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Predicting High-Risk Disease Using Tissue Biomarkers

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Active Surveillance for Localized Prostate Cancer

Part of the book series: Current Clinical Urology ((CCU))

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Abstract

Personalized medicine in the management of patients with prostate cancer is limited to the integration of patient attributes such as age, genetic risk, and comorbidities with specific clinical-pathological variables including serum prostate-specific antigen (PSA); imaging and features from the diagnostic prostate needle biopsy or prostatectomy specimen including tumor differentiation (i.e., Gleason); and volume and extent of disease (i.e., tumor length and/or percentage, number of positive cores at diagnosis, or pathological stage post surgery including margin status). Although the development of various clinical statistical instruments such as nomograms has provided a mechanism to interrogate such variables, most urologists rely on basic prognostic features of stage, grade, and PSA along with clinical judgment to define and understand individual risk and predict health outcomes. The reality is that the majority of patients diagnosed with prostate cancer today are in the lower, intermediate-risk category (biopsy Gleason score 6–7, PSA < 10 ng, ≤T2) where clinical variables are not able to sufficiently discriminate patients with respect to appropriate treatment and disease course (Adamy et al., J Urol 185:477–482, 2011; Yatani et al., Jpn J Clin Oncol 19:319–326, 1989; Abate-Shen et al., Differentiation 76:717–727, 2008). Furthermore, unlike other tumor types such as breast cancer, there are no routine ancillary diagnostic studies performed on the prostate needle biopsy or the prostatectomy specimen to support and refine the prognosis and treatment decision process for the individual patient.

In this chapter, we will provide an overview of prostate cancer biomarker discovery and validation strategies to identify aggressive disease at diagnosis and include an assessment of the technological advances, molecular strategies, and future methodologies to identify novel mechanisms of growth, invasion, and metastasis. We will also discuss the impact of pre-analytic specimen variability on study results and explore how advances in functional histology and mathematical models will play a central role in the development and incorporation of a systems-based platform to identify men at high risk of aggressive disease.

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Authors and Affiliations

  1. Department of Pathology, The Mount Sinai Hospital, One Gustave L. Levy Place, 1194, 10029-6574, New York, NY, USA

    Michael J. Donovan M.D., Ph.D. & Carlos Cordon-Cardo M.D., Ph.D.

  2. Department of Genetics and Genomic Sciences, The Mount Sinai School of Medicine, One Gustave L. Levy Place, 1194, 10029-6574, New York, NY, USA

    Carlos Cordon-Cardo M.D., Ph.D.

Authors
  1. Michael J. Donovan M.D., Ph.D.
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  2. Carlos Cordon-Cardo M.D., Ph.D.
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Corresponding author

Correspondence to Michael J. Donovan M.D., Ph.D. .

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Editors and Affiliations

  1. University of Toronto, Sunnybrook Health Sciences Centre, Division of Urology, Toronto, M4N 3M5, Ontario, Canada

    Laurence Klotz

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Donovan, M.J., Cordon-Cardo, C. (2012). Predicting High-Risk Disease Using Tissue Biomarkers. In: Klotz, L. (eds) Active Surveillance for Localized Prostate Cancer. Current Clinical Urology. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-61779-912-9_3

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  • DOI: https://doi.org/10.1007/978-1-61779-912-9_3

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  • Publisher Name: Humana Press, Totowa, NJ

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  • Online ISBN: 978-1-61779-912-9

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