Utility of Incorporating Genetic Variants for the Early Detection of Prostate Cancer

Genetic polymorphisms at 19q13.33 are associated with [-2]proPSA (p2PSA) levels and provide additional predictive value to prostate health index for prostate cancer

BACKGROUND

Prostate health index (phi), a derivative of [-2]proPSA (p2PSA), has shown better accuracy than prostate-specific antigen (PSA) in prostate cancer (PCa) detection. The present study was to investigate whether previously identified PSA-associated single nucleotide polymorphisms (SNPs) influence p2PSA or phi levels and lead to potential clinical utility.

METHODS

We conducted an observational prospective study with 2268 consecutive patients who underwent prostate biopsy in three tertiary medical centers from August 2013 to March 2019. Genotyping data of the 46 candidate genes with a ± 100 kb window were tested for association with p2PSA and phi levels using linear regression. Multivariable logistic regression models were performed and internally validated using repeated tenfold cross-validation. We further calculated personalized phi cutoff values based on the significant genotypes. Discriminative performance was assessed using decision curve analysis and net reclassification improvement (NRI) index.

RESULTS

We detected 11 significant variants at 19q13.33 which were p2PSA-associated independent of PCa. The most significant SNP, rs198978 in KLK2 (P_combined  = 5.73 × 10^-9 ), was also associated with phi values (P_combined  = 3.20 × 10^-6 ). Compared to the two commonly used phi cutoffs of 27.0 and 36.0, the personalized phi cutoffs had a significant NRI for PCa ranged from 5.23% to 9.70% among men carrying variant types (all p < .01).

CONCLUSION

Rs198978, is independently associated with p2PSA values, and can improve the diagnostic ability of phi for PCa using personalized cutoff values.

Battling Chemoresistance in Cancer: Root Causes and Strategies to Uproot Them

With nearly 10 million deaths, cancer is the leading cause of mortality worldwide. Along with major key parameters that control cancer treatment management, such as diagnosis, resistance to the classical and new chemotherapeutic reagents continues to be a significant problem. Intrinsic or acquired chemoresistance leads to cancer recurrence in many cases that eventually causes failure in the successful treatment and death of cancer patients. Various determinants, including tumor heterogeneity and tumor microenvironment, could cause chemoresistance through a diverse range of mechanisms. In this review, we summarize the key determinants and the underlying mechanisms by which chemoresistance appears. We then describe which strategies have been implemented and studied to combat such a lethal phenomenon in the management of cancer treatment, with emphasis on the need to improve the early diagnosis of cancer complemented by combination therapy.

The PROFILE Feasibility Study: Targeted Screening of Men With a Family History of Prostate Cancer

BACKGROUND

A better assessment of individualized prostate cancer (PrCa) risk is needed to improve screening. The use of the prostate-specific antigen (PSA) level for screening in the general population has limitations and is not currently advocated. Approximately 100 common single nucleotide polymorphisms (SNPs) have been identified that are associated with the risk of developing PrCa. The PROFILE pilot study explored the feasibility of using SNP profiling in men with a family history (FH) of PrCa to investigate the probability of detecting PrCa at prostate biopsy (PB). The primary aim of this pilot study was to determine the safety and feasibility of PrCa screening using transrectal ultrasound-guided PB with or without diffusion-weighted magnetic resonance imaging (DW-MRI) in men with a FH. A secondary aim was to evaluate the potential use of SNP profiling as a screening tool in this population.

PATIENTS AND METHODS

A total of 100 men aged 40-69 years with a FH of PrCa underwent PB, regardless of their baseline PSA level. Polygenic risk scores (PRSs) were calculated for each participant using 71 common PrCa susceptibility alleles. We treated the disease outcome at PB as the outcome variable and evaluated its associations with the PRS, PSA level, and DW-MRI findings using univariate logistic regression.

RESULTS

Of the 100 men, 25 were diagnosed with PrCa, of whom 12 (48%) had clinically significant disease. Four adverse events occurred and no deaths. The PSA level and age at study entry were associated with PrCa at PB (p = .00037 and p = .00004, respectively).

CONCLUSION

The results of the present pilot study have demonstrated that PB is a feasible and safe method of PrCa screening in men with a FH, with a high proportion of PrCa identified requiring radical treatment. It is feasible to collect data on PrCa-risk SNPs to evaluate their combined effect as a potential screening tool. A larger prospective study powered to detect statistical associations is in progress.

IMPLICATIONS FOR PRACTICE

Prostate biopsy is a feasible and safe approach to prostate cancer screening in men with a family history and detects a high proportion of prostate cancer that needs radical treatment. Calculating a polygenic risk score using prostate cancer risk single nucleotide polymorphisms could be a potential future screening tool for prostate cancer.

Improved prediction of complex diseases by common genetic markers: state of the art and further perspectives

Reliable risk assessment of frequent, but treatable diseases and disorders has considerable clinical and socio-economic relevance. However, as these conditions usually originate from a complex interplay between genetic and environmental factors, precise prediction remains a considerable challenge. The current progress in genotyping technology has resulted in a substantial increase of knowledge regarding the genetic basis of such diseases and disorders. Consequently, common genetic risk variants are increasingly being included in epidemiological models to improve risk prediction. This work reviews recent high-quality publications targeting the prediction of common complex diseases. To be included in this review, articles had to report both, numerical measures of prediction performance based on traditional (non-genetic) risk factors, as well as measures of prediction performance when adding common genetic variants to the model. Systematic PubMed-based search finally identified 55 eligible studies. These studies were compared with respect to the chosen approach and methodology as well as results and clinical impact. Phenotypes analysed included tumours, diabetes mellitus, and cardiovascular diseases. All studies applied one or more statistical measures reporting on calibration, discrimination, or reclassification to quantify the benefit of including SNPs, but differed substantially regarding the methodological details that were reported. Several examples for improved risk assessments by considering disease-related SNPs were identified. Although the add-on benefit of including SNP genotyping data was mostly moderate, the strategy can be of clinical relevance and may, when being paralleled by an even deeper understanding of disease-related genetics, further explain the development of enhanced predictive and diagnostic strategies for complex diseases.

Incorporating Known Genetic Variants Does Not Improve the Accuracy of PSA Testing to Identify High Risk Prostate Cancer on Biopsy

INTRODUCTION

Prostate-specific antigen (PSA) testing is a widely accepted screening method for prostate cancer, but with low specificity at thresholds giving good sensitivity. Previous research identified four single nucleotide polymorphisms (SNPs) principally associated with circulating PSA levels rather than with prostate cancer risk (TERT rs2736098, FGFR2 rs10788160, TBX3 rs11067228, KLK3 rs17632542). Removing the genetic contribution to PSA levels may improve the ability of the remaining biologically-determined variation in PSA to discriminate between high and low risk of progression within men with identified prostate cancer. We investigate whether incorporating information on the PSA-SNPs improves the discrimination achieved by a single PSA threshold in men with raised PSA levels.

MATERIALS AND METHODS

Men with PSA between 3-10 ng/mL and histologically-confirmed prostate cancer were categorised as high or low risk of progression (Low risk: Gleason score≤6 and stage T1-T2a; High risk: Gleason score 7-10 or stage T2C). We used the combined genetic effect of the four PSA-SNPs to calculate a genetically corrected PSA risk score. We calculated the Area under the Curve (AUC) to determine how well genetically corrected PSA risk scores distinguished men at high risk of progression from low risk men.

RESULTS

The analysis includes 868 men with prostate cancer (Low risk: 684 (78.8%); High risk: 184 (21.2%)). Receiver operating characteristic (ROC) curves indicate that including the 4 PSA-SNPs does not improve the performance of measured PSA as a screening tool for high/low risk prostate cancer (measured PSA level AUC = 59.5% (95% CI: 54.7,64.2) vs additionally including information from the 4 PSA-SNPs AUC = 59.8% (95% CI: 55.2,64.5) (p-value = 0.40)).

CONCLUSION

We demonstrate that genetically correcting PSA for the combined genetic effect of four PSA-SNPs, did not improve discrimination between high and low risk prostate cancer in men with raised PSA levels (3-10 ng/mL). Replication and gaining more accurate estimates of the effects of the 4 PSA-SNPs and additional variants associated with PSA levels and not prostate cancer could be obtained from subsequent GWAS from larger prospective studies.

Multigene panels in prostate cancer risk assessment: a systematic review

PURPOSE

Single-nucleotide polymorphism (SNP) panel tests have been proposed for use in the detection of, and prediction of risk for, prostate cancer and as prognostic indicator in affected men. A systematic review was undertaken to address three research questions to evaluate the analytic validity, clinical validity, clinical utility, and prognostic validity of SNP-based panels.

METHODS

Data sources comprised MEDLINE, Cochrane CENTRAL, Cochrane Database of Systematic Reviews, and EMBASE; these were searched from inception to April 2013. The gray-literature searches included contact with manufacturers. Eligible studies included English-language studies evaluating commercially available SNP panels. Study selection and risk of bias assessment were undertaken by two independent reviewers.

RESULTS

Twenty-one studies met eligibility criteria. All focused on clinical validity and evaluated 18 individual panels with 2 to 35 SNPs. All had poor discriminative ability (overall area under receiver-operator characteristic curves, 58-74%; incremental gain resulting from inclusion of SNP data, 2.5-11%) for predicting risk of prostate cancer and/or distinguishing between aggressive and asymptomatic/latent disease. The risk of bias of the studies, as assessed by the Newcastle Ottawa Scale (NOS) and Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tools, was moderate.

CONCLUSION

The evidence on currently available SNP panels is insufficient to assess analytic validity, and at best the panels assessed would add a small and clinically unimportant improvement to factors such as age and family history in risk stratification (clinical validity). No evidence on the clinical utility of current panels is available.Genet Med 18 6, 535-544.

Incorporation of personal single nucleotide polymorphism (SNP) data into a national level electronic health record for disease risk assessment, part 3: an evaluation of SNP incorporated national health information system of Turkey for prostate cancer

Background

A personalized medicine approach provides opportunities for predictive and preventive medicine. Using genomic, clinical, environmental, and behavioral data, the tracking and management of individual wellness is possible. A prolific way to carry this personalized approach into routine practices can be accomplished by integrating clinical interpretations of genomic variations into electronic medical records (EMRs)/electronic health records (EHRs). Today, various central EHR infrastructures have been constituted in many countries of the world, including Turkey.

Objective

As an initial attempt to develop a sophisticated infrastructure, we have concentrated on incorporating the personal single nucleotide polymorphism (SNP) data into the National Health Information System of Turkey (NHIS-T) for disease risk assessment, and evaluated the performance of various predictive models for prostate cancer cases. We present our work as a three part miniseries: (1) an overview of requirements, (2) the incorporation of SNP data into the NHIS-T, and (3) an evaluation of SNP data incorporated into the NHIS-T for prostate cancer.

Methods

In the third article of this miniseries, we have evaluated the proposed complementary capabilities (ie, knowledge base and end-user application) with real data. Before the evaluation phase, clinicogenomic associations about increased prostate cancer risk were extracted from knowledge sources, and published predictive genomic models assessing individual prostate cancer risk were collected. To evaluate complementary capabilities, we also gathered personal SNP data of four prostate cancer cases and fifteen controls. Using these data files, we compared various independent and model-based, prostate cancer risk assessment approaches.

Results

Through the extraction and selection processes of SNP-prostate cancer risk associations, we collected 209 independent associations for increased risk of prostate cancer from the studied knowledge sources. Also, we gathered six cumulative models and two probabilistic models. Cumulative models and assessment of independent associations did not have impressive results. There was one of the probabilistic, model-based interpretation that was successful compared to the others. In envirobehavioral and clinical evaluations, we found that some of the comorbidities, especially, would be useful to evaluate disease risk. Even though we had a very limited dataset, a comparison of performances of different disease models and their implementation with real data as use case scenarios helped us to gain deeper insight into the proposed architecture.

Conclusions

In order to benefit from genomic variation data, existing EHR/EMR systems must be constructed with the capability of tracking and monitoring all aspects of personal health status (genomic, clinical, environmental, etc) in 24/7 situations, and also with the capability of suggesting evidence-based recommendations. A national-level, accredited knowledge base is a top requirement for improved end-user systems interpreting these parameters. Finally, categorization using similar, individual characteristics (SNP patterns, exposure history, etc) may be an effective way to predict disease risks, but this approach needs to be concretized and supported with new studies.

Genetic variation in prostate-specific antigen-detected prostate cancer and the effect of control selection on genetic association studies

BACKGROUND

Only a minority of the genetic components of prostate cancer risk have been explained. Some observed associations of SNPs with prostate cancer might arise from associations of these SNPs with circulating prostate-specific antigen (PSA) because PSA values are used to select controls.

METHODS

We undertook a genome-wide association study (GWAS) of screen-detected prostate cancer (ProtecT: 1,146 cases and 1,804 controls); meta-analyzed the results with those from the previously published UK Genetic Prostate Cancer Study (1,854 cases and 1,437 controls); investigated associations of SNPs with prostate cancer using either "low" (PSA < 0.5 ng/mL) or "high" (PSA ≥ 3 ng/mL, biopsy negative) PSA controls; and investigated associations of SNPs with PSA.

RESULTS

The ProtecT GWAS confirmed previously reported associations of prostate cancer at three loci: 10q11.23, 17q24.3, and 19q13.33. The meta-analysis confirmed associations of prostate cancer with SNPs near four previously identified loci (8q24.21,10q11.23, 17q24.3, and 19q13.33). When comparing prostate cancer cases with low PSA controls, alleles at genetic markers rs1512268, rs445114, rs10788160, rs11199874, rs17632542, rs266849, and rs2735839 were associated with an increased risk of prostate cancer, but the effect-estimates were attenuated to the null when using high PSA controls (Pheterogeneity in effect-estimates < 0.04). We found a novel inverse association of rs9311171-T with circulating PSA.

CONCLUSIONS

Differences in effect-estimates for prostate cancer observed when comparing low versus high PSA controls may be explained by associations of these SNPs with PSA.

IMPACT

These findings highlight the need for inferences from genetic studies of prostate cancer risk to carefully consider the influence of control selection criteria.

Predictions for the future of kallikrein-related peptidases in molecular diagnostics

Kallikrein-related peptidases (KLKs) form a cancer-related ensemble of serine proteases. This multigene family hosts the most widely used cancer biomarker that is PSA-KLK3, with millions of tests performed annually worldwide. The present report provides an overview of the biomarker potential of the extended KLK family (KLK1-KLK15) in various disease settings and envisages approaches that could lead to additional KLK-driven applications in future molecular diagnostics. Particular focus is given on the inclusion of KLKs into multifaceted cancer biomarker panels that provide enhanced diagnostic, prognostic and/or predictive accuracy in several human malignancies. Such panels have been described so far for prostate, ovarian, lung and colorectal cancers. The role of KLKs as biomarkers in non-malignant disease settings, such as Alzheimer's disease and multiple sclerosis, is also commented upon. Predictions are given on the challenges and future directions regarding clinically oriented KLK research.

Human kallikrein-2 gene and protein expression predicts prostate cancer at repeat biopsy

Purpose

The human kallikrein-2 (hK2) protein and two single nucleotide polymorphism (SNPs) (rs2664155, rs198977) of the gene are associated with prostate cancer risk. We examined whether hK2 protein and gene SNPs predict prostate cancer at the time of repeat biopsy.

Methods

We prospectively offered a repeat biopsy to men with a negative prostate biopsy performed for a PSA >4.0 ng/mL or abnormal Digital Rectal Exam (DRE) between 2001–2005. We genotyped and measured serum hK2 levels in 941 men who underwent a repeat prostate biopsy. Logistic regression analyses were conducted to determine the significance of KLK2 SNPs and hK2 levels for predicting cancer at repeat biopsy.

Results

Of the 941 patients, 180 (19.1%) were found to have cancer. The rs198977 SNP was positively associated with cancer at repeat biopsy (OR variant T allele = 1.8, 95% CI: 1.04-3.13, p = 0.049). When combined, the odds ratio for prostate cancer for patients with high hK2 levels and the variant T-allele of rs198977 was 3.77 (95% CI: 1.94-7.32, p < 0.0001), compared to patients with low hK2 levels and the C-allele. The addition of hK2 levels and KLK2 rs198977 to the baseline predictive model did not significantly increase the area under the curve from a baseline model of 0.67 to 0.69 (p = 0.6).

Conclusions

The KLK2 SNP rs198977 was positively associated with hK2 levels and predicts prostate cancer at the time of repeat prostate biopsy. Further characterization of the KLK2 gene will be needed to determine its clinical utility.

Prostate cancer in young men: an important clinical entity

Prostate cancer is considered a disease of older men (aged >65 years), but today over 10% of new diagnoses in the USA occur in young men aged ≤55 years. Early-onset prostate cancer, that is prostate cancer diagnosed at age ≤55 years, differs from prostate cancer diagnosed at an older age in several ways. Firstly, among men with high-grade and advanced-stage prostate cancer, those diagnosed at a young age have a higher cause-specific mortality than men diagnosed at an older age, except those over age 80 years. This finding suggests that important biological differences exist between early-onset prostate cancer and late-onset disease. Secondly, early-onset prostate cancer has a strong genetic component, which indicates that young men with prostate cancer could benefit from evaluation of genetic risk. Furthermore, although the majority of men with early-onset prostate cancer are diagnosed with low-risk disease, the extended life expectancy of these patients exposes them to long-term effects of treatment-related morbidities and to long-term risk of disease progression leading to death from prostate cancer. For these reasons, patients with early-onset prostate cancer pose unique challenges, as well as opportunities, for both research and clinical communities. Current data suggest that early-onset prostate cancer is a distinct phenotype-from both an aetiological and clinical perspective-that deserves further attention.

The role of single nucleotide polymorphisms in predicting prostate cancer risk and therapeutic decision making

Prostate cancer (PCa) is a major health care problem because of its high prevalence, health-related costs, and mortality. Epidemiological studies have suggested an important role of genetics in PCa development. Because of this, an increasing number of single nucleotide polymorphisms (SNPs) had been suggested to be implicated in the development and progression of PCa. While individual SNPs are only moderately associated with PCa risk, in combination, they have a stronger, dose-dependent association, currently explaining 30% of PCa familial risk. This review aims to give a brief overview of studies in which the possible role of genetic variants was investigated in clinical settings. We will highlight the major research questions in the translation of SNP identification into clinical practice.

The genetic epidemiology of prostate cancer and its clinical implications

Worldwide, familial and epidemiological studies have generated considerable evidence of an inherited component to prostate cancer. Indeed, rare highly penetrant genetic mutations have been implicated. Genome-wide association studies (GWAS) have also identified 76 susceptibility loci associated with prostate cancer risk, which occur commonly but are of low penetrance. However, these mutations interact multiplicatively, which can result in substantially increased risk. Currently, approximately 30% of the familial risk is due to such variants. Evaluating the functional aspects of these variants would contribute to our understanding of prostate cancer aetiology and would enable population risk stratification for screening. Furthermore, understanding the genetic risks of prostate cancer might inform predictions of treatment responses and toxicities, with the goal of personalized therapy. However, risk modelling and clinical translational research are needed before we can translate risk profiles generated from these variants into use in the clinical setting for targeted screening and treatment.

Cumulative risk impact of five genetic variants associated with papillary thyroid carcinoma

BACKGROUND

Two recent genome-wide association studies (GWASs) identified five single nucleotide polymorphisms (SNPs; rs965513, rs944289, rs966423, rs2439302, and rs116909374) associated with papillary thyroid carcinoma (PTC). Each variant showed highly significant but moderate to low disease risk. Here we assessed the cumulative risk and predictive value of the five SNPs.

METHODS

We genotyped two cohorts of individuals, 747 PTC cases and 1047 controls from Ohio and 1795 PTC cases and 2090 controls from Poland. Cumulative genetic risk scores were calculated using unweighted and weighted approaches.

RESULTS

All five SNPs showed significant association with PTC. The average cumulative risk score in cases was significantly higher than in controls (p<2.2×10(-16)). Each additional risk allele increased the risk of having PTC by 1.51 [95% confidence interval (CI) 1.4, 1.64] in Ohio and by 1.35 [95% CI 1.27, 1.44] in Poland. An analysis was performed weighing risk alleles by effect size and assigning individuals to three weighted risk score groups, low (≤2), medium (2-5), and high (>5). Individuals in the high group were significantly more susceptible to PTC compared with individuals in the low group with an odds ratio of 8.7 [95% CI 5.8, 13.3] in Ohio and 4.24 [95% CI 3.10, 5.84] in Poland. Almost identical results were obtained when follicular variant PTCs and microPTCs were omitted. These five SNPs explained 11% of the familial risk of thyroid cancer in the Ohio cohort and 6% in the Polish cohort.

CONCLUSION

As the genetic risk score increases, the risk of having PTC increases. However, the predictive power of the cumulative effect of these five variants is only moderately high and clinical use may not be feasible until more variants are detected.

The presence of prostate cancer at biopsy is predicted by a number of genetic variants

Several single nucleotide polymorphisms (SNPs) have been associated with an elevated risk of prostate cancer risk. It is not established if they are useful in predicting the presence of prostate cancer at biopsy or if they can be used to define a low-risk group of men. In this study, 4,548 men underwent a prostate biopsy because of an elevated prostate specific antigen (PSA; ≥4 ng/mL) or an abnormal digital rectal examination (DRE). All men were genotyped for 11 selected SNPs. The effect of each SNP, alone and in combination, on prostate cancer prevalence was studied. Of 4,548 men: 1,834 (40.3%) were found to have cancer. A positive association with prostate cancer was seen for 5 of 11 SNPs studied (rs1800629, rs1859962, rs1447295, rs4430796, rs11228565). The cancer detection rate rose with the number of SNP risk alleles from 29% for men with no variant to 63% for men who carried seven or more risk alleles (OR = 4.2; p = 0.002). The SNP data did not improve the predictive power of clinical factors (age, PSA and DRE) for detecting prostate cancer (AUC: 0.726 vs. 0.735; p = 0.4). We were unable to define a group of men with a sufficiently low prevalence of prostate cancer that a biopsy might have been avoided. In conclusion, our data do not support the routine use of SNP polymorphisms as an adjunct test to be used on the context of prostate biopsy for Polish men with an abnormal screening test.

Prostate cancer genomics by high-throughput technologies: genome-wide association study and sequencing analysis

Prostate cancer (PC) is the most common malignancy in males. It is evident that genetic factors at both germline and somatic levels play critical roles in prostate carcinogenesis. Recently, genome-wide association studies (GWAS) by high-throughput genotyping technology have identified more than 70 germline variants of various genes or chromosome loci that are significantly associated with PC susceptibility. They include multiple 8q24 loci, prostate-specific genes, and metabolism-related genes. Somatic alterations in PC genomes have been explored by high-throughput sequencing technologies such as whole-genome sequencing and RNA sequencing, which have identified a variety of androgen-responsive events and fusion transcripts represented by E26 transformation-specific (ETS) gene fusions. Recent innovations in high-throughput genomic technologies have enabled us to analyze PC genomics more comprehensively, more precisely, and on a larger scale in multiple ethnic groups to increase our understanding of PC genomics and biology in germline and somatic studies, which can ultimately lead to personalized medicine for PC diagnosis, prevention, and therapy. However, these data indicate that the PC genome is more complex and heterogeneous than we expected from GWAS and sequencing analyses.

A genetic score can identify men at high risk for prostate cancer among men with prostate-specific antigen of 1-3 ng/ml

BACKGROUND

The diagnostic performance of a genetic score based on single nucleotide polymorphisms (SNPs) is unknown in the prostate-specific antigen (PSA) range of 1-3 ng/ml. A substantial proportion of men in this PSA span have prostate cancer (PCa), but biomarkers to determine who should undergo a prostate biopsy are lacking.

OBJECTIVE

To evaluate whether a genetic risk score identifies men in the PSA range of 1-3 ng/ml who are at higher risk for PCa.

DESIGN, SETTING, AND PARTICIPANTS

Men aged 50-69 yr with PSA 1-3 ng/ml and without a previous prostate biopsy were selected from the STHLM2 cohort. Of 2696 men, 49 SNPs were genotyped, and a polygenic risk score was calculated. Of these men, 860 were invited according to risk score, and 172 underwent biopsy.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The risk of PCa was assessed using univariate and multivariate logistic regression analysis.

RESULTS AND LIMITATIONS

PCa was diagnosed in 47 of 172 participants (27%), with Gleason sum 6 in 36 of 47 men (77%) and Gleason sum ≥7 in 10 of 47 men (21%); one man had intraductal cancer. The genetic score was a significant predictor of a positive biopsy (p=0.028), even after adjusting for PSA, ratio of free to total PSA, prostate volume, age, and family history. There was an increase in the odds ratio of 1.60 (95% confidence interval, 1.05-2.45) with increasing genetic risk score. The absolute risk difference of positive biopsy was 19 percentage points, comparing the high and low genetic risk group (37% vs 18%).

CONCLUSIONS

A risk score based on SNPs predicts biopsy outcome in previously unbiopsied men with PSA 1-3 ng/ml. Introducing a genetic-based risk stratification tool can increase the proportion of men being classified in line with their true risk of PCa.

Genetic variants associated with predisposition to prostate cancer and potential clinical implications

Prostate cancer is the commonest cancer in the developed world. There is an inherited component to this disease as shown in familial and twin studies. However, the discovery of these variants has been difficult. The emergence of genome-wide association studies has led to the identification of over 46 susceptibility loci. Their clinical utility to predict risk, response to treatment, or treatment toxicity, remains undefined. Large consortia are needed to achieve adequate statistical power to answer these genetic-clinical and genetic-epidemiological questions. International collaborations are currently underway to link genetic with clinical/epidemiological data to develop risk prediction models, which could direct screening and treatment programs.

Concordance of tumor differentiation among brothers with prostate cancer

BACKGROUND

Genetic factors seem to be of greater importance in prostate cancer than in other forms of cancer. Studies have suggested familial concordance in survival, but the extent to which that is due to tumor characteristics is not known.

OBJECTIVE

We hypothesized that a brother of an index case with prostate cancer is at particularly increased risk of prostate cancer with the same tumor differentiation as the index case.

DESIGN, SETTING AND PARTICIPANTS

We identified 21,930 brothers of index cases with prostate cancer in the Prostate Cancer Data Base Sweden and followed them up for incidence of prostate cancer.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The relative risk of Gleason score-specific prostate cancer in the cohort of brothers was estimated by using the standardized incidence ratio (SIR) stratified by Gleason score of the index case. We estimated 95% confidence intervals (CIs) assuming a Poisson distribution.

RESULTS AND LIMITATIONS

Among brothers of index cases with Gleason score 8-10 cancer, the SIR was 2.53 (95% CI, 1.97-3.21) for a Gleason score 2-6 cancer and 4.00 (95% CI, 2.63-5.82) for a Gleason score 8-10 cancer. SIR for Gleason score 2-6 cancer among brothers decreased with time since the date of the index cases' diagnoses, whereas the risk of Gleason 8-10 cancer increased over time for brothers of index cases with Gleason 8-10 cancer (p for trend = 0.009).

CONCLUSIONS

Brothers of men with high-grade prostate cancer are at particularly increased risk of high-grade prostate cancer. Likewise, there is a concordance of less malignant prostate cancers within families. These findings may have direct clinical relevance for counseling men with a family history of prostate cancer.

Prostate cancer genomics, biology, and risk assessment through genome-wide association studies

Prostate cancer (PC) is the most common malignancy observed in men. It is evident that genetic factors play some important roles in PC etiology. Recently, genome-wide association studies in diverse ethnic groups have identified more than 40 germline variants of various genes or chromosomal loci that are significantly associated with PC susceptibility, including multiple 8q24 loci, prostate-specific genes, metabolic and hormone-related genes, and many regions where no coding gene is annotated. However, there are only a few variants or genes for which biological significance or functions have been elucidated so far. The greatest challenge related to genome-wide association studies loci in prostate genomics is to understand the functional consequences of these PC-associated loci and their involvement in PC biology and carcinogenesis. There have been attempts to determine PC risk estimations by combining multiple PC-associated variants for clinical tests, and these can identify a very minor population with high risk of PC. However, they cannot distinguish risk of aggressive PC from that of non-aggressive PC. Further identification of PC-susceptibility loci in larger genome-wide association studies cohorts and biological insights gained from such functional analyses have the potential to translate into clinical benefits, including the development of reliable biomarkers, risk estimation, and effective strategies for screening and prevention of PC.

New variants at 10q26 and 15q21 are associated with aggressive prostate cancer in a genome-wide association study from a prostate biopsy screening cohort

PURPOSE

To identify and examine polymorphisms of genes associated with aggressive and clinical significant forms of prostate cancer among a screening cohort.

EXPERIMENTAL DESIGN

We conducted a genome-wide association study among patients with aggressive forms of prostate cancer and biopsy-proven normal controls ascertained from a prostate cancer screening program. We then examined significant associations of specific polymorphisms among a prostate cancer screened cohort to examine their predictive ability in detecting prostate cancer.

RESULTS

We found significant associations between aggressive prostate cancer and five single nucleotide polymorphisms (SNPs) in the 10q26 (rs10788165, rs10749408, and rs10788165, p value for association 1.3 × 10(-10 ) to 3.2 × 10(-11) ) and 15q21 (rs4775302 and rs1994198, p values for association 3.1 × 10(-8 ) to 8.2 × 10(-9)) regions. Results of a replication study done in 3439 patients undergoing a prostate biopsy, revealed certain combinations of these SNPs to be significantly associated not only with prostate cancer but with aggressive forms of prostate cancer using an established classification criterion for prostate cancer progression (odds ratios for intermediate to high-risk disease 1.8-3.0, p value 0.003-0.001). These SNP combinations were also important clinical predictors for prostate cancer detection based on nomogram analysis that assesses prostate cancer risk.

CONCLUSIONS

Five SNPs were found to be associated with aggressive forms of prostate cancer. We demonstrated potential clinical applications of these associations.

Evaluation of multiple risk-associated single nucleotide polymorphisms versus prostate-specific antigen at baseline to predict prostate cancer in unscreened men

BACKGROUND

Although case-control studies have identified numerous single nucleotide polymorphisms (SNPs) associated with prostate cancer, the clinical role of these SNPs remains unclear.

OBJECTIVE

Evaluate previously identified SNPs for association with prostate cancer and accuracy in predicting prostate cancer in a large prospective population-based cohort of unscreened men.

DESIGN, SETTING, AND PARTICIPANTS

This study used a nested case-control design based on the Malmö Diet and Cancer cohort with 943 men diagnosed with prostate cancer and 2829 matched controls. Blood samples were collected between 1991 and 1996, and follow-up lasted through 2005.

MEASUREMENTS

We genotyped 50 SNPs, analyzed prostate-specific antigen (PSA) in blood from baseline, and tested for association with prostate cancer using the Cochran-Mantel-Haenszel test. We further developed a predictive model using SNPs nominally significant in univariate analysis and determined its accuracy to predict prostate cancer.

RESULTS AND LIMITATIONS

Eighteen SNPs at 10 independent loci were associated with prostate cancer. Four independent SNPs at four independent loci remained significant after multiple test correction (p<0.001). Seven SNPs at five independent loci were associated with advanced prostate cancer defined as clinical stage≥T3 or evidence of metastasis at diagnosis. Four independent SNPs were associated with advanced or aggressive cancer defined as stage≥T3, metastasis, Gleason score≥8, or World Health Organization grade 3 at diagnosis. Prostate cancer risk prediction with SNPs alone was less accurate than with PSA at baseline (area under the curve of 0.57 vs 0.79), with no benefit from combining SNPs with PSA. This study is limited by our reliance on clinical diagnosis of prostate cancer; there are likely undiagnosed cases among our control group.

CONCLUSIONS

Only a few previously reported SNPs were associated with prostate cancer risk in the large prospective Diet and Cancer cohort in Malmö, Sweden. SNPs were less useful in predicting prostate cancer risk than PSA at baseline.

Genetic correction of PSA values using sequence variants associated with PSA levels

Measuring serum levels of the prostate-specific antigen (PSA) is the most common screening method for prostate cancer. However, PSA levels are affected by a number of factors apart from neoplasia. Notably, around 40% of the variability of PSA levels in the general population is accounted for by inherited factors, suggesting that it may be possible to improve both sensitivity and specificity by adjusting test results for genetic effects. To search for sequence variants that associate with PSA levels, we performed a genome-wide association study and follow-up analysis using PSA information from 15,757 Icelandic and 454 British men not diagnosed with prostate cancer. Overall, we detected a genome-wide significant association between PSA levels and single-nucleotide polymorphisms (SNPs) at six loci: 5p15.33 (rs2736098), 10q11 (rs10993994), 10q26 (rs10788160), 12q24 (rs11067228), 17q12 (rs4430796), and 19q13.33 [rs17632542 (KLK3: I179T)], each with P(combined) <3 × 10(-10). Among 3834 men who underwent a biopsy of the prostate, the 10q26, 12q24, and 19q13.33 alleles that associate with high PSA levels are associated with higher probability of a negative biopsy (odds ratio between 1.15 and 1.27). Assessment of association between the six loci and prostate cancer risk in 5325 cases and 41,417 controls from Iceland, the Netherlands, Spain, Romania, and the United States showed that the SNPs at 10q26 and 12q24 were exclusively associated with PSA levels, whereas the other four loci also were associated with prostate cancer risk. We propose that a personalized PSA cutoff value, based on genotype, should be used when deciding to perform a prostate biopsy.

Improvement of prostate cancer detection by integrating the PSA test with miRNA expression profiling

Prostate-specific antigen (PSA) test is limited in prostate cancer diagnosis due to its inaccuracy. A new approach which integrates the PSA test with miRNA profiling was investigated to improve prostate cancer diagnosis. Six prostate cancer-related miRNAs (miR-16, -21, -34c, -101, -125b, -141) were tested in five cultured prostate cell lines and 20 human prostate specimens. We found that the miRNA expression profiles were significantly different between nontumorigenic and tumorigenic cell lines and specimens. Positive predictive value analysis of prostate cancer was increased from 40% to 87.5% by integrating patient PSA blood levels with miR-21 and miR-141 profiles.

Impact of genotyping on outcome of prostatic biopsies: a multicenter prospective study

Single nucleotide polymorphisms (SNPs) have been associated with prostate cancer (PCa) risk and tumor aggressiveness in retrospective studies. To assess the value of genotyping in a clinical setting, we evaluated the correlation between three genotypes (rs1447295 and rs6983267[8q24] and rs4054823[17p12]) and prostatic biopsy outcome prospectively in a French population of Caucasian men. Five hundred ninety-eight patients with prostatic-specific antigen (PSA) >4 ng/mL or abnormal digital rectal examination (DRE) participated in this prospective, multicenter study. Age, familial history of PCa, body mass index (BMI), data of DRE, International Prostate Symptom Score (I-PSS) score, PSA value and prostatic volume were collected prospectively before prostatic biopsy. Correlation between genotypes and biopsy outcome (positive or negative) and Gleason score (≤6 or >6) were studied by univariate and multivariable analysis. rs1447295 and rs6983267 risk variants were found to be associated with the presence of PCa in univariate analysis. rs6983267 genotype remained significantly linked to a positive biopsy (odds ratio [OR] = 1.66, 95% confidence interval [CI]: 1.06-2.59, P = 0.026) in multivariable analysis, but rs1447295 genotype did not (OR = 1.47, 95% CI: 0.89-2.43, P = 0.13).When biopsy outcome was stratified according to Gleason score, risk variants of rs1447295 were associated with aggressive disease (Gleason score ≥7) in univariate and multivariable analysis (OR = 2.05 95% CI: 1.10-3.79, P = 0.023). rs6983267 GG genotype was not related to aggressiveness. The results did not reach significance concerning rs4054823 for any analysis. This inaugural prospective evaluation thus confirmed potential usefulness of genotyping PCa assessment. Ongoing clinical evaluation of larger panels of SNPs will detail the actual impact of genetic markers on clinical practice.

The evolving role of familial history for prostate cancer

BACKGROUND

family history of prostate cancer is a risk factor for prostate cancer occurrence. Differently from other neoplasms no major predisposing gene has been identified.

MATERIAL AND METHODS

this review article presents the controversial results of studies about the prognostic and predictive role of family history in prostate cancer, reports the discovered predisposing genes, and biologic and pathologic findings.

RESULTS

mortality from PC remains a significant health care problem, but no trial investigated if it changed in presence of positive family history. The largest family study yet published concluded that men with family history are diagnosed and die at earlier ages than men without it. However, it failed to stress the prognostic value of family history. Genome-wide association studies of prostate cancer have identified a number of genetic variants at different loci in different populations. Prostate neoplasms of patients with positive family history exhibit a different pattern of expression of genes related with estrogen and androgen metabolism within the tumor. High-penetrance and low-penetrance genes in diagnosis and prognosis of prostate cancer, difficulties to define a classification and to quantify relative risks of single genes, documented gene-environment interactions are discussed.

CONCLUSION

family history stands for both shared genetic and environmental factors and their interaction. The availability of prostate-specific antigen test could explain partly the high familial risk, among brothers or shortly after the diagnosis of prostate cancer. Polymorphisms in genes associated with prostate cancer probably represent the most part of familial prostate cancer burden. An increasing knowledge of disregulated cellular pathways of lethal prostate cancer could define which of all genetic alterations have a role in defining new preventive and therapeutic strategies.

Assessment of clinical validity of a breast cancer risk model combining genetic and clinical information

BACKGROUND

The Gail model is widely used for the assessment of risk of invasive breast cancer based on recognized clinical risk factors. In recent years, a substantial number of single-nucleotide polymorphisms (SNPs) associated with breast cancer risk have been identified. However, it remains unclear how to effectively integrate clinical and genetic risk factors for risk assessment.

METHODS

Seven SNPs associated with breast cancer risk were selected from the literature and genotyped in white non-Hispanic women in a nested case-control cohort of 1664 case patients and 1636 control subjects within the Women's Health Initiative Clinical Trial. SNP risk scores were computed based on previously published odds ratios assuming a multiplicative model. Combined risk scores were calculated by multiplying Gail risk estimates by the SNP risk scores. The independence of Gail risk and SNP risk was evaluated by logistic regression. Calibration of relative risks was evaluated using the Hosmer-Lemeshow test. The performance of the combined risk scores was evaluated using receiver operating characteristic curves. The net reclassification improvement (NRI) was used to assess improvement in classification of women into low (<1.5%), intermediate (1.5%-2%), and high (>2%) categories of 5-year risk. All tests of statistical significance were two-sided.

RESULTS

The SNP risk score was nearly independent of Gail risk. There was good agreement between predicted and observed SNP relative risks. In the analysis for receiver operating characteristic curves, the combined risk score was more discriminating, with area under the curve of 0.594 compared with area under the curve of 0.557 for Gail risk alone (P < .001). Classification also improved for 5.6% of case patients and 2.9% of control subjects, showing an NRI value of 0.085 (P = 1.0 × 10⁻⁵). Focusing on women with intermediate Gail risk resulted in an improved NRI of 0.195 (P = 8.6 × 10⁻⁵).

CONCLUSIONS

Combining validated common genetic risk factors with clinical risk factors resulted in modest improvement in classification of breast cancer risks in white non-Hispanic postmenopausal women. Classification performance was further improved by focusing on women at intermediate risk.

Improved prediction of biochemical recurrence after radical prostatectomy by genetic polymorphisms

PURPOSE

Single nucleotide polymorphisms are inherited genetic variations that can predispose or protect individuals against clinical events. We hypothesized that single nucleotide polymorphism profiling may improve the prediction of biochemical recurrence after radical prostatectomy.

MATERIALS AND METHODS

We performed a retrospective, multi-institutional study of 703 patients treated with radical prostatectomy for clinically localized prostate cancer who had at least 5 years of followup after surgery. All patients were genotyped for 83 prostate cancer related single nucleotide polymorphisms using a low density oligonucleotide microarray. Baseline clinicopathological variables and single nucleotide polymorphisms were analyzed to predict biochemical recurrence within 5 years using stepwise logistic regression. Discrimination was measured by ROC curve AUC, specificity, sensitivity, predictive values, net reclassification improvement and integrated discrimination index.

RESULTS

The overall biochemical recurrence rate was 35%. The model with the best fit combined 8 covariates, including the 5 clinicopathological variables prostate specific antigen, Gleason score, pathological stage, lymph node involvement and margin status, and 3 single nucleotide polymorphisms at the KLK2, SULT1A1 and TLR4 genes. Model predictive power was defined by 80% positive predictive value, 74% negative predictive value and an AUC of 0.78. The model based on clinicopathological variables plus single nucleotide polymorphisms showed significant improvement over the model without single nucleotide polymorphisms, as indicated by 23.3% net reclassification improvement (p = 0.003), integrated discrimination index (p <0.001) and likelihood ratio test (p <0.001). Internal validation proved model robustness (bootstrap corrected AUC 0.78, range 0.74 to 0.82). The calibration plot showed close agreement between biochemical recurrence observed and predicted probabilities.

CONCLUSIONS

Predicting biochemical recurrence after radical prostatectomy based on clinicopathological data can be significantly improved by including patient genetic information.

Molecular biomarkers in prostate cancer

BACKGROUND

After more than two decades of clinical use, serum prostate-specific antigen (PSA) has increased the early diagnosis of prostate cancer, detecting the disease even when small volumes are present. Although stage migration of prostate cancer has occurred, PSA has well-known limitations, despite attempts at refinement and modification, such as the use of PSA velocity, which have been used to improve it. New biomarkers for prostate cancer have been discovered, with promising early results.

OBJECTIVE/METHODS

This article reviews the ubiquitous current literature on biomarkers in prostate cancer. A search using MEDLINE and EMBASE databases was performed and those articles reporting biomarkers in prostate cancer with clinically significant findings in terms of detection were analyzed. Immunohistochemical markers were not considered for this review.

RESULTS/CONCLUSION

Despite many markers being promising, no single marker has satisfied the criteria as a perfect candidate. Limited clinical use of IL-6, TGF-β1 and PCA3 has commenced, and further widespread availability of these tests is expected in the coming years. The future lies in artificial neural networks and panels of markers instead of individual assays. Although PSA has some well-known limitations, it is at present the best marker available for prostate cancer when used in conjunction with nomograms or risk calculators.

Prostate-specific antigen-based screening for prostate cancer in the third millennium: useful or hype?

Prostate cancer is the most prevalent malignancy in men and the third leading cause of cancer deaths worldwide. Although the wide-spread introduction of total prostate-specific antigen (tPSA) testing has revolutionized the approach to the managed care of this disease, there are some biological, analytical, clinical, and economical issues that argue against the cost-effectiveness of tPSA-based population screening for early identification of cancer. The on-going standardization/harmonization efforts, along with the outcomes of recent epidemiological investigations, demonstrate that the current tPSA thresholds might be revised and possibly recalculated according to several demographical variables, such as age, ethnicity, genotype, family history, and body mass index. A major shortcoming of tPSA screening is the lack of reliable evidences of reduction in prostate cancer-associated mortality, due to the large lead-time because of the indolent growth rate, the impossibility to differentiate high-grade from indolent cancers, and the treatment-associated morbidity. Since no single tPSA cut-off was proven able to efficiently identify men at higher risk of death, the jeopardy of over-diagnosis and over-treatment is also tangible. The large expenditure is an additional source of concern. Finally, a wide-spread population screening also carries several ethical, social, and psychological implications, which might overwhelm the potential benefits.