Assessment of a prognostic model, PSA metrics and toxicities in metastatic castrate resistant prostate cancer using data from Project Data Sphere (PDS).

Bethany Pitcher,Robert J Hamilton,Leila Khoja,Melania Pintilie,Anthony M Joshua,Kald Abdallah,Lucia R Languino

doi:10.1371/journal.pone.0170544

Abstract

BackgroundPrognostic models in metastatic castrate resistant prostate cancer (mCRPC) may have clinical utility. Using data from PDS, we aimed to 1) validate a contemporary prognostic model (Templeton et al., 2014) 2) evaluate prognostic impact of concomitant medications and PSA decrease 3) evaluate factors associated with docetaxel toxicity.MethodsWe accessed data on 2,449 mCRPC patients in PDS. The existing model was validated with a continuous risk score, time-dependent receiver operating characteristic (ROC) curves, and corresponding time-dependent Area under the Curve (tAUC). The prognostic effects of concomitant medications and PSA response were assessed by Cox proportional hazards models. One year tAUC was calculated for multivariable prognostic model optimized to our data. Conditional logistic regression models were used to assess associations with grade 3/4 adverse events (G3/4 AE) at baseline and after cycle 1 of treatment.ResultsDespite limitations of the PDS data set, the existing model was validated; one year AUC, was 0.68 (95% CI 95% CI, .66 to .71) to 0.78 (95%CI, .74 to .81) depending on the subset of datasets used. A new model was constructed with an AUC of .74 (.72 to .77). Concomitant medications low molecular weight heparin and warfarin were associated with poorer survival, Metformin and Cox2 inhibitors were associated with better outcome. PSA response was associated with survival, the effect of which was greatest early in follow-up. Age was associated with baseline risk of G3/4 AE. The odds of experiencing G3/4 AE later on in treatment were significantly greater for subjects who experienced a G3/4 AE in their first cycle (OR 3.53, 95% CI 2.53–4.91, p < .0001).ConclusionDespite heterogeneous data collection protocols, PDS provides access to large datasets for novel outcomes analysis. In this paper, we demonstrate its utility for validating existing models and novel model generation including the utility of concomitant medications in outcome analyses, as well as the effect of PSA response on survival and toxicity prediction.

Highlights

It is commonly appreciated that the natural history of prostate cancer varies widely [1,2,3]
Concomitant medications low molecular weight heparin and warfarin were associated with poorer survival, Metformin and Cox2 inhibitors were associated with better outcome
Age was associated with baseline risk of grade 3/4 adverse events (G3/4 adverse event (AE))

Summary

Introduction

It is commonly appreciated that the natural history of prostate cancer varies widely [1,2,3]. There are an increasing array of prognostic tools available in the localized disease setting [4, 5] in the metastatic disease state, the utility of prognostic scoring systems has had limited clinical impact in the pre- or post-chemotherapy setting, likely due to the rapid evolution of treatment paradigms (S1 Table) [6,7,8]. With these advances, the optimal choice of sequencing of treatments is unknown. Using data from PDS, we aimed to 1) validate a contemporary prognostic model (Templeton et al, 2014) 2) evaluate prognostic impact of concomitant medications and PSA decrease 3) evaluate factors associated with docetaxel toxicity.

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