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.
We conducted a genome-wide association study for testicular germ cell tumor (TGCT), genotyping 307,666 SNPs in 730 cases and 1,435 controls from the UK and replicating associations in a further 571 ...cases and 1,806 controls. We found strong evidence for susceptibility loci on chromosome 5 (per allele OR = 1.37 (95% CI = 1.19-1.58), P = 3 × 10−13), chromosome 6 (OR = 1.50 (95% CI = 1.28-1.75), P = 10−13) and chromosome 12 (OR = 2.55 (95% CI = 2.05-3.19), P = 10−31). KITLG, encoding the ligand for the receptor tyrosine kinase KIT, which has previously been implicated in the pathogenesis of TGCT and the biology of germ cells, may explain the association on chromosome 12.
Genetic evidence of disease association has often been used as a basis for selecting of drug targets for complex common diseases. Likewise, the propagation of genetic evidence through gene or protein ...interaction networks has been shown to accurately infer novel disease associations at genes for which no direct genetic evidence can be observed. However, an empirical test of the utility of combining these approaches for drug discovery has been lacking. In this study, we examine genetic associations arising from an analysis of 648 UK Biobank GWAS and evaluate whether targets identified as proxies of direct genetic hits are enriched for successful drug targets, as measured by historical clinical trial data. We find that protein networks formed from specific functional linkages such as protein complexes and ligand-receptor pairs are suitable for even naïve guilt-by-association network propagation approaches. In addition, more sophisticated approaches applied to global protein-protein interaction networks and pathway databases, also successfully retrieve targets enriched for clinically successful drug targets. We conclude that network propagation of genetic evidence can be used for drug target identification.
To determine whether a simple small vessel disease (SVD) score, which uses information available on rapid visual assessment of clinical MRI scans, predicts risk of cognitive decline and dementia, ...above that provided by simple clinical measures.
Three prospective longitudinal cohort studies (SCANS St George's Cognition and Neuroimaging in Stroke, RUN DMC Radboud University Nijmegen Diffusion Imaging and Magnetic Resonance Imaging Cohort, and the ASPS Austrian Stroke Prevention Study), which covered a range of SVD severity from mild and asymptomatic to severe and symptomatic, were included. In all studies, MRI was performed at baseline, cognitive tests repeated during follow-up, and progression to dementia recorded prospectively. Outcome measures were cognitive decline and onset of dementia during follow-up. We determined whether the SVD score predicted risk of cognitive decline and future dementia. We also determined whether using the score to select a group of patients with more severe disease would reduce sample sizes for clinical intervention trials.
In a pooled analysis of all 3 cohorts, the score improved prediction of dementia (area under the curve AUC, 0.85; 95% confidence interval CI, 0.81-0.89) compared with that from clinical risk factors alone (AUC, 0.76; 95% CI, 0.71-0.81). Predictive performance was higher in patients with more severe SVD. Power calculations showed selecting patients with a higher score reduced sample sizes required for hypothetical clinical trials by 40%-66% depending on the outcome measure used.
A simple SVD score, easily obtainable from clinical MRI scans and therefore applicable in routine clinical practice, aided prediction of future dementia risk.
Recent studies based on genome-wide association, linkage, and admixture scan analysis have reported associations of various genetic variants in 8q24 with susceptibility to breast, prostate, and ...colorectal cancer. This locus lies within a 1.18-Mb region that contains no known genes but is bounded at its centromeric end by FAM84B and at its telomeric end by c-MYC, two candidate cancer susceptibility genes. To investigate the associations of specific loci within 8q24 with specific cancers, we genotyped the nine previously reported cancer-associated single-nucleotide polymorphisms across the region in four case–control sets of prostate (1854 case subjects and 1894 control subjects), breast (2270 case subjects and 2280 control subjects), colorectal (2299 case subjects and 2284 control subjects), and ovarian (1975 case subjects and 3411 control subjects) cancer. Five different haplotype blocks within this gene desert were specifically associated with risks of different cancers. One block was solely associated with risk of breast cancer, three others were associated solely with the risk of prostate cancer, and a fifth was associated with the risk of prostate, colorectal, and ovarian cancer, but not breast cancer. We conclude that there are at least five separate functional variants in this region.
Folate-pathway gene polymorphisms have been implicated in several cancers and investigated inconclusively in relation to prostate cancer. We conducted a systematic review, which identified nine ...case-control studies (eight included, one excluded). We also included data from four genome-wide association studies and from a case-control study nested within the UK population-based Prostate Testing for Cancer and Treatment study. We investigated by meta-analysis the effects of eight polymorphisms: MTHFR C677T (rs1801133; 12 studies; 10,745 cases; 40,158 controls), MTHFR A1298C (rs1801131; 5 studies; 3,176 cases; 4,829 controls), MTR A2756G (rs1805087; 8 studies; 7,810 cases; 37,543 controls), MTRR A66G (rs1801394; 4 studies; 3,032 cases; 4,515 controls), MTHFD1 G1958A (rs2236225; 6 studies; 7,493 cases; 36,941 controls), SLC19A1/RFC1 G80A (rs1051266; 4 studies; 6,222 cases; 35,821 controls), SHMT1 C1420T (rs1979277; 2 studies; 2,689 cases; 4,110 controls), and FOLH1 T1561C (rs202676; 5 studies; 6,314 cases; 35,190 controls). The majority (10 of 13) of eligible studies had 100% Caucasian subjects; only one study had <90% Caucasian subjects. We found weak evidence of dominant effects of two alleles: MTR 2756A>G random effects pooled odds ratio, 1.06 (1.00-1.12); P = 0.06 (P = 0.59 for heterogeneity across studies) and SHMT1 1420C>T random effects pooled odds ratio, 1.11 (1.00-1.22); P = 0.05 (P = 0.38 for heterogeneity across studies). We found no effect of MTHFR 677C>T or any of the other alleles in dominant, recessive or additive models, or in comparing a/a versus A/A homozygous. Neither did we find any difference in effects on advanced or localized cancers. Our meta-analysis suggests that known common folate-pathway single nucleotide polymorphisms do not have significant effects on susceptibility to prostate cancer.
What's known on the subject? and What does the study add?
Family history (FH) is a major risk factor for the development of prostate cancer. The search for genetic variants has led to genome‐wide ...association studies (GWAS), which have so far reported 47 susceptibility loci that predispose men to prostate cancer. However, the use of genetics or FH status in predicting clinical outcomes after prostate cancer diagnosis remains uncertain. Guidelines currently exist for clinicians and patients summarising evidence relating to the best outcomes of different prostate cancer treatment methods. Genetics and FH could potentially add to this stratification.
Our study aimed to ascertain the potential prognostic roles of FH or genetic risk scores in patients managed by active surveillance.
Objectives
To explore the potential prognostic role of family history (FH) of prostate cancer and prostate cancer risk single nucleotide polymorphisms (SNPs) in patients undergoing active surveillance (AS) for prostate cancer.
This is the first study to date, which has investigated the potential prognostic role of SNP profiles in an AS cohort
Patients and Methods
FH data were collected from patients in the Royal Marsden Hospital AS study.
In all, 39 prostate cancer‐risk SNPs identified from published genome wide association studies (GWAS) were genotyped using the Sequenom Platform and TaqMan™ assays from available DNA.
The cumulative genetic‐risk scores for each patient were then calculated using the weighted effect estimated from previous GWAS (log‐additive model).
FH status and the genetic‐risk scores were assessed against adverse outcomes in AS, time to treatment and adverse histology on repeat biopsy, using univariable and multivariable Cox regression models to address time to treatment; and binary logistic regression to address biopsy upgrade.
Results
Of 471 patients, 55 (13.6%) had adverse histology on repeat biopsies and 145 (30.8%) had deferred treatment.
On univariate analysis, there was no significant relationship between FH of prostate cancer in any degree of relation, and adverse histology or time to treatment.
For risk score analyses, 386 patients' DNA was studied; and there was also no relationship found between the calculated genetic risk scores and adverse histology or time to treatment (P = 0.573 and P = 0.965, respectively).
The retrospective study design and the few events were the main limitation of the study.
Conclusions
There is currently insufficient data to support the use of FH status or prostate cancer SNP profile risk scores as prognostic factors in AS and these should not be used to influence management decisions.
As more genetic variants are discovered this may change and should be reassessed in multicentre AS cohorts.
This study aimed to quantify the probability of overdiagnosis of prostate cancer by polygenic risk.
We calculated the polygenic risk score based on 66 known prostate cancer susceptibility variants ...for 17,012 men aged 50-69 years (9,404 men identified with prostate cancer and 7,608 with no cancer) derived from three UK-based ongoing studies. We derived the probabilities of overdiagnosis by quartiles of polygenic risk considering that the observed prevalence of screen-detected prostate cancer is a combination of underlying incidence, mean sojourn time (MST), test sensitivity, and overdiagnosis.
Polygenic risk quartiles 1 to 4 comprised 9, 18, 25, and 48% of the cases, respectively. For a prostate-specific antigen test sensitivity of 80% and MST of 9 years, 43, 30, 25, and 19% of the prevalent screen-detected cancers in quartiles 1 to 4, respectively, were likely to be overdiagnosed cancers. Overdiagnosis decreased with increasing polygenic risk, with 56% decrease between the lowest and the highest polygenic risk quartiles.
Targeting screening to men at higher polygenic risk could reduce the problem of overdiagnosis and lead to a better benefit-to-harm balance in screening for prostate cancer.
There have been few genome-wide association studies (GWAS) of prostate cancer among diverse populations. To search for novel prostate cancer risk variants, we conducted GWAS of prostate cancer in ...Japanese and Latinos. In addition, we tested prostate cancer risk variants and developed genetic risk models of prostate cancer for Japanese and Latinos.
Our first-stage GWAS of prostate cancer included Japanese (cases/controls = 1,033/1,042) and Latino (cases/controls = 1,043/1,057) from the Multiethnic Cohort (MEC). Significant associations from stage I (P < 1.0 × 10(-4)) were examined in silico in GWAS of prostate cancer (stage II) in Japanese (cases/controls = 1,583/3,386) and Europeans (cases/controls = 1,854/1,894).
No novel stage I single-nucleotide polymorphism (SNP) outside of known risk regions reached genome-wide significance. For Japanese, in stage I, the most notable putative novel association was seen with 10 SNPs (P ≤ 8.0 × 10(-6)) at chromosome 2q33; however, this was not replicated in stage II. For Latinos, the most significant association was observed with rs17023900 at the known 3p12 risk locus (stage I: OR = 1.45; P = 7.01 × 10(-5) and stage II: OR = 1.58; P = 3.05 × 10(-7)). The majority of the established risk variants for prostate cancer, 79% and 88%, were positively associated with prostate cancer in Japanese and Latinos (stage I), respectively. The cumulative effects of these variants significantly influence prostate cancer risk (OR per allele = 1.10; P = 2.71 × 10(-25) and OR = 1.07; P = 1.02 × 10(-16) for Japanese and Latinos, respectively).
Our GWAS of prostate cancer did not identify novel genome-wide significant variants. However, our findings show that established risk variants for prostate cancer significantly contribute to risk among Japanese and Latinos.
Mean telomere length (TL) in blood cells is heritable and has been reported to be associated with risks of several diseases, including cancer. We conducted a meta-analysis of three GWAS for TL (total ...n=2240) and selected 1629 variants for replication via the "iCOGS" custom genotyping array. All ∼200 000 iCOGS variants were analysed with TL, and those displaying associations in healthy controls (n = 15 065) were further tested in breast cancer cases (n = 11 024). We found a novel TL association (Ptrend < 4 × 10(-10)) at 3p14.4 close to PXK and evidence (Ptrend < 7 × 10(-7)) for TL loci at 6p22.1 (ZNF311) and 20q11.2 (BCL2L1). We additionally confirmed (Ptrend < 5 × 10(-14)) the previously reported loci at 3q26.2 (TERC), 5p15.3 (TERT) and 10q24.3 (OBFC1) and found supportive evidence (Ptrend < 5 × 10(-4)) for the published loci at 2p16.2 (ACYP2), 4q32.2 (NAF1) and 20q13.3 (RTEL1). SNPs tagging these loci explain TL differences of up to 731 bp (corresponding to 18% of total TL in healthy individuals), however, they display little direct evidence for association with breast, ovarian or prostate cancer risks.
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