CAPweb: a bioinformatics CGH array Analysis Platform

Assessing variations in DNA copy number is crucial for understanding constitutional or somatic diseases, particularly cancers. The recently developed array-CGH (comparative genomic hybridization) technology allows this to be investigated at the genomic level. We report the availability of a web tool for analysing array-CGH data. CAPweb (CGH array Analysis Platform on the Web) is intended as a user-friendly tool enabling biologists to completely analyse CGH arrays from the raw data to the visualization and biological interpretation. The user typically performs the following bioinformatics steps of a CGH array project within CAPweb: the secure upload of the results of CGH array image analysis and of the array annotation (genomic position of the probes); first level analysis of each array, including automatic normalization of the data (for correcting experimental biases), breakpoint detection and status assignment (gain, loss or normal); validation or deletion of the analysis based on a summary report and quality criteria; visualization and biological analysis of the genomic profiles and results through a user-friendly interface. CAPweb is accessible at

Spatial normalization of array-CGH data

BACKGROUND: Array-based comparative genomic hybridization (array-CGH) is a recently developed technique for analyzing changes in DNA copy number. As in all microarray analyses, normalization is required to correct for experimental artifacts while preserving the true biological signal. We investigated various sources of systematic variation in array-CGH data and identified two distinct types of spatial effect of no biological relevance as the predominant experimental artifacts: continuous spatial gradients and local spatial bias. Local spatial bias affects a large proportion of arrays, and has not previously been considered in array-CGH experiments. RESULTS: We show that existing normalization techniques do not correct these spatial effects properly. We therefore developed an automatic method for the spatial normalization of array-CGH data. This method makes it possible to delineate and to eliminate and/or correct areas affected by spatial bias. It is based on the combination of a spatial segmentation algorithm called NEM (Neighborhood Expectation Maximization) and spatial trend estimation. We defined quality criteria for array-CGH data, demonstrating significant improvements in data quality with our method for three data sets coming from two different platforms (198, 175 and 26 BAC-arrays). CONCLUSION: We have designed an automatic algorithm for the spatial normalization of BAC CGH-array data, preventing the misinterpretation of experimental artifacts as biologically relevant outliers in the genomic profile. This algorithm is implemented in the R package MANOR (Micro-Array NORmalization), which is described at and available from the Bioconductor site . It can also be tested on the CAPweb bioinformatics platform at

Live-Cell Chromosome Dynamics and Outcome of X Chromosome Pairing Events during ES Cell Differentiation

SummaryRandom X inactivation represents a paradigm for monoallelic gene regulation during early ES cell differentiation. In mice, the choice of X chromosome to inactivate in XX cells is ensured by monoallelic regulation of Xist RNA via its antisense transcription unit Tsix/Xite. Homologous pairing events have been proposed to underlie asymmetric Tsix expression, but direct evidence has been lacking owing to their dynamic and transient nature. Here we investigate the live-cell dynamics and outcome of Tsix pairing in differentiating mouse ES cells. We find an overall increase in genome dynamics including the Xics during early differentiation. During pairing, however, Xic loci show markedly reduced movements. Upon separation, Tsix expression becomes transiently monoallelic, providing a window of opportunity for monoallelic Xist upregulation. Our findings reveal the spatiotemporal choreography of the X chromosomes during early differentiation and indicate a direct role for pairing in facilitating symmetry-breaking and monoallelic regulation of Xist during random X inactivation

Precision medicine in cancer: Challenges and recommendations from an EU-funded cervical cancer biobanking study

Background:Cervical cancer (CC) remains a leading cause of gynaecological cancer-related mortality worldwide. CC pathogenesis is triggered when human papillomavirus (HPV) inserts into the genome, resulting in tumour suppressor gene inactivation and oncogene activation. Collecting tumour and blood samples is critical for identifying these genetic alterations.Methods:BIO-RAIDs is the first prospective molecular profiling clinical study to include a substantial biobanking effort that used uniform high-quality standards and control of samples. In this European Union (EU)-funded study, we identified the challenges that were impeding the effective implementation of such a systematic and comprehensive biobanking effort.Results:The challenges included a lack of uniform international legal and ethical standards, complexities in clinical and molecular data management, and difficulties in determining the best technical platforms and data analysis techniques. Some difficulties were encountered by all investigators, while others affected only certain institutions, regions, or countries.Conclusions:The results of the BIO-RAIDs programme highlight the need to facilitate and standardise regulatory procedures, and we feel that there is also a need for international working groups that make recommendations to regulatory bodies, governmental funding agencies, and academic institutions to achieve a proficient biobanking programme throughout EU countries. This represents the first step in precision medicine

EMA - A R package for Easy Microarray data analysis

<p>Abstract</p> <p>Background</p> <p>The increasing number of methodologies and tools currently available to analyse gene expression microarray data can be confusing for non specialist users.</p> <p>Findings</p> <p>Based on the experience of biostatisticians of Institut Curie, we propose both a clear analysis strategy and a selection of tools to investigate microarray gene expression data. The most usual and relevant existing R functions were discussed, validated and gathered in an easy-to-use R package (EMA) devoted to gene expression microarray analysis. These functions were improved for ease of use, enhanced visualisation and better interpretation of results.</p> <p>Conclusions</p> <p>Strategy and tools proposed in the EMA R package could provide a useful starting point for many microarrays users. EMA is part of Comprehensive R Archive Network and is freely available at <url>http://bioinfo.curie.fr/projects/ema/</url>.</p

A genomic and transcriptomic approach for a differential diagnosis between primary and secondary ovarian carcinomas in patients with a previous history of breast cancer

<p>Abstract</p> <p>Background</p> <p>The distinction between primary and secondary ovarian tumors may be challenging for pathologists. The purpose of the present work was to develop genomic and transcriptomic tools to further refine the pathological diagnosis of ovarian tumors after a previous history of breast cancer.</p> <p>Methods</p> <p>Sixteen paired breast-ovary tumors from patients with a former diagnosis of breast cancer were collected. The genomic profiles of paired tumors were analyzed using the Affymetrix GeneChip^®Mapping 50 K Xba Array or Genome-Wide Human SNP Array 6.0 (for one pair), and the data were normalized with ITALICS (ITerative and Alternative normaLIzation and Copy number calling for affymetrix Snp arrays) algorithm or Partek Genomic Suite, respectively. The transcriptome of paired samples was analyzed using Affymetrix GeneChip^®Human Genome U133 Plus 2.0 Arrays, and the data were normalized with gc-Robust Multi-array Average (gcRMA) algorithm. A hierarchical clustering of these samples was performed, combined with a dataset of well-identified primary and secondary ovarian tumors.</p> <p>Results</p> <p>In 12 of the 16 paired tumors analyzed, the comparison of genomic profiles confirmed the pathological diagnosis of primary ovarian tumor (n = 5) or metastasis of breast cancer (n = 7). Among four cases with uncertain pathological diagnosis, genomic profiles were clearly distinct between the ovarian and breast tumors in two pairs, thus indicating primary ovarian carcinomas, and showed common patterns in the two others, indicating metastases from breast cancer. In all pairs, the result of the transcriptomic analysis was concordant with that of the genomic analysis.</p> <p>Conclusions</p> <p>In patients with ovarian carcinoma and a previous history of breast cancer, SNP array analysis can be used to distinguish primary and secondary ovarian tumors. Transcriptomic analysis may be used when primary breast tissue specimen is not available.</p

Prognostic impact of vitamin B6 metabolism in lung cancer

Patients with non-small cell lung cancer (NSCLC) are routinely treated with cytotoxic agents such as cisplatin. Through a genome-wide siRNA-based screen, we identified vitamin B6 metabolism as a central regulator of cisplatin responses in vitro and in vivo. By aggravating a bioenergetic catastrophe that involves the depletion of intracellular glutathione, vitamin B6 exacerbates cisplatin-mediated DNA damage, thus sensitizing a large panel of cancer cell lines to apoptosis. Moreover, vitamin B6 sensitizes cancer cells to apoptosis induction by distinct types of physical and chemical stress, including multiple chemotherapeutics. This effect requires pyridoxal kinase (PDXK), the enzyme that generates the bioactive form of vitamin B6. In line with a general role of vitamin B6 in stress responses, low PDXK expression levels were found to be associated with poor disease outcome in two independent cohorts of patients with NSCLC. These results indicate that PDXK expression levels constitute a biomarker for risk stratification among patients with NSCLC.publishedVersio

Biostatistical Algorithms for OMICS Data in oncology. Application to DNA copy number microarray Experiments

Diplôme : Dr. d'UniversitéCancer is a major cause of death and lots of effort must be made to defeat the disease. Microarray technology is a powerful tool very helpful in oncology in order to better understand the molecular mechanisms involved in tumoral progression. We know that cancer is due to a modification of the gene regulation. Then, the study of gene expression in tumours is a valuable information in order to understand the biology of the disease and to identify new prognostic and predictive factors so that the clinician can tailor the therapy for each patient. Besides the modification of gene expression, tumours have chromosome alterations and especially a change of their DNA copy number. There are microarrays which allow the quantification of DNA copy number. The raw data obtained from the microarray technology need appropriate statistical processing so that they can be biologically and clinically meaningful. This is precisely the goal of the present work. Thus, statistical methods have been developed in order to normalise and extract the biological information from microarrays devoted to the study of DNA copy number in tumours. The methods have been applied in uveal melanoma in order to identify high-risk tumours. The integrative analysis of different types of molecular profiles is a challenge in biostatistics. Therefore, a statistical method able to combine both gene expression and DNA copy number data has been developed in the framework of supervised classification. The statistical properties of the method have been studied and its performance has been evaluated on both simulated and real data.Le cancer est une cause principale de décès et d'importants efforts doivent être réalisés pour vaincre la maladie. La technologie des microarrays est un puissant outil de recherche en oncologie pour comprendre les mécanismes de la progression tumorale qui est due à une perturbation de la régulation des gènes. Par conséquent, l'étude de leur niveau d'expression dans les tumeurs offre une perspective pour comprendre les mécanismes biologiques de la maladie et identifier de nouveaux facteurs pronostiques et prédictifs qui aideront le clinicien à choisir la thérapie de chaque patients. Par ailleurs, les tumeurs présentent un changement du nombre de copies d'ADN dont la quantification est aussi possible par microarray. L'utilisation des données de microarray nécessite un traitement statistique approprié permettant de transformer les données brutes en données interprétables biologiquement et cliniquement. Ainsi, nous avons développé des méthodes statistiques qui visent à normaliser et extraire l'information biologique issue des microarrays dédiés à l'étude du nombre de copies d'ADN des tumeurs. Nos méthodes ont permis la caractérisation des tumeurs de haut-risque métastatique dans le mélanome uvéal. Par ailleurs, un des enjeux de l'analyse biostatistique des données de microarrays consiste en l'analyse intégrée de différents types de profils moléculaires. Ainsi, une méthode statistique qui combine les données d'expression de gènes et du nombre de copie d'ADN obtenues par microarrays a été développée dans un contexte de classification supervisée. Les propriétés statistiques de la méthode ont été étudiées et ses performances estimées sur des données simulées et réelles

Akgorithmes biostatistiques pour les données omiques en oncologie - Application à l'étude du nombre de copies d'ADN à partir des expériences de microarray

Cancer is a major cause of death and lots of effort must be made to defeat the disease. Microarray technology is a powerful tool very helpful in oncology in order to better understand the molecular mechanisms involved in tumoral progression. We know that cancer is due to a modification of the gene regulation. Then, the study of gene expression in tumours is a valuable information in order to understand the biology of the disease and to identify new prognostic and predictive factors so that the clinician can tailor the therapy for each patient. Besides the modification of gene expression, tumours have chromosome alterations and especially a change of their DNA copy number. There are microarrays which allow the quantification of DNA copy number. The raw data obtained from the microarray technology need appropriate statistical processing so that they can be biologically and clinically meaningful. This is precisely the goal of the present work. Thus, statistical methods have been developed in order to normalise and extract the biological information from microarrays devoted to the study of DNA copy number in tumours. The methods have been applied in uveal melanoma in order to identify high-risk tumours. The integrative analysis of different types of molecular profiles is a challenge in biostatistics. Therefore, a statistical method able to combine both gene expression and DNA copy number data has been developed in the framework of supervised classification. The statistical properties of the method have been studied and its performance has been evaluated on both simulated and real data.Le cancer est une cause principale de décès et d'importants eorts doivent être réalisés pour vaincre la maladie. La technologie des microarrays est un puissant outil de recherche en oncologie pour comprendre les mécanismes de la progression tumorale qui est due à une perturbation de la régulation des gènes. Par conséquent, l'étude de leur niveau d'expression dans les tumeurs offre une perspective pour comprendre les mécanismes biologiques de la maladie et identier de nouveaux facteurs pronostiques et prédictifs qui aideront le clinicien à choisir la thérapie de chaque patients. Par ailleurs, les tumeurs présentent un changement du nombre de copies d'ADN dont la quantication est aussi possible par microarray. L'utilisation des données de microarray nécessite un traitement statistique approprié permettant de transformer les données brutes en données interprétables biologiquement et cliniquement. Ainsi, nous avons développé des méthodes statistiques qui visent à normaliser et extraire l'information biologique issue des microarrays dédiés à l'étude du nombre de copies d'ADN des tumeurs. Nos méthodes ont permis la caractérisation des tumeurs de haut-risque métastatique dans le mélanome uvéal. Par ailleurs, un des enjeux de l'analyse biostatistique des données de microarrays consiste en l'analyse intégrée de différents types de prols moléculaires. Ainsi, une méthode statistique qui combine les données d'expression de gènes et du nombre de copie d'ADN obtenues par microarrays a été développée dans un contexte de classication supervisée. Les propriétés statistiques de la méthode ont été étudiées et ses performances estimées sur des données simulées et réelles

Stability-based comparison of class discovery methods for DNA copy number profiles.

International audienceArray-CGH can be used to determine DNA copy number, imbalances in which are a fundamental factor in the genesis and progression of tumors. The discovery of classes with similar patterns of array-CGH profiles therefore adds to our understanding of cancer and the treatment of patients. Various input data representations for array-CGH, dissimilarity measures between tumor samples and clustering algorithms may be used for this purpose. The choice between procedures is often difficult. An evaluation procedure is therefore required to select the best class discovery method (combination of one input data representation, one dissimilarity measure and one clustering algorithm) for array-CGH. Robustness of the resulting classes is a common requirement, but no stability-based comparison of class discovery methods for array-CGH profiles has ever been reported. We applied several class discovery methods and evaluated the stability of their solutions, with a modified version of Bertoni's [Formula: see text]-based test [1]. Our version relaxes the assumption of independency required by original Bertoni's [Formula: see text]-based test. We conclude that Minimal Regions of alteration (a concept introduced by [2]) for input data representation, sim [3] or agree [4] for dissimilarity measure and the use of average group distance in the clustering algorithm produce the most robust classes of array-CGH profiles. The software is available from http://bioinfo.curie.fr/projects/cgh-clustering. It has also been partly integrated into "Visualization and analysis of array-CGH"(VAMP)[5]. The data sets used are publicly available from ACTuDB [6]