Effect of surgical procedures on prostate tumor gene expression profiles

Biobanking of derivatives from radical retropubic and robot-assisted laparoscopic prostatectomy tissues as part of the prostate cancer biorepository network

BACKGROUND

The goal of the Prostate Cancer Biorepository Network (PCBN) is to develop a biorepository with high-quality, well-annotated specimens obtained in a systematic, reproducible fashion using optimized and standardized protocols, and an infrastructure to facilitate the growth of the resource and its wide usage by the prostate cancer research community. An emerging area of concern in the field of prostate cancer biobanking is an apparent shift in the proportion of surgical procedures performed for prostate cancer treatment from radical retropubic prostatectomy (RRP) to robot-assisted laparoscopic prostatectomy (RALP). Our study aimed to determine the potential impact of the RALP procedure on the detection of known prostate cancer biomarkers, and the subsequent suitability of RALP-derived specimens for prostate cancer biomarker studies.

METHODS

DNA and RNA were extracted from RRP and RALP specimens. Quality assessment was conducted using spectrophotometric analysis and RNA was analyzed for RNA integrity number (RIN) and by real-time reverse-transcription PCR (qRT-PCR) for racemase, hepsin, ERG, TMPRSS2-ERG gene fusions, and the microRNAs miR-26a, miR-26b, miR-141, and miR-221.

RESULTS

We demonstrate that extraction of derivatives from frozen tissues from RRP and RALP specimens yields samples of equally high quality as assessed by spectrophotometric and RIN analysis. Likewise, expression levels of genes analyzed by qRT-PCR did not differ between RRP and RALP-derived tissues.

CONCLUSIONS

Our studies indicate that samples obtained from RALP specimens may be suitable for prostate cancer biomarker studies-an important finding given the current shift in surgical procedures for prostate cancer treatment.

Gene panel model predictive of outcome in patients with prostate cancer

In men at high risk for prostate cancer, established clinical and pathological parameters provide only limited prognostic information. Here we analyzed a French cohort of 103 prostate cancer patients and developed a gene panel model predictive of outcome in this group of patients. The model comprised of a 15-gene TaqMan Low-Density Array (TLDA) card, with gene expressions compared to a standardized reference. The RQ value for each gene was calculated, and a scoring system was developed. Summing all the binary scores (0 or 1) corresponding to the 15 genes, a global score is obtained between 0 and 15. This global score can be compared to Gleason score (0 to 10) by recalculating it into a 0-10 scaled score. A scaled score ≥2 suggested that the patient is suffering from a prostate cancer, and a scaled score ≥7 flagged aggressive cancer. Statistical analyses demonstrated a strongly significant linear correlation (p=3.50E-08) between scaled score and Gleason score for this prostate cancer cohort (N=103). These results support the capacity of this designed 15 target gene TLDA card approach to predict outcome in prostate cancer, opening up a new avenue for personalized medicine through future independent replication and applications for rapid identification of aggressive prostate cancer phenotypes for early intervention.