Search PubMed⌕ Search

Biomedical subjects

Fatima Sanchez-Cabo

Publications and source records attributed to Fatima Sanchez-Cabo.

4 recordsLinked to original sources

Effector memory T cells, early metastasis, and survival in colorectal cancer.

BACKGROUND: The role of tumor-infiltrating immune cells in the early metastatic invasion of colorectal cancer is unknown. METHODS: We studied pathological signs of early metastatic invasion (venous emboli and lymphatic and perineural invasion) in 959 specimens of resected colorectal cancer. The local immune response within the tumor was studied by flow cytometry (39 tumors), low-density-array real-time polymerase-chain-reaction assay (75 tumors), and tissue microarrays (415 tumors). RESULTS: Univariate analysis showed significant differences in disease-free and overall survival according to the presence or absence of histologic signs of early metastatic invasion (P<0.001). Multivariate Cox analysis showed that an early conventional pathological tumor-node-metastasis stage (P<0.001) and the absence of early metastatic invasion (P=0.04) were independently associated with increased survival. As compared with tumors with signs of early metastatic invasion, tumors without such signs had increased infiltrates of immune cells and increased levels of messenger RNA (mRNA) for products of type 1 helper effector T cells (CD8, T-BET [T-box transcription factor 21], interferon regulatory factor 1, interferon-gamma, granulysin, and granzyme B) but not increased levels of inflammatory mediators or immunosuppressive molecules. The two types of tumors had significant differences in the levels of expression of 65 combinations of T-cell markers, and hierarchical clustering showed that markers of T-cell migration, activation, and differentiation were increased in tumors without signs of early metastatic invasion. The latter type of tumors also had increased numbers of CD8+ T cells, ranging from early memory (CD45RO+CCR7-CD28+CD27+) to effector memory (CD45RO+CCR7-CD28-CD27-) T cells. The presence of high levels of infiltrating memory CD45RO+ cells, evaluated immunohistochemically, correlated with the absence of signs of early metastatic invasion, a less advanced pathological stage, and increased survival. CONCLUSIONS: Signs of an immune response within colorectal cancers are associated with the absence of pathological evidence of early metastatic invasion and with prolonged survival.

Analysis of Variance↗

A quantization method based on threshold optimization for microarray short time series.

BACKGROUND: Reconstructing regulatory networks from gene expression profiles is a challenging problem of functional genomics. In microarray studies the number of samples is often very limited compared to the number of genes, thus the use of discrete data may help reducing the probability of finding random associations between genes. RESULTS: A quantization method, based on a model of the experimental error and on a significance level able to compromise between false positive and false negative classifications, is presented, which can be used as a preliminary step in discrete reverse engineering methods. The method is tested on continuous synthetic data with two discrete reverse engineering methods: Reveal and Dynamic Bayesian Networks. CONCLUSION: The quantization method, evaluated in comparison with two standard methods, 5% threshold based on experimental error and rank sorting, improves the ability of Reveal and Dynamic Bayesian Networks to identify relations among genes.

Algorithms↗

PathwayExplorer: web service for visualizing high-throughput expression data on biological pathways.

While generation of high-throughput expression data is becoming routine, the fast, easy, and systematic presentation and analysis of these data in a biological context is still an obstacle. To address this need, we have developed PathwayExplorer, which maps expression profiles of genes or proteins simultaneously onto major, currently available regulatory, metabolic and cellular pathways from KEGG, BioCarta and GenMAPP. PathwayExplorer is a platform-independent web server application with an optional standalone Java application using a SOAP (simple object access protocol) interface. Mapped pathways are ranked for the easy selection of the pathway of interest, displaying all available genes of this pathway with their expression profiles in a selectable and intuitive color code. Pathway maps produced can be downloaded as PNG, JPG or as high-resolution vector graphics SVG. The web service is freely available at https://pathwayexplorer.genome.tugraz.at; the standalone client can be downloaded at http://genome.tugraz.at.

Computer Graphics↗

Global gene expression profiling reveals widespread yet distinctive translational responses to different eukaryotic translation initiation factor 2B-targeting stress pathways.

Global inhibition of protein synthesis is a hallmark of many cellular stress conditions. Even though specific mRNAs defy this (e.g., yeast GCN4 and mammalian ATF4), the extent and variation of such resistance remain uncertain. In this study, we have identified yeast mRNAs that are translationally maintained following either amino acid depletion or fusel alcohol addition. Both stresses inhibit eukaryotic translation initiation factor 2B, but via different mechanisms. Using microarray analysis of polysome and monosome mRNA pools, we demonstrate that these stress conditions elicit widespread yet distinct translational reprogramming, identifying a fundamental role for translational control in the adaptation to environmental stress. These studies also highlight the complex interplay that exists between different stages in the gene expression pathway to allow specific preordained programs of proteome remodeling. For example, many ribosome biogenesis genes are coregulated at the transcriptional and translational levels following amino acid starvation. The transcriptional regulation of these genes has recently been connected to the regulation of cellular proliferation, and on the basis of our results, the translational control of these mRNAs should be factored into this equation.

Amino Acids↗