Cellular and molecular pharmacology of antiestrogen action and resistance

Antiestrogen therapy remains one of the most widely used and effective treatments for the management of endocrine responsive breast cancers. This reflects the ability of antiestrogens to compete with estrogens for binding to estrogen receptors. Whereas response rates of up to 70% are reported in patients with tumors expressing estrogen and progesterone receptors, most responsive tumors will eventually acquire resistance. The most important factor in de novo resistance is lack of expression of these receptors. However, the mechanisms driving resistance in tumors that express estrogen and/or progesterone receptors are unclear. A tamoxifen-stimulated phenotype has been described, but seems to occur only in a minority of patients. Most tumors (>80%) may become resistant through other, less well defined, resistance mechanisms. These may be multifactorial, including changes in immunity, host endocrinology, and drug pharmacokinetics. Significant changes within the tumor cells may also occur, including alterations in the ratio of the estrogen receptor alpha:beta forms and/or other changes in estrogen receptor-driven transcription complex function. These may lead to perturbations in the gene network signaling downstream of estrogen receptors. Cells may also alter paracrine and autocrine growth factor interactions, potentially producing a ligand-independent activation of estrogen receptors by mitogen-activated protein kinases. Antiestrogens can affect the function of intracellular proteins and signaling that may, or may not, involve estrogen receptor-mediated events. These include changes in oxidative stress responses, specific protein kinase C isoform activation, calmodulin function, and cell membrane structure/function.

Maternal and prepubertal diet, mammary development and breast cancer risk

At present, we do not know what causes sporadic breast cancer. Environmental factors,particularly diet, appear to explain at least 70% of newly diagnosed breast cancers, but it is not clear what these factors are. We propose that the lack of progress in this area is due to a lack of considering the effect of timing of environmental and dietary exposures on the breast. The evidence provided above suggests that an in utero exposure to an estrogenic environment-including that caused by diet [high (n-6) PUFA or genistein]-increases breast cancer risk. This increase may be mediated by an increased presence of TEB in the mammary epithelial tree and increased ER-alpha levels, reduced ER-beta levels or both. Prepubertal estrogenic exposure, in contrast, reduces later risk of developing breast cancer. The protective effect of estrogens may be mediated by early epithelial differentiation, reduced presence of ER-alpha and increased levels of ER-beta in the mammary gland. The challenge we are now facing is to determine whether the data obtained mainly through the use of animal models is relevant to women and if so, how we might be able to modulate pregnancy and childhood estrogenic exposure by appropriate dietary modifications to reduce breast cancer risk in women.

Molecular and pharmacological aspects of antiestrogen resistance

Endocrine therapy is effective in approximately one-third of all breast cancers and up to 80% of tumors that express both estrogen and progesterone receptors. Despite the low toxicity, good overall response rates, and additional benefits associated with its partial agonist activity, most Tamoxifen-responsive breast cancers acquire resistance. The development of new antiestrogens, both steroidal and non-steroidal, provides the opportunity for the development of non-cross-resistant therapies and the identification of additional mechanisms of action and resistance. Drug-specific pharmacologic mechanisms may confer a resistance phenotype, reflecting the complexities of both tumor biology/pharmacology and the molecular endocrinology of steroid hormone action. However, since all antiestrogens will be effective only in cells that express estrogen receptors (ER), many mechanisms will likely be directly related to ER expression and signaling. For example, loss of ER expression/function is likely to confer a cross-resistance phenotype across all structural classes of antiestrogens. Altered expression of ERalpha and ERbeta, and/or signaling from transcription complexes driven by these receptors, may produce drug-specific resistance phenotypes. We have begun to study the possible changes in gene expression that may occur as cells acquire resistance to steroidal and non-steroidal antiestrogens. Our preliminary studies implicate the altered expression of several estrogen-regulated genes. However, resistance to antiestrogens is likely to be a multigene phenomenon, involving a network of interrelated signaling pathways. The way in which this network is adapted by cells may vary among tumors, consistent with the existence of a highly plastic and adaptable genotype within breast cancer cells.

Comparative tracking error analysis of five different optical tracking systems

OBJECTIVE

Effective utilization of an optical tracking system for image-based surgical guidance requires optimal placement of the dynamic reference frame (DRF) with respect to the tracking camera. Unlike other studies that measure the overall accuracy of a particular navigation system, this study investigates the precision of one component of the navigation system: the optical tracking system (OTS). The precision of OTS measurements is quantified as jitter. By measuring jitter, one can better understand how system inaccuracies depend on the position of the DRF with respect to the camera.

MATERIALS AND METHODS

Both FlashPointtrade mark (Image Guided Technologies, Inc., Boulder, Colorado) and Polaristrade mark (Northern Digital Inc., Ontario, Canada) optical tracking systems were tested in five different camera and DRF configurations. A linear testing apparatus with a software interface was designed to facilitate data collection. Jitter measurements were collected over a single quadrant within the camera viewing volume, as symmetry was assumed about the horizontal and vertical axes.

RESULTS

Excluding the highest 5% of jitter, the FlashPoint cameras had an RMS jitter range of 0.028 +/- 0.012 mm for the 300 mm model, 0.051 +/- 0.038 mm for the 580 mm model, and 0.059 +/- 0.047 mm for the 1 m model. The Polaris camera had an RMS jitter range of 0.058 +/- 0.037 mm with an active DRF and 0.115 +/- 0.075 mm with a passive DRF.

CONCLUSION

Both FlashPoint and Polaris have jitter less than 0.11 mm, although the error distributions differ significantly. Total jitter for all systems is dominated by the component measured in the axis directed away from the camera.