Targeted Mass Spectrometry Assays for Specific Quantification of Urinary proPSA Isoforms.

Prostate cancer (PCa) is the second leading cause of male cancer-related deaths in the United States. The pre-mature forms of prostate-specific antigen (PSA), proPSA, were shown to be associated with PCa. However, there is a technical challenge in the development of antibody-based immunoassays for specific recognition of each individual proPSA isoform. Herein, we report the development of highly specific, antibody-free, targeted mass spectrometry assays for simultaneous quantification of [-2], [-4], [-5], and [-7] proPSA isoforms in voided urine. The newly developed proPSA assays capitalize on Lys-C digestion to generate surrogate peptides with appropriate length (9-16 amino acids) along with long-gradient liquid chromatography separation. The assay utility of these isoform markers was evaluated in a cohort of 30 well-established clinical urine samples for distinguishing PCa patients from healthy controls. Under the 95% confidence interval, the combination of [-2] and [-4] proPSA isoforms yields the area under curve (AUC) of 0.86, and the AUC value for the combined all four isoforms was calculated to be 0.85. We have further verified [-2]proPSA, the dominant isoform, in an independent cohort of 34 clinical urine samples. Validation of proPSA isoforms in large-scale cohorts is needed to demonstrate their potential clinical utility.

Accommodating heterogeneous missing data patterns for prostate cancer risk prediction.

BACKGROUND: We compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk factors from the end-user. METHODS: Ten North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG) were used for fitting a risk prediction tool for clinically significant prostate cancer, defined as Gleason grade group ≥ 2 on standard TRUS prostate biopsy. One large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach. RESULTS: Among 12,703 biopsies from 10 training cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to 1,757 of 5,540 (32%) in the external validation cohort. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL, under-predicting risks as percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had the worst CIL (-13.3%). The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. For end-users of the risk tool, two risk factors were mandatory: serum prostate-specific antigen (PSA) and age, and ten were optional: digital rectal exam, prostate volume, prior negative biopsy, 5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer family history. CONCLUSION: Developers of clinical risk prediction tools should optimize use of available data and sources even in the presence of high amounts of missing data and offer options for users with missing risk factors.

Prediction of future risk of any and higher-grade prostate cancer based on the PLCO and SELECT trials.

BACKGROUND: A model was built that characterized effects of individual factors on five-year prostate cancer (PCa) risk in the Prostate, Lung, Colon, and Ovarian Cancer Screening Trial (PLCO) and the Selenium and Vitamin E Cancer Prevention Trial (SELECT). This model was validated in a third San Antonio Biomarkers of Risk (SABOR) screening cohort. METHODS: A prediction model for 1- to 5-year risk of developing PCa and Gleason > 7 PCa (HG PCa) was built on PLCO and SELECT using the Cox proportional hazards model adjusting for patient baseline characteristics. Random forests and neural networks were compared to Cox proportional hazard survival models, using the trial datasets for model building and the SABOR cohort for model evaluation. The most accurate prediction model is included in an online calculator. RESULTS: The respective rates of PCa were 8.9%, 7.2%, and 11.1% in PLCO (n = 31,495), SELECT (n = 35,507), and SABOR (n = 1790) over median follow-up of 11.7, 8.1 and 9.0 years. The Cox model showed higher prostate-specific antigen (PSA), BMI and age, and African American race to be associated with PCa and HGPCa. Five-year risk predictions from the combined SELECT and PLCO model effectively discriminated risk in the SABOR cohort with C-index 0.76 (95% CI [0.72, 0.79]) for PCa, and 0.74 (95% CI [0.65,0.83]) for HGPCa. CONCLUSIONS: A 1- to 5-year PCa risk prediction model developed from PLCO and SELECT was validated with SABOR and implemented online. This model can individualize and inform shared screening decisions.

African-specific improvement of a polygenic hazard score for age at diagnosis of prostate cancer.

Polygenic hazard score (PHS) models are associated with age at diagnosis of prostate cancer. Our model developed in Europeans (PHS46) showed reduced performance in men with African genetic ancestry. We used a cross-validated search to identify single nucleotide polymorphisms (SNPs) that might improve performance in this population. Anonymized genotypic data were obtained from the PRACTICAL consortium for 6253 men with African genetic ancestry. Ten iterations of a 10-fold cross-validation search were conducted to select SNPs that would be included in the final PHS46+African model. The coefficients of PHS46+African were estimated in a Cox proportional hazards framework using age at diagnosis as the dependent variable and PHS46, and selected SNPs as predictors. The performance of PHS46 and PHS46+African was compared using the same cross-validated approach. Three SNPs (rs76229939, rs74421890 and rs5013678) were selected for inclusion in PHS46+African. All three SNPs are located on chromosome 8q24. PHS46+African showed substantial improvements in all performance metrics measured, including a 75% increase in the relative hazard of those in the upper 20% compared to the bottom 20% (2.47-4.34) and a 20% reduction in the relative hazard of those in the bottom 20% compared to the middle 40% (0.65-0.53). In conclusion, we identified three SNPs that substantially improved the association of PHS46 with age at diagnosis of prostate cancer in men with African genetic ancestry to levels comparable to Europeans.

Comparison of rectal swab, glove tip, and participant-collected stool techniques for gut microbiome sampling.

BACKGROUND: Studies of the gut microbiome are becoming increasingly important. Such studies require stool collections that can be processed or frozen in a timely manner so as not to alter the microbial content. Due to the logistical difficulties of home-based stool collection, there has been a challenge in selecting the appropriate sample collection technique and comparing results from different microbiome studies. Thus, we compared stool collection and two alternative clinic-based fecal microbiome collection techniques, including a newer glove-based collection method. RESULTS: We prospectively enrolled 22 adult men from our prostate cancer screening cohort SABOR (San Antonio Biomarkers of Risk for prostate cancer) in San Antonio, TX, from 8/2018 to 4/2019. A rectal swab and glove tip sample were collected from each participant during a one-time visit to our clinics. A single stool sample was collected at the participant's home. DNA was isolated from the fecal material and 16 s rRNA sequencing of the V1-V2 and V3-V4 regions was performed. We found the gut microbiome to be similar in richness and evenness, noting no differences in alpha diversity among the collection methods. The stool collection method, which remains the gold-standard method for the gut microbiome, proved to have different community composition compared to swab and glove tip techniques (p< 0.001) as measured by Bray-Curtis and unifrac distances. There were no significant differences in between the swab and glove tip samples with regard to beta diversity (p> 0.05). Despite differences between home-based stool and office-based fecal collection methods, we noted that the distance metrics for the three methods cluster by participant indicating within-person similarities. Additionally, no taxa differed among the methods in a Linear Discriminant Analysis Effect Size (LEfSe) analysis comparing all-against-all sampling methods. CONCLUSION: The glove tip method provides similar gut microbiome results as rectal swab and stool microbiome collection techniques. The addition of a new office-based collection technique could help easy and practical implementation of gut microbiome research studies and clinical practice.

A genome-wide association study of prostate cancer in Latinos.

Latinos represent <1% of samples analyzed to date in genome-wide association studies of cancer. The clinical value of genetic information in guiding personalized medicine in populations of non-European ancestry will require additional discovery and risk locus characterization efforts across populations. In the present study, we performed a GWAS of prostate cancer (PrCa) in 2,820 Latino PrCa cases and 5,293 controls to search for novel PrCa risk loci and to examine the generalizability of known PrCa risk loci in Latino men. We also conducted a genetic admixture-mapping scan to identify PrCa risk alleles associated with local ancestry. Genome-wide significant associations were observed with 84 variants all located at the known PrCa risk regions at 8q24 (128.484-128.548) and 10q11.22 (MSMB gene). In admixture mapping, we observed genome-wide significant associations with local African ancestry at 8q24. Of the 162 established PrCa risk variants that are common in Latino men, 135 (83.3%) had effects that were directionally consistent as previously reported, among which 55 (34.0%) were statistically significant with p < 0.05. A polygenic risk model of the known PrCa risk variants showed that, compared to men with average risk (25th-75th percentile of the polygenic risk score distribution), men in the top 10% had a 3.19-fold (95% CI: 2.65, 3.84) increased PrCa risk. In conclusion, we found that the known PrCa risk variants can effectively stratify PrCa risk in Latino men. Larger studies in Latino populations will be required to discover and characterize genetic risk variants for PrCa and improve risk stratification for this population.

Multi-cohort modeling strategies for scalable globally accessible prostate cancer risk tools.

BACKGROUND: Online clinical risk prediction tools built on data from multiple cohorts are increasingly being utilized for contemporary doctor-patient decision-making and validation. This report outlines a comprehensive data science strategy for building such tools with application to the Prostate Biopsy Collaborative Group prostate cancer risk prediction tool. METHODS: We created models for high-grade prostate cancer risk using six established risk factors. The data comprised 8492 prostate biopsies collected from ten institutions, 2 in Europe and 8 across North America. We calculated area under the receiver operating characteristic curve (AUC) for discrimination, the Hosmer-Lemeshow test statistic (HLS) for calibration and the clinical net benefit at risk threshold 15%. We implemented several internal cross-validation schemes to assess the influence of modeling method and individual cohort on validation performance. RESULTS: High-grade disease prevalence ranged from 18% in Zurich (1863 biopsies) to 39% in UT Health San Antonio (899 biopsies). Visualization revealed outliers in terms of risk factors, including San Juan VA (51% abnormal digital rectal exam), Durham VA (63% African American), and Zurich (2.8% family history). Exclusion of any cohort did not significantly affect the AUC or HLS, nor did the choice of prediction model (pooled, random-effects, meta-analysis). Excluding the lowest-prevalence Zurich cohort from training sets did not statistically significantly change the validation metrics for any of the individual cohorts, except for Sunnybrook, where the effect on the AUC was minimal. Therefore the final multivariable logistic model was built by pooling the data from all cohorts using logistic regression. Higher prostate-specific antigen and age, abnormal digital rectal exam, African ancestry and a family history of prostate cancer increased risk of high-grade prostate cancer, while a history of a prior negative prostate biopsy decreased risk (all p-values < 0.004). CONCLUSIONS: We have outlined a multi-cohort model-building internal validation strategy for developing globally accessible and scalable risk prediction tools.

Association between variants in genes involved in the immune response and prostate cancer risk in men randomized to the finasteride arm in the Prostate Cancer Prevention Trial.

BACKGROUND: We reported that some, but not all single nucleotide polymorphisms (SNPs) in select immune response genes are associated with prostate cancer, but not individually with the prevalence of intraprostatic inflammation in the Prostate Cancer Prevention Trial (PCPT) placebo arm. Here, we investigated whether these same SNPs are associated with risk of lower- and higher-grade prostate cancer in men randomized to finasteride, and with prevalence of intraprostatic inflammation among controls. Methods A total of 16 candidate SNPs in IL1ß, IL2, IL4, IL6, IL8, IL10, IL12(p40), IFNG, MSR1, RNASEL, TLR4, and TNFA and 7 tagSNPs in IL10 were genotyped in 625 white prostate cancer cases, and 532 white controls negative for cancer on an end-of-study biopsy nested in the PCPT finasteride arm. We used logistic regression to estimate log-additive odds ratios (OR) and 95% confidence intervals (CI) adjusting for age and family history. RESULTS: Minor alleles of rs2243250 (T) in IL4 (OR = 1.46, 95% CI 1.03-2.08, P-trend = 0.03), rs1800896 (G) in IL10 (OR = 0.77, 95% CI 0.61-0.96, P-trend = 0.02), rs2430561 (A) in IFNG (OR = 1.33, 95% CI 1.02-1.74; P-trend = 0.04), rs3747531 (C) in MSR1 (OR = 0.55, 95% CI 0.32-0.95; P-trend = 0.03), and possibly rs4073 (A) in IL8 (OR = 0.81, 95% CI 0.64-1.01, P-trend = 0.06) were associated with higher- (Gleason 7-10; N = 222), but not lower- (Gleason 2-6; N = 380) grade prostate cancer. In men with low PSA (<2 ng/mL), these higher-grade disease associations were attenuated and/or no longer significant, whereas associations with higher-grade disease were apparent for minor alleles of rs1800795 (C: OR = 0.70, 95% CI 0.51-0.94, P-trend = 0.02) and rs1800797 (A: OR = 0.72, 95% CI 0.53-0.98, P-trend = 0.04) in IL6. While some IL10 tagSNPs were associated with lower- and higher-grade prostate cancer, distributions of IL10 haplotypes did not differ, except possibly between higher-grade cases and controls among those with low PSA (P = 0.07). We did not observe an association between the studied SNPs and intraprostatic inflammation in the controls. CONCLUSION: In the PCPT finasteride arm, variation in genes involved in the immune response, including possibly IL8 and IL10 as in the placebo arm, may be associated with prostate cancer, especially higher-grade disease, but not with intraprostatic inflammation. We cannot rule out PSA-associated detection bias or chance due to multiple testing.

MBDDiff: an R package designed specifically for processing MBDcap-seq datasets.

BACKGROUND: Since its initial discovery in 1975, DNA methylation has been intensively studied and shown to be involved in various biological processes, such as development, aging and tumor progression. Many experimental techniques have been developed to measure the level of DNA methylation. Methyl-CpG binding domain-based capture followed by high-throughput sequencing (MBDCap-seq) is a widely used method for characterizing DNA methylation patterns in a genome-wide manner. However, current methods for processing MBDCap-seq datasets does not take into account of the region-specific genomic characteristics that might have an impact on the measurements of the amount of methylated DNA (signal) and background fluctuation (noise). Thus, specific software needs to be developed for MBDCap-seq experiments. RESULTS: A new differential methylation quantification algorithm for MBDCap-seq, MBDDiff, was implemented. To evaluate the performance of the MBDDiff algorithm, a set of simulated signal based on negative binomial and Poisson distribution with parameters estimated from real MBDCap-seq datasets accompanied with different background noises were generated, and then performed against a set of commonly used algorithms for MBDCap-seq data analysis in terms of area under the ROC curve (AUC), number of false discoveries and statistical power. In addition, we also demonstrated the effective of MBDDiff algorithm to a set of in-house prostate cancer samples, endometrial cancer samples published earlier, and a set of public-domain triple negative breast cancer samples to identify potential factors that contribute to cancer development and recurrence. CONCLUSIONS: In this paper we developed an algorithm, MBDDiff, designed specifically for datasets derived from MBDCap-seq. MBDDiff contains three modules: quality assessment of datasets and quantification of DNA methylation; determination of differential methylation of promoter regions; and visualization functionalities. Simulation results suggest that MBDDiff performs better compared to MEDIPS and DESeq in terms of AUC and the number of false discoveries at different levels of background noise. MBDDiff outperforms MEDIPS with increased backgrounds noise, but comparable performance when noise level is lower. By applying MBDDiff to several MBDCap-seq datasets, we were able to identify potential targets that contribute to the corresponding biological processes. Taken together, MBDDiff provides user an accurate differential methylation analysis for data generated by MBDCap-seq, especially under noisy conditions.

Adding genetic risk score to family history identifies twice as many high-risk men for prostate cancer: Results from the prostate cancer prevention trial.

BACKGROUND: While family history (FH) has been widely used to provide risk information, it captures only a small proportion of subjects with higher genetic susceptibility. Our objective is to assess whether a genetic risk score (GRS) calculated from prostate cancer (PCa) risk-associated single nucleotide polymorphisms (SNPs) can supplement FH for more effective risk stratification for PCa screening decision-making. METHODS: A GRS was calculated based on 29 PCa risk-associated SNPs for 4,528 men of European descent in the placebo arm of the Prostate Cancer Prevention Trial (PCPT). At study entry, participants were free of PCa diagnosis. Performance of FH and GRS were measured by observed detection rate of PCa and high-grade PCa (Gleason score ≥7) during the 7-year study. RESULTS: GRS was a significant predictor of PCa in men with or without a positive FH (P = 1.18 × 10(-4) and P = 4.50 × 10(-16) , respectively). Using FH alone, as expected, the 17% of men who were FH+ had a PCa detection rate that was significantly higher (29.02%) than FH- men (23.43%, P = 0.001). When both FH+ or GRS >1.4 are considered, more than twice as many men (36%) can be classified as higher risk, as evidenced by a significantly higher PCa detection rate (30.98%) than in the remaining men (20.61%, P = 5.30 × 10(-15) ). If targeting only FH+ men, four out of five PCa cases would go undetected, as would a similarly large fraction (∼80%) of high-grade PCa cases. In comparison, if targeting FH+ or GRS >1.4 men, almost half of all PCa cases would be detected, including 45% of high-grade PCa cases. CONCLUSIONS: A prostate cancer GRS can supplement family history to better identify higher risk men for targeted intervention. Prostate 76:1120-1129, 2016. © 2016 Wiley Periodicals, Inc.

Key genes involved in the immune response are generally not associated with intraprostatic inflammation in men without a prostate cancer diagnosis: Results from the prostate cancer prevention trial.

BACKGROUND: We previously reported that both intraprostatic inflammation and SNPs in genes involved in the immune response are associated with prostate cancer risk and disease grade. In the present study, we evaluated the association between these SNPs and intraprostatic inflammation in men without a prostate cancer diagnosis. METHODS: Included in this cross-sectional study were 205 white controls from a case-control study nested in the placebo arm of the Prostate Cancer Prevention Trial. We analyzed inflammation data from the review of H&E-stained prostate tissue sections from biopsies performed per protocol at the end of the trial irrespective of clinical indication, and data for 16 SNPs in key genes involved in the immune response (IL1ß, IL2, IL4, IL6, IL8, IL10, IL12(p40), IFNG, MSR1, RNASEL, TLR4, TNFA; 7 tagSNPs in IL10). Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI) for the association between carrying at least one minor allele and having at least one biopsy core (of a mean of three reviewed) with inflammation. RESULTS: None of the SNPs evaluated was statistically significantly associated with having at least one core with inflammation. However, possible inverse associations were present for carrying the minor allele of rs2069762 (G) in IL2 (OR = 0.51, 95%CI 0.25-1.02); carrying two copies of the minor allele of rs1800871 (T) of IL10 (OR = 0.29, 95%CI 0.08-1.00); and carrying the minor allele of rs486907 (A) in RNASEL (OR = 0.52, 95%CI 0.26-1.06). After creating a genetic risk score from the three SNPs possibly associated with inflammation, the odds of inflammation increased with increasing number of risk alleles (P-trend = 0.008). CONCLUSION: While our findings do not generally support a cross-sectional link between individual SNPs in key genes involved in the immune response and intraprostatic inflammation in men without a prostate cancer diagnosis, they do suggest that some of these variants when in combination may be associated with intraprostatic inflammation in benign tissue.

Association of androgen metabolism gene polymorphisms with prostate cancer risk and androgen concentrations: Results from the Prostate Cancer Prevention Trial.

BACKGROUND: Prostate cancer is highly influenced by androgens and genes. The authors investigated whether genetic polymorphisms along the androgen biosynthesis and metabolism pathways are associated with androgen concentrations or with the risk of prostate cancer or high-grade disease from finasteride treatment. METHODS: A nested case-control study from the Prostate Cancer Prevention Trial using data from men who had biopsy-proven prostate cancer (cases) and a group of biopsy-negative, frequency-matched controls was conducted to investigate the association of 51 single nucleotide polymorphisms (SNPs) in 12 genes of the androgen pathway with overall (total), low-grade, and high-grade prostate cancer incidence and serum hormone concentrations. RESULTS: There were significant associations of genetic polymorphisms in steroid 5α-reductase 1 (SRD5A1) (reference SNPs: rs3736316, rs3822430, rs1560149, rs248797, and rs472402) and SRD5A2 (rs2300700) with the risk of high-grade prostate cancer in the placebo arm of the Prostate Cancer Prevention Trial; 2 SNPs were significantly associated with an increased risk (SRD5A1 rs472402 [odds ratio, 1.70; 95% confidence interval, 1.05-2.75; Ptrend = .03] and SRD5A2 rs2300700 [odds ratio, 1.94; 95% confidence interval, 1.19-3.18; Ptrend = .01]). Eleven SNPs in SRD5A1, SRD5A2, cytochrome P450 family 1, subfamily B, polypeptide 1 (CYP1B1), and CYP3A4 were associated with modifying the mean concentrations of serum androgen and sex hormone-binding globulin; and 2 SNPs (SRD5A1 rs824811 and CYP1B1 rs10012; Ptrend < .05) consistently and significantly altered all androgen concentrations. Several SNPs (SRD5A1 rs3822430, SRD5A2 rs2300700, CYP3A43 rs800672, and CYP19 rs700519; Ptrend < .05) were significantly associated with both circulating hormone levels and prostate cancer risk. CONCLUSIONS: Germline genetic variations of androgen-related pathway genes are associated with serum androgen concentrations and the risk of prostate cancer. Further studies to examine the functional consequence of novel causal variants are warranted. Cancer 2016;122:2332-2340. © 2016 American Cancer Society.

Serial Percent Free Prostate Specific Antigen in Combination with Prostate Specific Antigen for Population Based Early Detection of Prostate Cancer.

PURPOSE: We characterized the diagnostic properties of serial percent free prostate specific antigen in relation to prostate specific antigen in a multiethnic, multiracial cohort of healthy men. MATERIALS AND METHODS: A total of 6,982 percent free prostate specific antigen and prostate specific antigen measurements were obtained from participants in a greater than 12-year Texas screening study comprising 1,625 men who never underwent biopsy, 497 who underwent 1 or more biopsies negative for prostate cancer and 61 diagnosed with prostate cancer. We evaluated the ROC AUC of percent free prostate specific antigen and the proportion of patients with fluctuating values across multiple visits determined according to 2 thresholds (less than 15% vs 25%). The proportion of cancer cases in which percent free prostate specific antigen indicated a positive test before prostate specific antigen greater than 4 ng/ml did and the number of negative biopsies that would have been spared by negative percent free prostate specific antigen test results were calculated. RESULTS: Percent free prostate specific antigen fluctuated around its threshold of less than 25% (less than 15%) in 38.3% (78.1%), 42.2% (20.9%), and 11.4% (25.7%) of patients never biopsied, and with negative and positive biopsies, respectively. At the same thresholds, percent free prostate specific antigen tested positive earlier than prostate specific antigen in 71.4% and 34.2% of cancer cases, respectively. Among men with multiple negative biopsies and PSA greater than 4 ng/ml, percent free PSA would have tested negative in 31.6% and 65.8%, respectively. CONCLUSIONS: Percent free prostate specific antigen should accompany prostate specific antigen testing to potentially spare unnecessary biopsies or detect cancer earlier. When near the threshold, both tests should be repeated due to commonly observed fluctuation.

Processing of voided urine for prostate cancer RNA biomarker analysis.

BACKGROUND: Voided urine samples have been shown to contain cells released from prostate tumors. Could good quality RNA from cells in urine be obtained from every donor for multimarker analysis? In addition, could urine donation be as simple as possible, a practical consideration for a lab test, without involving a prostate massage (as indicated for PCA3 testing), which precludes frequent collection; needing it done at a specific time of day (e.g., first or second urine); and requiring prompt processing of samples in clinics with limited molecular biology capability? METHODS: Collected urine samples were pelleted, and the RNA isolated was processed for cDNA synthesis and in vitro transcription to generate amplified sense aRNA. The resultant aRNA was rigorously analyzed for possible introduced changes. DMSO was used as a cell preservative for frozen storage of urine samples. RESULTS: Good quality aRNA was obtained for over 100 samples collected at two different institutions. The process of RNA amplification removed co-isolated DNA in some samples, which did not affect RNA amplification. Amplification did not amplify genes that were absent and produce other expression alterations. The sense aRNA could be used to generate urinary transcriptomes specific to individual patients. No chaotropic agents for RNA preservation were added to the urine samples so that the supernatant could be used for analysis of secreted protein biomarkers. The time of donation was not important since patients were seen during the entire day. DMSO was an effective cell preservative for freezing urine. CONCLUSIONS: Urinary RNA can be readily isolated and amplified for prostate cancer biomarker analysis. Individual patients had unique set of transcripts derived from their tumor.

Variation in genes involved in the immune response and prostate cancer risk in the placebo arm of the Prostate Cancer Prevention Trial.

BACKGROUND: We previously found that inflammation in benign prostate tissue is associated with an increased odds of prostate cancer, especially higher-grade disease. Since part of this link may be due to genetics, we evaluated the association between single nucleotide polymorphisms (SNPs) in immune response genes and prostate cancer in the placebo arm of the Prostate Cancer Prevention Trial. METHODS: We genotyped 16 candidate SNPs in IL1ß, IL2, IL4, IL6, IL8, IL10, IL12(p40), IFNG, MSR1, RNASEL, TLR4, and TNFA and seven tagSNPs in IL10 in 881 prostate cancer cases and 848 controls negative for cancer on an end-of-study biopsy. Cases and controls were non-Hispanic white and frequency matched on age and family history. We classified cases as lower (Gleason sum <7; N = 674) and higher (7-10; N = 172) grade, and used logistic regression to estimate odds ratios (OR) and 95% confidence intervals (CI) adjusting for age and family history. RESULTS: The minor allele (C) of rs3212227 in IL12(p40) was associated with an increased risk of total (log additive: OR = 1.30, 95%CI 1.10-1.53; P-trend = 0.0017) and lower-grade (OR = 1.36, 95%CI 1.15-1.62; P-trend = 0.0004) prostate cancer. The minor allele (A) of rs4073 in IL8 was possibly associated with a decreased risk of higher-grade (OR = 0.81, 95%CI 0.64-1.02; P-trend = 0.07), but not total disease. None of the other candidates was associated with risk. The minor alleles of IL10 tagSNPs rs1800890 (A; OR = 0.87, 95%CI: 0.75-0.99; P-trend = 0.04) and rs3021094 (C; OR = 1.31, 95%CI 1.03-1.66, P-trend = 0.03) were associated with risk; the latter also with lower- (P-trend = 0.04) and possibly higher- (P-trend = 0.06) grade disease. These patterns were similar among men with PSA <2 ng/ml at biopsy. CONCLUSION: Variation in some immune response genes may be associated with prostate cancer risk. These associations were not fully explained by PSA-associated detection bias. Our findings generally support the role of inflammation in the etiology of prostate cancer.

DNA methylation screening of primary prostate tumors identifies SRD5A2 and CYP11A1 as candidate markers for assessing risk of biochemical recurrence.

BACKGROUND: Altered DNA methylation in CpG islands of gene promoters has been implicated in prostate cancer (PCa) progression and can be used to predict disease outcome. In this study, we determine whether methylation changes of androgen biosynthesis pathway (ABP)-related genes in patients' plasma cell-free DNA (cfDNA) can serve as prognostic markers for biochemical recurrence (BCR). METHODS: Methyl-binding domain capture sequencing (MBDCap-seq) was used to identify differentially methylated regions (DMRs) in primary tumors of patients who subsequently developed BCR or not, respectively. Methylation pyrosequencing of candidate loci was validated in cfDNA samples of 86 PCa patients taken at and/or post-radical prostatectomy (RP) using univariate and multivariate prediction analyses. RESULTS: Putative DMRs in 13 of 30 ABP-related genes were found between tumors of BCR (n = 12) versus no evidence of disease (NED) (n = 15). In silico analysis of The Cancer Genome Atlas data confirmed increased DNA methylation of two loci-SRD5A2 and CYP11A1, which also correlated with their decreased expression, in tumors with subsequent BCR development. Their aberrant cfDNA methylation was also associated with detectable levels of PSA taken after patients' post-RP. Multivariate analysis of the change in cfDNA methylation at all of CpG sites measured along with patient's treatment history predicted if a patient will develop BCR with 77.5% overall accuracy. CONCLUSIONS: Overall, increased DNA methylation of SRD5A2 and CYP11A1 related to androgen biosynthesis functions may play a role in BCR after patients' RP. The correlation between aberrant cfDNA methylation and detectable PSA in post-RP further suggests their utility as predictive markers for PCa recurrence. .

Incorporation of detailed family history from the Swedish Family Cancer Database into the PCPT risk calculator.

PURPOSE: A detailed family history provides an inexpensive alternative to genetic profiling for individual risk assessment. We updated the PCPT Risk Calculator to include detailed family histories. MATERIALS AND METHODS: The study included 55,168 prostate cancer cases and 638,218 controls from the Swedish Family Cancer Database who were 55 years old or older in 1999 and had at least 1 male first-degree relative 40 years old or older and 1 female first-degree relative 30 years old or older. Likelihood ratios, calculated as the ratio of risk of observing a specific family history pattern in a prostate cancer case compared to a control, were used to update the PCPT Risk Calculator. RESULTS: Having at least 1 relative with prostate cancer increased the risk of prostate cancer. The likelihood ratio was 1.63 for 1 first-degree relative 60 years old or older at diagnosis (10.1% of cancer cases vs 6.2% of controls), 2.47 if the relative was younger than 60 years (1.5% vs 0.6%), 3.46 for 2 or more relatives 60 years old or older (1.2% vs 0.3%) and 5.68 for 2 or more relatives younger than 60 years (0.05% vs 0.009%). Among men with no diagnosed first-degree relatives the likelihood ratio was 1.09 for 1 or more second-degree relatives diagnosed with prostate cancer (12.7% vs 11.7%). Additional first-degree relatives with breast cancer, or first-degree or second-degree relatives with prostate cancer compounded these risks. CONCLUSIONS: A detailed family history is an independent predictor of prostate cancer compared to commonly used risk factors. It should be incorporated into decision making for biopsy. Compared with other costly biomarkers it is inexpensive and universally available.

Intermediate-Term Risk of Prostate Cancer is Directly Related to Baseline Prostate Specific Antigen: Implications for Reducing the Burden of Prostate Specific Antigen Screening.

PURPOSE: Prostate specific antigen screening is controversial, as a large number of men must be screened annually to achieve a benefit. We sought to determine whether baseline prostate specific antigen could reliably predict subsequent risk of prostate cancer and risk of consequential prostate cancer. MATERIALS AND METHODS: A multiethnic cohort of 2,923 prostate cancer-free men was recruited between 2000 and 2012, and followed for a median of 7.5 years. Baseline prostate specific antigen was stratified into 6 strata and relative hazards of prostate cancer detection for each prostate specific antigen stratum were estimated, adjusting for ethnicity, family history and age. RESULTS: During followup 289 patients were diagnosed with prostate cancer. Men with baseline prostate specific antigen in the lowest stratum (0.1 to 1.0 ng/ml) were at greatly reduced risk for prostate cancer during followup. This half of the cohort with prostate specific antigen 1.0 ng/ml or less were at 3.4% (95% CI 2.1, 4.5) 10-year risk of prostate cancer and 90% of the cancers were low risk. By comparison the other half were at 15% to 39% risk of cancer detection with a 39% risk in the highest stratum (3 to 10 ng/ml). CONCLUSIONS: Optimal prostate specific antigen screening frequency for men with a prostate specific antigen level of 0.1 to 1.0 ng/ml may be up to every 10 years. This approach has the potential to dramatically reduce the cost of screening, decreasing over detection of inconsequential tumors, while maintaining detection of tumors for which treatment has been proven to reduce prostate cancer mortality.

Active Surveillance is an Appropriate Management Strategy for a Proportion of Men Diagnosed with Prostate Cancer by Prostate Specific Antigen Testing.

PURPOSE: The purpose of this study was to determine the fraction of men who would qualify for active surveillance in a population based cohort diagnosed with prostate cancer. In those who qualified and subsequently underwent primary treatment with radical prostatectomy, we assessed the rate of upgrading and up staging. MATERIALS AND METHODS: SABOR is a Clinical and Epidemiologic Center of the EDRN (Early Detection Research Network), NCI (National Cancer Institute), with 3,828 men enrolled at the time of review. Of these men 320 were diagnosed with prostate cancer, of whom 281 had sufficient data for review. These 281 cases were reviewed to determine suitability for active surveillance using 2 sets of criteria. Criteria 1 were prostate specific antigen density less than 15%, 2 or fewer cores involved with cancer, Gleason score 6 or less and cancer involving 50% or less of biopsy volume. Criteria 2 were 4 or fewer cores with Gleason 3 + 3 cancer and only 1 core of Gleason 3 + 4 cancer with up to 15% of core involved with Gleason 3 + 4 disease. For those undergoing radical prostatectomy, we examined rates of up staging and upgrading. RESULTS: Of the 281 patients, 187 (67%) qualified for active surveillance under criteria 1 and/or 2. Treatment data were available on 178 patients, and 74 underwent radical prostatectomy. Using the initial biopsy, 14 men (33.1%) who met criteria 1 and 9 (25%) who met criteria 2 were upgraded and/or up staged on final pathological review. By comparison, 38% of those who did not qualify for active surveillance were upgraded and/or up staged. CONCLUSIONS: In a population based cohort, two-thirds of men diagnosed with prostate cancer qualify for active surveillance. Less restricted criteria for surveillance may be appropriate based on similar rates of upgrading/up staging at radical prostatectomy.

Improving patient prostate cancer risk assessment: Moving from static, globally-applied to dynamic, practice-specific risk calculators.

Clinical risk calculators are now widely available but have generally been implemented in a static and one-size-fits-all fashion. The objective of this study was to challenge these notions and show via a case study concerning risk-based screening for prostate cancer how calculators can be dynamically and locally tailored to improve on-site patient accuracy. Yearly data from five international prostate biopsy cohorts (3 in the US, 1 in Austria, 1 in England) were used to compare 6 methods for annual risk prediction: static use of the online US-developed Prostate Cancer Prevention Trial Risk Calculator (PCPTRC); recalibration of the PCPTRC; revision of the PCPTRC; building a new model each year using logistic regression, Bayesian prior-to-posterior updating, or random forests. All methods performed similarly with respect to discrimination, except for random forests, which were worse. All methods except for random forests greatly improved calibration over the static PCPTRC in all cohorts except for Austria, where the PCPTRC had the best calibration followed closely by recalibration. The case study shows that a simple annual recalibration of a general online risk tool for prostate cancer can improve its accuracy with respect to the local patient practice at hand.