Clinical significance of altered nm23-H1, EGFR, RB and p53 expression in bilharzial bladder cancer

<p>Abstract</p> <p>Background</p> <p>Clinical characterization of bladder carcinomas is still inadequate using the standard clinico-pathological prognostic markers. We assessed the correlation between <it>nm23-H1</it>, <it>Rb, EGFR </it>and <it>p53 </it>in relation to the clinical outcome of patients with muscle invasive bilharzial bladder cancer (MI-BBC).</p> <p>Methods</p> <p><it>nm23-H1</it>, <it>Rb, EGFR and p53 </it>expression was assessed in 59 MI-BBC patients using immunohistochemistry and reverse transcription (RT-PCR) and was correlated to the standard clinico-pathological prognostic factors, patient's outcome and the overall survival (OS) rate.</p> <p>Results</p> <p>Overexpression of <it>EGFR </it>and <it>p53 </it>proteins was detected in 66.1% and 35.6%; respectively. Loss of <it>nm23-H1</it>and <it>Rb </it>proteins was detected in 42.4% and 57.6%; respectively. Increased <it>EGFR and </it>loss of <it>nm23-H1 </it>RNA were detected in 61.5% and 36.5%; respectively. There was a statistically significant correlation between <it>p53 </it>and <it>EGFR </it>overexpression (<it>p </it>< 0.0001), <it>nm23 </it>loss (protein and RNA), lymph node status (<it>p </it>< 0.0001); between the incidence of local recurrence and <it>EGFR </it>RNA overexpression (p= 0.003) as well as between the incidence of metastasis and altered <it>Rb </it>expression (<it>p </it>= 0.026), <it>p53 </it>overexpression (<it>p </it>< 0.0001) and mutation (<it>p </it>= 0.04). Advanced disease stage correlated significantly with increased <it>EGFR </it>(protein and RNA) (<it>p </it>= 0.003 & 0.01), reduced <it>nm23-H1 </it>RNA (<it>p </it>= 0.02), altered <it>Rb </it>(<it>p </it>= 0.023), and <it>p53 </it>overexpression (<it>p </it>= 0.004). OS rates correlated significantly, in univariate analysis, with <it>p53 </it>overexpression (<it>p </it>= 0.011), increased <it>EGFR </it>(protein and RNA, <it>p </it>= 0.034&0.031), <it>nm23-H1 RNA </it>loss (<it>p </it>= 0.021) and aberrations of ≥ 2 genes. However, multivariate analysis showed that only high <it>EGFR </it>overexpression, metastatic recurrence, high tumor grade and the combination of ≥ 2 affected markers were independent prognostic factors.</p> <p>Conclusion</p> <p><it>nm23-H1, EGFR </it>and <it>p53 </it>could be used as prognostic biomarkers in MI-BBC patients. In addition to the standard pathological prognostic factors, a combination of these markers (≥ 2) has synergistic effects in stratifying patients into variable risk groups. The higher is the number of altered biomarkers, the higher will be the risk of disease progression and death.</p

Managing model adaptation by precise detection of metamodel changes

Abstract. Technological and business changes influence the evolution of software systems. When this happens, the software artifacts may need to be adapted to the changes. This need is rapidly increasing in systems built using the Model-Driven Engineering (MDE) paradigm. An MDE system basically consists of metamodels, terminal models, and transformations. The evolution of a metamodel may render its related terminal models and transformations invalid. This paper proposes a three-step solution that automatically adapts terminal models to their evolving metamodels. The first step computes the equivalences and (simple and complex) changes between a given metamodel, and a former version of the same metamodel. The second step translates the equivalences and differences into an adaptation transformation. This transformation can then be executed in a third step to adapt to the new version any terminal model conforming to the former version. We validate our ideas by implementing a prototype based on the AtlanMod Model Management Architecture (AMMA) platform. We present the accuracy and performance that the prototype delivers on two concrete examples: a Petri Net metamodel from the research literature, and the Netbeans Java metamodel