Three-dimensional growth as multicellular spheroid activates the proangiogenic phenotype of colorectal carcinoma cells via LFA-1-dependent VEGF: implications on hepatic micrometastasis

Background: The recruitment of vascular stromal and endothelial cells is an early event occurring during cancer cell growth at premetastatic niches, but how the microenvironment created by the initial three-dimensional (3D) growth of cancer cells affects their angiogenesis-stimulating potential is unclear. Methods: The proangiogenic profile of CT26 murine colorectal carcinoma cells was studied in seven-day cultured 3D-spheroids of <300 mu m in diameter, produced by the hanging-drop method to mimic the microenvironment of avascular micrometastases prior to hypoxia occurrence. Results: Spheroid-derived CT26 cells increased vascular endothelial growth factor (VEGF) secretion by 70%, which in turn increased the in vitro migration of primary cultured hepatic sinusoidal endothelium (HSE) cells by 2-fold. More importantly, spheroid-derived CT26 cells increased lymphocyte function associated antigen (LFA)-1-expressing cell fraction by 3-fold; and soluble intercellular adhesion molecule (ICAM)-1, given to spheroid-cultured CT26 cells, further increased VEGF secretion by 90%, via cyclooxygenase (COX)-2-dependent mechanism. Consistent with these findings, CT26 cancer cells significantly increased LFA-1 expression in non-hypoxic avascular micrometastases at their earliest inception within hepatic lobules in vivo; and angiogenesis also markedly increased in both subcutaneous tumors and hepatic metastases produced by spheroid-derived CT26 cells. Conclusion: 3D-growth per se enriched the proangiogenic phenotype of cancer cells growing as multicellular spheroids or as subclinical hepatic micrometastases. The contribution of integrin LFA-1 to VEGF secretion via COX-2 was a micro environmental-related mechanism leading to the pro-angiogenic activation of soluble ICAM-1-activated colorectal carcinoma cells. This mechanism may represent a new target for specific therapeutic strategies designed to block colorectal cancer cell growth at a subclinical micrometastatic stage within the liver.

Recognition of Membrane-Bound Fusion-Peptide/MPER Complexes by the HIV-1 Neutralizing 2F5 Antibody : Implications for Anti-2F5 Immunogenicity

13 p.

Evaluation of Alpha 1-Antitrypsin and the Levels of mRNA Expression of Matrix Metalloproteinase 7, Urokinase Type Plasminogen Activator Receptor and COX-2 for the Diagnosis of Colorectal Cancer

8 p.

On the optimal usage of labelled examples in semi-supervised multi-class classification problems

In recent years, the performance of semi-supervised learning has been theoretically investigated. However, most of this theoretical development has focussed on binary classification problems. In this paper, we take it a step further by extending the work of Castelli and Cover [1] [2] to the multi-class paradigm. Particularly, we consider the key problem in semi-supervised learning of classifying an unseen instance x into one of K different classes, using a training dataset sampled from a mixture density distribution and composed of l labelled records and u unlabelled examples. Even under the assumption of identifiability of the mixture and having infinite unlabelled examples, labelled records are needed to determine the K decision regions. Therefore, in this paper, we first investigate the minimum number of labelled examples needed to accomplish that task. Then, we propose an optimal multi-class learning algorithm which is a generalisation of the optimal procedure proposed in the literature for binary problems. Finally, we make use of this generalisation to study the probability of error when the binary class constraint is relaxed.

Properties of convergence of a class of iterative processes generated by sequences of self-mappings with applications to switched dynamic systems

This article investigates the convergence properties of iterative processes involving sequences of self-mappings of metric or Banach spaces. Such sequences are built from a set of primary self-mappings which are either expansive or non-expansive self-mappings and some of the non-expansive ones can be contractive including the case of strict contractions. The sequences are built subject to switching laws which select each active self-mapping on a certain activation interval in such a way that essential properties of boundedness and convergence of distances and iterated sequences are guaranteed. Applications to the important problem of stability of dynamic switched systems are also given.

Advantages and Versatility of Fluorescence-Based Methodology to Characterize the Functionality of LDLR and Class Mutation Assignment

Familial hypercholesterolemia (FH) is a common autosomal codominant disease with a frequency of 1:500 individuals in its heterozygous form. The genetic basis of FH is most commonly mutations within the LDLR gene. Assessing the pathogenicity of LDLR variants is particularly important to give a patient a definitive diagnosis of FH. Current studies of LDLR activity ex vivo are based on the analysis of I-125-labeled lipoproteins (reference method) or fluorescent-labelled LDL. The main purpose of this study was to compare the effectiveness of these two methods to assess LDLR functionality in order to validate a functional assay to analyse LDLR mutations. LDLR activity of different variants has been studied by flow cytometry using FITC-labelled LDL and compared with studies performed previously with I-125-labeled lipoproteins. Flow cytometry results are in full agreement with the data obtained by the I-125 methodology. Additionally confocal microscopy allowed the assignment of different class mutation to the variants assayed. Use of fluorescence yielded similar results than I-125-labeled lipoproteins concerning LDLR activity determination, and also allows class mutation classification. The use of FITC-labelled LDL is easier in handling and disposal, cheaper than radioactivity and can be routinely performed by any group doing LDLR functional validations.