole population for the benefit of only a subset. We hypothesized that making post-prostatectomy treatment decisions using genomics-based risk prediction estimates would improve cancer and quality of life outcomes. Methods: We developed a state-transition model to simulate outcomes over a 10 year horizon for a cohort of post-prostatectomy patients. Outcomes included cancer progression rates at 5 and 10 years, overall survival, and quality-adjusted survival with reductions for treatment, side effects, and cancer stage. We compared outcomes using population-level versus individuallevel risk of cancer progression, and for genomics-based care versus usual care treatment recommendations. Results: Cancer progression outcomes, expected life-years (LYs), and expected quality-adjusted life-years (QALYs) were significantly different when individual genomics-based cancer progression risk estimates were used in place of population-level risk estimates. Use of the genomic classifier to guide treatment decisions provided small, but statistically significant, improvements in model outcomes. We observed an additional 0.03 LYs and 0.07 QALYs, a 12% relative increase in the 5-year recurrence-free survival probability, and a 4% relative reduction in the 5-year probability of metastatic disease or death. Conclusions: The use of genomics-based risk prediction to guide treatment decisions may improve outcomes for prostate cancer patients. This study offers a framework for individualized decision analysis, and can be extended to incorporate a wide range of personal attributes to enable delivery of patient-centered tools for informed decision-making.

Original languageEnglish (US)
Article numbere0116866
JournalPLoS One
Volume10
Issue number4
DOIs
StatePublished - Apr 2 2015

Fingerprint

Decision Support Techniques
Decision theory
prostatic neoplasms
Prostatic Neoplasms
Classifiers
genomics
neoplasms
Genomics
quality-adjusted life year
Prostatectomy
risk estimate
Neoplasms
Quality-Adjusted Life Years
adjuvants
Therapeutics
Survival
therapeutics
prediction
Population
decision support systems

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Evaluating the clinical impact of a genomic classifier in prostate cancer using individualized decision analysis. / Lobo, Jennifer Mason; Dicker, Adam P.; Buerki, Christine; Daviconi, Elai; Karnes, Robert Jeffrey; Jenkins, Robert Brian; Patel, Nirav; Den, Robert B.; Showalter, Timothy N.

In: PLoS One, Vol. 10, No. 4, e0116866, 02.04.2015.

Research output: Contribution to journalArticle

Lobo, Jennifer Mason ; Dicker, Adam P. ; Buerki, Christine ; Daviconi, Elai ; Karnes, Robert Jeffrey ; Jenkins, Robert Brian ; Patel, Nirav ; Den, Robert B. ; Showalter, Timothy N. / Evaluating the clinical impact of a genomic classifier in prostate cancer using individualized decision analysis. In: PLoS One. 2015 ; Vol. 10, No. 4.
@article{40ba11298c5c4da793864d061af44a71,
title = "Evaluating the clinical impact of a genomic classifier in prostate cancer using individualized decision analysis",
abstract = "Background: Currently there is controversy surrounding the optimal way to treat patients with prostate cancer in the post-prostatectomy setting. Adjuvant therapies carry possible benefits of improved curative results, but there is uncertainty in which patients should receive adjuvant therapy. There are concerns about giving toxicity to a whole population for the benefit of only a subset. We hypothesized that making post-prostatectomy treatment decisions using genomics-based risk prediction estimates would improve cancer and quality of life outcomes. Methods: We developed a state-transition model to simulate outcomes over a 10 year horizon for a cohort of post-prostatectomy patients. Outcomes included cancer progression rates at 5 and 10 years, overall survival, and quality-adjusted survival with reductions for treatment, side effects, and cancer stage. We compared outcomes using population-level versus individuallevel risk of cancer progression, and for genomics-based care versus usual care treatment recommendations. Results: Cancer progression outcomes, expected life-years (LYs), and expected quality-adjusted life-years (QALYs) were significantly different when individual genomics-based cancer progression risk estimates were used in place of population-level risk estimates. Use of the genomic classifier to guide treatment decisions provided small, but statistically significant, improvements in model outcomes. We observed an additional 0.03 LYs and 0.07 QALYs, a 12{\%} relative increase in the 5-year recurrence-free survival probability, and a 4{\%} relative reduction in the 5-year probability of metastatic disease or death. Conclusions: The use of genomics-based risk prediction to guide treatment decisions may improve outcomes for prostate cancer patients. This study offers a framework for individualized decision analysis, and can be extended to incorporate a wide range of personal attributes to enable delivery of patient-centered tools for informed decision-making.",
author = "Lobo, {Jennifer Mason} and Dicker, {Adam P.} and Christine Buerki and Elai Daviconi and Karnes, {Robert Jeffrey} and Jenkins, {Robert Brian} and Nirav Patel and Den, {Robert B.} and Showalter, {Timothy N.}",
year = "2015",
month = "4",
day = "2",
doi = "10.1371/journal.pone.0116866",
language = "English (US)",
volume = "10",
journal = "PLoS One",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "4",

}

TY - JOUR

T1 - Evaluating the clinical impact of a genomic classifier in prostate cancer using individualized decision analysis

AU - Lobo, Jennifer Mason

AU - Dicker, Adam P.

AU - Buerki, Christine

AU - Daviconi, Elai

AU - Karnes, Robert Jeffrey

AU - Jenkins, Robert Brian

AU - Patel, Nirav

AU - Den, Robert B.

AU - Showalter, Timothy N.

PY - 2015/4/2

Y1 - 2015/4/2

N2 - Background: Currently there is controversy surrounding the optimal way to treat patients with prostate cancer in the post-prostatectomy setting. Adjuvant therapies carry possible benefits of improved curative results, but there is uncertainty in which patients should receive adjuvant therapy. There are concerns about giving toxicity to a whole population for the benefit of only a subset. We hypothesized that making post-prostatectomy treatment decisions using genomics-based risk prediction estimates would improve cancer and quality of life outcomes. Methods: We developed a state-transition model to simulate outcomes over a 10 year horizon for a cohort of post-prostatectomy patients. Outcomes included cancer progression rates at 5 and 10 years, overall survival, and quality-adjusted survival with reductions for treatment, side effects, and cancer stage. We compared outcomes using population-level versus individuallevel risk of cancer progression, and for genomics-based care versus usual care treatment recommendations. Results: Cancer progression outcomes, expected life-years (LYs), and expected quality-adjusted life-years (QALYs) were significantly different when individual genomics-based cancer progression risk estimates were used in place of population-level risk estimates. Use of the genomic classifier to guide treatment decisions provided small, but statistically significant, improvements in model outcomes. We observed an additional 0.03 LYs and 0.07 QALYs, a 12% relative increase in the 5-year recurrence-free survival probability, and a 4% relative reduction in the 5-year probability of metastatic disease or death. Conclusions: The use of genomics-based risk prediction to guide treatment decisions may improve outcomes for prostate cancer patients. This study offers a framework for individualized decision analysis, and can be extended to incorporate a wide range of personal attributes to enable delivery of patient-centered tools for informed decision-making.

AB - Background: Currently there is controversy surrounding the optimal way to treat patients with prostate cancer in the post-prostatectomy setting. Adjuvant therapies carry possible benefits of improved curative results, but there is uncertainty in which patients should receive adjuvant therapy. There are concerns about giving toxicity to a whole population for the benefit of only a subset. We hypothesized that making post-prostatectomy treatment decisions using genomics-based risk prediction estimates would improve cancer and quality of life outcomes. Methods: We developed a state-transition model to simulate outcomes over a 10 year horizon for a cohort of post-prostatectomy patients. Outcomes included cancer progression rates at 5 and 10 years, overall survival, and quality-adjusted survival with reductions for treatment, side effects, and cancer stage. We compared outcomes using population-level versus individuallevel risk of cancer progression, and for genomics-based care versus usual care treatment recommendations. Results: Cancer progression outcomes, expected life-years (LYs), and expected quality-adjusted life-years (QALYs) were significantly different when individual genomics-based cancer progression risk estimates were used in place of population-level risk estimates. Use of the genomic classifier to guide treatment decisions provided small, but statistically significant, improvements in model outcomes. We observed an additional 0.03 LYs and 0.07 QALYs, a 12% relative increase in the 5-year recurrence-free survival probability, and a 4% relative reduction in the 5-year probability of metastatic disease or death. Conclusions: The use of genomics-based risk prediction to guide treatment decisions may improve outcomes for prostate cancer patients. This study offers a framework for individualized decision analysis, and can be extended to incorporate a wide range of personal attributes to enable delivery of patient-centered tools for informed decision-making.

UR - http://www.scopus.com/inward/record.url?scp=84926611269&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84926611269&partnerID=8YFLogxK

U2 - 10.1371/journal.pone.0116866

DO - 10.1371/journal.pone.0116866

M3 - Article

C2 - 25837660

AN - SCOPUS:84926611269

VL - 10

JO - PLoS One

JF - PLoS One

SN - 1932-6203

IS - 4

M1 - e0116866

ER -