several drugs are approved in prostate cancer (PC), both in localized and metastatic setting. Challenge of daily practice is the sequencing of available agents for optimal disease management. Trying to extract actionable information from the overall history of disease for each patient remains a difficult task but could provide new insights for better sequencing. This retrospective analysis aimed to follow-up patients included in the Rising-PSA phase 3 clinical trial (R-PSA-CP-03) until death or last contact.
we retrospectively analyzed therapies received by pts included in R-PSA at the HEGP hospital (Paris, France). Drugs were coded in 8 categories: LH: LHRH modulators, AA: anti-androgens, AA2: new generation AA, DC: docetaxel, CZ: cabazitaxel, EX: blinded experimental drugs, P: therapeutic pause, PCB: placebo(experimental). Sequence rank, therapy duration and their interaction was estimated using both a conditional repeated events model (CREM) and a multi-state model (MSM) based on Markov process stratified on disease setting. Covariables included in the models were: age and Gleason score at inclusion time.
152 pts included between 01/2003 and 09/2007 were followed > 10years. Metastatic progression: 70(46%). Death: 31(20%). Median age(y): 64(51-80)), Gleason ≥8: 47(31%). Median (range) number of sequences received: M0=8(1-15) & M1=6(1-19) including pauses. Number of times each therapy was used whatever the sequence (%M0/%M1): LH(48/10), AA(6/6), AA2(0/22), DC(10/10), CZ (0/10), EX(1/6), PCB(0/2), P(35/34). Upon CREM, the overall model fitted perfectly well the time on therapies and their sequence (robust estimation: p
to our knowledge, this is the first attempt to model the entire course of PC taking into account both therapies and sequence. Given the complexity of our model, these results should be validated with further studies and methods.
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All authors have declared no conflicts of interest.