Development of Noninvasive Biomarkers for Diagnosing and Monitoring Nonindolent Prostate Cancer

Abstract

While prostate cancer (PCa) is the most common solid organ malignancy in men, only 20 30% progress to metastatic disease. Men with indolent disease are offered a treatment plan that involves active surveillance (AS). However, there is a significant risk of under-grading the tumor and repeated invasive biopsies are required. We hypothesized that transcripts associated with high Gleason grade cancers are quantifiable in urine samples from men with prostate cancer, and that measurements of grade-associated transcripts will reflect the presence of higher-grade non-indolent tumors. By gene expression analysis (from microdissected Gleason-pattern (GP) 3 and GP4 PCa), in combination with publically available Gleason-associated transcriptional profiles, we have created a 46-gene panel that differentiates high Gleason from low Gleason grade PCa. Moreover, we have found that up-regulation of several GP-associated transcripts, such as RELN, associate with adverse clinical outcomes. We validated the GP4-associated upregulation of candidate genes by qPCR. Additionally, we have started to measure by qPCR the transcript levels for 6-genes in urine sediments from patients undergoing biopsy. Although, a significant difference exists between negative biopsy and PCa for two genes, no significant differences were found between GS6 and GS 8 biopsies. Urine sediments from patients undergoing radical prostatectomy are necessary to test their accuracy in predicting high versus low grade tumors. The discovery of high grade-associated transcripts in urine from patients with presumed indolent prostate cancer could substantially improve accurate staging of prostate tumors and clinical management decisions.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2013
Accession Number
ADA580378

Entities

People

  • Daniella Bianchi-frias

Organizations

  • Fred Hutchinson Cancer Center

Tags

DTIC Thesaurus Topics

  • Accuracy
  • Cancer
  • Data Sets
  • Diseases And Disorders
  • Dna Microarrays
  • Factor Analysis
  • Gene Expression
  • Information Science
  • Microarray Analysis
  • Neoplasms
  • Prostate
  • Prostate Cancer
  • Regression Analysis
  • Sediments
  • Statistical Analysis
  • Surveillance
  • Tissues

Fields of Study

  • Biology

Readers

  • Molecular and genetic basis of cancer.
  • Oncology and Biomarker-Based Cancer Detection.

Technology Areas

  • Space