Abstract

Both steroid hormones and peptide growth factors are known to control the growth and progression of breast cancers [1,2]. Human and animal breast carcinomas have receptors for steroid and peptide hormones, and both in vitro and in vivo studies have indicated that tumor proliferation rate and overall growth are dependent on these ligand-receptor systems [3,4]. Approximately one third to one half of all breast cancer cases show estrogen-dependent growth [3], and clinical studies have indicated that patients whose tumors have estrogen or progesterone receptors (ER, PR) have biologically less aggressive tumors and live longer than those patients whose tumors lack ER or PR [1,5]. However, some receptor-positive breast cancers are not normally responsive to steroid hormone regulation, behave much more aggressively, and reduce patient survival [5,6]. One explanation for this discrepancy between steroid-receptor positivity and tumor behavior is that the tumor may lose certain intracellular mechanisms that mediate hormonal growth control. A second explanation is that these receptor-positive tumors are heterogeneous and contain subpopulations of receptor-negative cells capable of more aggressive behavior. A third possibility is that the more aggressive tumor cell behavior is derived from the influence of other hormones, such as polypeptide hormones and growth factors. These latter factors may play autocrine and paracrine roles in determining aggressive breast tumor behavior, including a more rapid tumor proliferation rate and steroid hormone resistance. For this reason it is important to quantitate tumor receptor levels in concert with histological analysis and other techniques to identify the cells in tumors that contain receptors for steroid hormones and polypeptide growth factors.KeywordsBreast CancerEpidermal Growth Factor ReceptorInsulin ReceptorBreast Cancer TissueNormal Breast TissueThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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