A Comparison of Five Methods for Selecting Tagging Single-Nucleotide Polymorphisms

Our goal was to compare methods for tagging single-nucleotide polymorphisms (tagSNPs) withrespect to the power to detect disease association under differing haplotype-disease associationmodels. We were also interested in the effect that SNP selection samples, consisting of eithercases, controls, or a mixture, would have on power. We investigated five previously describedalgorithms for choosing tagSNPS: two that picked SNPs based on haplotype structure (Chapmanhaplotypicand Stram), two that picked SNPs based on pair-wise allelic association (Chapman-allelicand Cousin), and one control method that chose equally spaced SNPs (Zhai). In two diseaseassociatedregions from the Genetic Analysis Workshop 14 simulated data, we tested theassociation between tagSNP genotype and disease over the tagSNP sets chosen by each methodfor each sampling scheme. This was repeated for 100 replicates to estimate power. The two allelicmethods chose essentially all SNPs in the region and had nearly optimal power. The two haplotypicmethods chose about half as many SNPs. The haplotypic methods had poor performance comparedto the allelic methods in both regions. We expected an improvement in power when the selectionsample contained cases; however, there was only moderate variation in power between thesampling approaches for each method. Finally, when compared to the haplotypic methods, thereference method performed as well or worse in the region with ancestral disease haplotypestructure

Genomic “Dark Matter” in Prostate Cancer: Exploring the Clinical Utility of ncRNA as Biomarkers

Prostate cancer is the most diagnosed cancer among men in the United States. While the majority of patients who undergo surgery (prostatectomy) will essentially be cured, about 30–40% men remain at risk for disease progression and recurrence. Currently, patients are deemed at risk by evaluation of clinical factors, but these do not resolve whether adjuvant therapy will significantly attenuate or delay disease progression for a patient at risk. Numerous efforts using mRNA-based biomarkers have been described for this purpose, but none have successfully reached widespread clinical practice in helping to make an adjuvant therapy decision. Here, we assess the utility of non-coding RNAs as biomarkers for prostate cancer recurrence based on high-resolution oligonucleotide microarray analysis of surgical tissue specimens from normal adjacent prostate, primary tumors, and metastases. We identify differentially expressed non-coding RNAs that distinguish between the different prostate tissue types and show that these non-coding RNAs can predict clinical outcomes in primary tumors. Together, these results suggest that non-coding RNAs are emerging from the “dark matter” of the genome as a new source of biomarkers for characterizing disease recurrence and progression. While this study shows that non-coding RNA biomarkers can be highly informative, future studies will be needed to further characterize the specific roles of these non-coding RNA biomarkers in the development of aggressive disease

The oestrogen receptor alpha-regulated lncRNA NEAT1 is a critical modulator of prostate cancer

The androgen receptor (AR) plays a central role in establishing an oncogenic cascade that drives prostate cancer progression. Some prostate cancers escape androgen dependence and are often associated with an aggressive phenotype. The oestrogen receptor alpha (ERα) is expressed in prostate cancers, independent of AR status. However, the role of ERα remains elusive. Using a combination of chromatin immunoprecipitation (ChIP) and RNA-sequencing data, we identified an ERα-specific non-coding transcriptome signature. Among putatively ERα-regulated intergenic long non-coding RNAs (lncRNAs), we identified nuclear enriched abundant transcript 1 (NEAT1) as the most significantly overexpressed lncRNA in prostate cancer. Analysis of two large clinical cohorts also revealed that NEAT1 expression is associated with prostate cancer progression. Prostate cancer cells expressing high levels of NEAT1 were recalcitrant to androgen or AR antagonists. Finally, we provide evidence that NEAT1 drives oncogenic growth by altering the epigenetic landscape of target gene promoters to favour transcription

A comparison of two logistic regression approaches for case-control data with missing haplotypes

In a case-control study, subjects are selected according to disease status and their risk factors are determined retrospectively. When risk factors are fully observed for all subjects, maximum-likelihood inference of disease associations may be obtained by applying prospective logistic regression to case-control data as though it were collected prospectively. We investigate the statistical properties of prospective maximum-likelihood (PML) inference of disease associations with risk factors known as haplotypes when haplotype phase is not fully observed in some subjects. We motivate applying PhlL to case-control data and compare PML to an estimating equation (EE) approach developed specifically for such data. We conduct limited simulations of case-control data to investigate the bias of PhlL and EE, both in estimated haplotype risks and in their standard errors. PhlL performed well in the simulation configurations we considered. By contrast, EE gave anticonservative inference when there was marked haplotype ambiguity

A comparison of five methods for selecting tagging single-nucleotide polymorphisms

Our goal was to compare methods for tagging single-nucleotide polymorphisms (tagSNPs) with respect to the power to detect disease association under differing haplotype-disease association models. We were also interested in the effect that SNP selection samples, consisting of either cases, controls, or a mixture, would have on power. We investigated five previously described algorithms for choosing tagSNPS: two that picked SNPs based on haplotype structure (Chapman-haplotypic and Stram), two that picked SNPs based on pair-wise allelic association (Chapman-allelic and Cousin), and one control method that chose equally spaced SNPs (Zhai). In two disease-associated regions from the Genetic Analysis Workshop 14 simulated data, we tested the association between tagSNP genotype and disease over the tagSNP sets chosen by each method for each sampling scheme. This was repeated for 100 replicates to estimate power. The two allelic methods chose essentially all SNPs in the region and had nearly optimal power. The two haplotypic methods chose about half as many SNPs. The haplotypic methods had poor performance compared to the allelic methods in both regions. We expected an improvement in power when the selection sample contained cases; however, there was only moderate variation in power between the sampling approaches for each method. Finally, when compared to the haplotypic methods, the reference method performed as well or worse in the region with ancestral disease haplotype structure.Medicine, Department ofMedicine, Faculty ofNon UBCReviewedFacult

Combined value of validated clinical and genomic risk stratification tools for predicting prostate cancer mortality in a high-risk prostatectomy cohort.

BackgroundRisk prediction models that incorporate biomarkers and clinicopathologic variables may be used to improve decision making after radical prostatectomy (RP). We compared two previously validated post-RP classifiers-the Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) and the Decipher genomic classifier (GC)-to predict prostate cancer-specific mortality (CSM) in a contemporary cohort of RP patients.ObjectiveTo evaluate the combined prognostic ability of CAPRA-S and GC to predict CSM.Design, setting, and participantsA cohort of 1010 patients at high risk of recurrence after RP were treated at the Mayo Clinic between 2000 and 2006. High risk was defined by any of the following: preoperative prostate-specific antigen >20 ng/ml, pathologic Gleason score ≥8, or stage pT3b. A case-cohort random sample identified 225 patients (with cases defined as patients who experienced CSM), among whom CAPRA-S and GC could be determined for 185 patients.Outcome measurements and statistical analysisThe scores were evaluated individually and in combination using concordance index (c-index), decision curve analysis, reclassification, cumulative incidence, and Cox regression for the prediction of CSM.Results and limitationsAmong 185 men, 28 experienced CSM. The c-indices for CAPRA-S and GC were 0.75 (95% confidence interval [CI], 0.55-0.84) and 0.78 (95% CI, 0.68-0.87), respectively. GC showed higher net benefit on decision curve analysis, but a score combining CAPRA-S and GC did not improve the area under the receiver-operating characteristic curve after optimism-adjusted bootstrapping. In 82 patients stratified to high risk based on CAPRA-S score ≥6, GC scores were likewise high risk for 33 patients, among whom 17 had CSM events. GC reclassified the remaining 49 men as low to intermediate risk; among these men, three CSM events were observed. In multivariable analysis, GC and CAPRA-S as continuous variables were independently prognostic of CSM, with hazard ratios (HRs) of 1.81 (p<0.001 per 0.1-unit change in score) and 1.36 (p=0.01 per 1-unit change in score). When categorized into risk groups, the multivariable HR for high CAPRA-S scores (≥6) was 2.36 (p=0.04) and was 11.26 (p<0.001) for high GC scores (≥0.6). For patients with both high GC and high CAPRA-S scores, the cumulative incidence of CSM was 45% at 10 yr. The study is limited by its retrospective design.ConclusionsBoth GC and CAPRA-S were significant independent predictors of CSM. GC was shown to reclassify many men stratified to high risk based on CAPRA-S ≥6 alone. Patients with both high GC and high CAPRA-S risk scores were at markedly elevated post-RP risk for lethal prostate cancer. If validated prospectively, these findings suggest that integration of a genomic-clinical classifier may enable better identification of those post-RP patients who should be considered for more aggressive secondary therapies and clinical trials.Patient summaryThe Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) and the Decipher genomic classifier (GC) were significant independent predictors of prostate cancer-specific mortality. These findings suggest that integration of a genomic-clinical classifier may enable better identification of those post-radical prostatectomy patients who should be considered for more aggressive secondary therapies and clinical trials