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 are evaluating molecular and clinical predictors at diagnosis to distinguish lethal and indolent prostate cancer. In a related project, we have 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 information 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 now are testing the 12-gene and additional tumor markers within a prostatectomy cohort of 950 men from the Physicians Health Study and Health Professionals Follow-up Study. We have constructed high-density tumor tissue microarrays on the cohort, and have undertaken immunohistochemistry to characterize protein expression. We have also completed abstraction of clinical data from medical records and pathology reports. Preliminary analyses and model building to test the discriminatory ability of the gene markers and clinical data are now underway, 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, 2007
Accession Number
ADA475306

Entities

People

  • Lorelei A. Mucci

Organizations

  • Brigham and Women's Hospital

Tags

DTIC Thesaurus Topics

  • Biomedical Research
  • Cancer
  • Classification
  • Computer Programming
  • Databases
  • Discrimination
  • Diseases And Disorders
  • Health Services
  • Medical Personnel
  • Neoplasms
  • Pathology
  • Prostate
  • Prostate Cancer
  • Public Health
  • Statistical Analysis
  • Therapy
  • Tissues

Fields of Study

  • Medicine

Readers

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  • Regression Analysis.
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