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Accession Number:

AD1166351

Title:

Radiogenomic Characterization of Prostate Cancer: Distinguishing Aggressive From Indolent Disease

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Report Date:

2021-10-01

Abstract:

Over the past several years, there has been growing utilization of multi-parametric magnetic resonance imaging (mpMRI) to detect aggressive or high-grade PCa. Even though close to 20 % of aggressive PCa are missed by mpMRI, this imaging modality is currently being used to guide treatment decisions, such as for active surveillance and delineating areas for focal therapy. The goal of this project is to improve the detection of aggressive(Gleason 7) PCa by combining mpMRI and urinary biomarkers. Building on our prior work on using PCA3 and TMPRSS2:ERG to detect aggressive PCa, we have developed a novel urine-based targeted next generation sequencing (NGS) assay to detect PCa. We hypothesize that aggressive PCa harbors unique molecular alterations that impact detection by mpMRI or a urine-based sequencing assay. To test this hypothesis, we propose the following Specific Aims: 1):To assess the accuracy of a novel urine-based NGS assay for the detection of high-grade PCa. We will perform targeted DNA/RNA NGS on already collected pre-biopsy post-DRE urine specimens in patients who underwent radical prostatectomy (RP) at the University of Michigan (U-M). The molecular profile of patients with high-grade (cases) versus low-grade (controls) PCa will be compared. 2): To comprehensively characterize the genomic and transcriptomic alterations associated with cancer visibility on mpMRI. We will collect formalin-fixed paraffin-embedded (FFPE) radical prostatectomy (RP) specimens with multiple foci of cancer in men who had mpMRI prior to RP, with an emphasis on those with multiple Gleason grades. Where available, corresponding matched pelvic lymph node specimens with metastasis will also be collected. Targeted multiplexed PCR-based NGS will be performed to characterize and compare the molecular profiles of visible and invisible PCa foci on mpMRI. 3): Determine and compare tissue versus a urine-based prognostic assays to predict upgrading at RP.

Pages:

46

File Size:

3.73MB

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Distribution Statement:

Approved For Public Release

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