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Canary TMA

Feng, ZidingFred Hutchinson Cancer Research Center
Biomarkers to be evaluated include (but are not limited to): FLNA (ABP280), AMACR, CDK7, ITGA5, JAG1, SLC4A1AP, MIB1 (MKi67), MTA1, MUC1, TP63, KLK3 (PSA), TPD52. These initial candidate biomarkers were chosen based on their inclusion in a 12-gene model shown to be predictive of aggressive prostate cancer (Bismar et al, 2006). We anticipate that the initial set of “validation” TMAs constructed in this study will have the capacity to evaluate approximately 100 candidate biomarkers. If resources allow, a second set of “discovery” TMAs will be constructed, allowing for the evaluation of an addition 100 candidates.
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Prostate and Urologic Cancers Research Group

This protocol describes a multi-center, retrospective, case-cohort tissue microarray (TMA) study to evaluate tissue biomarkers for their ability to predict recurrent prostate cancer at the time of radical prostatectomy (RP). Candidate biomarkers will be assessed by performing tissue localization studies on TMAs containing recurrent prostate cancer and non-recurrent prostate cancer. De-identified data will be transferred to a central repository for statistical analysis. Participating institutions will use a variation of case-cohort sampling to randomly select a subset of patients from a retrospectively constructed RP cohort and/or perform selected assays on the cohort. The study endpoint is time to recurrence; of primary interest is five year recurrence free survival. Recurrent prostate cancer is defined by 1) a single serum prostate-specific antigen (PSA) level greater than 0.2 ng/mL after RP and/or 2) receipt of salvage or secondary therapy after RP and/or 3) clinical or radiological evidence of metastatic disease. Non-recurrent prostate cancer is defined as disease with no evidence of recurrence.

The 5 year biochemical (PSA) recurrence-free survival rate after radical prostatectomy to treat prostate cancer has been estimated at 84% or lower depending on a number of risk factors (e.g., Gleason grade, surgical margins, and pre-operative PSA levels; Han et al, 2003). Identifying the subset of patients with potentially recurrent disease at the time of radical prostatectomy will allow for immediate clinical management of aggressive disease and patient counseling, as well as inform the scheduling of patient surveillance. Although various models have been constructed to predict the probability of recurrent disease based on clinical and pathologic parameters, as well as pre-surgery PSA levels (Stephenson et al, 2005; Pound et al, 1999), these models could be improved with the addition of new biomarkers. This study will create a valuable resource for validating tissue biomarkers of recurrent prostate cancer.
Data analysis: For discovery and model building, Cox regression for case-cohort design will be used to analyze the time-to-recurrence data. The sampling probabilities for different subgroups will be used as a weighting factor to make the analysis relevant to the whole RP cohort (Samuelsen et al, 2007) . The linear score obtained from Cox regression will be treated as a composite biomarker and a threshold will be identified for desired sensitivity/specificity. For validation analysis, we assume the test, either a single biomarker or a composite biomarker, has been developed from discovery study or from other studies. For the primary objective, sensitivity at 98% specificity will be estimated from the data and a 95% one-sided confidence interval will be constructed. The sampling probabilities will be used to weigh different groups in calculating sensitivity/specificity for the whole cohort, making the estimates more applicable for general RP population. If the biomarker is a 4-level ordinal single biomarker, we will calculate sensitivity and its 95% confidence interval at the level corresponding to at least 98% specificity (assuming it is achievable). If the confidence interval covers the null hypothesized sensitivity 0.15, we will conclude that the candidate biomarker does not meet our criteria of performing significantly better than 15% sensitivity at 98% specificity. Similarly, for the secondary objective, we will construct a 95% one-sided confidence interval for the specificity at 98% sensitivity. If the confidence interval covers the null hypothesized specificity 0.10, we will conclude that the candidate biomarker does not meet our criteria of performing significantly better than 10% specificity at 98% sensitivity. To examine the effect of surgery site on biomarker levels, we will use a Cox regression model including biomarker, site indicator, and site indicator by biomarker interaction terms in the model. If the interaction term is statistically significant, we will first investigate if some site specific standardization (such as using percentile in controls within site) will eliminate such interactions and enable us to create a combined ROC curve. Otherwise, outcomes will be analyzed by each clinical site and ROC curves will be plotted for each site and we will investigate the possible explanations for such differences.

No datasets are currently associated with this protocol.

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