Imaging Prostatic Lipids to Distinguish Aggressive Prostate Cancer

Abstract

In this application, we propose to build upon our current work to determine the association between fatty acid synthase (FAS) overexpression and intraprostatic fat as measured by in-vivo imaging using proton magnetic resonance spectroscopy imaging in the prediction of prostate disease aggressiveness. Mechanisms linking fatty acid synthase overexpression, lipid accumulation, lipid oxidation, and tumor aggressiveness will be explored using metabolomics. Employing a cross-sectional design we will recruit 50 men with low-grade and 50 men with high grade prostate cancer post-diagnosis as determined prior to prostatectomy. Each patient will complete one proton magnetic resonance spectroscopy imaging session and provide access to his prostatectomy tissue. Among men diagnosed with low grade (proposed as more indolent) and high grade (proposed as more aggressive) prostate cancer (as determined by Gleason scoring) we propose to: 1) Determine the correlation between FAS expression in prostatectomy samples and the amount of intraprostatic lipid using 1H magnetic resonance spectroscopic imaging (proton MRSI) with an endorectal coil. 2) Identify the association between FAS expression and FAS activity in prostatectomy samples, intraprostatic lipid as measured by MRSI and prostate tumor aggressiveness. 3) To quantify key metabolic intermediates involved in lipid metabolism, mitochondrial function, inflammation, and apoptosis in the prostatectomy samples.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2015
Accession Number
ADA626329

Entities

People

  • Jonathan Q Purnell

Organizations

  • Oregon Health & Science University

Tags

DTIC Thesaurus Topics

  • Data Acquisition
  • Data Analysis
  • Department Of Defense
  • Diseases And Disorders
  • Fatty Acids
  • Lipids
  • Magnetic Resonance
  • Medical Personnel
  • Metabolomics
  • Neoplasms
  • Physicians
  • Prostate
  • Prostate Cancer
  • Resonance
  • Spectroscopy
  • Statistical Analysis
  • Tissues

Fields of Study

  • Medicine

Readers

  • Immunology and Pathology
  • Medical Imaging.
  • Oncology and Biomarker-Based Cancer Detection.