M2 - Analysis and integration of transcriptomic data from spontaneous tumor development in a murine tumor model

Type de poste
Niveau d'étude minimal
Durée du poste
Contrat renouvelable
Contrat non renouvelable
Date de prise de fonction
Date de fin de validité de l'annonce
Nom de la structure d'accueil

Campus Scientifique de Luminy
163, Avenue de Luminy
13009 Marseille

Bianca Habermann, Group Leader 'Computational Biology'
Flavio Maina, Group Leader 'Signalling networks for stemness and tumorigenesis'
Email du/des contacts

• Thesis description:
Cancer biology is nowadays one of the most important research areas. Patient-oriented databases such as the Cancer Genome Atlas (TCGA, [1]) collect and store data from cancer patients, including expression or mutation data. Yet, with human data, we lack the possibility to trace the different stages of the tumorigenic program, from pre-tumorigenic stages to tumour initiation and progression towards aggressiveness, with metastatic events. In our lab, we have generated a mouse model that expresses a specific receptor tyrosine kinase (RTK), MET, at slightly increased levels and which spontaneously develops liver or mammary gland tumours (according to the tissue in which enhanced MET is driven). Our model gives us the unique opportunity to use healthy tissues, pre-tumorigenic tissues, early tumours, and advanced tumours to monitor the gene expression changes occurring during the process of tumorigenesis [2,3]. We have collected transcriptomic data from liver and mammary gland samples corresponding to these 4 different stages. In this project, we will use data integration of these collections of data from liver and mammary gland tissues to identify key genes de- regulated at different stages during the tumorigenesis process. Furthermore, we will perform SNP-calling on RNA-seq data to analyse potential alterations observed during tumour development. We will furthermore perform enrichment analysis of pathways and gene ontology terms, as well as with epigenetic data to identify regulatory modules responsible for tumour growth. Finally, we will integrate our transcriptomic data with patient data available from TCGA to identify related tumour types in patients and therefore matching patient subsets to our tumour model.

• Required techniques:
Data mining; RNA-seq analysis; NGS data analysis and integration; programming and scripting in R/Python and Bash

• References:
[1] The Cancer Genome Atlas Research Network, Nature Genetics 45, 1113-20 (2013). doi:10.1038/ng.2764.
[2] Fan Y. et al. J. of Hepatology, 70(3): 470-482 (2019). PMID: 30529386.
[3] Arechederra M. et al. Nature Communications, Aug 8;9(1): 3164 (2018). PMID: 30089774.

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