Comparison of two methods for estimating absolute risk of prostate cancer based on single nucleotide polymorphisms and family history

Development of risk prediction models for glioma based on genome-wide association study findings and comprehensive evaluation of predictive performances

Over 14 common single nucleotide polymorphisms (SNP) have been consistently identified from genome-wide association studies (GWAS) as associated with glioma risk in European background. The extent to which and how these genetic variants can improve the prediction of glioma risk has was not been investigated. In this study, we employed three independent case-control datasets in Chinese populations, tested GWAS signals in dataset1, validated association results in dataset2, developed prediction models in dataset2 for the consistently replicated SNPs, refined the consistently replicated SNPs in dataset3 and developed tailored models for Chinese populations. For model construction, we aggregated the contribution of multiple SNPs into genetic risk scores (count GRS and weighed GRS) or predicted risks from logistic regression analyses (PRFLR).

In dataset2, the area under receiver operating characteristic curves (AUC) of the 5 consistently replicated SNPs by PRFLR(SNPs) was 0.615, higher than those of all GRSs(ranging from 0.607 to 0.611, all P>0.05). The AUC of genetic profile significantly exceeded that of family history (fmc) alone (AUC=0.535, all P<0.001). The best model in our study comprised “PRURA +fmc” (AUC=0.646) in dataset3. Further model assessment analyses provided additional evidence.

This study indicates that genetic markers have potential value for risk prediction of glioma.

Risk assessment models for genetic risk predictors of lung cancer using two-stage replication for Asian and European populations

In the past ten years, great successes have been accumulated by taking advantage of both candidate-gene studies and genome-wide association studies. However, limited studies were available to systematically evaluate the genetic effects for lung cancer risk with large-scale and different ethnic populations. We systematically reviewed relevant literatures and filtered out 241 important genetic variants identified in 124 articles. A two-stage case-control study within specific subgroups was performed to assess the effects [Training set: 2,331 cases vs. 3,077 controls (Chinese population); testing set: 1,937 cases vs. 1,984 controls (European population)]. Variable selection and model development were used LASSO penalized regression and genetic risk score (GRS) system. Further change in area under the receiver operator characteristic curves (AUC) made by the epidemiologic model with and without GRS was used to compare predictions. It kept 38 genetic variants in our study and the ratios of lung cancer risk for subjects in the upper quartile GRS was three times higher compared to that in the low quartile (odds ratio: 4.64, 95% CI: 3.87–5.56). In addition, we found that adding genetic predictors to smoking risk factor-only model improved lung cancer predictive value greatly: AUC, 0.610 versus 0.697 (P < 0.001). Similar performance was derived in European population and the combined two data sets. Our findings suggested that genetic predictors could improve the predictive ability of risk model for lung cancer and highlighted the application among different populations, indicating that the lung cancer risk assessment model will be a promising tool for high risk population screening and prediction.

A simple-to-use method incorporating genomic markers into prostate cancer risk prediction tools facilitated future validation

OBJECTIVES

To incorporate single-nucleotide polymorphisms (SNPs) into the Prostate Cancer Prevention Trial Risk Calculator (PCPTRC).

STUDY DESIGN AND SETTING

A multivariate random-effects meta-analysis of likelihood ratios (LRs) for 30 validated SNPs was performed, allowing the incorporation of linkage disequilibrium. LRs for an SNP were defined as the ratio of the probability of observing the SNP in prostate cancer cases relative to controls and estimated by published allele or genotype frequencies. LRs were multiplied by the PCPTRC prior odds of prostate cancer to provide updated posterior odds.

RESULTS

In the meta-analysis (prostate cancer cases/controls = 386,538/985,968), all but two of the SNPs had at least one statistically significant allele LR (P < 0.05). The two SNPs with the largest LRs were rs16901979 [LR = 1.575 for one risk allele, 2.552 for two risk alleles (homozygous)] and rs1447295 (LR = 1.307 and 1.887, respectively).

CONCLUSION

The substantial investment in genome-wide association studies to discover SNPs associated with prostate cancer risk and the ability to integrate these findings into the PCPTRC allows investigators to validate these observations, to determine the clinical impact, and to ultimately improve clinical practice in the early detection of the most common cancer in men.

Polymorphisms influencing prostate-specific antigen concentration may bias genome-wide association studies on prostate cancer

BACKGROUND

Genome-wide association studies (GWAS) have produced weak (OR = 1.1-1.5) but significant associations between single nucleotide polymorphisms (SNPs) and prostate cancer. However, these associations may be explained by detection bias caused by SNPs influencing PSA concentration. Thus, in a simulation study, we quantified the extent of bias in the association between a SNP and prostate cancer when the SNP influences PSA concentration.

METHODS

We generated 2,000 replicate cohorts of 20,000 men using real-world estimates of prostate cancer risk, prevalence of carrying ≥1 minor allele, PSA concentration, and the influence of a SNP on PSA concentration. We modeled risk ratios (RR) of 1.00, 1.25, and 1.50 for the association between carrying ≥1 minor allele and prostate cancer. We calculated mean betas from the replicate cohorts and quantified bias under each scenario.

RESULTS

Assuming no association between a SNP and prostate cancer, the estimated mean bias in betas ranged from 0.02 to 0.10 for ln PSA being 0.05 to 0.20 ng/mL higher in minor allele carriers; the mean biased RRs ranged from 1.03 to 1.11. Assuming true RRs = 1.25 and 1.50, the biased RRs were as large as 1.39 and 1.67, respectively.

CONCLUSION

Estimates of the association between SNPs and prostate cancer can be biased to the magnitude observed in published GWAS, possibly resulting in type I error. However, large associations (RR > 1.10) may not fully be explained by this bias.

IMPACT

The influence of SNPs on PSA concentration should be considered when interpreting results from GWAS on prostate cancer.

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.

Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status

OBJECTIVE

There is increasing interest in adding common genetic variants identified through genome wide association studies (GWAS) to breast cancer risk prediction models. First results from such models showed modest benefits in terms of risk discrimination. Heterogeneity of breast cancer as defined by hormone-receptor status has not been considered in this context. In this study we investigated the predictive capacity of 32 GWAS-detected common variants for breast cancer risk, alone and in combination with classical risk factors, and for tumours with different hormone receptor status.

MATERIAL AND METHODS

Within the Breast and Prostate Cancer Cohort Consortium, we analysed 6009 invasive breast cancer cases and 7827 matched controls of European ancestry, with data on classical breast cancer risk factors and 32 common gene variants identified through GWAS. Discriminatory ability with respect to breast cancer of specific hormone receptor-status was assessed with the age adjusted and cohort-adjusted concordance statistic (AUROC(a)). Absolute risk scores were calculated with external reference data. Integrated discrimination improvement was used to measure improvements in risk prediction.

RESULTS

We found a small but steady increase in discriminatory ability with increasing numbers of genetic variants included in the model (difference in AUROC(a) going from 2.7% to 4%). Discriminatory ability for all models varied strongly by hormone receptor status.

DISCUSSION AND CONCLUSIONS

Adding information on common polymorphisms provides small but statistically significant improvements in the quality of breast cancer risk prediction models. We consistently observed better performance for receptor-positive cases, but the gain in discriminatory quality is not sufficient for clinical application.

A comparison of Bayesian and frequentist approaches to incorporating external information for the prediction of prostate cancer risk

We present the most comprehensive comparison to date of the predictive benefit of genetics in addition to currently used clinical variables, using genotype data for 33 single-nucleotide polymorphisms (SNPs) in 1,547 Caucasian men from the placebo arm of the REduction by DUtasteride of prostate Cancer Events (REDUCE®) trial. Moreover, we conducted a detailed comparison of three techniques for incorporating genetics into clinical risk prediction. The first method was a standard logistic regression model, which included separate terms for the clinical covariates and for each of the genetic markers. This approach ignores a substantial amount of external information concerning effect sizes for these Genome Wide Association Study (GWAS)-replicated SNPs. The second and third methods investigated two possible approaches to incorporating meta-analysed external SNP effect estimates - one via a weighted PCa 'risk' score based solely on the meta analysis estimates, and the other incorporating both the current and prior data via informative priors in a Bayesian logistic regression model. All methods demonstrated a slight improvement in predictive performance upon incorporation of genetics. The two methods that incorporated external information showed the greatest receiver-operating-characteristic AUCs increase from 0.61 to 0.64. The value of our methods comparison is likely to lie in observations of performance similarities, rather than difference, between three approaches of very different resource requirements. The two methods that included external information performed best, but only marginally despite substantial differences in complexity.

Developing genetic epidemiological models to predict risk for nasopharyngeal carcinoma in high-risk population of China

To date, the only established model for assessing risk for nasopharyngeal carcinoma (NPC) relies on the sero-status of the Epstein-Barr virus (EBV). By contrast, the risk assessment models proposed here include environmental risk factors, family history of NPC, and information on genetic variants. The models were developed using epidemiological and genetic data from a large case-control study, which included 1,387 subjects with NPC and 1,459 controls of Cantonese origin. The predictive accuracy of the models were then assessed by calculating the area under the receiver-operating characteristic curves (AUC). To compare the discriminatory improvement of models with and without genetic information, we estimated the net reclassification improvement (NRI) and integrated discrimination index (IDI). Well-established environmental risk factors for NPC include consumption of salted fish and preserved vegetables and cigarette smoking (in pack years). The environmental model alone shows modest discriminatory ability (AUC = 0.68; 95% CI: 0.66, 0.70), which is only slightly increased by the addition of data on family history of NPC (AUC = 0.70; 95% CI: 0.68, 0.72). With the addition of data on genetic variants, however, our model’s discriminatory ability rises to 0.74 (95% CI: 0.72, 0.76). The improvements in NRI and IDI also suggest the potential usefulness of considering genetic variants when screening for NPC in endemic areas. If these findings are confirmed in larger cohort and population-based case-control studies, use of the new models to analyse data from NPC-endemic areas could well lead to earlier detection of NPC.

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.

Functional annotation of risk loci identified through genome-wide association studies for prostate cancer

BACKGROUND

The majority of established prostate cancer (PCa) risk-associated single nucleotide polymorphisms (SNPs) identified from genome-wide association studies do not fall into protein coding regions. Therefore, the mechanisms by which these SNPs affect PCa risk remain unclear. Here, we used a series of bioinformatic tools and databases to provide possible molecular insights into the actions of risk SNPs.

METHODOLOGY/PRINCIPAL FINDINGS

We performed a comprehensive assessment of the potential functional impact of 33 SNPs that were identified and confirmed as associated with PCa risk in previous studies. For these 33 SNPs and additional SNPs in linkage disequilibrium (LD) (r(2)  ≥ 0.5), we first mapped them to genomic functional annotation databases, including the encyclopedia of DNA elements (ENCODE), 11 genomic regulatory elements databases defined by the University of California Santa Cruz (UCSC) table browser, and androgen receptor (AR)-binding sites defined by a ChIP-chip technique. Enrichment analysis was then carried out to assess whether the risk SNP blocks were enriched in the various annotation sets. Risk SNP blocks were significantly enriched over that expected by chance in two annotation sets, including AR-binding sites (P = 0.003), and FoxA1-binding sites (P = 0.05). About one-third of the 33 risk SNP blocks are located within AR-binding regions.

CONCLUSIONS/SIGNIFICANCE

The significant enrichment of risk SNPs in AR-binding sites may suggest a potential molecular mechanism for these SNPs in PCa initiation, and provide guidance for future functional studies.

Combining 33 genetic variants with prostate-specific antigen for prediction of prostate cancer: longitudinal study

The aim of this study was to investigate if a genetic risk score including 33 common genetic variants improves prediction of prostate cancer when added to measures of prostate-specific antigen (PSA). We conducted a case-control study nested within the Northern Sweden Health and Disease Cohort (NSHDC), a prospective cohort in northern Sweden. A total of 520 cases and 988 controls matched for age, and date of blood draw were identified by linkage between the regional cancer register and the NSHDC. Receiver operating characteristic curves with area under curve (AUC) estimates were used as measures of prostate cancer prediction. The AUC for the genetic risk score was 64.3% [95% confidence interval (CI) = 61.4-67.2], and the AUC for total PSA and the ratio of free to total PSA was 86.2% (95% CI = 84.4-88.1). A model including the genetic risk score, total PSA and the ratio of free to total PSA increased the AUC to 87.2% (95% CI = 85.4-89.0, p difference = 0.002). The addition of a genetic risk score to PSA resulted in a marginal improvement in prostate cancer prediction that would not seem useful for clinical risk assessment.

The application of genetics and genomics to cancer prevention

Advances in technology have accelerated the translation of genetics and genomics into the arena of cancer prevention. This provides unique opportunities to individualize cancer risk prediction so early intervention can either modify risk or allow for early diagnosis thereby potentially decreasing the morbidity and mortality of cancer and containing costs. While the full potential of these genetic/genomic discoveries have yet to be realized, many have clear clinical relevance such as the value of family history and/or tumor profiling to identify those who may harbor a mutation in a cancer susceptibility gene and are therefore candidates for genetic testing. Here, we provide an overview of the scope of genetic and genomic influences on cancer risk assessment and the entire spectrum of cancer prevention.