Kinetic models of PSA decrease after surgery in prostate tumor diseases as a help for clinician interpretation
Benoit You (1,2) , Paul Perrin (1,2), Philippe Paparel (1,2), Gilles Freyer (1,2), Olivier Colomban (1) Brigitte Tranchand (1,3), Pascal Girard (1)
(1)EA3738 Faculté de Médecine Lyon Sud, Université de Lyon, Lyon, France (2)Hospices civils de Lyon, Lyon, France (3) Centre Léon Bérard, Lyon, France
Objectives: In order to characterize Prostate Specific Antigen (PSA) compartments releases (anatomy transitional and peripheral zones) and prognostic value of PSA decrease on patient's outcome, we built a kinetic model of PSA decline after prostate surgery in 109 patients with prostate tumor diseases.
Patients: Fifty four Prostate Benign Hyperplasia patients treated with adenomectomy (PBH=adenoma developed within transitional zone; n=54, median age 62 years, creatinin clearance (CCL) 80 mL/min) and fifty five limited prostate cancer patients treated with radical prostatectomy (n=55; median age:69 years, CCR: 84 mL/min; 6.76 PSA assays/patient, differentiation score Gleason 7, pathology stage T2N0 to pT3N1) were included in a retrospective study.
Data: Data base involved 553 post-surgery values of serum PSA over a 4 years period (mean 3.35 PSA assays/patient in adenomectomy group and 6.76 in prostatectomy group) with a median follow-up of 97 days for adenomectomy group and 285 days for prostatectomy group.
Models: PSA declines were fitted according to multi-exponential models using NONMEM v5 with FOCE INTER. All parameters were considered with inter-individual variability (IIV) and patient's covariates were tested in models in order to reduce IIV.
Since the post-adenomectomy PSA showed a re-growth after few weeks linked to the PSA prostate residual peripheral zone production, a third exponential was added in the model to describe this phenomenon.Afterwards models were validated using visual predictive check.
Relationships between PSA decline profile and on one hand patients outcome (PSA biologic relapse: RLPS: 0=no, 1=Yes) and on the other hand 18 months relapse free survival (RFS%)) were determined using S-PLUS.
The best model was a bi-exponential one using multiplicative error.
After adenomectomy for PBH (n=54), PSA was fitted by:
The final IIV of first, second and third exponential rates were 58%, 83% and 66% respectively.
According to this equation, we can infer the individual predicted PSA production by the transitional zone (median 0.126 ng/mL/ tissue gramme) which is close to the literature values evaluated by anatomopathology. Moreover, the PSA prostate peripheral production is estimated to be about 0.75ng/mL.
After radical prostatectomy for prostate cancer (n=55), PSA decrease (from Day 0 to 30 after surgery) was described by:
PSA(t)= ((8.58 -0.042*CCL)*EXP(-0.21*t)) + (1.84 + 1.87*RLPS)*EXP(-0.39*t)
IIV of first and second exponential rates were 33% and 53% respectively. This equation allowed us to calculate the PSA Area Under the Curve (AUC).Patients who presented a RLPS had an increased PSA AUC (33.16 vs 29.10 ng/mL.day, p=0.04).
Total PSA production was assumed to be the sum of: the PSA prostate transitional zone (adenoma) production, PSA prostate peripheral zone production and PSA cancer production. Consequently, the predicted PSA prostate cancer compartment production was estimated to be 0.038 ng/mL/gramme of cancer tissue. Relapse free survival was significantly better in patients with a small PSA cancer compartment production (linear regression, p=0.03).
Conclusions: Using modeling in 2 prostate tumor diseases, we not only determined prostate transitional and peripheral compartments production (results consistent with literature) but also PSA cancer compartment production. We showed the influence of renal function on PSA. Our results confirm the relationship between PSA decrease and cancer relapse. We planned to build a model describing relapse risk according to PSA AUC in order to give to physicians a tool for PSA interpretation after surgery.