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Urology. 2001 Apr;57(4 Suppl 1):224-9.

Prostate cancer chemoprevention: Strategies for designing efficient clinical trials.

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National Cancer Institute, Rockville, Maryland 20852, USA.


A chemoprevention (CP) strategy has evolved for conducting efficient clinical trials for prostate cancer (PCa) prevention. It integrates five key components, including agents, biomarkers, cohorts, designs, and endpoints. The rationale for the CP strategy relates to the natural history of prostate cancer. There is a wide array of natural and synthetic agents that hold promise for inhibiting, reversing, or modulating the transition from normal to precancer and from precancer to cancer. These agent classes include antiandrogens, antiestrogens, phytoestrogens, antioxidants, anti-inflammatory (proapoptotic) agents, antiproliferation/antidifferentiation agents, signal transduction modulators of receptor tyrosine kinase and ras farnesylation, antiangiogenesis agents, insulinlike growth factor (IGF)-1, peroxisome proliferator-activator receptor modulators (-gamma and -delta), and gene-based interventions. Biomarkers and endpoints are guided by the level of evidence required (eg, phase 1, 2, 3). Two candidate surrogate endpoints (SE) based on histology are high-grade prostatic intraepithelial neoplasia (HGPIN) and computer-assisted image analysis of dysplastic lesions. Phase 1 trials use standard endpoints of safety, pharmacokinetics and limited pharmacodynamics. Phase 2 trials use endpoints of modulation of biomarkers and correlation with histology. Phase 3 trials use endpoints of clinical benefit, such as cancer incidence reduction and quality of life. Validation of a biomarker as a SE involves correlation of the biomarker with clinical benefit. Cohorts (target populations) for phase 2/3 trials include the general population of men over age 50 with a normal prostate-specific antigen (PSA), subjects with a strong family history of PCa, subjects with elevated PSA/negative biopsy, and subjects with HGPIN/negative biopsy. These at-risk populations reflect key individual risk factors (age, race, serum PSA [free/total]; serum IGF-1/IGF binding protein (IGFBP)-3; 1, 25(OH)(2) D3; family history of PCa; carriers of PCa susceptibility genes [ELAC2, CYP3A4, SRD5A2, etc.]; and histology such as atypia and HGPIN) that could be combined into a multivariate risk model for PCa. The probability of cancer risk (recurrence) is a key factor that impacts on the clinical trial design (power, sample size, and primary endpoint). Multivariate predictive mathematical models for biochemical recurrence after radical prostatectomy by decreasing sample size and time to clinical outcomes maximize trial efficiency and identify the patients most likely to benefit from secondary prevention. The two large primary prevention trials, Prostate Cancer Prevention Trial/Seleninium and Vitamin E Chemoprevention Trial (PCPT/ SELECT), in low- and average-risk subjects have sample sizes of 18,000 to 32,000, with a treatment duration of 7 years to detect a 25% reduction in biopsy-proven PCa. Subjects with HGPIN have the highest known cancer risk (approximately 50% at 3 years), and thus require a small sample size (n = 450) to detect a 33% reduction in cancer incidence. A schema involving three sequential trials for agent registration is described. In summary, a CP strategy that incorporates well-defined agents, clinical and validated SE, and high-risk cohorts defined by genetic and acquired risk factors in a series of well-designed randomized controlled trials provides an efficient pathway for evaluating and approving new agents for PCa prevention.

[Indexed for MEDLINE]

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