Molecular and Clinical Predictors of Aggressive Prostate Cancer

Abstract

While prostate cancer is an important cause of cancer mortality, most men diagnosed with early prostate cancer experience an indolent course. We evaluated molecular and clinical predictors to distinguish lethal and indolent prostate cancer. In a related project, we tested the predictive value of a previously identified multigene tumor signature, a 12-gene model. Risk classification based on the 12-gene model predicted development of lethal disease 20 years hence. The best discrimination came from combining data from the 12-gene markers and clinical data, which perfectly classified the lowest risk stratum where no one died of cancer and provided greater discriminatory ability (AUC 0.78) than the clinical model alone (AUC 0.71), p=0.04. We tested the top 5-genes from the model, and additional tumor markers, within a prostatectomy cohort of 950 men from the Physicians? Health Study and Health Professionals Follow-up Study. We constructed high-density tumor tissue microarrays, and undertook immunohistochemistry to characterize protein expression. We abstracted clinical data from medical records and pathology reports. We undertook statistical analyses and model building to test the discriminatory ability of the gene markers and clinical data, with the ultimate goal to provide prognostication of lethal and indolent prostate cancer.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2008
Accession Number
ADA503189

Entities

People

  • Lorelei A. Mucci

Organizations

  • Brigham and Women's Hospital

Tags

DTIC Thesaurus Topics

  • Biomedical Research
  • Cancer
  • Discrimination
  • Diseases And Disorders
  • Health Services
  • Information Science
  • Medical Personnel
  • Neoplasms
  • Oncology
  • Pathology
  • Preventive Medicine
  • Prostate
  • Prostate Cancer
  • Public Health
  • Statistical Analysis
  • Statistics
  • Tissues

Fields of Study

  • Medicine

Readers

  • Oncology and Biomarker-Based Cancer Detection.