Biobanking after Radical Prostatectomy: Comparison of Slice and Punch Protocols.

Abstract

Background: Biobanking of prostate cancer tissue is crucial for advancing biomarker-guided precision medicine. However, there is no standardized optimal protocol for biobanking prostatectomy specimens. This study aims to compare the representativeness and sustainability of two biobanking protocols-"Punch" and "Slice"-currently used in Norway. Methods: Fresh frozen tissue from 40 radical prostatectomy specimens was biobanked using both the Punch and Slice protocols. Following macroscopic evaluation, a 2 mm thick transverse slice of the prostate (Slice protocol) was collected and stored in an ultra-freezer for future drill biopsy subsampling, guided by histopathological assessment of adjacent formalin-fixed, paraffin-embedded tissue sections. After the slice was collected, five cylindrical tissue samples were punched from the cut surfaces (Punch protocol). Statistical analyses were conducted to compare the sampling precision and time consumption of both protocols. Results: Cancerous tissue was successfully sampled in 87.5% of cases using the Punch protocol and 75% of cases using the Slice protocol. Both methods yielded comparable results in terms of the number of cancerous cores and the ability to sample tissue representing the highest Gleason grade. The mean biobanking time of tissue slices was 4.9 minutes compared to 15.1 minutes for the ready-to-use tissue punches. Both methods have previously been shown to provide high-quality RNA extracts. Conclusion: Both biobanking protocols are effective for sampling prostate cancer tissue, with no significant difference in precision or quality. The choice between protocols should consider factors such as resource availability, tissue quantity, and specific research needs. The Punch protocol is less resource-intensive overall, while the Slice protocol collects vastly more tissue, has a shorter period of ischemia, and provides detailed mapping of biobanked components, allowing for further subsampling at multiple time points.