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.

Human epidermal growth factor receptor 2 (HER2) extracellular domain levels are associated with progression-free survival in patients with HER2-positive metastatic breast cancer receiving lapatinib monotherapy.

BACKGROUND: Changes in serum human epidermal growth factor receptor 2 (HER2) levels associated with clinical outcomes, including objective response rate, progression-free survival (PFS), and overall survival have been reported in patients with metastatic breast cancer (MBC) receiving trastuzumab and chemotherapy. This study investigated whether baseline or changes in serum HER2 correlated with overall response rate (ORR) and/or PFS in patients with MBC receiving first-line lapatinib monotherapy. METHODS: The EGF20009 study investigated lapatinib monotherapy in 138 HER2-positive patients with MBC previously untreated for their metastatic disease. Serum was collected and assessed at baseline and every 4 weeks for 16 weeks after treatment initiation. Disease assessment was performed at weeks 8 and 12 and every 12 weeks thereafter. A ≥ 20% decrease or increase in serum HER2 was defined as a significant change. RESULTS: Seventy-nine percent of patients had elevated baseline serum HER2. Baseline serum HER2 was associated with ORR (P = .043) but not PFS. Patients with a ≥ 20% decrease from baseline of serum HER2 at weeks 4, 8, 12, and 16 had a significantly increased ORR and prolonged PFS. Conversely, those with a ≥ 20% increase from baseline had a significantly lower ORR and shorter PFS. CONCLUSION: Significant decreases in serum HER2 levels during the first 16 weeks of lapatinib monotherapy were associated with better clinical outcome (longer PFS and increased ORR) in HER2-positive MBC patients.

Potential impact of adding genetic markers to clinical parameters in predicting prostate biopsy outcomes in men following an initial negative biopsy: findings from the REDUCE trial.

BACKGROUND: Several germline single nucleotide polymorphisms (SNPs) have been consistently associated with prostate cancer (PCa) risk. OBJECTIVE: To determine whether there is an improvement in PCa risk prediction by adding these SNPs to existing predictors of PCa. DESIGN, SETTING, AND PARTICIPANTS: Subjects included men in the placebo arm of the randomized Reduction by Dutasteride of Prostate Cancer Events (REDUCE) trial in whom germline DNA was available. All men had an initial negative prostate biopsy and underwent study-mandated biopsies at 2 yr and 4 yr. Predictive performance of baseline clinical parameters and/or a genetic score based on 33 established PCa risk-associated SNPs was evaluated. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Area under the receiver operating characteristic curves (AUC) were used to compare different models with different predictors. Net reclassification improvement (NRI) and decision curve analysis (DCA) were used to assess changes in risk prediction by adding genetic markers. RESULTS AND LIMITATIONS: Among 1654 men, genetic score was a significant predictor of positive biopsy, even after adjusting for known clinical variables and family history (p = 3.41 × 10(-8)). The AUC for the genetic score exceeded that of any other PCa predictor at 0.59. Adding the genetic score to the best clinical model improved the AUC from 0.62 to 0.66 (p<0.001), reclassified PCa risk in 33% of men (NRI: 0.10; p=0.002), resulted in higher net benefit from DCA, and decreased the number of biopsies needed to detect the same number of PCa instances. The benefit of adding the genetic score was greatest among men at intermediate risk (25th percentile to 75th percentile). Similar results were found for high-grade (Gleason score ≥ 7) PCa. A major limitation of this study was its focus on white patients only. CONCLUSIONS: Adding genetic markers to current clinical parameters may improve PCa risk prediction. The improvement is modest but may be helpful for better determining the need for repeat prostate biopsy. The clinical impact of these results requires further study.