"Identification of Pediatric high-risk tumor master regulators: a pan-cancer study (2022-13-SCHLEIERMACHER_CA)" project details

× Help section

To download a PDF version of the PhD thesis project, please click on the PDF document in orange.

General information

Application closed

2022-13-SCHLEIERMACHER_CA

Computational biology; Pediatric cancer; Genomics data; Tumor heterogeneity; Master regulators

Identification of Pediatric high-risk tumor master regulators: a pan-cancer study

Director(s) and team

Gudrun Schleiermacher & Florence Cavalli

Computational Biology and Integrative Genomics of Cancer /








Translational Pediatric Oncology

Abstract

The PhD project will be conducted as part of computational biology lab and translational pediatric oncology lab of Drs. Cavalli and Schleiermacher’s. The Cavalli lab investigates tumor heterogeneity using genomic approaches to explore clinically relevant aspects of brain and more generally pediatric tumor biology. The Schleiermacher lab aims to characterize biomarkers in solid tumors, particularly neuroblastoma, to study the underlying mechanisms leading to the observed alterations and to develop new therapeutic approaches.The mechanisms driving treatment resistance and the recurrent tumors of pediatric patients are still largely unknown. Clinicians have very few therapeutic options when a pediatric tumor relapses, it is, therefore, essential to better characterize them, discover the key genes driving recurrences to open the door to further research on novel treatment. An avenue to increase our understanding of aggressive tumors is to identify, the master regulators (MRs) that drive the transcriptional output. Analyzing a unique large-scale pan pediatric cancer dataset generated as part of the national MAPPYACTS and MICCHADO programs (20 tumor types, > 1000 tumors with multi-omics sequencing data; RNA-seq, WES (Whole Exome Sequencing)), coupled with high-level clinical data and CRISRP-Cas9 essential gene screen results, we will identify the MRs of high-risk pediatric tumor types as well as the ones driving the recurrent tumors. Integration of the genomics and clinical data will allow us to further decipher the treatment effects taking into account somatic alterations. In addition, analysis of CIRSPR-Cas9 essential gene screens on cell lines and integration of these results will allow us to further improve our MR identification pipeline and understanding of tumor progression mechanism. We will therefore pinpoint the most relevant genes that will be further validated and increase our understanding of the transcriptional programs driving pediatric tumors evolution

Requirements to apply for the PhD thesis project

Applicants should have a strong desire to increase the knowledge of pediatric tumors biology through bioinformatics analyses. He/she should have a strong background in bioinformatics, statistics, or computer science with knowledge and interest in biology. Wet lab experience is a plus but not compulsory. The applicant should show solid capacity for independent and creative thinking and a desire to work closely with clinicians and biologists. Experience in statistical analysis using R is essential and working in a Unix environment is desired