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International audience; Background. Glioblastomas are heterogeneous tumors composed of a necrotic and tumor core and an invasive periphery.Methods. Here, we performed a proteomics analysis of laser-capture micro-dissected glioblastoma core and invasive areas of patient-derived xenografts.Results. Bioinformatics analysis identified enriched proteins in central and invasive tumor areas. Novel markers of invasion were identified, the genes proteolipid protein 1 (PLP1) and Dynamin-1 (DNM1), which were subsequently validated in tumors and by functional assays.Conclusions. In summary, our results identify new networks and molecules that may play an important role in glioblastoma development and may constitute potential novel therapeutic targets.

International audience; Basal-like breast cancers are among the most aggressive cancers and effective targeted therapies are still missing. In order to identify new therapeutic targets, we performed Methyl-Seq and RNA-Seq of 10 breast cancer cell lines with different phenotypes. We confirmed that breast cancer subtypes cluster the RNA-Seq data but not the Methyl-Seq data. Basal-like tumor hypermethylated phenotype was not confirmed in our study but RNA-Seq analysis allowed to identify 77 genes significantly overexpressed in basal-like breast cancer cell lines. Among them, 48 were overexpressed in triple negative breast cancers of TCGA data. Some molecular functions were overrepresented in this candidate gene list. Genes involved in antioxydation, such as SOD1, MGST3 and PRDX or cadherin-binding genes, such as PFN1, ITGB1 and ANXA1, could thus be considered as basal like breast cancer biomarkers. We then sought if these genes were linked to BRCA1, since this gene is often inactivated in basal-like breast cancers. Nine genes were identified overexpressed in both basal-like breast cancer cells and BRCA1 mutated cells. Amongst them, at least 3 genes code for proteins implicated in epithelial cell migration and epithelial to mesenchymal transition (VIM, ITGB1 and RhoA). Our study provided several potential therapeutic targets for triple negative and BRCA1 mutated breast cancers. It seems that migration and mesenchymal properties acquisition of basal-like breast cancer cells is a key functional pathway in these tumors with a high metastatic potential.

Cancer results from the excessive proliferation of cells decending from the same founder cell and following a Darwinian process of diversification and selection. This process is defined by the accumulation of genetic and epigenetic alterations whose characterization is a key element for establishing a therapy that would specifically target tumor cells. The advent of new high-throughput sequencing technologies enables this characterization at the molecular level. This technological revolution has led to the development of numerous bioinformatics methods. In this thesis, we are particularly interested in the development of new computational methods for the analysis of sequencing data of tumor samples allowing precise identification of tumor-specific alterations and an accurate description of tumor subpopulations. In the first chapter, we explore methods for identifying single nucleotide alterations in targeted sequencing data and apply them to a cohort of breast cancer patients. We introduce two new methods of analysis, each tailored to a particular sequencing technology, namely Roche 454 and Pacific Biosciences. In the first case, we adapted existing approaches to the particular case of transcript sequencing. In the second case, when using conventional approaches, we were confronted with a high background noise resulting in a high rate of false positives. We have developed a new method, MICADo, based on the De Bruijn graphs and making possible an effective distinction between patient-specific alterations and alterations common to the cohort, which makes the results usable in a clinical context. Second chapter deals with the identification of copy number alterations. We describe the approach put in place for their efficient identification from very low coverage data. The main contribution of this work is the development of a strategy for statistical analysis in order to emphasise local and global changes in the genome that occurred during the treatment administered to patients with breast cancer. Our method is based on the construction of a linear model to establish scores of differences between samples before and after treatment. In the third chapter, we focus on the problem of clonal reconstruction. This problem has recently gathered a lot of interest, but it still lacks a well-established formal framework. We first propose a formalization of the clonal reconstruction problem. Then we use this formalism to put in place a method based on Gaussian mixture models. Our method uses single nucleotide and copy number alterations - such as those discussed in the previous two chapters - to characterize and quantify different clonal populations present in a tumor sample., Le cancer résulte de la prolifération excessive de cellules qui dérivent toutes de la même cellule initiatrice et suivent un processus Darwinien de diversification et de sélection. Ce processus est défini par l'accumulation d'altérations génétiques et épigénétiques dont la caractérisation est un élément majeur pour pouvoir proposer une thérapie ciblant spécifiquement les cellules tumorales. L'avènement des nouvelles technologies de séquençage haut débit permet cette caractérisation à un niveau moléculaire. Cette révolution technologique a entraîné le développement de nombreuses méthodes bioinformatiques. Dans cette thèse, nous nous intéressons particulièrement au développement de nouvelles méthodes computationnelles d'analyse de données de séquençage d'échantillons tumoraux permettant une identification précise d'altérations spécifiques aux tumeurs et une description fine des sous populations tumorales. Dans le premier chapitre, il s'agît d'étudier des méthodes d'identification d'altérations ponctuelles dans le cadre de séquençage ciblé, appliquées à une cohorte de patientes atteintes du cancer du sein. Nous décrivons deux nouvelles méthodes d'analyse, chacune adaptée à une technologie de séquençage, spécifiquement Roche 454 et Pacifique Biosciences.Dans le premier cas, nous avons adapté des approches existantes au cas particulier de séquences de transcrits. Dans le second cas, nous avons été confronté à un bruit de fond élevé entraînant un fort taux de faux positifs lors de l'utilisation d'approches classiques. Nous avons développé une nouvelle méthode, MICADo, basée sur les graphes de De Bruijn et permettant une distinction efficace entre les altérations spécifiques aux patients et les altérations communes à la cohorte, ce qui rend les résultats exploitables dans un contexte clinique. Le second chapitre aborde l'identification d'altérations de nombre de copies. Nous décrivons l'approche mise en place pour leur identification efficace à partir de données de très faible couverture. L'apport principal de ce travail consiste en l'élaboration d'une stratégie d'analyse statistique afin de mettre en évidence des changements locaux et globaux au niveau du génome survenus durant le traitement administré à des patientes atteintes de cancer du sein. Notre méthode repose sur la construction d'un modèle linéaire permettant d'établir des scores de différences entre les échantillons avant et après traitement. Dans le troisième chapitre, nous nous intéressons au problème de reconstruction clonale. Cette problématique récente est actuellement en plein essor, mais manque cependant d'un cadre formel bien établi. Nous proposons d'abord une formalisation du problème de reconstruction clonale. Ensuite nous utilisons ce formalisme afin de mettre en place une méthode basée sur les modèles de mélanges Gaussiens. Cette méthode utilise les altérations ponctuelles et de nombre de copies - comme celles abordées dans les deux chapitres précédents - afin de caractériser et quantifier les différentes populations clonales présentes dans un échantillon tumoral.

International audience; To determine the TP53 mutations present in a patient cohort (~1500 patients), breast tumor TP53 mRNA was sequenced with PacBio technology. However, none of theexisting tools, e.g. VarScan, GATK, has proven to be suitable for this type of data. Indeed, in addition to the high sequencing error rate generated by PacBio (~15%), thetumor biopsies are contaminated by healthy tissue. This makes it difficult to differentiate real mutations as they are lost within the high background noise. To circumventthis problem, we have developed a method for detecting mutations using De Bruijn graphs: MICADo for Mutations In Cancer Data.

International audience; One in eight women are affected by breast cancer. Most of them receive hormonal therapy. Neoadjuvant hormonal therapy is a form of hormonal therapy given before surgery. Treatment for 6 months causes tumours to shrink, after which residual tumour is removed by surgery. Unfortunately, in some cases, the tumour cells are resistant to hormonal therapy and the patients relapse. This can be caused by intra-tumour heterogeneity: hormonal therapy eliminates drug-sensitive clones, leaving behind resistant clones. Understanding why some clones are resistant and what their characteristics are may lead to the development of alternative therapies. We compare DNA copy number profiles before and after treatment in the case of ER+ breast cancers. Very low depth sequencing was performed (Illumina GAIIx technology) on biopsies from breast tumours, before and after treatment. Reads were aligned to the human genome hg19 (bwa). CNAnorm was used to partition reference genome in intervals G = (i 1 , ...,i n) of non-overlapping sliding windows. Number of reads was converted to a count vector C = (c 1 , ...,c n) and then to a ratio vector with respect to a pool of normal female DNA.