Translation of Prognostic and Pharmacodynamic Biomarkers from Trial to Non-trial Patients with Metastatic Castration-resistant Prostate Cancer Treated with Docetaxel

Abstract

Aims We conducted a pooled analysis of four randomised controlled trials and a non-trial retrospective dataset to study the changes in serum prostate-specific antigen (PSA) concentrations during treatment and its impact on survival in men treated with docetaxel for metastatic castration-resistant prostate cancer. We also compared the outcomes and pre-treatment prognostic factors between trial and non-trial patients. Materials and methods Data were obtained from four randomised controlled trials and a non-trial cohort from a tertiary cancer centre. The PSA kinetics covariates chosen were absolute value (PSAT), best percentage change (BPCH) and tumour growth rate (K). The association between the covariates collected and overall survival was assessed within a Cox proportional hazards model. How well a covariate captured the difference between trial and non-trial patients was assessed by reporting on models with or without trial status as a covariate. Results We reviewed individual datasets of 2282 patients. The median overall survival for trial patients was 20.4 (95% confidence interval 19.6–22.2) months and for the non-trial cohort was 12.4 (10.7–14.7) months (P < 0.001). Of the pre-treatment factors, we found that only lactate dehydrogenase fully captured the difference in prognosis between the trial and non-trial cohorts. All PSA kinetic metrics appeared to be prognostic in both the trial and non-trial patients. However, the effect size was reduced in non-trial versus trial patients (interaction P < 0.001). Of the time-dependent covariates, we found that BPCH best captured the difference between trial and non-trial patient prognosis. Conclusions The analysis presented here highlights how data from open-source trial databases can be combined with emerging clinical practice databases to assess differences between trial versus non-trial patients for particular treatments. These results highlight the importance of developing prognostic models using both pre-treatment and time-dependent biomarkers of new treatments.