Alkaline Phosphatase Kinetics Predict Metastasis among Prostate Cancer Patients Who Experience Relapse following Radical Prostatectomy
Abstract
Introduction. Metastasis prostate cancer (CaP) occurs in a small fraction of patients. Improved prognostication of disease progression is a critical challenge. This study examined alkaline phosphatase velocity (APV) in predicting distant metastasis-free survival (DMFS). Materials and Methods. This retrospective cohort study examined CaP patients enrolled in the Center for Prostate Disease Research (CPDR) multicenter national database who underwent RP and experienced BCR (n=1783). BCR was defined as a PSA ≥ 0.2 ng/mL at ≥ 8 weeks post-RP, followed by at least one confirmatory PSA ≥ 0.2 ng/mL or initiation of salvage therapy. APV was computed as the slope of the linear regression line of all alkaline phosphatase (AP) values after BCR and prior to distant metastasis. APV values in the uppermost quartile were defined as “rapid” and compared to the lower three quartiles combined (“slower”). Unadjusted Kaplan Meier (KM) estimation curves and multivariable Cox proportional hazards analysis were used to examine predictors of DMFS. Results. Of the 1783 eligible patients who experienced post-RP BCR, 701 (39.3%) had necessary AP data for APV calculation. PSA doubling time (PSADT) and APV were strongly associated (p=0.008). No differences in APV were observed across race. In KM analysis, significantly poorer DMFS was observed among the rapid versus slower APV group (Log-rank p=0.003). In multivariable analysis, a rapid APV was predictive of a twofold increased probability of DMFS (HR = 2.2; 95% CI = 1.2, 3.9; p = 0.008), controlling for key study covariates. Conclusions. Building on previous work, this study found that rapid APV was a strong predictor of DMFS for a broader group of CaP patients, those who undergo post-RP BCR who were enrolled in a longitudinal cohort with long-term follow-up and equal health care access. APV is worth considering as a complementary clinical factor for predicting DMFS.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 28, 2018
- Source ID
- 10.1155/2018/4727089
Entities
People
- Adam R. Metwalli
- Carolyn A. Salter
- Claire Kuo
- Inger L. Rosner
- Jennifer Cullen
- Jordan Dimitrakoff
- Lauren M Hurwitz
- Yongmei Chen
Organizations
- Food and Drug Administration
- National Cancer Institute
- Uniformed Services University of the Health Sciences
- Walter Reed National Military Medical Center