Senior INSERM researcher, leader of the computational biology group within the « Functional Genomics of Solid Tumors » team at Cordeliers Research Center. INSERM excellence award (2015). Institut Necker Fondation Tourre best post-doctoral student award (2015).
Topics of interest
Cancer genomics, bioinformatics, machine learning
Project in Prairie
Discovering cancer-causing mutations using deep learning approaches. Eric Letouzé will develop deep learning approaches to predict celltype specific regulatory features of gene expression, splicing and translation from the DNA sequence, and use these tools to discover new driver events among the millions of non-coding mutations identified in human cancer genomes.
Next-generation sequencing technologies have allowed the identification of millions of mutations in tens of thousands of tumor samples. Yet, the vast majority of driver mutations functionally associated with cancer development lies within <2% of the genome encoding protein-coding genes. Although non-coding mutations can dramatically modulate the expression of cancer genes, predicting their precise functional impact remains extremely challenging. By developing deep neural networks able to learn regulatory features from the DNA sequence, we will be able to predict which mutations are likely to alter the expression of oncogenes and tumor suppressors, and unravel the missing drivers within the huge pancancer mutation catalogues available in public databases.