Performance of clinicopathologic models in men with high risk localized prostate cancer: impact of a 22-gene genomic classifier.
Aged
Biomarkers, Tumor
/ genetics
Cohort Studies
Disease Progression
Humans
Kallikreins
/ blood
Male
Middle Aged
Models, Statistical
Neoplasm Metastasis
Nomograms
Prognosis
Prostate-Specific Antigen
/ blood
Prostatectomy
Prostatic Neoplasms
/ blood
ROC Curve
Retrospective Studies
Risk Factors
Transcriptome
Journal
Prostate cancer and prostatic diseases
ISSN: 1476-5608
Titre abrégé: Prostate Cancer Prostatic Dis
Pays: England
ID NLM: 9815755
Informations de publication
Date de publication:
12 2020
12 2020
Historique:
received:
28
10
2019
accepted:
09
03
2020
revised:
24
01
2020
pubmed:
2
4
2020
medline:
18
9
2021
entrez:
2
4
2020
Statut:
ppublish
Résumé
Prostate cancer exhibits biological and clinical heterogeneity even within established clinico-pathologic risk groups. The Decipher genomic classifier (GC) is a validated method to further risk-stratify disease in patients with prostate cancer, but its performance solely within National Comprehensive Cancer Network (NCCN) high-risk disease has not been undertaken to date. A multi-institutional retrospective study of 405 men with high-risk prostate cancer who underwent primary treatment with radical prostatectomy (RP) or radiation therapy (RT) with androgen-deprivation therapy (ADT) at 11 centers from 1995 to 2005 was performed. Cox proportional hazards models were used to determine the hazard ratios (HR) for the development of metastatic disease based on clinico-pathologic variables, risk groups, and GC score. The area under the receiver operating characteristic curve (AUC) was determined for regression models without and with the GC score. Over a median follow-up of 82 months, 104 patients (26%) developed metastatic disease. On univariable analysis, increasing GC score was significantly associated with metastatic disease ([HR]: 1.34 per 0.1 unit increase, 95% confidence interval [CI]: 1.19-1.50, p < 0.001), while age, serum PSA, biopsy GG, and clinical T-stage were not (all p > 0.05). On multivariable analysis, GC score (HR: 1.33 per 0.1 unit increase, 95% CI: 1.19-1.48, p < 0.001) and GC high-risk (vs low-risk, HR: 2.95, 95% CI: 1.79-4.87, p < 0.001) were significantly associated with metastasis. The addition of GC score to regression models based on NCCN risk group improved model AUC from 0.46 to 0.67, and CAPRA from 0.59 to 0.71. Among men with high-risk prostate cancer, conventional clinico-pathologic data had poor discrimination to risk stratify development of metastatic disease. GC score was a significant and independent predictor of metastasis and may help identify men best suited for treatment intensification/de-escalation.
Sections du résumé
BACKGROUND
Prostate cancer exhibits biological and clinical heterogeneity even within established clinico-pathologic risk groups. The Decipher genomic classifier (GC) is a validated method to further risk-stratify disease in patients with prostate cancer, but its performance solely within National Comprehensive Cancer Network (NCCN) high-risk disease has not been undertaken to date.
METHODS
A multi-institutional retrospective study of 405 men with high-risk prostate cancer who underwent primary treatment with radical prostatectomy (RP) or radiation therapy (RT) with androgen-deprivation therapy (ADT) at 11 centers from 1995 to 2005 was performed. Cox proportional hazards models were used to determine the hazard ratios (HR) for the development of metastatic disease based on clinico-pathologic variables, risk groups, and GC score. The area under the receiver operating characteristic curve (AUC) was determined for regression models without and with the GC score.
RESULTS
Over a median follow-up of 82 months, 104 patients (26%) developed metastatic disease. On univariable analysis, increasing GC score was significantly associated with metastatic disease ([HR]: 1.34 per 0.1 unit increase, 95% confidence interval [CI]: 1.19-1.50, p < 0.001), while age, serum PSA, biopsy GG, and clinical T-stage were not (all p > 0.05). On multivariable analysis, GC score (HR: 1.33 per 0.1 unit increase, 95% CI: 1.19-1.48, p < 0.001) and GC high-risk (vs low-risk, HR: 2.95, 95% CI: 1.79-4.87, p < 0.001) were significantly associated with metastasis. The addition of GC score to regression models based on NCCN risk group improved model AUC from 0.46 to 0.67, and CAPRA from 0.59 to 0.71.
CONCLUSIONS
Among men with high-risk prostate cancer, conventional clinico-pathologic data had poor discrimination to risk stratify development of metastatic disease. GC score was a significant and independent predictor of metastasis and may help identify men best suited for treatment intensification/de-escalation.
Identifiants
pubmed: 32231245
doi: 10.1038/s41391-020-0226-2
pii: 10.1038/s41391-020-0226-2
pmc: PMC10184170
mid: NIHMS1893915
doi:
Substances chimiques
Biomarkers, Tumor
0
KLK3 protein, human
EC 3.4.21.-
Kallikreins
EC 3.4.21.-
Prostate-Specific Antigen
EC 3.4.21.77
Types de publication
Journal Article
Multicenter Study
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
646-653Subventions
Organisme : NCI NIH HHS
ID : P50 CA186786
Pays : United States
Organisme : NCI NIH HHS
ID : T32 CA180984
Pays : United States
Organisme : NCI NIH HHS
ID : P50CA186786
Pays : United States
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