METHODS: To this end, we undertook a pilot genome-wide CNV analysis approach in 36 subjects (18 patients with high-grade PCa and 18 controls that were matched by age and ethnicity) in search of more accurate biomarkers that could potentially explain susceptibility toward high-grade PCa. We conducted this study using the array comparative genomic hybridization technique. Array results were validated in 92 independent samples (46 high-grade PCa, 23 benign prostatic hyperplasia, and 23 healthy controls) using polymerase chain reaction-based copy number counting method.
RESULTS: A total of 314 CNV regions were found to be unique to PCa subjects in this cohort (P<0.05). A log2 ratio-based copy number analysis revealed 5 putative rare or novel CNV loci or both associated with susceptibility to PCa. The CNV gain regions were 1q21.3, 15q15, 7p12.1, and a novel CNV in PCa 12q23.1, harboring ARNT, THBS1, SLC5A8, and DDC genes that are crucial in the p53 and cancer pathways. A CNV loss and deletion event was observed at 8p11.21, which contains the SFRP1 gene from the Wnt signaling pathway. Cross-comparison analysis with genes associated to PCa revealed significant CNVs involved in biological processes that elicit cancer pathogenesis via cytokine production and endothelial cell proliferation.
CONCLUSION: In conclusion, we postulated that the CNVs identified in this study could provide an insight into the development of advanced PCa.
METHODS: We used three single nucleotide polymorphisms (SNPs) (rs8176746, rs505922, and rs8176704) to determine ABO genotype in 2,774 aggressive prostate cancer cases and 4,443 controls from the Breast and Prostate Cancer Cohort Consortium (BPC3). Unconditional logistic regression was used to calculate age and study-adjusted odds ratios and 95% confidence intervals for the association between blood type, genotype, and risk of aggressive prostate cancer (Gleason score ≥8 or locally advanced/metastatic disease (stage T3/T4/N1/M1).
RESULTS: We found no association between ABO blood type and risk of aggressive prostate cancer (Type A: OR = 0.97, 95%CI = 0.87-1.08; Type B: OR = 0.92, 95%CI =n0.77-1.09; Type AB: OR = 1.25, 95%CI = 0.98-1.59, compared to Type O, respectively). Similarly, there was no association between "dose" of A or B alleles and aggressive prostate cancer risk.
CONCLUSIONS: ABO blood type was not associated with risk of aggressive prostate cancer.
MATERIALS AND METHODS: From a PSA screening initiative, 161 men were shown to have elevated PSA levels in their blood and underwent prostatic tissue biopsy. DNA was extracted from the blood, and exon 1 of the AR gene amplified by PCR and sequenced. The number of CAG repeat sequences were counted and compared to the immunohistochemical expression of ERG and AR in the matched tumour biopsies.
RESULTS: Of men with elevated PSA, 89 were diagnosed with prostate cancer, and 72 with benign prostatic hyperplasia (BPH). There was no significant difference in the length of the CAG repeat in men with prostate cancer and BPH. The CAG repeat length was not associated with; age, PSA or tumour grade, though a longer CAG repeat was associated with tumour stage. ERG and AR were expressed in 36% and 86% of the cancers, respectively. There was no significant association between CAG repeat length and ERG or AR expression. However, there was a significant inverse relationship between ERG and AR expression. In addition, a significantly great proportion of Indian men had ERG positive tumours, compared to men of Malay or Chinese descent.
CONCLUSIONS: CAG repeat length is not associated with prostate cancer or expression of ERG or AR. However, ERG appears to be more common in the prostate cancers of Malaysian Indian men than in the prostate cancers of other Malaysian ethnicities and its expression in this study was inversely related to AR expression.
MATERIALS AND METHOD: 180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy.
RESULTS: 166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer.
CONCLUSIONS: Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.
OBJECTIVE: To test if SNPs associated with other traits may also affect the risk of aggressive prostate cancer.
DESIGN, SETTING, AND PARTICIPANTS: SNPs implicated in any phenotype other than prostate cancer (p≤10(-7)) were identified through the catalog of published GWAS and tested in 2891 aggressive prostate cancer cases and 4592 controls from the Breast and Prostate Cancer Cohort Consortium (BPC3). The 40 most significant SNPs were followed up in 4872 aggressive prostate cancer cases and 24,534 controls from the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium.
OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Odds ratios (ORs) and 95% confidence intervals (CIs) for aggressive prostate cancer were estimated.
RESULTS AND LIMITATIONS: A total of 4666 SNPs were evaluated by the BPC3. Two signals were seen in regions already reported for prostate cancer risk. rs7014346 at 8q24.21 was marginally associated with aggressive prostate cancer in the BPC3 trial (p=1.6×10(-6)), whereas after meta-analysis by PRACTICAL the summary OR was 1.21 (95% CI 1.16-1.27; p=3.22×10(-18)). rs9900242 at 17q24.3 was also marginally associated with aggressive disease in the meta-analysis (OR 0.90, 95% CI 0.86-0.94; p=2.5×10(-6)). Neither of these SNPs remained statistically significant when conditioning on correlated known prostate cancer SNPs. The meta-analysis by BPC3 and PRACTICAL identified a third promising signal, marked by rs16844874 at 2q34, independent of known prostate cancer loci (OR 1.12, 95% CI 1.06-1.19; p=4.67×10(-5)); it has been shown that SNPs correlated with this signal affect glycine concentrations. The main limitation is the heterogeneity in the definition of aggressive prostate cancer between BPC3 and PRACTICAL.
CONCLUSIONS: We did not identify new SNPs for aggressive prostate cancer. However, rs16844874 may provide preliminary genetic evidence on the role of the glycine pathway in prostate cancer etiology.
PATIENT SUMMARY: We evaluated whether genetic variants associated with several traits are linked to the risk of aggressive prostate cancer. No new such variants were identified.
OBJECTIVE: To report the first year's screening results for all men at enrollment in the study.
DESIGN, SETTING AND PARTICIPANTS: We recruited men aged 40-69 yr with germline BRCA1/2 mutations and a control group of men who have tested negative for a pathogenic BRCA1 or BRCA2 mutation known to be present in their families. All men underwent prostate-specific antigen (PSA) testing at enrollment, and those men with PSA >3 ng/ml were offered prostate biopsy.
OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: PSA levels, PCa incidence, and tumour characteristics were evaluated. The Fisher exact test was used to compare the number of PCa cases among groups and the differences among disease types.
RESULTS AND LIMITATIONS: We recruited 2481 men (791 BRCA1 carriers, 531 BRCA1 controls; 731 BRCA2 carriers, 428 BRCA2 controls). A total of 199 men (8%) presented with PSA >3.0 ng/ml, 162 biopsies were performed, and 59 PCas were diagnosed (18 BRCA1 carriers, 10 BRCA1 controls; 24 BRCA2 carriers, 7 BRCA2 controls); 66% of the tumours were classified as intermediate- or high-risk disease. The positive predictive value (PPV) for biopsy using a PSA threshold of 3.0 ng/ml in BRCA2 mutation carriers was 48%-double the PPV reported in population screening studies. A significant difference in detecting intermediate- or high-risk disease was observed in BRCA2 carriers. Ninety-five percent of the men were white, thus the results cannot be generalised to all ethnic groups.
CONCLUSIONS: The IMPACT screening network will be useful for targeted PCa screening studies in men with germline genetic risk variants as they are discovered. These preliminary results support the use of targeted PSA screening based on BRCA genotype and show that this screening yields a high proportion of aggressive disease.
PATIENT SUMMARY: In this report, we demonstrate that germline genetic markers can be used to identify men at higher risk of prostate cancer. Targeting screening at these men resulted in the identification of tumours that were more likely to require treatment.
OBJECTIVE: To report the utility of PSA screening, PrCa incidence, positive predictive value of PSA, biopsy, and tumour characteristics after 3 yr of screening, by BRCA status.
DESIGN, SETTING, AND PARTICIPANTS: Men aged 40-69 yr with a germline pathogenic BRCA1/2 mutation and male controls testing negative for a familial BRCA1/2 mutation were recruited. Participants underwent PSA screening for 3 yr, and if PSA > 3.0 ng/ml, men were offered prostate biopsy.
OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: PSA levels, PrCa incidence, and tumour characteristics were evaluated. Statistical analyses included Poisson regression offset by person-year follow-up, chi-square tests for proportion t tests for means, and Kruskal-Wallis for medians.
RESULTS AND LIMITATIONS: A total of 3027 patients (2932 unique individuals) were recruited (919 BRCA1 carriers, 709 BRCA1 noncarriers, 902 BRCA2 carriers, and 497 BRCA2 noncarriers). After 3 yr of screening, 527 men had PSA > 3.0 ng/ml, 357 biopsies were performed, and 112 PrCa cases were diagnosed (31 BRCA1 carriers, 19 BRCA1 noncarriers, 47 BRCA2 carriers, and 15 BRCA2 noncarriers). Higher compliance with biopsy was observed in BRCA2 carriers compared with noncarriers (73% vs 60%). Cancer incidence rate per 1000 person years was higher in BRCA2 carriers than in noncarriers (19.4 vs 12.0; p = 0.03); BRCA2 carriers were diagnosed at a younger age (61 vs 64 yr; p = 0.04) and were more likely to have clinically significant disease than BRCA2 noncarriers (77% vs 40%; p = 0.01). No differences in age or tumour characteristics were detected between BRCA1 carriers and BRCA1 noncarriers. The 4 kallikrein marker model discriminated better (area under the curve [AUC] = 0.73) for clinically significant cancer at biopsy than PSA alone (AUC = 0.65).
CONCLUSIONS: After 3 yr of screening, compared with noncarriers, BRCA2 mutation carriers were associated with a higher incidence of PrCa, younger age of diagnosis, and clinically significant tumours. Therefore, systematic PSA screening is indicated for men with a BRCA2 mutation. Further follow-up is required to assess the role of screening in BRCA1 mutation carriers.
PATIENT SUMMARY: We demonstrate that after 3 yr of prostate-specific antigen (PSA) testing, we detect more serious prostate cancers in men with BRCA2 mutations than in those without these mutations. We recommend that male BRCA2 carriers are offered systematic PSA screening.
METHODS: Immunoblotting, immunofluorescence, quantitative real-time PCR and flow cytometry assays investigated the expression of E-cadherin and CXCR3 isoforms. Intrasplenic inoculation of human prostate cancer (PCa) cells with spontaneous metastasis to the liver analyzed E-cadherin and CXCR3-B expression during cancer progression in vivo.
RESULTS: We found reciprocal regulation of E-cadherin and CXCR3 isoforms. E-cadherin surface expression promoted CXCR3-B presentation on the cell membrane, and to a lesser extent increased its mRNA and total protein levels. In turn, forced expression of CXCR3-A reduced E-cadherin expression level, whereas CXCR3-B increased E-cadherin in PCa. Meanwhile, a positive correlation of E-cadherin and CXCR3-B expression was found both in experimental PCa liver micro-metastases and patients' tissue.
CONCLUSIONS: CXCR3-B and E-cadherin positively correlated in vitro and in vivo in PCa cells and liver metastases, whereas CXCR3-A negatively regulated E-cadherin expression. These results suggest that CXCR3 isoforms may play important roles in cancer progression and dissemination via diametrically regulating tumor's phenotype.