Potential Prognostic and Diagnostic Application of a Novel Monoclonal Antibody Against Keratinocyte Growth Factor Receptor

KGFR is involved in the pathogenesis of several human cancers. In this study, we generated and characterized a monoclonal antibody specific to KGFR (SC-101 mAb) and evaluated its potential use in basic research and as a diagnostic and prognostic tool. The specificity and biological activity of the SC-101 mAb were evaluated by Western blotting, immunofluorescence, and immunoprecipitation analyses on various cell lines. KGFR expression in breast, pancreatic, and thyroid carcinoma was assessed by immunohistochemistry (IHC) with SC-101 mAb. KGFR expression levels revealed by SC-101 mAb resulted to increase proportionally with tumor grade in breast and pancreatic cancer. In addition, SC-101 mAb was able to detect KGFR down-modulation in thyroid cancer. SC-101 mAb might represent a useful tool for basic research applications, and it could also contribute to improve the accuracy of diagnosis and prognosis of epithelial tumors.     

Simulating gene-gene and gene-environment interactions in complex diseases: Gene-Environment iNteraction Simulator 2

ABSTRACT: BACKGROUND: The analysis of complex diseases is an important problem in human genetics. Because multifactoriality isexpected to play a pivotal role, many studies are currently focused on collecting information on the geneticand environmental factors that potentially influence these diseases. However, there is still a lack of efficientand thoroughly tested statistical models that can be used to identify implicated features and theirinteractions. Simulations using large biologically realistic data sets with known gene-gene andgene-environment interactions that influence the risk of a complex disease are a convenient and useful wayto assess the performance of statistical methods. RESULTS: The Gene-Environment iNteraction Simulator 2 (GENS2) simulates interactions among two genetic and oneenvironmental factor and also allows for epistatic interactions. GENS2 is based on data with realisticpatterns of linkage disequilibrium, and imposes no limitations either on the number of individuals to besimulated or on number of non-predisposing genetic/environmental factors to be considered. The GENS2tool is able to simulate gene-environment and gene-gene interactions. To make the Simulator more intuitive,the input parameters are expressed as standard epidemiological quantities. GENS2 is written in Pythonlanguage and takes advantage of operators and modules provided by the simuPOP simulation environment.It can be used through a graphical or a command-line interface and is freely available fromhttp://sourceforge.net/projects/gensim. The software is released under the GNU General Public Licenseversion 3.0. CONCLUSIONS: Data produced by GENS2 can be used as a benchmark for evaluating statistical tools designed for theidentification of gene-gene and gene-environment interactions.