Using prognosis to guide inclusion criteria, define standardized end-points and stratify follow up in active surveillance for prostate cancer

To test whether using disease prognosis can inform a rational approach to Active Surveillance (AS) for early prostate cancer METHODS: We previously developed the Cambridge Prognostics Groups (CPG), a 5-tiered model that uses PSA, Grade Group and stage to predict cancer survival outcomes. We applied the CPG model to a UK and a Swedish prostate cancer cohort to test differences in prostate cancer mortality (PCM) in men managed conservatively or by upfront treatment in CPG2 and 3 (which subdivides the intermediate-risk classification) versus CPG1 (low-risk). We then applied the CPG model to a contemporary UK AS cohort which was optimally characterised at baseline for disease burden to identify predictors of true prognostic progression. Results were retested in an external AS cohort from Spain.

In a UK cohort (n=3659) 10-year PCM was 2.3% in CPG1, 1.5% & 3.5% in treated/untreated CPG2 and 1.9% & 8.6% in treated/untreated CPG3. In the Swedish cohort (n=27,942): 10-year PCM was 1.0% in CPG1, 2.2% & 2.7% in treated/untreated CPG2 and 6.1% & 12.5% in treated/untreated CPG3. We then tested using progression to CPG3 as a hard endpoint in a modern surveillance cohort (n=133). During follow-up (median 3.5 years) only 6% (8/133) progressed to CPG3. Predictors of progression were a PSA density ≥0.15 and CPG2 at diagnosis. Progression occurred in 1%, 8% and 21% of men with neither factor, only one or both. In an independent Spanish AS cohort (n=143) the corresponding rates were 3%, 10% and 14%.

Using disease prognosis allows a rational approach to inclusion criteria, discontinuation triggers and risk-stratified management in AS. This article is protected by copyright. All rights reserved.

 2019 May 7. doi: 10.1111/bju.14800. [Epub ahead of print]