Personalized prostate cancer screening: improving PSA tests with genomic information

Prostate-specific antigen screening for prostate cancer: Diagnostic performance, clinical thresholds, and strategies for refinement

Prostate specific antigen (PSA) has transformed the diagnosis and treatment of prostate cancer by enabling early detection at global scale. Due to expression in both benign and malignant cells, PSA-based prostate cancer screening using single cut-points yields imperfect diagnostic performance and has led to the detection and over-treatment of low-grade prostate cancer. Additional challenges in the interpretation of PSA include substantial inter and intrapersonal variation, differences with age and prostate volume, and selection of standardized PSA value cutoffs for clinical application. In response, refinements to PSA including risk and age-based thresholds, age and genetic adjustments, PSA density, percentage free PSA, and PSA velocity have been proposed and extensively studied. In this review, we focus on the clinical role of PSA as a screening biomarker with a particular emphasis on its test characteristics, clinically actionable thresholds, and strategies to refine its clinical interpretation.

Performance of African-ancestry-specific polygenic hazard score varies according to local ancestry in 8q24

BACKGROUND

We previously developed an African-ancestry-specific polygenic hazard score (PHS46+African) that substantially improved prostate cancer risk stratification in men with African ancestry. The model consists of 46 SNPs identified in Europeans and 3 SNPs from 8q24 shown to improve model performance in Africans. Herein, we used principal component (PC) analysis to uncover subpopulations of men with African ancestry for whom the utility of PHS46+African may differ.

MATERIALS AND METHODS

Genotypic data were obtained from the PRACTICAL consortium for 6253 men with African genetic ancestry. Genetic variation in a window spanning 3 African-specific 8q24 SNPs was estimated using 93 PCs. A Cox proportional hazards framework was used to identify the pair of PCs most strongly associated with the performance of PHS46+African. A calibration factor (CF) was formulated using Cox coefficients to quantify the extent to which the performance of PHS46+African varies with PC.

RESULTS

CF of PHS46+African was strongly associated with the first and twentieth PCs. Predicted CF ranged from 0.41 to 2.94, suggesting that PHS46+African may be up to 7 times more beneficial to some African men than others. The explained relative risk for PHS46+African varied from 3.6% to 9.9% for individuals with low and high CF values, respectively. By cross-referencing our data set with 1000 Genomes, we identified significant associations between continental and calibration groupings.

CONCLUSION

We identified PCs within 8q24 that were strongly associated with the performance of PHS46+African. Further research to improve the clinical utility of polygenic risk scores (or models) is needed to improve health outcomes for men of African ancestry.

Common genetic and clinical risk factors: association with fatal prostate cancer in the Cohort of Swedish Men

BACKGROUND

Clinical variables-age, family history, genetics-are used for prostate cancer risk stratification. Recently, polygenic hazard scores (PHS46, PHS166) were validated as associated with age at prostate cancer diagnosis. While polygenic scores are associated with all prostate cancer (not specific for fatal cancers), PHS46 was also associated with age at prostate cancer death. We evaluated if adding PHS to clinical variables improves associations with prostate cancer death.

METHODS

Genotype/phenotype data were obtained from a nested case-control Cohort of Swedish Men (n = 3279; 2163 with prostate cancer, 278 prostate cancer deaths). PHS and clinical variables (family history, alcohol intake, smoking, heart disease, hypertension, diabetes, body mass index) were tested via univariable Cox proportional hazards models for association with age at prostate cancer death. Multivariable Cox models with/without PHS were compared with log-likelihood tests.

RESULTS

Median age at last follow-up/prostate cancer death was 78.0 (IQR: 72.3-84.1) and 81.4 (75.4-86.3) years, respectively. On univariable analysis, PHS46 (HR 3.41 [95% CI 2.78-4.17]), family history (HR 1.72 [1.46-2.03]), alcohol (HR 1.74 [1.40-2.15]), diabetes (HR 0.53 [0.37-0.75]) were each associated with prostate cancer death. On multivariable analysis, PHS46 (HR 2.45 [1.99-2.97]), family history (HR 1.73 [1.48-2.03]), alcohol (HR 1.45 [1.19-1.76]), diabetes (HR 0.62 [0.42-0.90]) all remained associated with fatal disease. Including PHS46 or PHS166 improved multivariable models for fatal prostate cancer (p < 10^-15).

CONCLUSIONS

PHS had the most robust association with fatal prostate cancer in a multivariable model with common risk factors, including family history. Adding PHS to clinical variables may improve prostate cancer risk stratification strategies.

Additional SNPs improve risk stratification of a polygenic hazard score for prostate cancer

BACKGROUND

Polygenic hazard scores (PHS) can identify individuals with increased risk of prostate cancer. We estimated the benefit of additional SNPs on performance of a previously validated PHS (PHS46).

MATERIALS AND METHOD

180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy.

RESULTS

166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer.

CONCLUSIONS

Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.

Comprehensive Analysis of Multiple Cohort Datasets Deciphers the Utility of Germline Single-Nucleotide Polymorphisms in Prostate Cancer Diagnosis

Prostate cancer susceptibility is a polygenic trait. We aimed to examine the controversial diagnostic utility of single-nucleotide polymorphisms (SNP) for prostate cancer. We analyzed two datasets collected from Europeans and one from Africans. These datasets were generated by the genome-wide association studies, that is, CGEMS, BPC3, and MEC-Africans, respectively. About 540,000 SNPs, including 61 risk markers that constitute a panel termed MK-61, were commonly genotyped. For each dataset, we augmented the MK-61 panel to generate an MK-61⁺ one by adding several thousands of SNPs that were moderately associated with prostate cancer occurrence in external dataset(s). We assessed the diagnostic utility of both panels by measuring their predictive strength for prostate cancer occurrence with AUC statistics. We calculated the theoretical AUCs using quantitative genetics model-based formulae and obtained the empirical estimates via 10-fold cross-validation using statistical and machine learning techniques. For the MK-61 panel, the 95% confidence intervals of the theoretical AUCs (AUC-CI.95) were 0.578-0.655, 0.596-0.656, and 0.539-0.596 in the CGEMS, BPC3, and MEC-Africans cohorts, respectively. For the MK-61⁺ panels, the corresponding AUC-CI.95 were 0.617-0.663, 0.527-0.736, and 0.547-0.565. The empirical AUCs largely fell within the theoretical interval. A promising result (AUC = 0.703, FNR = 0.354, FPR = 0.353) was obtained in the BPC3 cohort when the MK-61⁺ panel was used. In the CGEMS cohort, the MK-61⁺ panel complemented PSA in predicting the disease status of PSA ≥ 2.0 ng/mL samples. This study demonstrates that augmented risk SNP panels can enhance prostate cancer prediction for males of European ancestry, especially those with [Formula: see text]ng/mL. PREVENTION RELEVANCE: This study demonstrates that augmented risk SNP panels can enhance prostate cancer prediction for males of European ancestry, especially those with PSA ≥ 2 ng/mL.

Polygenic hazard score is associated with prostate cancer in multi-ethnic populations

Genetic models for cancer have been evaluated using almost exclusively European data, which could exacerbate health disparities. A polygenic hazard score (PHS1) is associated with age at prostate cancer diagnosis and improves screening accuracy in Europeans. Here, we evaluate performance of PHS2 (PHS1, adapted for OncoArray) in a multi-ethnic dataset of 80,491 men (49,916 cases, 30,575 controls). PHS2 is associated with age at diagnosis of any and aggressive (Gleason score ≥ 7, stage T3-T4, PSA ≥ 10 ng/mL, or nodal/distant metastasis) cancer and prostate-cancer-specific death. Associations with cancer are significant within European (n = 71,856), Asian (n = 2,382), and African (n = 6,253) genetic ancestries (p < 10-180). Comparing the 80th/20th PHS2 percentiles, hazard ratios for prostate cancer, aggressive cancer, and prostate-cancer-specific death are 5.32, 5.88, and 5.68, respectively. Within European, Asian, and African ancestries, hazard ratios for prostate cancer are: 5.54, 4.49, and 2.54, respectively. PHS2 risk-stratifies men for any, aggressive, and fatal prostate cancer in a multi-ethnic dataset.

A Genetic Risk Score to Personalize Prostate Cancer Screening, Applied to Population Data

BACKGROUND

A polygenic hazard score (PHS), the weighted sum of 54 SNP genotypes, was previously validated for association with clinically significant prostate cancer and for improved prostate cancer screening accuracy. Here, we assess the potential impact of PHS-informed screening.

METHODS

United Kingdom population incidence data (Cancer Research United Kingdom) and data from the Cluster Randomized Trial of PSA Testing for Prostate Cancer were combined to estimate age-specific clinically significant prostate cancer incidence (Gleason score ≥7, stage T3-T4, PSA ≥10, or nodal/distant metastases). Using HRs estimated from the ProtecT prostate cancer trial, age-specific incidence rates were calculated for various PHS risk percentiles. Risk-equivalent age, when someone with a given PHS percentile has prostate cancer risk equivalent to an average 50-year-old man (50-year-standard risk), was derived from PHS and incidence data. Positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was calculated using PHS-adjusted age groups.

RESULTS

The expected age at diagnosis of clinically significant prostate cancer differs by 19 years between the 1st and 99th PHS percentiles: men with PHS in the 1st and 99th percentiles reach the 50-year-standard risk level at ages 60 and 41, respectively. PPV of PSA was higher for men with higher PHS-adjusted age.

CONCLUSIONS

PHS provides individualized estimates of risk-equivalent age for clinically significant prostate cancer. Screening initiation could be adjusted by a man's PHS.

IMPACT

Personalized genetic risk assessments could inform prostate cancer screening decisions.

Polygenic hazard score to guide screening for aggressive prostate cancer: development and validation in large scale cohorts

OBJECTIVES

To develop and validate a genetic tool to predict age of onset of aggressive prostate cancer (PCa) and to guide decisions of who to screen and at what age.

DESIGN

Analysis of genotype, PCa status, and age to select single nucleotide polymorphisms (SNPs) associated with diagnosis. These polymorphisms were incorporated into a survival analysis to estimate their effects on age at diagnosis of aggressive PCa (that is, not eligible for surveillance according to National Comprehensive Cancer Network guidelines; any of Gleason score ≥7, stage T3-T4, PSA (prostate specific antigen) concentration ≥10 ng/L, nodal metastasis, distant metastasis). The resulting polygenic hazard score is an assessment of individual genetic risk. The final model was applied to an independent dataset containing genotype and PSA screening data. The hazard score was calculated for these men to test prediction of survival free from PCa.

SETTING

Multiple institutions that were members of international PRACTICAL consortium.

PARTICIPANTS

All consortium participants of European ancestry with known age, PCa status, and quality assured custom (iCOGS) array genotype data. The development dataset comprised 31 747 men; the validation dataset comprised 6411 men.

MAIN OUTCOME MEASURES

Prediction with hazard score of age of onset of aggressive cancer in validation set.

RESULTS

In the independent validation set, the hazard score calculated from 54 single nucleotide polymorphisms was a highly significant predictor of age at diagnosis of aggressive cancer (z=11.2, P<10-16). When men in the validation set with high scores (>98th centile) were compared with those with average scores (30th-70th centile), the hazard ratio for aggressive cancer was 2.9 (95% confidence interval 2.4 to 3.4). Inclusion of family history in a combined model did not improve prediction of onset of aggressive PCa (P=0.59), and polygenic hazard score performance remained high when family history was accounted for. Additionally, the positive predictive value of PSA screening for aggressive PCa was increased with increasing polygenic hazard score.

CONCLUSIONS

Polygenic hazard scores can be used for personalised genetic risk estimates that can predict for age at onset of aggressive PCa.

Genome-wide association study of prostate-specific antigen levels identifies novel loci independent of prostate cancer

Prostate-specific antigen (PSA) levels have been used for detection and surveillance of prostate cancer (PCa). However, factors other than PCa—such as genetics—can impact PSA. Here we present findings from a genome-wide association study (GWAS) of PSA in 28,503 Kaiser Permanente whites and 17,428 men from replication cohorts. We detect 40 genome-wide significant (P<5 × 10⁻⁸) single-nucleotide polymorphisms (SNPs): 19 novel, 15 previously identified for PSA (14 of which were also PCa-associated), and 6 previously identified for PCa only. Further analysis incorporating PCa cases suggests that at least half of the 40 SNPs are PSA-associated independent of PCa. The 40 SNPs explain 9.5% of PSA variation in non-Hispanic whites, and the remaining GWAS SNPs explain an additional 31.7%; this percentage is higher in younger men, supporting the genetic basis of PSA levels. These findings provide important information about genetic markers for PSA that may improve PCa screening, thereby reducing over-diagnosis and over-treatment.

Prostate-specific antigen is used as a biomarker of prostate cancer, but levels can be affected by other factors not related to cancer. Here, the authors find genes associated with prostate specific antigen levels in healthy men, which could be used to reduce over-diagnosis and over-treatment.

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.

Contribution of genetic variation rs266882 to prostate-specific antigen levels in healthy controls with serum PSA below 2.0 ng/ml

We evaluated the impact of genetic variation in the prostate-specific antigen (PSA) gene (rs266882) on serum PSA levels in healthy men as well as risk factors for benign prostate hypertrophy (BPH) and prostate cancer. The study population comprised 91 men with PSA levels below 2.0 ng/ml as healthy controls, 78 men with PSA 2-10 ng/ml as a BPH group, and 128 prostate cancer patients, all in Korea. DNA was amplified by polymerase chain reaction and the product was sequenced. We found that PSA levels were associated with a G/A polymorphism only in healthy controls. The transition, however, was not associated with PSA levels of BPH and cancer patients, nor was it a risk factor. In conclusion, this genetic factor is important for determining serum PSA levels in the naive group, whereas the disruption of prostatic architecture in BPH or prostate cancer may be a major determining factor for PSA levels.

Historical prostate cancer screening and treatment outcomes from a single institution

OBJECTIVE

To quantify outcomes of individuals diagnosed and treated for prostate cancer in a single institution.

DESIGN

Retrospective electronic chart abstraction.

SETTING

Marshfield Clinic, the largest private multispecialty group practice in Wisconsin, and one of the largest in the United States, provides health care services annually to approximately 385,000 unique patients through 1.8 million annual patient encounters.

PARTICIPANTS

Individuals within the Marshfield Clinic cancer registry who had been diagnosed with prostate cancer between 1960 and 2009.

METHODS

Electronic chart abstraction from the cancer registry and the electronic medical record was conducted (N=6,181). Data abstracted included age at diagnosis; stage and grade of tumor; prostate specific antigen (PSA) values before, at, and after diagnosis; initial cancer treatment; follow-up time; subsequent cancer treatments; evidence of metastasis; age of death; and cause of death, if known.

RESULTS

The average age of prostate cancer diagnosis has decreased from 70-71 years in the 1960's and 1970's to an average age at diagnosis of 67 years in the 2000's (P<0.001). This decrease in age occurred within the decades of implementation of PSA screening. Approximately 74% of men diagnosed with prostate cancer within the PSA screening era had at least one PSA test, and the presence of a PSA test did not appear to change treatment outcome. Age, grade, and stage were the biggest predictors of prostate cancer outcome. There was no difference in event-free survival between current treatment types (radical prostatectomy, brachytherapy, photon treatment, or intensity-modulated radiation therapy) (2003 or later) when stratified by age (greater than 85%, 5-year event-free survival P=0.85); however, more events occurred with older external beam radiation treatment regimens (1993-2003) (70% to 75%, 5-year event-free survival P=0.001).

CONCLUSION

Individuals diagnosed and treated for prostate cancer within the Marshfield Clinic comprehensive care setting follow national trends with a decreased age of diagnosis since the advent of PSA screening. Outcomes for individuals treated within the Clinic system are also comparable to national trends.

Diverse functional networks of Tbx3 in development and disease

The T-box transcription factor Tbx3 plays multiple roles in normal development and disease. In order to function in different tissues and on different target genes, Tbx3 binds transcription factors or other cofactors specific to temporal or spatial locations. Examining the development of the mammary gland, limbs, and heart as well as the biology of stem cells and cancer provides insights into the diverse and common functions that Tbx3 can perform. By either repressing or activating transcription of target genes in a context-dependent manner, Tbx3 is able to modulate differentiation of immature progenitor cells, control the rate of cell proliferation, and mediate cellular signaling pathways. Because the direct regulators of these cellular processes are highly context-dependent, it is essential that Tbx3 has the flexibility to regulate transcription of a large group of targets, but only become a active on a small cohort of them at any given time or place. Moreover, Tbx3 must be responsive to the variety of different upstream factors that are present in different tissues. Only by understanding the network of genes, proteins, and molecules with which Tbx3 interacts can we hope to understand the role that Tbx3 plays in normal development and how its aberrant expression can lead to disease. Because of its myriad functions in disparate developmental and disease contexts, Tbx3 is an ideal candidate for a systems-based approach to genetic function and interaction.

National Cancer Institute Prostate Cancer Genetics Workshop

Compelling evidence supports a genetic component to prostate cancer susceptibility and aggressiveness. Recent genome-wide association studies have identified more than 30 single-nucleotide polymorphisms associated with prostate cancer susceptibility. It remains unclear, however, whether such genetic variants are associated with disease aggressiveness--one of the most important questions in prostate cancer research today. To help clarify this and substantially expand research in the genetic determinants of prostate cancer aggressiveness, the first National Cancer Institute Prostate Cancer Genetics Workshop assembled researchers to develop plans for a large new research consortium and patient cohort. The workshop reviewed the prior work in this area and addressed the practical issues in planning future studies. With new DNA sequencing technology, the potential application of sequencing information to patient care is emerging. The workshop, therefore, included state-of-the-art presentations by experts on new genotyping technologies, including sequencing and associated bioinformatics issues, which are just beginning to be applied to cancer genetics.