Mots-Clés

Bioinformatique cancer tumeurs cérébrales RNA sequencing Epigénétique ATAC-seq single-cell hétérogénéité

Description

Internship in Computational Biology/Cancer Genomics

Study of glioma brain tumor heterogeneity - analysis of snATAC-seq profiles

The Cavalli Lab is part of the "Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer" Unit (U900 INSERM, Mines ParisTech, Institut Curie) at Institut Curie, which consists of ~90 researchers and students. It is a very active and growing interdisciplinary team of bioinformaticians, biologists, physicians, mathematicians, statisticians, physicists, and computer scientists.

The Cavalli Lab (located at Institut Curie St-Cloud, west of Paris), aims to decipher tumor heterogeneity, one of the main causes of treatment resistance. We investigate the complexity of tumor biology and temporal and intra-tumoral heterogeneity using computational analysis of data from cutting-edge sequencing efforts. The goal of our genomic approach is to explore clinically relevant aspects of brain tumor biology using patient samples.

The goal of the internship is to contribute to the analysis of a unique cohort of glioma brain tumor samples assayed by single-nucleus multiome (parallel RNA-seq and ATAC-seq), generated in collaboration with oncologists. The single-nucleus RNA-seq data gives access to the transcriptomic information at the single cell level for thousands of cells in a given tumor. Our cohort of ~150K cells allows us to identify the different malignant cell types composing the tumors and assess tumor heterogeneity. In addition, we have for the same nuclei the open chromatin profiles thanks to the snATAC-seq part of the data. In this internship, we would like to explore these snATAC-seq profiles in more detail, identifying the cell state cis-co-accessibility networks (with the CICERO tool) and evaluating cell plasticity. We would like to investigate if the RNA and ATAC profiles are “in sync” along the course of tumor development, or if, at the epigenetic level, cells maintain the ability to evolve towards multiple malignant states. Can the landscape of chromatin accessibility help us to identify which cells are likely to evolve towards a given tumor state? As half of our samples are recurrent tumors, do these recurrent tumors have specific epigenetic landscapes and/or features of plasticity as compared to initial primary tumors? More generally, a major goal of the internship is to maximise the information inferred from the ATAC assay, and to review and implement state-of-the-art analysis approaches to see what further insights the ATAC assay can give on tumor development and biology.

This project will contribute to the larger study of the 60 samples snRNA/ATAC-seq primary-recurrent paired sample cohort we are analysing focusing on performing computational analysis to reveal more information from the snATAC-seq profiles.

Skills

• Experience working in a Unix environment and statistical analysis
• Programming skills (R, Python, bash)
• Knowledge/Experience with bioinformatics and biostatistics tools for bulk RNA-seq, ATAC-seq and/or single-cell RNA-seq analysis (R, Bioconductor)