MicroRNAs have demonstrated importance in a large number of carcinogenesis processes ranging from initiation, propagation and the appearance of metastases. They raise many hopes as therapeutic treatment targets. However, the drug candidate MRX34 (which mimics a microRNA) proved to be a failure in patients because it was too toxic. It is therefore urgent to better understand the mode of action of microRNAs in order to design new therapeutic strategies.
The thesis project proposes to use two cutting-edge technologies for this: microRNA/mRNA co-sequencing data, at the single cell level, and artificial intelligence techniques (AI, including neural networks and XGBoost ). It will benefit from the contribution of two other projects, which end in 2025 (an overlap of a few months with the CFR thesis): a thesis financed by Pfizer-INSERM, and a multi-team project financed by the cancer plan. These two projects have already enabled rigorous statistical analysis of co-sequencing data at the single cell level, which will be used during the PhD work. A collaboration, already initiated, is planned with Gipsa-Lab, Grenoble, specialist in machine learning / AI.