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Discovering the causes of cancer and the means of prevention

Endogenous Hormones

Produced within the body's cells

Our bodies naturally produce hormones as part of normal healthy functioning. It is well-known that hormones, especially sex-steroid hormones like estrogen, are related to cancer initiation and progression. DCEG researchers are actively investigating how the body’s own hormone production may be related to risk for breast, endometrial, and ovarian cancer as well as testicular cancer and other malignancies. We study not only levels of circulating estrogens in the body, but also individual variability in the ratio of estrogen metabolites.

Estrogens play important roles in the pathophysiology of breast tumors and are recognized causal factors in the etiology of breast cancer; this central insight has led to many of the available preventive and therapeutic interventions for breast cancer. Endogenous estrogens may also play causal roles in endometrial and ovarian cancers and could also be important in male reproductive cancers, such as male breast cancer, testicular cancer, and prostate cancer. Substantial inter-individual variability has been observed in levels of circulating and excreted estrogens and estrogen metabolites (jointly referred to as EM) among men, postmenopausal women, and premenopausal women. Because EM vary with respect to bioavailability, affinity to estrogen receptors, and mutagenic potential, investigators have hypothesized that variations in estrogen profiles may account for interindividual differences in cancer risks. Until recently, it has been difficult to study this hypothesis in epidemiologic settings. Recent development of a liquid chromatography-tandem mass spectrometry (LC/MS2) method for measurement of estrogens and estrogen metabolites in urine (Xu et al., Anal Chem 2006;78:1553) and serum (Xu et al., Anal Chem 2007;79:7813) represents an important methodologic development because it provides an assay with characteristics that make it feasible for use in epidemiologic studies: high sensitivity, specificity, reliability, and scalability for high-throughput work.