Description

Lung adenocarcinoma (LUAD) remains the leading cause of cancer-related mortality, and therapeutic success with immune checkpoint blockade or KRAS-targeted inhibitors is limited to a subset of patients. Although oncogenic RAS activity is known to shape the tumour microenvironment through several mechanisms, the immune ecosystems associated with RAS activation remain poorly defined, partly because RAS pathway activation extends well beyond KRAS mutation status and complicates analysis in large patient cohorts. A clearer understanding of the immune consequences of oncogenic RAS signalling is therefore essential to advancing personalised therapy.
Our laboratory developed a transcription-based method (RAS84) to stratify LUAD tumours according to RAS activity. Using this approach and bulk RNA Seq deconvolution, we have shown that distinct levels of RAS activity correspond to distinct immune signatures. This PhD project will extend those findings by characterising the RAS-dependent immune landscape using single-cell and spatial transcriptomics from a Gustave Roussy patient cohort, complemented by CITE-Seq datasets from preclinical models treated with KRAS inhibitors. Applying RAS84 across these multi-omics datasets will enable the identification of immune and stromal states, ligand–receptor communication networks, and spatially organised ecosystems linked to oncogenic RAS. Integration of human tumours with perturbation data from mouse models will allow the validation of immune features directly modulated by RAS signalling.
The PhD candidate will work within a synergistic environment bringing together data scientists and wet-lab experimentalists. They will contribute to advancing our understanding of LUAD, starting with human samples, and collaborate with team members to experimentally validate emerging hypotheses.

Compétences requises

Essential: - Master’s degree in bioinformatics or an equivalent subject with a significant computational analysis component. - Experienced in programming in R or Python - Familiarity with Linux and HPC environment - An interest in analysing complex genomics and transcriptomics datasets - Fluent in English (labs are run in English) - Open, collegiate, communicative and happy to work in a collaborative scientific environment Desirable: - Knowledge of statistical analysis - Familiarity with cancer biology and immunology

Bibliographie

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Mots clés

Bioinformatique, multi-omique, transcriptomique spatiale, scRNA-Seq, adenocarcinome du poumon, KRAS