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Functional characterization of pseudogenes as new biomarker in prostate cancer

$202,928R21FY2014CANIH

University Of Michigan At Ann Arbor, Ann Arbor MI

Investigators

Linked publications, trials & patents

Abstract

DESCRIPTION (provided by applicant): Prostate cancer remains the most commonly diagnosed cancer and second leading cancer-related death in men in the United States with 238,590 new diagnosis and ~29,720 deaths in 2013 (ACS). Accurate diagnosis and early intervention will reduce the number of deaths due to metastatic prostate cancer. Prostate cancer diagnosis is based initially on the controversial serum prostate specific antigen (PSA) level. To overcome the problems associated with testing PSA level, identification of accurate and reliable cancer-specific molecular markers will reduce unnecessary biopsies for those with benign conditions and direct patients with aggressive disease for appropriate treatment. The genetic basis of prostate cancer can be classified into groups based on specific driving molecular aberrations. About 50-60% of prostate cancer patients are known to have E26 transformation specific (ETS) gene rearrangements and overexpression of SPINK1 was identified in 5-10% of ETS-negative prostate cancer. Recently, we reported the identification of druggable gene fusions involving RAF kinase genes (SLC45A3-BRAF and ESRP1-RAF1) in 1-2% of ETS negative prostate cancer. The genetic basis of the remaining 30-40% of ETS-negative prostate cancer remains unknown. In this proposal we utilized next generation sequencing technology to study the transcriptional landscape of prostate cancer on a genome-wide scale for pseudogene expression. In our pilot study using 89 prostate cancer samples we identified 8 candidate pseudogenes and a novel fusion gene involving a pseudogene expressed only in prostate cancer. Based on the encouraging preliminary results on the functional role of the candidate pseudogenes, we will prioritize our studies with a focus on the functional characterization of CXADRP2 and KLK4-KLKP1. In specific aim 1: We will expand the validation of candidate pseudogenes in a large cohort of prostate cancer to understand their unique role in prostate cancer. In our preliminary validation studies, we confirmed the expression of CXADRP2 in ETS fusion-negative prostate cancer and KLK4-KLKP1 in ETS fusion positive prostate cancer. Further using RNA in situ hybridization assay we showed significant increase in the expression of KLK4-KLKP1 in high Gleason score (GS-7 or above) prostate cancer indicating its potential role in aggressive prostate cancer. We also presented preliminary data on the expression of these two pseudogenes in post-DRE urine samples, indicating the potential to screen by non-invasive methods. With the strong correlation with ETS -positive and ETS -negative prostate cancer for CXADRP2 and KLK4-KLKP1; respectively, we will correlate the expression with known prostate specific non-coding RNA PCA3 and PCAT-1 to understand the mutually exclusive function of pseudogenes in subsets of prostate cancer. Given the unique 54 amino acid peptide from KLKP1, we will develop KLKP1 specific antibody to screen by western blot analysis and immunohistochemistry. In specific aim 2: We showed strong functional data using in vitro and in vivo studies. We also show that either of the gene has any effect on the other, suggesting an independent mechanism in prostate cancer. We will perform gene expression microarray analysis in cells with overexpression to understand the molecular mechanisms involved and potential signaling pathways affected by the pseudogenes. Further, based on the encouraging results from the in vivo CAM assay, we will validate the oncogenic properties in murine models. We anticipate that validation of these new molecular markers will add into the armamentarium of prostate cancer diagnostics to identify patients that will develop metastatic disease who require early and aggressive treatment strategies.

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Functional characterization of pseudogenes as new biomarker in prostate cancer · GrantIndex