PubMed · 4555592
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G R Prout. 1972-07-13. Bladder carcinoma.. https://doi.org/10.1056/nejm197207132870207
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E-cadherin mutations are found in 50% of diffuse-type gastric carcinoma, but not in intestinal gastric carcinoma. Because cell-cell adhesion mediated by E-cadherin plays an important role in epithelial cell survival, E-cadherin mutations could alter the apoptotic behavior of tumor cells. p53 and Bcl-2 family members are also important regulators of cellular apoptosis. This is the first study that investigates the relationship between E-cadherin gene mutation and p53 gene mutation, p53 accumulation, Bcl-2 expression, and Ki-67 expression in diffuse-type gastric carcinoma (24 cases, E-cadherin mutation status: wild-type in 8 patients and mutant in 16 patients). The mutation status of exons 5-8 of p53 was analyzed by denaturing high pressure liquid chromatography (DHPLC) in formalin-fixed, paraffin-embedded tumor sections, followed by direct sequencing of cases with aberrant chromatographic patterns. p53 mutations were found in 1 of 8 tumors without E-cadherin mutation (12.5%) and in 1 of 16 tumors with E-cadherin mutation (6.3%), a difference that was not statistically significant (p = 1.00). p53 accumulation was found in 8 of 24 tumors (33.3%) by immunohistochemical staining. p53 accumulation was significantly more frequent in tumors without E-cadherin mutations (5 of 8 tumors, 62.5%) than in gastric carcinoma tissues with E-cadherin mutations (3 of 16 tumors, 18.8%, p = 0.03). Bcl-2 staining was not observed in gastric carcinoma cells without E-cadherin mutations, but was detectable in 5 of 16 tumors with E-cadherin mutations (31.3%), a difference that was not statistically significant (p = 0.13). No relationship was observed between Ki-67 staining and the E-cadherin mutation status (p = 1.00). These data suggest that the presence of E-cadherin mutations can significantly alter the accumulation of the apoptosis-regulating p53 protein, whereas no correlation with the p53 mutation status or with Ki-67 staining was observed.
The nitroreductase (NR)/CB1954 enzyme prodrug system has given promising results in preclinical studies and is currently being assessed in phase I clinical trials. It is well established that there is an immune component to the bystander effect observed with other systems such as thymidine kinase and cytosine deaminase; however, such an effect has not previously been described using NR. We have preliminary data suggesting an immune bystander effect with NR to further examine these effects and their potential enhancement by cytokines, an adenoviral vector containing CMV-NR, an internal ribosome entry site (IRES) and the gene for murine GM-CSF (mGM-CSF) was constructed. The NR-GM-CSF virus was validated in 2 experimental models and demonstrated increased therapeutic efficacy in the MC26 murine colorectal tumour model. These data illustrate that the combination of suicide gene therapy using NR and CB1954 with immune stimulation via GM-CSF gives an improved response compared to either modality alone and suggests that the immune component of this response may be beneficial in combating unresectable, metastatic disease and preventing tumour recurrence.
The application of DNA microarray technology for analysis of gene expression creates enormous opportunities to accelerate the pace in understanding living systems and identification of target genes and pathways for drug development and therapeutic intervention. Parallel monitoring of the expression profiles of thousands of genes seems particularly promising for a deeper understanding of cancer biology and the identification of molecular signatures supporting the histological classification schemes of neoplastic specimens. However, the increasing volume of data generated by microarray experiments poses the challenge of developing equally efficient methods and analysis procedures to extract, interpret, and upgrade the information content of these databases. Herein, a computational procedure for pattern identification, feature extraction, and classification of gene expression data through the analysis of an autoassociative neural network model is described. The identified patterns and features contain critical information about gene-phenotype relationships observed during changes in cell physiology. They represent a rational and dimensionally reduced base for understanding the basic biology of the onset of diseases, defining targets of therapeutic intervention, and developing diagnostic tools for the identification and classification of pathological states. The proposed method has been tested on two different microarray datasets-Golub's analysis of acute human leukemia [Golub et al. (1999) Science 286:531-537], and the human colon adenocarcinoma study presented by Alon et al. [1999; Proc Natl Acad Sci USA 97:10101-10106]. The analysis of the neural network internal structure allows the identification of specific phenotype markers and the extraction of peculiar associations among genes and physiological states. At the same time, the neural network outputs provide assignment to multiple classes, such as different pathological conditions or tissue samples, for previously unseen instances.