An update of the mechanisms of resistance to EGFR-tyrosine kinase inhibitors in breast cancer: gefitinib (Iressatrade mark)-induced changes in the expression and nucleo-cytoplasmic trafficking of HER-ligands (Review)

Intrinsic resistance to the epidermal growth factor receptor (EGFR; HER1) tyrosine kinase inhibitor (TKI) gefitinib, and more generally to EGFR TKIs, is a common phenomenon in breast cancer. The availability of molecular criteria for predicting sensitivity to EGFR-TKIs is, therefore, the most relevant issue for their correct use and for planning future research. Though it appears that in non-small-cell lung cancer (NSCLC) response to gefitinib is directly related to the occurrence of specific mutations in the EGFR TK domain, breast cancer patients cannot be selected for treatment with gefitinib on the same basis as such EGFR mutations have been reported neither in primary breast carcinomas nor in several breast cancer cell lines. Alternatively, there is a general agreement on the hypothesis that the occurrence of molecular alterations that activate transduction pathways downstream of EGFR (i.e., MEK1/MEK2 - ERK1/2 MAPK and PI-3'K - AKT growth/survival signaling cascades) significantly affect the response to EGFR TKIs in breast carcinomas. However, there are no studies so far addressing a role of EGF-related ligands as intrinsic breast cancer cell modulators of EGFR TKI efficacy. We recently monitored gene expression profiles and sub-cellular localization of HER-1/-2/-3/-4 related ligands (i.e., EGF, amphiregulin, transforming growth factor-α, ß-cellulin, epiregulin and neuregulins) prior to and after gefitinib treatment in a panel of human breast cancer cell lines. First, gefitinibinduced changes in the endogenous levels of EGF-related ligands correlated with the natural degree of breast cancer cell sensitivity to gefitinib. While breast cancer cells intrinsically resistant to gefitinib (IC50 ≥15 μM) markedly up-regulated (up to 600 times) the expression of genes codifying for HERspecific ligands, a significant down-regulation (up to 106 times) of HER ligand gene transcription was found in breast cancer cells intrinsically sensitive to gefitinib (IC50 ≤1 μM). Second, loss of HER1 function differentially regulated the nuclear trafficking of HER-related ligands. While gefitinib treatment induced an active import and nuclear accumulation of the HER ligand NRG in intrinsically gefitinib-resistant breast cancer cells, an active export and nuclear loss of NRG was observed in intrinsically gefitinib-sensitive breast cancer cells. In summary, through in vitro and pharmacodynamic studies we have learned that, besides mutations in the HER1 gene, oncogenic changes downstream of HER1 are the key players regulating gefitinib efficacy in breast cancer cells. It now appears that pharmacological inhibition of HER1 function also leads to striking changes in both the gene expression and the nucleo-cytoplasmic trafficking of HER-specific ligands, and that this response correlates with the intrinsic degree of breast cancer sensitivity to the EGFR TKI gefitinib. The relevance of this previously unrecognized intracrine feedback to gefitinib warrants further studies as cancer cells could bypass the antiproliferative effects of HER1-targeted therapeutics without a need for the overexpression and/or activation of other HER family members and/or the activation of HER-driven downstream signaling cascades

Proposal of a parallel architecture for a motion detection algorithm

This paper proposes a parallel architecture for estimation of the motion of an underwater robot. It is well known that image processing requires a huge amount of computation, mainly at low-level processing where the algorithms are dealing with a great number of data. In a motion estimation algorithm, correspondences between two images have to be solved at the low level. In the underwater imaging, normalised correlation can be a solution in the presence of non-uniform illumination. Due to its regular processing scheme, parallel implementation of the correspondence problem can be an adequate approach to reduce the computation time. Taking into consideration the complexity of the normalised correlation criteria, a new approach using parallel organisation of every processor from the architecture is proposed