A novel prognostic model predicting overall survival in patients with metastatic castration-resistant prostate cancer receiving standard chemotherapy: A multi-trial cohort analysis

Abstract

PURPOSE: Generalizable, updated, and easy-to-use prognostic models for patients with metastatic castration-resistant prostate cancer (mCRPC) are lacking. We developed a nomogram predicting the overall survival (OS) of mCRPC patients receiving standard chemotherapy using data from five randomized clinical trials (RCTs). METHODS: Patients enrolled in the control arm of five RCTs (ASCENT 2, VENICE, CELGENE/MAINSAIL, ENTHUSE 14, and ENTHUSE 33) were randomly split between training (n = 1636, 70%) and validation cohorts (n = 700, 30%). In the training cohort, Cox regression tested the prognostic significance of all available variables as a predictor of OS. Independent predictors of OS on multivariable analysis were used to construct a novel multivariable model (nomogram). The accuracy of this model was tested in the validation cohort using time-dependent area under the curve (tAUC) and calibration curves. RESULTS: Most of the patients were aged 65-74 years (44.5%) and the median (interquartile range) follow-up time was 13.9 (8.9-20.2) months. At multivariable analysis, the following were independent predictors of OS in mCRPC patients: sites of metastasis (visceral vs. bone metastasis, hazard ratio [HR]: 1.24), prostate-specific antigen (HR: 1.00), aspartate transaminase (HR: 1.01), alkaline phosphatase (HR: 1.00), body mass index (HR: 0.97), and hemoglobin (≥13 g/dl vs./dl, HR: 0.41; all p \u3c 0.05). A nomogram based on these variables was developed and showed favorable discrimination (tAUC at 12 and 24 months: 73% and 72%, respectively) and calibration characteristics on external validation. CONCLUSION: A new prognostic model to predict OS of patients with mCRPC undergoing first line chemotherapy was developed. This can help urologists/oncologists in counseling patients and might be useful to better stratify patients for future clinical trials