Abstract
Objectives To investigate the added value of assessing transcripts for the long cAMP phosphodiesterase-4D (PDE4D) isoforms, PDE4D5 and PDE4D9, regarding the prognostic power of the ‘CAPRA & PDE4D7' combination risk model to predict longitudinal postsurgical biological outcomes in prostate cancer. Patients and Methods RNA was extracted from both biopsy punches of resected tumours (606 patients; RP cohort) and diagnostic needle biopsies (168 patients; DB cohort). RT-qPCR was performed in order to determine PDE4D5, PDE4D7, and PDE4D9 transcript scores in both study cohorts. By RNA sequencing, we determined the TMPRSS2-ERG fusion status of each tumour sample in the RP cohort. Kaplan-Meier survival analyses were then applied to correlate the PDE4D5, PDE4D7 and PDE4D9 scores with postsurgical patient outcomes. Logistic regression was then used to combine the clinical CAPRA score with PDE4D5, PDE4D7, and PDE4D9 scores in order to build a ‘CAPRA & PDE4D5/7/9' regression model. ROC and decision curve analysis was used to estimate the net benefit of the ‘CAPRA & PDE4D5/7/9' risk model. Results Kaplan-Meier survival analysis, on the RP cohort, revealed a significant association of the PDE4D7 score with postsurgical biochemical recurrence (BCR) in the presence of the TMPRSS2-ERG gene rearrangement (logrank p<0.0001), compared to the absence of this gene fusion event (logrank p=0.08). In contrast, the PDE4D5 score was only significantly associated with BCR in TMPRSS2-ERG fusion negative tumours (logrank p<0.0001 vs. logrank p=0.4 for TMPRSS2-ERG+ tumours). This was similar for the PDE4D9 score although less pronounced compared to that of the PDE4D5 score (TMPRSS2ERG- logrank p<0.0001 vs. TMPRSS2ERG+ logrank p<0.005). In order to predict BCR after primary treatment, we undertook ROC analysis of the logistic regression combination model of the CAPRA score with the PDE4D5, PDE4D7, and PDE4D9 scores. For the DB cohort, this demonstrated significant differences in the AUC between the CAPRA and the PDE4D5/7/9 regression model vs. the CAPRA and PDE4D7 risk model (AUC 0.87 vs. 0.82; p=0.049) vs. the CAPRA score alone (AUC 0.87 vs. 0.77; p=0.005). The CAPRA and PDE4D5/7/9 risk model stratified 19.2% patients of the DB cohort to either ‘no risk of biochemical relapse' (NPV 100%) or the ‘start of any secondary treatment (NPV 100%)', over a follow-up period of up to 15 years. Decision curve analysis presented a clear, net benefit for the use of the novel CAPRA & PDE4D5/7/9 risk model compared to the clinical CAPRA score alone or the CAPRA and PDE4D7 model across all decision thresholds. Conclusion Association of the long PDE4D5, PDE4D7, and PDE4D9 transcript scores to prostate cancer patient outcome, after primary intervention, varies in opposite directions depending on the TMPRSS2-ERG genomic fusion background of the tumour. Adding transcript scores for the long PDE4D isoforms, PDE4D5 and PDE4D9, to our previously presented combination risk model of the combined ‘CAPRA & PDE4D7' score, in order to generate the CAPRA and PDE4D5/7/9 score, significantly improves the prognostic power of the model in predicting postsurgical biological outcomes in prostate cancer patients.
Highlights
Prostate cancer is the most common nonhematology tumour diagnosed in men in western countries [1, 2]
We explored the net benefit of avoiding primary treatment, based on the predicted risk of a PSA relapse after surgery for the CAPRA score by comparing the utility of the CAPRA and PDE4D7 model versus the CAPRA and PDE4D5/7/9 combination model
We have previously proposed that a predictive model of the clinical risk algorithm CAPRA, in combination with the prostate cancer biomarker PDE4D7, provides value to prostate cancer risk stratification [20, 21]
Summary
Prostate cancer is the most common nonhematology tumour diagnosed in men in western countries [1, 2]. The natural history of prostate tumours is heterogeneous with, in general, indolent characteristics It includes some forms of the disease that can develop into deadly cancers. Some patients with clinically lowrisk characteristics progress after initial intervention [5,6,7] while others, with more advanced pathological features will experience stable disease during periods of follow-up [8, 9]. This continues to pose the challenge of selecting the most optimal management strategy for each individual patient. More advanced protocols for inclusion/exclusion of men to conservative management, monitoring strategies while in AS and measures to switch to definitive treatment are required for optimal prostate cancer patient care [12]
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