Team Project

Glycoprotein Biomarkers for the Early Detection of Aggressive Prostate Cancer

Hui Zhang Supplement 2012
1. PSAD i s a biomarker to predict re-classification of AS patients on repeat biopsy (11, 13-14). 3. PSA derivatives (i.e. freePSA and (-2,-5,•-7)ProPSA)) in serum and tissue (18-20). 4. DNA contentand nuclear morphometry of AS biopsy (21-24). 5. Ki67; Our group found that Ki--67 to be univariately significant to predict PSA recurrence in long term follow-up CaP (22). 6. p300 and Nuclear Morphometry in CaP: Her-2/neu oncogene over-expression and DNA content: The FBBL at JHMI demonstrated that Her- 2Jneu as well as DNA content was increased significantly in biochemical progression, metastasis and CaP­ specific survival (22, 24-25). 8. Calci um channel, voltage dependent, I tvpe, alpha 1d subunit (CACNA1D): Periostin (POSTN
No design specified.
Prostate and Urologic Cancers Research Group

This proposal is focused on a means to improve the selection of men with indolent CaP to undergo AS and identify those men who unexpectedly may have very serious disease and fits within the scope of the EDRN research vision.

Aim #1 To select 140 retrospective (n=70 eventually had a catastrophic outcome and another n=70 cases where the outcome is as expected, a very low risk cancer). Dr. Epstein,pathologist, will be responsible for these tasks. Cases available for the project are listed in Table 1— These must be collected,reviewed and marked for cancer areas. Aim #2 Quantitative Nuclear Morphometry (QNM) and Molecular Biomarkers by MTI on AS cohort Optimization of five biomarkers using the Multiplex tissue immunoblotting (MTI)1quarter QNM technology is standardized and requires 15-20 minutes per case and we have 140 cases to run. QNM – this will require at least 5 qurtersa to collect cells from 140 cases, create the database and then analyze. MTI for (-5,-7)ProPSA, Ki67, Her2/neu, POSTN, &CACNA1D - We can only run about 5-6 cases per every 2 days and make one run per week for all the markers. A total of five quarters required.. Aim #3 Construct and validate computerized based histologic classifier using Cox proportional hazards analysis. Dr. Bruce Trock will assist in preparing the Cox proportional hazard models. A proportional hazards model will be developed using the predictors from the best model in Aim 2. For continuous variables with evidence of non-linearity we will explore alternative metrics using restricted cubic splines. We may use several approaches to modeling. If the number of predictors derived in Aim 1 is not large (<1/10th the number of biopsy progression events) we will include all predictors and bootstrap the model. Estimates after all data is collected and audited: 8-10 weeks (part-time basis)

There are currently no biomarkers annotated for this protocol.

No datasets are currently associated with this protocol.

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