A Novel Risk Score (P-score) Based on a Three-Gene Signature, for Estimating the Risk of Prostate Cancer-Specific Mortality

Abstract

Purpose: To develop and validate a risk score (P-score) algorithm which includes previously described three-gene signature and clinicopathological parameters to predict the risk of death from prostate cancer (PCa) in a retrospective cohort.

Patients and methods: A total of 591 PCa patients diagnosed between 2003 and 2008 in Stockholm, Sweden, with a median clinical follow-up time of 7.6 years (1-11 years) were included in this study. Expression of a three-gene signature (IGFBP3, F3, VGLL3) was measured in formalin-fixed paraffin-embedded material from diagnostic core needle biopsies (CNB) of these patients. A point-based scoring system based on a Fine-Gray competing risk model was used to establish the P-score based on the three-gene signature combined with PSA value, Gleason score and tumor stage at diagnosis. The endpoint was PCa-specific mortality, while other causes of death were treated as a competing risk. Out of the 591 patients, 315 patients (estimation cohort) were selected to develop the P-score. The P-score was subsequently validated in an independent validation cohort of 276 patients.

Results: The P-score was established ranging from the integers 0 to 15. Each one-unit increase was associated with a hazard ratio of 1.39 (95% confidence interval: 1.27-1.51, p < 0.001). The P-score was validated and performed better in predicting PCa-specific mortality than both D'Amico (0.76 vs 0.70) and NCCN (0.76 vs 0.71) by using the concordance index for competing risk. Similar improvement patterns are shown by time-dependent area under the curve. As demonstrated by cumulative incidence function, both P-score and gene signature stratified PCa patients into significantly different risk groups.

Conclusion: We developed the P-score, a risk stratification system for newly diagnosed PCa patients by integrating a three-gene signature measured in CNB tissue. The P-score could provide valuable decision support to distinguish PCa patients with favorable and unfavorable outcomes and hence improve treatment decisions.

Keywords: Prostatype; biomarker; biopsy; genetic testing; prognosis; prostate cancer.

Figure 1

Forest model to show univariable hazard ratio (HR) of individual genes in (A) the full dataset (N=591) and (B) the estimation dataset (N=315). HR, 95% confidence interval (CI) and p-value are presented.

Figure 2

(A): Cumulative incidence function (CIF) analysis of three-gene signature to predict PCa-specific mortality versus time in the full dataset (N=591). (Gene signature levels: 0 – no gene in high-risk category, 1 – one gene in high-risk category; 2 – two genes in high-risk category; 3 – all genes in high-risk category). (B) Cumulative incidence function analysis of P-score to predict time to PCa-specific mortality in the full dataset (N=591).

Figure 3

(A) Distribution of the P-score in the full dataset (N=591). Q1: quartile 1; Q3: quartile 3. (B) Distribution of the P-score in the NCCN risk groups (N=590; 0: low risk; 1: favorable intermediate risk; 2: unfavorable intermediate risk; 3: high risk; 4: very high risk). Yellow dots: Censored patients; red dots: patients who died of prostate cancer (PCa); blue dots: patients who died of other causes. Two dashed lines indicate cut-offs for separating low, intermediate, and high-risk P-scores.