Bioinformatique et biologie des systèmes du cancer

Publications de l’équipe

Année de publication : 2013

David Cohen, Inna Kuperstein, Emmanuel Barillot, Andrei Zinovyev, Laurence Calzone (2013 May 30)

From a biological hypothesis to the construction of a mathematical model.

Methods in molecular biology (Clifton, N.J.) : 107-25 : DOI : 10.1007/978-1-62703-450-0_6 En savoir plus
Résumé

Mathematical models serve to explain complex biological phenomena and provide predictions that can be tested experimentally. They can provide plausible scenarios of a complex biological behavior when intuition is not sufficient anymore. The process from a biological hypothesis to a mathematical model might be challenging for biologists that are not familiar with mathematical modeling. In this chapter we discuss a possible workflow that describes the steps to be taken starting from a biological hypothesis on a biochemical cellular mechanism to the construction of a mathematical model using the appropriate formalism. An important part of this workflow is formalization of biological knowledge, which can be facilitated by existing tools and standards developed by the systems biology community. This chapter aims at introducing modeling to experts in molecular biology that would like to convert their hypotheses into mathematical models.

Replier
Eric Bonnet, Laurence Calzone, Daniel Rovera, Gautier Stoll, Emmanuel Barillot, Andrei Zinovyev (2013 May 30)

Practical use of BiNoM: a biological network manager software.

Methods in molecular biology (Clifton, N.J.) : 127-46 : DOI : 10.1007/978-1-62703-450-0_7 En savoir plus
Résumé

The Biological Network Manager (BiNoM) is a software tool for the manipulation and analysis of biological networks. It facilitates the import and conversion of a set of well-established systems biology file formats. It also provides a large set of graph-based algorithms that allow users to analyze and extract relevant subnetworks from large molecular maps. It has been successfully used in several projects related to the analysis of large and complex biological data, or networks from databases. In this tutorial, we present a detailed and practical case study of how to use BiNoM to analyze biological networks.

Replier
Virginie Maire, Céline Baldeyron, Marion Richardson, Bruno Tesson, Anne Vincent-Salomon, Eléonore Gravier, Bérengère Marty-Prouvost, Leanne De Koning, Guillem Rigaill, Aurélie Dumont, David Gentien, Emmanuel Barillot, Sergio Roman-Roman, Stéphane Depil, Francisco Cruzalegui, Alain Pierré, Gordon C Tucker, Thierry Dubois (2013 May 24)

TTK/hMPS1 is an attractive therapeutic target for triple-negative breast cancer.

PloS one : e63712 : DOI : 10.1371/journal.pone.0063712 En savoir plus
Résumé

Triple-negative breast cancer (TNBC) represents a subgroup of breast cancers (BC) associated with the most aggressive clinical behavior. No targeted therapy is currently available for the treatment of patients with TNBC. In order to discover potential therapeutic targets, we searched for protein kinases that are overexpressed in human TNBC biopsies and whose silencing in TNBC cell lines causes cell death. A cohort including human BC biopsies obtained at Institut Curie as well as normal tissues has been analyzed at a gene-expression level. The data revealed that the human protein kinase monopolar spindle 1 (hMPS1), also known as TTK and involved in mitotic checkpoint, is specifically overexpressed in TNBC, compared to the other BC subgroups and healthy tissues. We confirmed by immunohistochemistry and reverse phase protein array that TNBC expressed higher levels of TTK protein compared to the other BC subgroups. We then determined the biological effects of TTK depletion by RNA interference, through analyses of tumorigenic capacity and cell viability in different human TNBC cell lines. We found that RNAi-mediated depletion of TTK in various TNBC cell lines severely compromised their viability and their ability to form colonies in an anchorage-independent manner. Moreover, we observed that TTK silencing led to an increase in H2AX phosphorylation, activation of caspases 3/7, sub-G1 cell population accumulation and high annexin V staining, as well as to a decrease in G1 phase cell population and an increased aneuploidy. Altogether, these data indicate that TTK depletion in TNBC cells induces apoptosis. These results point out TTK as a protein kinase overexpressed in TNBC that may represent an attractive therapeutic target specifically for this poor prognosis associated subgroup of breast cancer.

Replier
Paola Vera-Licona, Eric Bonnet, Emmanuel Barillot, Andrei Zinovyev (2013 Apr 30)

OCSANA: optimal combinations of interventions from network analysis.

Bioinformatics (Oxford, England) : 1571-3 : DOI : 10.1093/bioinformatics/btt195 En savoir plus
Résumé

Targeted therapies interfering with specifically one protein activity are promising strategies in the treatment of diseases like cancer. However, accumulated empirical experience has shown that targeting multiple proteins in signaling networks involved in the disease is often necessary. Thus, one important problem in biomedical research is the design and prioritization of optimal combinations of interventions to repress a pathological behavior, while minimizing side-effects. OCSANA (optimal combinations of interventions from network analysis) is a new software designed to identify and prioritize optimal and minimal combinations of interventions to disrupt the paths between source nodes and target nodes. When specified by the user, OCSANA seeks to additionally minimize the side effects that a combination of interventions can cause on specified off-target nodes. With the crucial ability to cope with very large networks, OCSANA includes an exact solution and a novel selective enumeration approach for the combinatorial interventions’ problem.

Replier

Année de publication : 2012

Virginie Maire, Fariba Némati, Marion Richardson, Anne Vincent-Salomon, Bruno Tesson, Guillem Rigaill, Eléonore Gravier, Bérengère Marty-Prouvost, Leanne De Koning, Guillaume Lang, David Gentien, Aurélie Dumont, Emmanuel Barillot, Elisabetta Marangoni, Didier Decaudin, Sergio Roman-Roman, Alain Pierré, Francisco Cruzalegui, Stéphane Depil, Gordon C Tucker, Thierry Dubois (2012 Nov 13)

Polo-like kinase 1: a potential therapeutic option in combination with conventional chemotherapy for the management of patients with triple-negative breast cancer.

Cancer research : 813-23 : DOI : 10.1158/0008-5472.CAN-12-2633 En savoir plus
Résumé

Breast cancers are composed of molecularly distinct subtypes with different clinical outcomes and responses to therapy. To discover potential therapeutic targets for the poor prognosis-associated triple-negative breast cancer (TNBC), gene expression profiling was carried out on a cohort of 130 breast cancer samples. Polo-like kinase 1 (PLK1) was found to be significantly overexpressed in TNBC compared with the other breast cancer subtypes. High PLK1 expression was confirmed by reverse phase protein and tissue microarrays. In triple-negative cell lines, RNAi-mediated PLK1 depletion or inhibition of PLK1 activity with a small molecule (BI-2536) induced an increase in phosphorylated H2AX, G(2)-M arrest, and apoptosis. A soft-agar colony assay showed that PLK1 silencing impaired clonogenic potential of TNBC cell lines. When cells were grown in extracellular matrix gels (Matrigel), and exposed to BI-2536, apoptosis was observed specifically in TNBC cancerous cells, and not in a normal cell line. When administrated as a single agent, the PLK1 inhibitor significantly impaired tumor growth in vivo in two xenografts models established from biopsies of patients with TNBC. Most importantly, the administration of BI-2536, in combination with doxorubicin + cyclophosphamide chemotherapy, led to a faster complete response compared with the chemotherapy treatment alone and prevented relapse, which is the major risk associated with TNBC. Altogether, our observations suggest PLK1 inhibition as an attractive therapeutic approach, in association with conventional chemotherapy, for the management of patients with TNBC.

Replier
Chong-Jian Chen, Nicolas Servant, Joern Toedling, Alexis Sarazin, Antonin Marchais, Evelyne Duvernois-Berthet, Valérie Cognat, Vincent Colot, Olivier Voinnet, Edith Heard, Constance Ciaudo, Emmanuel Barillot (2012 Oct 10)

ncPRO-seq: a tool for annotation and profiling of ncRNAs in sRNA-seq data.

Bioinformatics (Oxford, England) : 3147-9 : DOI : 10.1093/bioinformatics/bts587 En savoir plus
Résumé

Non-coding RNA (ncRNA) PROfiling in small RNA (sRNA)-seq (ncPRO-seq) is a stand-alone, comprehensive and flexible ncRNA analysis pipeline. It can interrogate and perform detailed profiling analysis on sRNAs derived from annotated non-coding regions in miRBase, Rfam and RepeatMasker, as well as specific regions defined by users. The ncPRO-seq pipeline performs both gene-based and family-based analyses of sRNAs. It also has a module to identify regions significantly enriched with short reads, which cannot be classified under known ncRNA families, thus enabling the discovery of previously unknown ncRNA- or small interfering RNA (siRNA)-producing regions. The ncPRO-seq pipeline supports input read sequences in fastq, fasta and color space format, as well as alignment results in BAM format, meaning that sRNA raw data from the three current major platforms (Roche-454, Illumina-Solexa and Life technologies-SOLiD) can be analyzed with this pipeline. The ncPRO-seq pipeline can be used to analyze read and alignment data, based on any sequenced genome, including mammals and plants.

Replier
Eric Bonnet, Laurence Calzone, Daniel Rovera, Gautier Stoll, Emmanuel Barillot, Andrei Zinovyev (2012 Sep 7)

BiNoM 2.0, a Cytoscape plugin for accessing and analyzing pathways using standard systems biology formats.

BMC systems biology : 18 : DOI : 10.1186/1752-0509-7-18 En savoir plus
Résumé

Public repositories of biological pathways and networks have greatly expanded in recent years. Such databases contain many pathways that facilitate the analysis of high-throughput experimental work and the formulation of new biological hypotheses to be tested, a fundamental principle of the systems biology approach. However, large-scale molecular maps are not always easy to mine and interpret.

Replier
Gautier Stoll, Eric Viara, Emmanuel Barillot, Laurence Calzone (2012 Aug 31)

Continuous time Boolean modeling for biological signaling: application of Gillespie algorithm.

BMC systems biology : 116 : DOI : 10.1186/1752-0509-6-116 En savoir plus
Résumé

Mathematical modeling is used as a Systems Biology tool to answer biological questions, and more precisely, to validate a network that describes biological observations and predict the effect of perturbations. This article presents an algorithm for modeling biological networks in a discrete framework with continuous time.

Replier
Nicolas Servant, Bryan R Lajoie, Elphège P Nora, Luca Giorgetti, Chong-Jian Chen, Edith Heard, Job Dekker, Emmanuel Barillot (2012 Aug 28)

HiTC: exploration of high-throughput ‘C’ experiments.

Bioinformatics (Oxford, England) : 2843-4 : DOI : 10.1093/bioinformatics/bts521 En savoir plus
Résumé

The R/Bioconductor package HiTC facilitates the exploration of high-throughput 3C-based data. It allows users to import and export ‘C’ data, to transform, normalize, annotate and visualize interaction maps. The package operates within the Bioconductor framework and thus offers new opportunities for future development in this field.

Replier
Loredana Martignetti, Karine Laud-Duval, Franck Tirode, Gaelle Pierron, Stéphanie Reynaud, Emmanuel Barillot, Olivier Delattre, Andrei Zinovyev (2012 Aug 1)

Antagonism pattern detection between microRNA and target expression in Ewing’s sarcoma.

PloS one : e41770 : DOI : 10.1371/journal.pone.0041770 En savoir plus
Résumé

MicroRNAs (miRNAs) have emerged as fundamental regulators that silence gene expression at the post-transcriptional and translational levels. The identification of their targets is a major challenge to elucidate the regulated biological processes. The overall effect of miRNA is reflected on target mRNA expression, suggesting the design of new investigative methods based on high-throughput experimental data such as miRNA and transcriptome profiles. We propose a novel statistical measure of non-linear dependence between miRNA and mRNA expression, in order to infer miRNA-target interactions. This approach, which we name antagonism pattern detection, is based on the statistical recognition of a triangular-shaped pattern in miRNA-target expression profiles. This pattern is observed in miRNA-target expression measurements since their simultaneously elevated expression is statistically under-represented in the case of miRNA silencing effect. The proposed method enables miRNA target prediction to strongly rely on cellular context and physiological conditions reflected by expression data. The procedure has been assessed on synthetic datasets and tested on a set of real positive controls. Then it has been applied to analyze expression data from Ewing’s sarcoma patients. The antagonism relationship is evaluated as a good indicator of real miRNA-target biological interaction. The predicted targets are consistently enriched for miRNA binding site motifs in their 3’UTR. Moreover, we reveal sets of predicted targets for each miRNA sharing important biological function. The procedure allows us to infer crucial miRNA regulators and their potential targets in Ewing’s sarcoma disease. It can be considered as a valid statistical approach to discover new insights in the miRNA regulatory mechanisms.

Replier
Valentina Boeva, Alban Lermine, Camille Barette, Christel Guillouf, Emmanuel Barillot (2012 Jul 26)

Nebula–a web-server for advanced ChIP-seq data analysis.

Bioinformatics (Oxford, England) : 2517-9 En savoir plus
Résumé

ChIP-seq consists of chromatin immunoprecipitation and deep sequencing of the extracted DNA fragments. It is the technique of choice for accurate characterization of the binding sites of transcription factors and other DNA-associated proteins. We present a web service, Nebula, which allows inexperienced users to perform a complete bioinformatics analysis of ChIP-seq data.

Replier
Loredana Martignetti, Andrei Zinovyev, Emmanuel Barillot (2012 Jul 20)

Identification of shortened 3′ untranslated regions from expression arrays.

Journal of bioinformatics and computational biology : 1241001 : DOI : 10.1142/S0219720012410016 En savoir plus
Résumé

Cancer cells have been recently shown to express high level of short 3’UTR isoforms that can escape miRNA-mediated regulation. We present here a computational procedure for systematically identifying shortened 3’UTRs by Affymetrix 3′ microarrays. The advantage of this technology compared to more recent and promising ones such as exon arrays and RNA-Seq is that, giving the relatively small cost, already existing datasets in public databases include a considerably higher number of experiments. Moreover, the design of Affymetrix Gene Chips is well-suited for 3’UTR analysis of a large number of genes. Initially, Affymetrix individual probes are regrouped into customized probesets mapping specifically the CDS or the 3’UTR of the transcript, according to RefSeq annotation. Then, candidate 3’UTR shortening events are identified by statistical differential expression analysis of customized probesets in different biological conditions. The procedure has been applied to expression data from two ovarian adenocarcinoma datasets. Selected gene sets are significantly enriched for annotated splice variant genes as well as genes involved in estrogen dependent cancer mechanisms, confirming the validity of the proposed procedure.

Replier
Maya Ridinger-Saison, Valentina Boeva, Pauline Rimmelé, Ivan Kulakovskiy, Isabelle Gallais, Benjamin Levavasseur, Caroline Paccard, Patricia Legoix-Né, François Morlé, Alain Nicolas, Philippe Hupé, Emmanuel Barillot, Françoise Moreau-Gachelin, Christel Guillouf (2012 Jul 14)

Spi-1/PU.1 activates transcription through clustered DNA occupancy in erythroleukemia.

Nucleic acids research : 8927-41 : DOI : 10.1093/nar/gks659 En savoir plus
Résumé

Acute leukemias are characterized by deregulation of transcriptional networks that control the lineage specificity of gene expression. The aberrant overexpression of the Spi-1/PU.1 transcription factor leads to erythroleukemia. To determine how Spi-1 mechanistically influences the transcriptional program, we combined a ChIP-seq analysis with transcriptional profiling in cells from an erythroleukemic mouse model. We show that Spi-1 displays a selective DNA-binding that does not often cause transcriptional modulation. We report that Spi-1 controls transcriptional activation and repression partially through distinct Spi-1 recruitment to chromatin. We revealed several parameters impacting on Spi-1-mediated transcriptional activation. Gene activation is facilitated by Spi-1 occupancy close to transcriptional starting site of genes devoid of CGIs. Moreover, in those regions Spi-1 acts by binding to multiple motifs tightly clustered and with similar orientation. Finally, in contrast to the myeloid and lymphoid B cells in which Spi-1 exerts a physiological activity, in the erythroleukemic cells, lineage-specific cooperating factors do not play a prevalent role in Spi-1-mediated transcriptional activation. Thus, our work describes a new mechanism of gene activation through clustered site occupancy of Spi-1 particularly relevant in regard to the strong expression of Spi-1 in the erythroleukemic cells.

Replier
Sylvie Troncale, Aurélie Barbet, Lamine Coulibaly, Emilie Henry, Beilei He, Emmanuel Barillot, Thierry Dubois, Philippe Hupé, Leanne de Koning (2012 Jul 5)

NormaCurve: a SuperCurve-based method that simultaneously quantifies and normalizes reverse phase protein array data.

PloS one : e38686 : DOI : 10.1371/journal.pone.0038686 En savoir plus
Résumé

Reverse phase protein array (RPPA) is a powerful dot-blot technology that allows studying protein expression levels as well as post-translational modifications in a large number of samples simultaneously. Yet, correct interpretation of RPPA data has remained a major challenge for its broad-scale application and its translation into clinical research. Satisfying quantification tools are available to assess a relative protein expression level from a serial dilution curve. However, appropriate tools allowing the normalization of the data for external sources of variation are currently missing.

Replier
Joern Toedling, Nicolas Servant, Constance Ciaudo, Laurent Farinelli, Olivier Voinnet, Edith Heard, Emmanuel Barillot (2012 Mar 3)

Deep-sequencing protocols influence the results obtained in small-RNA sequencing.

PloS one : e32724 : DOI : 10.1371/journal.pone.0032724 En savoir plus
Résumé

Second-generation sequencing is a powerful method for identifying and quantifying small-RNA components of cells. However, little attention has been paid to the effects of the choice of sequencing platform and library preparation protocol on the results obtained. We present a thorough comparison of small-RNA sequencing libraries generated from the same embryonic stem cell lines, using different sequencing platforms, which represent the three major second-generation sequencing technologies, and protocols. We have analysed and compared the expression of microRNAs, as well as populations of small RNAs derived from repetitive elements. Despite the fact that different libraries display a good correlation between sequencing platforms, qualitative and quantitative variations in the results were found, depending on the protocol used. Thus, when comparing libraries from different biological samples, it is strongly recommended to use the same sequencing platform and protocol in order to ensure the biological relevance of the comparisons.

Replier