Blood-Based Biomarkers of Aggressive Prostate Cancer

Purpose: Prostate cancer is a bimodal disease with aggressive and indolent forms. Current prostate-specific-antigen testing and digital rectal examination screening provide ambiguous results leading to both under-and over-treatment. Accurate, consistent diagnosis is crucial to risk-stratify patients and facilitate clinical decision making as to treatment versus active surveillance. Diagnosis is currently achieved by needle biopsy, a painful procedure. Thus, there is a clinical need for a minimally-invasive test to determine prostate cancer aggressiveness. A blood sample to predict Gleason score, which is known to reflect aggressiveness of the cancer, could serve as such a test. Materials and Methods: Blood mRNA was isolated from North American and Malaysian prostate cancer patients/controls. Microarray analysis was conducted utilizing the Affymetrix U133 plus 2·0 platform. Expression profiles from 255 patients/controls generated 85 candidate biomarkers. Following quantitative real-time PCR (qRT-PCR) analysis, ten disease-associated biomarkers remained for paired statistical analysis and normalization. Results: Microarray analysis was conducted to identify 85 genes differentially expressed between aggressive prostate cancer (Gleason score ≥8) and controls. Expression of these genes was qRT-PCR verified. Statistical analysis yielded a final seven-gene panel evaluated as six gene-ratio duplexes. This molecular signature predicted as aggressive (ie, Gleason score ≥8) 55% of G6 samples, 49% of G7(3+4), 79% of G7(4+3) and 83% of G8-10, while rejecting 98% of controls. Conclusion: In this study, we have developed a novel, blood-based biomarker panel which can be used as the basis of a simple blood test to identify men with aggressive prostate cancer and thereby reduce the overdiagnosis and overtreatment that currently results from diagnosis using PSA alone. We discuss possible clinical uses of the panel to identify men more likely to benefit from biopsy and immediate therapy versus those more suited to an “active surveillance” strategy

Prospective Randomized Controlled Trial Comparing Plasmakinetic Vaporesection and Conventional Transurethral Resection of the Prostate

Plasmakinetic vaporesection of the prostate (PKVP) using normal saline irrigation has the theoretical advantage of avoiding transurethral resection syndrome and minimizing blood loss. It may also shorten the operative time since tissue is resected instead of just vaporized. The aim of this study was to evaluate the efficiency, safety and advantages of PKVP compared with standard transurethral resection of the prostate (TURP) at a regional acute hospital. Methods: A total of 60 consecutive men admitted from a waiting list for surgery for benign prostatic hyperplasia (BPH) were prospectively randomized to either PKVP or TURP. Peri- and postoperative outcome data at 3 months were obtained. Results: The PKVP loop achieved a fast and sharp cutting action similar to that with the traditional TURP loop. Data analysis was based on 51 patients. There were no significant differences between the methods in resection time, postoperative catheterization time and hospital stay. The mean reductions in serum sodium 2 hours after PKVP and on postoperative day 1 were 0.52 mmol/L and 3.35 mmol/L, respectively, while mean reductions in haemoglobin were 0.36 g/dL and 0.24 g/dL, respectively. There was no significant difference in haemoglobin reductions between PKVP and TURP (p = 0.326 at 2 hours; p = 0.192 on day 1) and serum sodium (p = 0.757 at 2 hours; p = 0.888 on day 1). Both groups achieved comparable improvement in International Prostate Symptom Score (p = 0.862), quality-of-life score (p = 0.169) and peak flow rate (p = 0.96) at 3-month follow-up. Conclusion: PKVP achieved comparable results to traditional TURP and was an effective and safe procedure. However, it did not demonstrate obvious advantages over TURP in this acute regional hospital regular TURP list setting

High grade prostate cancer (Gleason score 8 and above) biomarker gene list and differential expression ratio in Cohort II verification sample set (80 disease and 102 controls).

<p># The 7 biomarkers were picked up from the 10 that were verified in Cohort II samples, using gene-ratio algorithm, based on the best AUC of combined gene-pair.</p>†<p>Determined by qRT-PCR analysis using SAMSN1 as a partner gene, gene ratio was calculated using delta delta Ct calculation.</p>‡<p>Calculated by Mann-Whitney test.</p>*<p>area under receiver-operating-characteristic curve.</p

Predictions for independent Cohort III and Cohort IV samples.

<p>The negative prediction rate for control cases is charted along with the positive prediction rates for cancer cases. PSA alone has high positive predictive rates for all cancer grades (>87%) but the combined PSA and RNA panel has lower positive prediction rates for the less aggressive G6 and G7(3+4) subgroups, 55%, and 49% respectively) while nearly the same positive prediction rate for the more aggressive G7(4+3) as G8 groups (79% and 83% respectively.</p

PCR data from a sample set of 122 PAXgene samples of prostate cancer from the G8 group and 138 PAXgene samples from the control group were performed in a 1000×2-fold cross-validation test.

<p>Histograms of AUC were plotted and compared; results showed AUCs from the PCR data were well separated from the null sets, with an overlap of less than 5%.</p

Gene identification and validation process.

<p>Gene Identification using Affymetrix U133Plus 2.0 GeneChip oligonucleotide arrays was carried out in Toronto, Canada, and Penang, Malaysia, in parallel. In Toronto, analysis was conducted on 166 samples (G8 = 42, G0 = 124). At the Malaysian site, 89 samples were profiled (49 G8, 40 G0). From microarray data analysis, 85 genes identified at both sites were tested in a series of quantitative real-time PCR verification studies. Twenty genes were verified through a Cohort I study on several cohorts of EDTA samples (total 245). These 20 genes were further tested in a Cohort II series of experiments on PAXgene samples (total 182), executed independently in Penang, Malaysia. 10 of the genes were verified, of which 7 genes became our final biomarkers and also confirmed in another independent sample set-Cohort III test (total 121).</p

Combined PSA and mRNA model.

<p>Inputs are CT values for genes and Log2 transformation of PSA in ng/ml.</p