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1.  The Potential of Genes and other Markers to Inform about Risk 
Advances in biotechnology have raised expectations that biomarkers, including genetic profiles, will yield information to accurately predict outcomes for individuals. However, results to date have been disappointing. In addition, statistical methods to quantify the predictive information in markers have not been standardized.
We discuss statistical techniques to summarize predictive information including risk distribution curves and measures derived from them that relate to decision making. Attributes of these measures are contrasted with alternatives such as receiver operating characteristic curves, R-squared, percent reclassification and net reclassification index. Data are generated from simple models of risk conferred by genetic profiles for individuals in a population. Statistical techniques are illustrated and the risk prediction capacities of different risk models are quantified.
Risk distribution curves are most informative and relevant to clinical practice. They show proportions of subjects classified into clinically relevant risk categories. In a population in which 10% have the outcome event and subjects are categorized as high risk if their risk exceeds 20%, we found to identify as high risk more than half of those destined to have an event, either 150 genes each with odds ratio of 1.5 or 250 genes each with odds ratio of 1.25 was required when the minor allele frequencies are 10%. We show that conclusions based on ROC curves may not be the same as conclusions based on risk distribution curves.
Many highly predictive genes will be required in order to identify substantial numbers of subjects at high risk.
PMCID: PMC2836397  PMID: 20160267
biomarkers; classification; discrimination; prediction; statistical methods
2.  Performance of a Single Assay for Both Type III and Type VI TMPRSS2:ERG Fusions in Noninvasive Prediction of Prostate Biopsy Outcome 
Clinical chemistry  2008;54(12):2007-2017.
TMPRSS2:ERG fusions are promising prostate cancer biomarkers. Because they can occur in multiple forms in a single cancer specimen, we developed a quantitative PCR test that detects both type III and type VI TMPRSS2:ERG fusions. The assay is quantified from a standard curve determined with a plasmid-cloned type III TMPRSS2:ERG fusion target.
We collected expressed prostatic secretion (EPS) under an institutional review board–approved, blinded, prospective study from 74 patients undergoing transrectal ultrasound-guided biopsy for prostate cancer. We compared the characteristic performance of the test for type III and type VI TMPRSS2:ERG fusions in predicting biopsy outcome and distinguishing between high and low Gleason scores with similar tests for the expression of PCA3 and DNA methylation levels of the APC, RARB, RASSF1, and GSTP1 genes. We used logistic regression to analyze the effects of multiple biomarkers in linear combinations.
Each test provided a significant improvement in characteristic performance over baseline digital rectal examination (DRE) plus serum prostate-specific antigen (PSA); however, the test for type III and type VI TMPRSS2:ERG fusions yielded the best performance in predicting biopsy outcome [area under the curve (AUC) 0.823, 95% CI 0.728–0.919, P < 0.001] and Gleason grade >7 (AUC 0.844, 95% CI 0.740–0.948, P < 0.001).
Although each test appears to have diagnostic value, PSA plus DRE plus type III and type VI TMPRSS2:ERG provided the best diagnostic performance in EPS specimens.
PMCID: PMC2977928  PMID: 18948370

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