Société Française de Bioinformatique

Mots-Clés

biologie spatiale, biologie des systèmes, cancer, transcriptomique

Description

Gastric cancer (GC) is a major health burden with relatively low survival rate even when most advanced medical care is available. Immune checkpoint inhibitors (ICI) have been approved in some countries for its treatment. However, alike for other tumors, non-responder rates to ICI are high, while there is no biomarker available to identify patients that would (or would not) benefit from such advanced and expensive treatment. Efforts to identify biomarkers from body fluids or whole tumor samples have largely failed, pointing out the need for a more fine-grained approach.

We have hence embarked on exploring the resistance to ICI in GC by using spatial transcriptomics. The latter enables the visualization of important immune structures as well as other stromal cells in the tumor microenvironment, permitting the exploration of their molecular dialogue. To this end we have recently developed an elegant approach [1] to study ligand-receptor interactions in spatial transcriptomic data that we wish to apply to GC. Identification of soluble factors participating in the immune-suppressive dialogue would largely benefit GC patients. This would potentially lead to biomarkers and new targets against resistance.

In this project, the student will use existing tools to infer how cellular networks and downstream signaling pathways vary in space over several tumors. Based on this information, recurrent and pertinent structures, which could be names as ecosystems or habitats, will be searched to evidence key factors in tumor immunity regulation. Code will be implemented in R.