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Analyse bioinformatique intégrative de l'hétérogénéité intratumorale génétique dans le neuroblastome à un niveau cellule unique

par Jaydutt Bhalshankar

Projet de thèse en Aspects moléculaires et cellulaires de la biologie

Sous la direction de Gudrun Schleiermacher et de Andreï Zinovyev.

Thèses en préparation à l'Université Paris sciences et lettres , dans le cadre de École doctorale Cancérologie, Biologie, Médecine, Santé , en partenariat avec Cancer, Hétérogénéité, Instabilité et Plasticité (laboratoire) , Diversité et Plasticité des tumeurs de l'enfant (DePiCT) (equipe de recherche) et de Institut Curie (Paris ; 1978-....) (établissement opérateur d'inscription) depuis le 17-10-2019 .


  • Résumé

    The molecular characterization of pediatric cancers is an important step in understanding the mechanisms of origin of these cancers and in identifying therapeutic targets. While a cancer is composed of several tumor cells of common origin, we have recently learned that these cells do not look alike, with the existence of different clones of cells defined by distinct genetic alterations. These clones play an important role in resistance and escape from treatments. Recent technological advances are now making single-cell analysis feasible, single-cell DNA approaches and more recently even Single-cell DNA that has been applied to tumour samples. Such an approach is particularly relevant in neuroblastoma (NB), a childhood cancer for which high-risk forms are associated with poor survival. This cancer is characterized by significant clinical and genetic heterogeneity and previous studies have highlighted the importance of spatial and temporal heterogeneity as well as the role of clonal evolution in tumor progression. However, no previous studies have been conducted at the level of a single cell. We now plan to explore the heterogeneity of NB, focusing on bioinformatics analysis of the genetic heterogeneity of NB tumor cells. In order to characterize intra-tumor genetic heterogeneity in detail, we will explore tumor cells at a single-cell level (SC) by analyzing both genetic alterations and expression patterns in individual tumor SC, to determine distinct patterns associated with each other in each cell, and to perform reconstruction of neuroblastoma phylogenetics. For this, we plan to use data from RNA and SC DNA sequencing, studying patient samples (primary tumor/bone marrow and a paired germline) and PDX model samples. Bioinformatics data is generated based on already established techniques, with single cells isolated from different samples and subjected to unique cell sequencing using 10xGenomics Chromium Single Cell technology, aimed at capturing and analyzing 1000 to 3000 cells per sample. The individual results will be compared to the overall analysis of the tumor, focusing on the specific alterations of the tumor cells. We will apply bioinformatics tools and develop other bioinformatics tools to describe distinct populations of tumor cells, linking genetic models to expression patterns. We will attempt to establish whether specific mutations are mutually exclusive or occur sequentially in the same cellular sublon clones. In addition, comparing the SC of primary tumors with metastatic sites will highlight the mechanisms leading to metastatic spread. Understanding the characteristics of SC will be important with respect to therapeutic approaches for patients with NB, helping to identify new combinations of therapeutic treatments targeting specific sub-clones.

  • Titre traduit

    Integrative bioinformatics analysis of genetic intratumor heterogeneity in single-cell neuroblastoma


  • Résumé

    The molecular characterization of pediatric cancers is an important step both to understand the mechanisms of origin of these cancers, and to identify therapeutic targets. While a cancer is made up of multiple tumor cells of a common origin, we recently learned that these cells are not alike, with the existence of different cell clones that are defined by distinct genetic alterations. These clones play an important role in resistance and escape to treatments. Recent technological advance now render single cell analysis feasible, with both single cell RNAseq and more recently even single cell DNA approaches having been applied to tumor samples. Such an approach is particularly relevant in neuroblastoma (NB), a childhood cancer for which high-risk forms are associated with poor survival. This cancer is characterized by significant clinical and genetic heterogeneity, and previous studies have highlighted the importance of both spatial and temporal heterogeneity as well as the role of clonal evolution in tumor progression. However, no previous studies have been performed at a single cell level. We now plan to explore heterogeneity of NB, focusing in this project on the bioinformatics analysis of genetic heterogeneity of NB tumor cells. Aiming for a detailed characterization of genetic intra-tumor heterogeneity, we will explore tumor cells at a single cell (SC) level by analyzing both genetic alterations and expression patterns in individual tumor SCs, for determination of distinct patterns associated with one another in each cell and for reconstruction of NB phylogenetics. For this we plan to use data from SC RNA and DNA sequencing in NB, studying patient samples (primary tumor/bone marrow and paired germline) and samples from PDX models. Bioinformatics data are generated based on already established techniques, with single cells isolated from different samples and subjected to single cell sequencing using the 10xGenomics Chromium Single Cell technology, aiming for a capture and analysis of 1000 – 3000 cells per sample. Individual results will be compared to the bulk tumor analysis, focusing on tumor cells specific alterations. We will apply bioinformatics tools and develop further bioinformatics tools to describe distinct tumor cell populations, linking genetic patterns to expression patterns. We will seek to establish whether specific mutations are mutually exclusive or occur sequentially in the same cellular subclones. Furthermore, the comparison of SC from the primary tumor versus metastatic sites will highlight mechanisms leading to metastatic spread. Understanding SC characteristics will be of importance with regards to therapeutical approaches for NB patients, by helping to identify new therapeutic treatment combinations targeting specific subclones.