University of Turin

About: University of Turin is a education organization based out in Turin, Piemonte, Italy. It is known for research contribution in the topics: Population & Cancer. The organization has 29607 authors who have published 77952 publications receiving 2480900 citations. The organization is also known as: Universita degli Studi di Torino & Università degli Studi di Torino.

Endothelin B receptor mediates the endothelial barrier to T cell homing to tumors and disables immune therapy.

Abstract: In spite of their having sufficient immunogenicity, tumor vaccines remain largely ineffective. The mechanisms underlying this lack of efficacy are still unclear. Here we report a previously undescribed mechanism by which the tumor endothelium prevents T cell homing and hinders tumor immunotherapy. Transcriptional profiling of microdissected tumor endothelial cells from human ovarian cancers revealed genes associated with the absence or presence of tumor-infiltrating lymphocytes (TILs). Overexpression of the endothelin B receptor (ETBR) was associated with the absence of TILs and short patient survival time. The ETBR inhibitor BQ-788 increased T cell adhesion to human endothelium in vitro, an effect countered by intercellular adhesion molecule-1 (ICAM-1) blockade or treatment with NO donors. In mice, ETBR neutralization by BQ-788 increased T cell homing to tumors; this homing required ICAM-1 and enabled tumor response to otherwise ineffective immunotherapy in vivo without changes in systemic antitumor immune response. These findings highlight a molecular mechanism with the potential to be pharmacologically manipulated to enhance the efficacy of tumor immunotherapy in humans. In spite of generating a tangible antitumor cellular immune response in peripheral blood, tumor vaccines have proven largely ineffective to date. The mechanisms underlying these failures remain unclear, but factors in the tumor microenvironment may be involved. The success of immune therapy partly depends on the ability of effector cells to infiltrate tumors, but the mechanisms governing homing of effector cells into tumors remain poorly understood. We hypothesized that insight into these mechanisms could be gained from the study of spontaneous antitumor immune response. A growing body of evidence shows that a variety of solid human tumors are spontaneously infiltrated by T cells. We have previously reported that intraepithelial TILs are detected in fewer than half of individuals with ovarian cancer 1 , and individuals with intraepithelial TILs have markedly improved survival 1 , a finding confirmed in ovarian and other cancers 2‐4 . T cell trafficking through lymphoid organs and peripheral tissues is tightly controlled through endothelial addressing signals regulating homing, adhesion and transendothelial migration. The endothelium is a crucial controller of T cell trafficking in homeostasis, autoimmunity and transplantation in humans, but the role of tumor endothelium in cancer immunotherapy has not been investigated to date. Because of the clear dichotomy between the presence and absence of intraepithelial TILs in ovarian cancer, we hypothesized that this would be a suitable model to investigate the role of tumor endothelium in regulating T cell homing. We undertook unbiased gene discovery

Search for neutral MSSM Higgs bosons at LEP

Abstract: The four LEP collaborations, ALEPH, DELPHI, L3 and OPAL, have searched for the neutral Higgs bosons which are predicted by the Minimal Supersymmetric standard model (MSSM). The data of the four collaborations are statistically combined and examined for their consistency with the background hypothesis and with a possible Higgs boson signal. The combined LEP data show no significant excess of events which would indicate the production of Higgs bosons. The search results are used to set upper bounds on the cross-sections of various Higgs-like event topologies. The results are interpreted within the MSSM in a number of “benchmark” models, including CP-conserving and CP-violating scenarios. These interpretations lead in all cases to large exclusions in the MSSM parameter space. Absolute limits are set on the parameter cosβ and, in some scenarios, on the masses of neutral Higgs bosons.

FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data

Abstract: Data clustering analysis has been extensively applied to extract information from gene expression profiles obtained with DNA microarrays. To this aim, existing clustering approaches, mainly developed in computer science, have been adapted to microarray data analysis. However, previous studies revealed that microarray datasets have very diverse structures, some of which may not be correctly captured by current clustering methods. We therefore approached the problem from a new starting point, and developed a clustering algorithm designed to capture dataset-specific structures at the beginning of the process. The clustering algorithm is named Fuzzy clustering by Local Approximation of MEmbership (FLAME). Distinctive elements of FLAME are: (i) definition of the neighborhood of each object (gene or sample) and identification of objects with "archetypal" features named Cluster Supporting Objects, around which to construct the clusters; (ii) assignment to each object of a fuzzy membership vector approximated from the memberships of its neighboring objects, by an iterative converging process in which membership spreads from the Cluster Supporting Objects through their neighbors. Comparative analysis with K-means, hierarchical, fuzzy C-means and fuzzy self-organizing maps (SOM) showed that data partitions generated by FLAME are not superimposable to those of other methods and, although different types of datasets are better partitioned by different algorithms, FLAME displays the best overall performance. FLAME is implemented, together with all the above-mentioned algorithms, in a C++ software with graphical interface for Linux and Windows, capable of handling very large datasets, named Gene Expression Data Analysis Studio (GEDAS), freely available under GNU General Public License. The FLAME algorithm has intrinsic advantages, such as the ability to capture non-linear relationships and non-globular clusters, the automated definition of the number of clusters, and the identification of cluster outliers, i.e. genes that are not assigned to any cluster. As a result, clusters are more internally homogeneous and more diverse from each other, and provide better partitioning of biological functions. The clustering algorithm can be easily extended to applications different from gene expression analysis.

Overt versus covert treatment for pain, anxiety, and Parkinson's disease

Abstract: Summary The recent introduction of covert administration of treatment to biomedical research has produced some interesting results, with many clinical and ethical implications. Concealed treatment has been used in people with nervous system conditions including pain, anxiety, and Parkinson's disease. The main finding is that when the patient is completely unaware that a treatment is being given, the treatment is less effective than when it is given overtly in accordance with routine medical practice. The difference between open and hidden administrations is thought to represent the placebo component of the treatment, even though no placebo has been given. The decreased effectiveness of hidden treatments indicates that knowledge about a treatment affects outcome and highlights the importance of the patient-provider interaction. In addition, by use of covert administration, the efficacy of some treatments can be assessed without the use of a placebo and associated ethical issues.