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Search Results: 1 - 10 of 32666 matches for " Daniel Baty "
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Therapeutic Antibodies for the Treatment of Pancreatic Cancer
Patrick Chames,Brigitte Kerfelec,Daniel Baty
The Scientific World Journal , 2010, DOI: 10.1100/tsw.2010.103
Abstract:
State of the Art in Tumor Antigen and Biomarker Discovery
Klervi Even-Desrumeaux,Daniel Baty,Patrick Chames
Cancers , 2011, DOI: 10.3390/cancers3022554
Abstract: Our knowledge of tumor immunology has resulted in multiple approaches for the treatment of cancer. However, a gap between research of new tumors markers and development of immunotherapy has been established and very few markers exist that can be used for treatment. The challenge is now to discover new targets for active and passive immunotherapy. This review aims at describing recent advances in biomarkers and tumor antigen discovery in terms of antigen nature and localization, and is highlighting the most recent approaches used for their discovery including “omics” technology.
Stability of gene contributions and identification of outliers in multivariate analysis of microarray data
Florent Baty, Daniel Jaeger, Frank Preiswerk, Martin M Schumacher, Martin H Brutsche
BMC Bioinformatics , 2008, DOI: 10.1186/1471-2105-9-289
Abstract: In this study, we developed a computationally efficient algorithm for: i) the assessment of the significance of gene contributions and ii) the identification of sample outliers in multivariate analysis of microarray data. The approach is based on the use of resampling methods including bootstrapping and jackknifing. A statistical package of R functions was developed. This package includes tools for both inferring the statistical significance of gene contributions and identifying outliers among samples.The methodology was successfully applied to three published data sets with varying levels of signal intensities. Its relevance was compared with alternative methods. Overall, it proved to be particularly effective for the evaluation of the stability of microarray data.Ordination methods are useful tools for the analysis of gene expression microarrays. Principal component analysis (PCA) and correspondence analysis (CA) have both been used to extract the main sources of variation present in highly multivariate microarray data [1,2]. The supervised counterparts of these approaches, including between-group analysis (BGA) [3] and analyses with respect to instrumental variables [4], were proposed to handle descriptive variables controlled in the design of the experiment (e.g. disease classes). When dealing with transcriptomics data, multivariate approaches are generally more appropriate than univariate strategies because they intrinsically take gene covariations and interactions into account.Constrained ordination methods are very efficient for sample classification and class prediction. They are flexible and can be used easily to identify groups of genes associated with classes of samples. Geometrical interpretations are generally required to investigate the gene-sample relationship. Genes of interest can also be ranked according to their discriminative power. However, considering the exploratory nature of these methods, it is not trivial to assess the significance of a giv
EGFR Exon-Level Biomarkers of the Response to Bevacizumab/Erlotinib in Non-Small Cell Lung Cancer
Florent Baty, Sacha Rothschild, Martin Früh, Daniel Betticher, Cornelia Dr?ge, Richard Cathomas, Daniel Rauch, Oliver Gautschi, Lukas Bubendorf, Susanne Crowe, Francesco Zappa, Miklos Pless, Martin Brutsche, Swiss Group for Clinical Cancer Research
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0072966
Abstract: Activating epidermal growth factor receptor (EGFR) mutations are recognized biomarkers for patients with metastatic non-small cell lung cancer (NSCLC) treated with EGFR tyrosine kinase inhibitors (TKIs). EGFR TKIs can also have activity against NSCLC without EGFR mutations, requiring the identification of additional relevant biomarkers. Previous studies on tumor EGFR protein levels and EGFR gene copy number revealed inconsistent results. The aim of the study was to identify novel biomarkers of the response to TKIs in NSCLC by investigating whole genome expression at the exon-level. We used exon arrays and clinical samples from a previous trial (SAKK19/05) to investigate the expression variations at the exon-level of 3 genes potentially playing a key role in modulating treatment response: EGFR, V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) and vascular endothelial growth factor (VEGFA). We identified the expression of EGFR exon 18 as a new predictive marker for patients with untreated metastatic NSCLC treated with bevacizumab and erlotinib in the first line setting. The overexpression of EGFR exon 18 in tumor was significantly associated with tumor shrinkage, independently of EGFR mutation status. A similar significant association could be found in blood samples. In conclusion, exonic EGFR expression particularly in exon 18 was found to be a relevant predictive biomarker for response to bevacizumab and erlotinib. Based on these results, we propose a new model of EGFR testing in tumor and blood.
Thermal synchrotron radiation from RRMHD simulations of the double tearing mode reconnection - Application to the Crab flares
Makoto Takamoto,J. Pétri,H. Baty
Physics , 2015, DOI: 10.1093/mnras/stv2163
Abstract: We study the magneto-hydrodynamic tearing instability occurring in a double current sheet configuration when a guide field is present. This is investigated by means of resistive relativistic magneto-hydrodynamic (RRMHD) simulations. Following the dynamics of the double tearing mode (DTM), we are able to compute synthetic synchrotron spectra in the explosive reconnection phase. The pulsar striped wind model represents a site where such current sheets are formed, including a guide field. The variability of the Crab nebula/pulsar system, seen as flares, can be therefore naturally explained by the DTM explosive phase in the striped wind. Our results indicate that the Crab GeV flare can be explained by the double tearing mode in the striped wind region if the magnetization parameter $\sigma$ is around $10^5$.
Explosive reconnection of double tearing modes in relativistic plasmas: application to the Crab flares
H. Baty,J. Petri,S. Zenitani
Physics , 2013, DOI: 10.1093/mnrasl/slt104
Abstract: Magnetic reconnection associated to the double tearing mode (DTM) is investigated by means of resistive relativistic magnetohydrodynamic (RRMHD) simulations. A linearly unstable double current sheet system in two dimensional cartesian geometry is considered. For initial perturbations of large enough longitudinal wavelengths, a fast reconnection event is triggered by a secondary instability that is structurally driven by the nonlinear evolution of the magnetic islands. The latter reconnection phase and time scale appear to weakly depend on the plasma resistivity and magnetization parameter. We discuss the possible role of such explosive reconnection dynamics to explain the MeV flares observed in the Crab pulsar nebula. Indeed the time scale and the critical minimum wavelength give constraints on the Lorentz factor of the striped wind and on the location of the emission region respectively.
Global Gene Expression Analysis of the Interaction between Cancer Cells and Osteoblasts to Predict Bone Metastasis in Breast Cancer
Michal Rajski, Brigitte Vogel, Florent Baty, Christoph Rochlitz, Martin Buess
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0029743
Abstract: Background Bone metastasis is a main cause of morbidity in breast cancer. Since breast cancer is a heterogeneous disease, the interactions of cancer cells with the skeletal host cells might also be diverse. We hypothesized that gene expression signatures induced by heterotypic interaction of breast cancer cells and osteoblasts might be of clinical relevance. Methodology/Principal Findings We established an ex vivo co-culture model using benign breast epithelial cells or a panel of 5 malignant breast epithelial cells in combination with primary human osteoblasts and determined associated gene expression changes with HEEBO microarrays. Pretreatment gene expression profiles of 295 early stage breast cancers published from the Netherlands Cancer Institute with a median follow up of 12.6 years allowed evaluating in vitro effects in the in vivo situation.The effects of the interaction between osteoblasts and breast cancer cell lines of different origin were very heterogeneous. Hs578T cells started to proliferate in co-culture with osteoblasts, SKBR-3 induced a TGF-β response and MDA-MB231 cells showed two distinct sets of up-regulated genes: A set of interferon response genes associated with an up-regulation of STAT1 was in vivo remarkably coherent providing a basis for segregation of tumors into two groups. In a uni-variate analysis, early stage tumors with high expression levels (n = 136) of this gene set had a significantly lower overall survival rate (p = 0.005) (63% at 10 years) than tumors with low expression levels (n = 159) (overall survival: 77% at 10 years). The second gene set was associated with IL-6 and did not significantly change the overall survival rate (p = 0.165), but was significantly associated with a shorter time to bone metastasis (p = 0.049; 74% vs. 83% at 10 years). Conclusion/Significance An IL-6 gene expression pattern induced by heterotypic interaction of breast cancer cells with osteoblasts in vitro is associated with a higher rate of bone metastasis in vivo.
Therapeutic bronchoscopy for malignant airway stenoses: Choice of modality and survival
Chhajed Prashant,Somandin Stephanie,Baty Florent,Mehta Ankur
Journal of Cancer Research and Therapeutics , 2010,
Abstract: Background: There are no data regarding the factors influencing the choice of therapeutic bronchoscopic modality in the management of malignant airway stenoses. Objectives: To assess the choice of therapeutic bronchoscopy modality and analyze factors influencing survival in patients with malignant central airway obstruction. Materials and Methods: We performed 167 procedures in 130 consecutive patients, for malignant central airway obstruction, over six years. Results: Laser was used either alone or in combination with stent insertion in 76% procedures. Laser only was used in 53% procedures for lesions below the main bronchi. Stents alone were used for extrinsic compression or stump insufficiency. Combined laser and stent insertion was most frequently used for lesions involving the trachea plus both main bronchi or only the main bronchi. The Dumon stent was preferred in lesions of the trachea and the right bronchial tree, the Ultraflex stent for lesions on the left side and stenoses below the main bronchi. Survival was better in patients with lung cancer, lesions restricted to one lung and when laser alone was used compared to esophageal cancer, metastases and tracheal involvement. Conclusion: The choice of different airway stents can be made based on the nature and site of the lesion. Dumon stents are suited for lesions in trachea and right main bronchus and the Ultraflex stents on the left side and stenoses beyond the main bronchi. Survival can be estimated based on the diagnosis, site of the lesion and treatment modality used.
Optimized between-group classification: a new jackknife-based gene selection procedure for genome-wide expression data
Florent Baty, Michel P Bihl, Guy Perrière, Aedín C Culhane, Martin H Brutsche
BMC Bioinformatics , 2005, DOI: 10.1186/1471-2105-6-239
Abstract: We propose an optimized between-group classification (OBC) which uses a jackknife-based gene selection procedure. OBC emphasizes classification accuracy rather than feature selection. OBC is a backward optimization procedure that maximizes the percentage of between group inertia by removing the least influential genes one by one from the analysis. This selects a subset of highly discriminative genes which optimize disease class prediction. We apply OBC to four datasets and compared it to other classification methods.OBC considerably improved the classification and predictive accuracy of BGA, when assessed using independent data sets and leave-one-out cross-validation.The R code is freely available [see 1] as well as supplementary information [see 2].Gene expression microarrays enable the simultaneous measurement of the expression levels of thousands of genes. Supervised classification of gene expression data aims to identify combinations of genes which give the best discrimination of groups of samples specified in advance. For such methods, which are classically used in disease class prediction, the identification of a subset of discriminating genes can be critical [1,2]. Indeed, a large proportion of genes are generally non-informative in terms of disease class prediction. A gain in classification and prediction performance can be expected when predictors are built upon a subset of highly discriminating genes [3,4].Several algorithms capable of selecting a subset of predictive genes were recently proposed [5]. These methods include a genetic algorithm [6], maximum difference subset algorithm (MDSS) [7], support vector machines [8,9], a shrunken centroids technique [2,10] and several which use of discriminant functions [11].However, two issues remain: 1) different subsets of genes may provide comparable optimal discriminations [1]; 2) it is generally difficult to determine the optimal number of genes for discrimination [12,13]. This number may vary according to the
Analysis with respect to instrumental variables for the exploration of microarray data structures
Florent Baty, Micha?l Facompré, Jan Wiegand, Joseph Schwager, Martin H Brutsche
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-422
Abstract: We present here a family of methods, the analyses with respect to instrumental variables, which can be easily applied to the particular case of microarray data. An illustrative example of analysis with instrumental variables is given in the case of microarray data investigating the effect of beverage intake on peripheral blood gene expression. This approach is compared to an ANOVA-based gene-by-gene statistical method.Instrumental variables analyses provide a simple way to control several sources of variation in a multivariate analysis of microarray data. Due to their flexibility, these methods can be associated with a large range of ordination techniques combined with one or several qualitative and/or quantitative descriptive variables.Microarray experiments essentially yield highly multivariate data. The number of variables measured in such data is generally far greater than the number of samples and numerous specific statistical approaches have been proposed. In this context, ordination methods proved to be powerful exploratory tools.Principal component analysis (PCA) and correspondence analysis (CA) are two dimensionality reduction techniques commonly applied in this area of microarray analysis [1,2]. In an unsupervised fashion, these techniques aim to summarize trends present in high-dimensional datasets.Besides the variables of direct interest (gene expression levels), one or several qualitative variables are sometimes used to describe features of the experimental design. In a clinical context, variables describing the phenotypic structures of a population are typically involved (e.g. "healthy controls" vs. "patients"). Several other variables can also be taken into account including information about temporal, treatment, individual effects, etc. Technical information can also be described. For example, laboratory effect and batch effect not rarely represent an important source of data variation. Overall, the descriptive variables can be classified into two ca
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