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

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.

Targeted Mass Spectrometry of a Clinically Relevant PSA Variant from Post-DRE Urines for Quantitation and Genotype Determination

PURPOSE

The rs17632542 single nucleotide polymorphism (SNP) results in lower serum prostate specific antigen (PSA) levels which may further mitigate against its clinical utility as a prostate cancer biomarker. Post-digital rectal exam (post-DRE) urine is a minimally invasive fluid that is currently utilized in prostate cancer diagnosis. To detect and quantitate the variant protein in urine.

EXPERIMENTAL DESIGN

Fifty-three post-DRE urines from rs17632542 genotyped individuals processed and analyzed by liquid chromatography/mass spectrometry (LC-MS) in a double-blinded randomized study. The ability to distinguish between homozygous wild-type, heterozygous, or homozygous variant is examined before unblinding.

RESULTS

Stable-isotope labeled peptides are used in the detection and quantitation of three peptides of interest in each sample using parallel reaction monitoring (PRM). Using these data, groupings are predicted using hierarchical clustering in R. Accuracy of the predictions show 100% concordance across the 53 samples, including individuals homozygous and heterozygous for the SNP.

CONCLUSIONS AND CLINICAL RELEVANCE

The study demonstrates that MS based peptide variant quantitation in urine could be useful in determining patient genotype expression. This assay provides a tool to evaluate the utility of PSA variant (rs17632542) in parallel with current and forthcoming urine biomarker panels.

Genetic predisposition to prostate cancer

INTRODUCTION

Prostate cancer (PrCa) is the commonest non-cutaneous cancer in men in the UK. Epidemiological evidence as well as twin studies points towards a genetic component contributing to aetiology.

SOURCES OF DATA

Key recently published literature.

AREAS OF AGREEMENT

A family history of PrCa doubles the risk of disease development in first-degree relatives. Linkage and genetic sequencing studies identified rare moderate-high-risk gene loci, which predispose to PrCa development when altered by mutation. Genome-wide association studies have identified common single-nucleotide polypmorphisms (SNPs), which confer a cumulative risk of PrCa development with increasing number of risk alleles. There are emerging data that castrate-resistant disease is associated with mutations in DNA repair genes.

AREAS OF CONTROVERSY

Linkage studies investigating possible high-risk loci leading to PrCa development identified possible loci on several chromosomes, but most have not been consistently replicated by subsequent studies. Germline SNPs related to prostate specific antigen (PSA) levels and to normal tissue radiosensitivity have also been identified though not all have been validated in subsequent studies.

GROWING POINTS

Utilizing germline SNP profiles as well as identifying high-risk genetic variants could target screening to high-risk groups, avoiding the drawbacks of PSA screening.

AREAS TIMELY FOR DEVELOPING RESEARCH

Incorporating genetics into PrCa screening is being investigated currently using both common SNP profiles and higher risk rare variants. Knowledge of germline genetic defects will allow the development of targeted screening programs, preventive strategies and the personalized treatment of PrCa.

The Role of Proteomics in Biomarker Development for Improved Patient Diagnosis and Clinical Decision Making in Prostate Cancer

Prostate Cancer (PCa) is the second most commonly diagnosed cancer in men worldwide. Although increased expression of prostate-specific antigen (PSA) is an effective indicator for the recurrence of PCa, its intended use as a screening marker for PCa is of considerable controversy. Recent research efforts in the field of PCa biomarkers have focused on the identification of tissue and fluid-based biomarkers that would be better able to stratify those individuals diagnosed with PCa who (i) might best receive no treatment (active surveillance of the disease); (ii) would benefit from existing treatments; or (iii) those who are likely to succumb to disease recurrence and/or have aggressive disease. The growing demand for better prostate cancer biomarkers has coincided with the development of improved discovery and evaluation technologies for multiplexed measurement of proteins in bio-fluids and tissues. This review aims to (i) provide an overview of these technologies as well as describe some of the candidate PCa protein biomarkers that have been discovered using them; (ii) address some of the general limitations in the clinical evaluation and validation of protein biomarkers; and (iii) make recommendations for strategies that could be adopted to improve the successful development of protein biomarkers to deliver improvements in personalized PCa patient decision making.