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Related Experiment Videos

Predicting high-risk disease using tissue biomarkers.

Michael J Donovan1, Carlos Cordon-Cardo

  • 1Department of Pathology, Mt. Sinai School of Medicine, New York City, NY 10029-6574, USA. Michael.Donovan@mssm.edu

Current Opinion in Urology
|March 13, 2013
PubMed
Summary
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Identifying aggressive prostate cancer at diagnosis is challenging. New biomarker strategies and systems pathology approaches are needed to personalize treatment and predict disease progression in men with prostate cancer.

Area of Science:

  • Oncology
  • Biomarker Discovery
  • Prostate Cancer Research

Background:

  • Limited tools exist for predicting prostate cancer progression risk in newly diagnosed men.
  • Accurate risk stratification is crucial for guiding treatment decisions and managing disease.
  • Prostate cancer exhibits molecular heterogeneity, complicating diagnosis and prognosis.

Purpose of the Study:

  • To review current prostate cancer biomarker discovery and validation strategies.
  • To identify methods for detecting aggressive, clinically significant prostate cancer at diagnosis.
  • To explore the use of prostate tissue and fluid samples for identifying disease markers.

Main Methods:

  • Review of clinical translational studies on prostate cancer genetics.
  • Analysis of biomarker discovery and validation strategies.

Related Experiment Videos

  • Evaluation of systems pathology platforms, including histomorphometry and multiplex biomarker assessment.

    • Main Results:

    • Aneuploidy and copy-number variation are implicated as predictors of aggressive prostate cancer.
    • NKX3.1 regulation by Pim-1 offers insights into disease course.
    • No routine tissue-based biomarkers currently exist for predicting prostate cancer progression risk at diagnosis.
    • TMPRSS2-ERG gene fusion is linked to early development but not unequivocally to aggressive disease.

    Conclusions:

    • A multimodal approach is essential due to prostate cancer's molecular heterogeneity.
    • Systems pathology platforms can integrate various data for prognostic algorithms.
    • Personalized diagnostic and treatment algorithms require advanced biomarker identification.