Sara Benlloch

Fine-mapping identifies multiple prostate cancer risk loci at 5p15, one of which associates with TERT expression

Associations between single nucleotide polymorphisms (SNPs) at 5p15 and multiple cancer types have been reported. We have previously shown evidence for a strong association between prostate cancer (PrCa) risk and rs2242652 at 5p15, intronic in the telomerase reverse transcriptase (TERT) gene that encodes TERT. To comprehensively evaluate the association between genetic variation across this region and PrCa, we performed a fine-mapping analysis by genotyping 134 SNPs using a custom Illumina iSelect array or Sequenom MassArray iPlex, followed by imputation of 1094 SNPs in 22 301 PrCa cases and 22 320 controls in The PRACTICAL consortium. Multiple stepwise logistic regression analysis identified four signals in the promoter or intronic regions of TERT that independently associated with PrCa risk. Gene expression analysis of normal prostate tissue showed evidence that SNPs within one of these regions also associated with TERT expression, providing a potential mechanism for predisposition to disease.

A genome-wide association scan (GWAS) for mean telomere length within the COGS project: identified loci show little association with hormone-related cancer risk

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.

The OncoArray Consortium: a Network for Understanding the Genetic Architecture of Common Cancers.

Background: Common cancers develop through a multistep process often including inherited susceptibility. Collaboration among multiple institutions, and funding from multiple sources, has allowed the development of an inexpensive genotyping microarray, the OncoArray. The array includes a genome-wide backbone, comprising 230,000 SNPs tagging most common genetic variants, together with dense mapping of known susceptibility regions, rare variants from sequencing experiments, pharmacogenetic markers, and cancer-related traits. Methods: The OncoArray can be genotyped using a novel technology developed by Illumina to facilitate efficient genotyping. The consortium developed standard approaches for selecting SNPs for study, for quality control of markers, and for ancestry analysis. The array was genotyped at selected sites and with prespecified replicate samples to permit evaluation of genotyping accuracy among centers and by ethnic background. Results: The OncoArray consortium genotyped 447,705 samples. A total of 494,763 SNPs passed quality control steps with a sample success rate of 97% of the samples. Participating sites performed ancestry analysis using a common set of markers and a scoring algorithm based on principal components analysis. Conclusions: Results from these analyses will enable researchers to identify new susceptibility loci, perform fine-mapping of new or known loci associated with either single or multiple cancers, assess the degree of overlap in cancer causation and pleiotropic effects of loci that have been identified for disease-specific risk, and jointly model genetic, environmental, and lifestyle-related exposures. Impact: Ongoing analyses will shed light on etiology and risk assessment for many types of cancer. Cancer Epidemiol Biomarkers Prev; 26(1); 126–35. ©2016 AACR.

Risk Analysis of Prostate Cancer in PRACTICAL, a Multinational Consortium, Using 25 Known Prostate Cancer Susceptibility Loci

Background: Genome-wide association studies have identified multiple genetic variants associated with prostate cancer risk which explain a substantial proportion of familial relative risk. These variants can be used to stratify individuals by their risk of prostate cancer. Methods: We genotyped 25 prostate cancer susceptibility loci in 40,414 individuals and derived a polygenic risk score (PRS). We estimated empirical odds ratios (OR) for prostate cancer associated with different risk strata defined by PRS and derived age-specific absolute risks of developing prostate cancer by PRS stratum and family history. Results: The prostate cancer risk for men in the top 1% of the PRS distribution was 30.6 (95% CI, 16.4–57.3) fold compared with men in the bottom 1%, and 4.2 (95% CI, 3.2–5.5) fold compared with the median risk. The absolute risk of prostate cancer by age of 85 years was 65.8% for a man with family history in the top 1% of the PRS distribution, compared with 3.7% for a man in the bottom 1%. The PRS was only weakly correlated with serum PSA level (correlation = 0.09). Conclusions: Risk profiling can identify men at substantially increased or reduced risk of prostate cancer. The effect size, measured by OR per unit PRS, was higher in men at younger ages and in men with family history of prostate cancer. Incorporating additional newly identified loci into a PRS should improve the predictive value of risk profiles. Impact: We demonstrate that the risk profiling based on SNPs can identify men at substantially increased or reduced risk that could have useful implications for targeted prevention and screening programs. Cancer Epidemiol Biomarkers Prev; 24(7); 1121–9. ©2015 AACR.

Evaluating genetic risk for prostate cancer among Japanese and Latinos

Background: 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. Methods: 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). Results: 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). Conclusion and Impact: 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. Cancer Epidemiol Biomarkers Prev; 21(11); 2048–58. ©2012 AACR.

Implications of polygenic risk-stratified screening for prostate cancer on overdiagnosis.

Purpose:This study aimed to quantify the probability of overdiagnosis of prostate cancer by polygenic risk.Methods: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.Results: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.Conclusion: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.Genet Med 17 10, 789–795.

Fine-Mapping the HOXB Region Detects Common Variants Tagging a Rare Coding Allele: Evidence for Synthetic Association in Prostate Cancer

The HOXB13 gene has been implicated in prostate cancer (PrCa) susceptibility. We performed a high resolution fine-mapping analysis to comprehensively evaluate the association between common genetic variation across the HOXB genetic locus at 17q21 and PrCa risk. This involved genotyping 700 SNPs using a custom Illumina iSelect array (iCOGS) followed by imputation of 3195 SNPs in 20,440 PrCa cases and 21,469 controls in The PRACTICAL consortium. We identified a cluster of highly correlated common variants situated within or closely upstream of HOXB13 that were significantly associated with PrCa risk, described by rs117576373 (OR 1.30, P = 2.62×10−14). Additional genotyping, conditional regression and haplotype analyses indicated that the newly identified common variants tag a rare, partially correlated coding variant in the HOXB13 gene (G84E, rs138213197), which has been identified recently as a moderate penetrance PrCa susceptibility allele. The potential for GWAS associations detected through common SNPs to be driven by rare causal variants with higher relative risks has long been proposed; however, to our knowledge this is the first experimental evidence for this phenomenon of synthetic association contributing to cancer susceptibility.

Prediction of individual genetic risk to prostate cancer using a polygenic score.

Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome‐wide significant level will improve disease prediction.

Genome-Wide Association Study of Prostate Cancer–Specific Survival

Background: Unnecessary intervention and overtreatment of indolent disease are common challenges in clinical management of prostate cancer. Improved tools to distinguish lethal from indolent disease are critical. Methods: We performed a genome-wide survival analysis of cause-specific death in 24,023 prostate cancer patients (3,513 disease-specific deaths) from the PRACTICAL and BPC3 consortia. Top findings were assessed for replication in a Norwegian cohort (CONOR). Results: We observed no significant association between genetic variants and prostate cancer survival. Conclusions: Common genetic variants with large impact on prostate cancer survival were not observed in this study. Impact: Future studies should be designed for identification of rare variants with large effect sizes or common variants with small effect sizes. Cancer Epidemiol Biomarkers Prev; 24(11); 1796–800. ©2015 AACR.

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. Cancer Epidemiol Biomarkers Prev; 23(7); 1356–65. ©2014 AACR.