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

Prediction of glucose excursions under uncertain parameters and food intake in intensive insulin therapy for type 1 diabetes mellitus

Considering the difficulty in the insulin dosage selection and the problem of hyper- and hypoglycaemia episodes in type 1 diabetes, dosage-aid systems appear as tremendously helpful for these patients. A model-based approach to this problem must unavoidably consider uncertainty sources such as the large intra-patient variability and food intake. This work addresses the prediction of glycaemia for a given insulin therapy face to parametric and input uncertainty, by means of modal interval analysis. As result, a band containing all possible glucose excursions suffered by the patient for the given uncertainty is obtained. From it, a safer prediction of possible hyper- and hypoglycaemia episodes can be calculated

Prediction of the bulking phenomenon in wastewater treatment plants

The control and prediction of wastewater treatment plants poses an important goal: to avoid breaking the environmental balance by always keeping the system in stable operating conditions. It is known that qualitative information — coming from microscopic examinations and subjective remarks — has a deep influence on the activated sludge process. In particular, on the total amount of effluent suspended solids, one of the measures of overall plant performance. The search for an input–output model of this variable and the prediction of sudden increases (bulking episodes) is thus a central concern to ensure the fulfillment of current discharge limitations. Unfortunately, the strong interrelation between variables, their heterogeneity and the very high amount of missing information makes the use of traditional techniques difficult, or even impossible. Through the combined use of several methods — rough set theory and artificial neural networks, mainly — reasonable prediction models are found, which also serve to show the different importance of variables and provide insight into the process dynamics

Prediction of postprandial blood glucose under intra-patient variability and uncertainty and its use in the design of insulin dosing strategies for type 1 diabetic patients

In this thesis I propose a novel method to estimate the dose and injection-to-meal time for low-risk intensive insulin therapy. This dosage-aid system uses an optimization algorithm to determine the insulin dose and injection-to-meal time that minimizes the risk of postprandial hyper- and hypoglycaemia in type 1 diabetic patients. To this end, the algorithm applies a methodology that quantifies the risk of experiencing different grades of hypo- or hyperglycaemia in the postprandial state induced by insulin therapy according to an individual patient’s parameters. This methodology is based on modal interval analysis (MIA). Applying MIA, the postprandial glucose level is predicted with consideration of intra-patient variability and other sources of uncertainty. A worst-case approach is then used to calculate the risk index. In this way, a safer prediction of possible hyper- and hypoglycaemic episodes induced by the insulin therapy tested can be calculated in terms of these uncertainties.