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Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
Published on: November 2, 2013
Hidden clues in prostate cancer - Lessons learned from clinical and pre-clinical approaches on diagnosis and risk
Roxanne Toivanen1, Laura H Porter2, Zhuoer Li2
1Department of Anatomy and Developmental Biology, Biomedicine Discovery Institute, Cancer Program, Monash University, Melbourne, Victoria, 3800, Australia; Prostate Cancer Research Program, Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, Victoria, 3002, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Victoria, 3010, Australia.
Abstract:
The heterogeneity of prostate cancer is evident at clinical, morphological and molecular levels. To aid clinical decision making, a three-tiered system for risk stratification is used to designate low-, intermediate-, and high-risk of disease progression. Intermediate-risk prostate cancers are the most frequently diagnosed, and even with common diagnostic features, can exhibit vastly different clinical progression. Thus, improved risk stratification methods are needed to better predict patient outcomes. Here, we provide an overview of the improvements in diagnosis/prognosis arising from advances in pathology reporting of prostate cancer, which can improve risk stratification, especially for patients with intermediate-risk disease. This review discusses updates to pathology reporting of morphological growth patterns, and proposes the utility of integrating prognostic biomarkers or innovative imaging techniques to enhance clinical decision-making. To complement clinical studies, experimental approaches using patient-derived tumors have highlighted important cellular and morphological features associated with aggressive disease that may impact treatment response. The intersection of urology, pathology and scientific disciplines is required to work towards a common goal of understanding disease pathogenesis, improving the stratification of patients with intermediate-risk disease and subsequently defining optimal treatment strategies using precision-based approaches.

