| Summary: | Prostate cancer is an ideal candidate for chemoprevention. Tea drinking is a possible explanation for the rarity of prostate cancer among Chinese men. Tea (Camellia sinensis) contains flavonoid polyphenols called catechins, believed to be responsible for this anti-carcinogenesis. In black tea these catechins are oxidised into theaflavins. Catechins and theaflavins both inhibit human prostate cancer cell proliferation in vitro. Catechins inhibit prostate cancer in the TRAMP mouse animal model of the disease. To determine if tea polyphenols warrant investigation in large trials, evidence is required from animal models and biomarkers of cancer prevention identified in small human studies. In the work presented here, prostate carcinogenesis was inhibited by orally administered tea polyphenols in the TRAMP mouse. Following 26 weeks of polyphenol administration median prostate masses were 0.54g, 0.28g and 1.01g for the theaflavin, catechin and control group respectively. This is the first in vivo evidence of prostate cancer chemoprevention by black tea theaflavins and adds to the previously published evidence for the same effect by green tea catechins. In the catechin group, this chemoprevention was associated with a significant reduction in the concentration of oxidative DNA adduct malondialdehydedeoxyguanosine (M1G) in prostate tissue. M1G, a marker of oxidative DNA damage, was therefore proposed as a putative biomarker of prostate cancer chemoprevention. A human trial was then performed involving 18 men randomised to receive four weeks of catechins, theaflavins or no polyphenol prior to transurethral resection of prostate. A significant reduction in M1G was detected in the DNA from prostate tissue of men who had received catechins. Tea polyphenols and particularly catechins may therefore represent prostate cancer prevention agents suitable for study in a larger human intervention trial however, this finding should be first be tested in further better designed biomarker studies using this result to inform decisions on study population size.
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