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

Compositional Data in Biomedical Research

Modern methods of compositional data analysis are not well known in biomedical research. Moreover, there appear to be few mathematical and statistical researchers working on compositional biomedical problems. Like the earth and environmental sciences, biomedicine has many problems in which the relevant scienti c information is encoded in the relative abundance of key species or categories. I introduce three problems in cancer research in which analysis of compositions plays an important role. The problems involve 1) the classi cation of serum proteomic pro les for early detection of lung cancer, 2) inference of the relative amounts of di erent tissue types in a diagnostic tumor biopsy, and 3) the subcellular localization of the BRCA1 protein, and it's role in breast cancer patient prognosis. For each of these problems I outline a partial solution. However, none of these problems is \solved". I attempt to identify areas in which additional statistical development is needed with the hope of encouraging more compositional data analysts to become involved in biomedical research

Métodos de estadística espacial para evaluar la influencia de factores medioambientales sobre la incidencia y mortalidad por cáncer

Los objetivos de la tesis son: 1.- Estudiar la relación entre la incidencia y mortalidad por cáncer y los factores medioambientales, en particular la contaminación atmosférica, controlando por factores socioeconómicos. 2.- Utilizar aquellos métodos de estadística espacial apropiados para cada tipo de diseño. 3.- Distinguir en los modelos las diferentes fuentes de extra-variabilidad espacial. 4.- Controlar el problema de exceso de ceros inherente a alguna de las neoplasias de interés medioambientales. Conclusiones: - Tanto la incidencia como la mortalidad de las neoplasias, presentaron dos fuentes de extravariación. La extravariaicón espacial, por la que unidades vecinas tienden a presentar razones de incidencia/mortalidad similares, y la heterogeneidad no espacial. En general la extravariabilidad espacial ha resultado ser mucho mayor que la no espacial. - Para suavizar las RIE/RME correspondientes a variables con un porcentaje de ceros superior al40-50% debe utilizarse un modelo que capture este comportamiento. - El mejor modelo en términos de ajuste para recoger el exceso de ceros en las variables de interés ha resultado ser el modelo mixto de riesgo relativo. - Las RIE/RME suavizadas presentan un patrón geográfico claro sólo en algunas neoplasias de interés medioambiental. - Parte de la variabilidad remanente en las RIE/RME suavizadas pudo ser explicada mediante la introducción de variables explicativas, en particular la contaminación atmosférica y variables socioeconómicas. -Como los contaminantes atmosféricos fueron observados en un diseño geoestadístico y las neoplasias de interés mediambiental lo fueron en un diseño en rejilla se modelizó la superficie de exposición. - El efecto del contaminante en cada municipio/sección censal se aproximó introduciendo en el modelo el valor promedio en cada área y la variabilidad intra-área. - El efecto del contaminante se consideró aleatorio, en el sentido de que podría ser diferente en cada una de las áreas. - Las condiciones socioeconómicas fueron otra de las variables que redujeron la variabilidad remanente en las RIE/RME suavizadas. -Las variables explicativas observadas con un diseño en rejilla, como el índice de privación, se introdujeron en el modelo como efectos fijos. - El efecto de la privación sobre la incidencia y/o mortalidad por cáncer de tráquea, bronquios y pulmón, controlando por contaminantes atmosféricos, fue mayor en las mujeres que en los hombres. -Altas concentraciones de contaminantes atmosféricos aumentan el riesgo de padecer neoplasias de interés medioambiental, controlando por condiciones socioeconómicas.