Société Française de Bioinformatique

Mots-Clés

Bioinformatics multiomics spatial transcriptomics lung adenocarinoma scRNA-seq cancer genomics cancer

Description

PhD Thesis: Characterisation of the RAS-dependent immune landscape in lung adenocarcinoma using single-cell technologies

The Inflammation and Cancer Plasticity laboratory at Gustave Roussy is looking to recruit a PhD candidate wishing to work at the forefront of cancer genomics and translational medicine. This PhD project offers the opportunity to work with the latest single-cell and spatial transcriptomic technologies and develop analysis strategies to answer key questions in cancer immunology. It will allow you to work within a rich research environment where you will develop the fundamental bioinformatics and data science skills required to analyse high-dimensional genomics data. With this project, you will have the opportunity to make impactful discoveries in cancer biology that can influence the treatment management of lung adenocarcinoma. Please take a look at the project details below and forward your CV if interested.

Project Details

Abstract

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.

Context

LUAD and therapeutical problematic

Non-small cell lung cancer (NSCLC) remains the leading cause of cancer-related deaths worldwide (1). Lung adenocarcinoma (LUAD), its most prevalent subtype, poses a major clinical challenge because of its high mortality and limited treatment efficacy. Whilst early diagnosis leads to surgical resection and better outcomes, most patients are diagnosed too late and cannot benefit from surgery. Immune checkpoint blockade (ICB) targeting the PD-1/PD-L1 axis to reactivate the immune system is now the standard of care for these patients. These therapies can offer long-term benefits, but only to a subset of patients (2,3). RAS inhibitors, including recently approved KRASG12C inhibitors, also work, in part, by indirectly reactivating the anti-tumour immune response (4,5). In contrast with immunotherapy, these targeted therapies have shown excellent response rates, but rapid resistance occurs in almost all patients receiving them (6).

KRAS in LUAD

A third of LUAD cases harbour KRAS mutations, leading to constitutive activation of the RAS signalling pathway. Beyond its well-known role in promoting cell proliferation and survival (7–10), oncogenic RAS signalling can directly drive immune evasion by increasing PD-L1 expression via the stabilisation of its CD274 mRNA (11), impairing IFN response, increasing expression of the immunosuppressive enzyme COX2 (12), and promoting immunosuppressive adenosine in the tumour microenvironment (TME) (13). This indicates that oncogenic RAS signalling can profoundly shape the TME composition and its phenotypic activation, thereby promoting tumour progression and influencing the therapeutic response to the abovementioned therapies.

KRAS mutation versus RAS transcriptional activation

Despite the expected impact of KRAS mutations on tumour progression and therapy resistance, there is no consensus in the literature regarding their predictive value for patient outcomes or response to therapy (14–18). Because 74% of lung adenocarcinoma (LUAD) tumours are mutated in one or more genes within the broader RAS pathway, from receptor tyrosine kinases to ERK MAP kinases and PI3 kinase (19), we think this could have complicated the study of oncogenic RAS in large patient cohorts. We therefore developed a stratification method based on RAS-regulated transcriptional activity (RAS84) to account for all alterations that could affect the RAS pathway. Using this method, we could demonstrate that RAS pathway activation was not restricted to KRAS-mutant tumours. We found that more than 80% of LUAD tumours show transcriptional signs of RAS pathway activity, including those with wild-type KRAS. These tumours fall into four groups, each with enrichment in distinct RAS target genes and characterised by concurrent alterations in STK11/LKB1, TP53, or CDKN2A. They also exhibit significant variations in response to therapy (13,20), indicating that refining LUAD patient stratification based on RAS transcriptional activity could improve personalised medicine.

Objectives

Given the central role of RAS signalling in determining clinical outcomes in LUAD, a more precise characterisation of the RAS-dependent tumour microenvironment is required to improve responses to immunotherapy, limit relapse under RAS-targeted treatments, and support more refined approaches to personalised medicine. With this project, we propose to delineate the tumour microenvironment associated with oncogenic RAS activity in LUAD using human tumour samples and a novel stratification method based on oncogenic RAS transcriptional activity.

Methods

The PhD candidate will have access to the MOSAIC cohort of 120 LUAD patients with Visium ST data, snRNA-Seq, bRNA-Seq, and WES at baseline, as well as CITE-Seq data from a preclinical experiment performed in two murine orthotopic, syngeneic lung cancer models treated or not with a KRAS inhibitor. The PhD candidate will use the human multi-omics spatial and single-cell dataset to correlate RAS activity with immune features identified in the data. The pre-clinical experimental data will be used to test whether modulating the RAS pathway in the tumour directly affects those features. The planned work packages are listed below.

Aim 1 - Classify Tumours Using the RAS84 Signature (Bulk RNA Seq)

The PhD candidate will work directly with the developer of RAS84 to apply the signature across the MOSAIC cohort. They will generate RAS activity groups, assess classification robustness and link RAS84 labels to genomic and clinical features. They will integrate these findings with single-cell and spatial analyses to build a unified map of RAS activity for the project.

Aim 2 - Characterise the RAS84 Specific Immune Landscape (snRNA Seq)

The candidate will analyse snRNA Seq data to define how RAS activity shapes immune and stromal composition. They will identify enriched cell populations, quantify pathway activation and relate these features to mutational profiles and known RAS-regulated mechanisms. They will use these results to uncover mechanistic signatures of RAS-dependent immunity.

Aim 3 - Map RAS Linked Spatial Ecosystems (Visium Spatial Transcriptomics)

The candidate will examine Visium datasets to locate spatially organised immune and stromal structures associated with RAS activity. They will identify spatial expression domains, characterise immune niches and detect stromal remodelling patterns across RAS84 groups. They will integrate spatial patterns with snRNA Seq cell states to produce a spatially resolved view of RAS-driven ecosystems.

Aim 4 - Reconstruct Cell–Cell Communication Networks

The candidate will integrate single-cell and spatial data to infer ligand–receptor interactions across RAS84 groups. They will identify signalling circuits that promote immune suppression, inflammation or altered therapy response. They will highlight communication pathways that may influence sensitivity to immunotherapy or RAS-targeted treatments.

Aim 5 - Link RAS Signalling to Microenvironmental Features

The candidate will evaluate how RAS signalling intensity aligns with the cellular and spatial features identified in earlier WPs. They will test associations with processes such as interferon signalling, PD L1 stabilisation, COX2-driven inflammation and adenosine-mediated suppression. Using both human and preclinical datasets, they will identify RAS-regulated pathways that drive specific microenvironmental phenotypes and may offer therapeutic opportunities.

Expected results

By the end of the PhD programme, we will have characterised the different immune landscapes across 5 RAS Activity Groups (RAGs) at a single-cell level; characterised RAS-dependent cell-cell communication pathways; and identified potential novel therapeutic targets associated with RAS signalling.

Bibliography

1. Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer J Clin. (2024);74(3):229–63.
2. Gandhi L, Rodriguez-Abreu D, Gadgeel S, Esteban E, Felip E, Angelis FD, et al. Pembrolizumab plus Chemotherapy in Metastatic Non–Small-Cell Lung Cancer. New England Journal of Medicine [Internet]. (2018 Apr 16);NEJMoa1801005-15.
3. Reck M, Rodríguez-Abreu D, Robinson AG, Hui R, Csőszi T, Fülöp A, et al. Pembrolizumab versus Chemotherapy for PD-L1–Positive Non–Small-Cell Lung Cancer. N Engl J Med. (2016);375(19):1823–33.
4. Mugarza E, Maldegem F van, Boumelha J, Moore C, Rana S, Sopena ML, et al. Therapeutic KRASG12C inhibition drives effective interferon-mediated antitumor immunity in immunogenic lung cancers. Sci Adv. (2022);8(29):eabm8780.
5. Canon J, Rex K, Saiki AY, Mohr C, Cooke K, Bagal D, et al. The clinical KRAS(G12C) inhibitor AMG 510 drives anti-tumour immunity. Nature. (2019 Nov 7);575(7781):217–23.
6. Langen AJ de, Johnson ML, Mazieres J, Dingemans AMC, Mountzios G, Pless M, et al. Sotorasib versus docetaxel for previously treated non-small-cell lung cancer with KRAS G12C mutation: a randomised, open-label, phase 3 trial. Lancet. (2023);401(10378):733–46.
7. Tao S, Wang S, Moghaddam SJ, Ooi A, Chapman E, Wong PK, et al. Oncogenic KRAS Confers Chemoresistance by Upregulating NRF2. Cancer Res. (2014 Dec 15);74(24):7430–41.
8. Grabocka E, Bar-Sagi D. Mutant KRAS Enhances Tumor Cell Fitness by Upregulating Stress Granules. Cell. (2016 Dec 15);167(7):1803-1813.e12.
9. Caiola E, Salles D, Frapolli R, Lupi M, Rotella G, Ronchi A, et al. Base excision repair-mediated resistance to cisplatin in KRAS(G12C) mutant NSCLC cells. Oncotarget. (2015 Jan 1);6(30):30072–87.
10. Pylayeva-Gupta Y, Grabocka E, Bar-Sagi D. RAS oncogenes: weaving a tumorigenic web. Nat Rev Cancer. (2011 Nov 1);11(11):761–74.
11. Coelho MA, de Carné Trécesson S, Rana S, Zecchin D, Moore C, Molina-Arcas M, et al. Oncogenic RAS Signaling Promotes Tumor Immunoresistance by Stabilizing PD-L1 mRNA. Immunity [Internet]. (2017 Dec 19);47(6):1083-1099.e6.
12. Boumelha J, Castro A de, Bah N, Cha H, Trécesson S de C, Rana S, et al. CRISPR-Cas9 screening identifies KRAS-induced COX-2 as a driver of immunotherapy resistance in lung cancer. Cancer Res. (2024);84(14):2231–46.
13. de Carné Trécesson S, East P, Pillsbury CE, Santos MS dos, Cha H, Colliver E, et al. Oncogenic RAS activity is linked to immune priming and adenosine-driven immune evasion in lung adenocarcinoma. bioRxiv. (2025);2025.11.25.690426.
14. Wiesweg M, Kasper S, Worm K, Herold T, Reis H, Sara L, et al. Impact of RAS mutation subtype on clinical outcome—a cross-entity comparison of patients with advanced non-small cell lung cancer and colorectal cancer. Oncogene. (2019 Apr 18);38(16):2953–66.
15. Cserepes M, Ostoros G, Lohinai Z, Raso E, Barbai T, Timar J, et al. Subtype-specific KRAS mutations in advanced lung adenocarcinoma: A retrospective study of patients treated with platinum-based chemotherapy. Eur J Cancer. (2014 Jul);50(10):1819–28.
16. Shepherd FA, Domerg C, Hainaut P, Jänne PA, Pignon JP, Graziano S, et al. Pooled Analysis of the Prognostic and Predictive Effects of KRAS Mutation Status and KRAS Mutation Subtype in Early-Stage Resected Non–Small-Cell Lung Cancer in Four Trials of Adjuvant Chemotherapy. J Clin Oncol. (2013 Jun 10);31(17):2173–81.
17. Sun L, Tan M, Cohen RB, Langer CJ, Mamtani R, Aggarwal C. Association Between KRAS Variant Status and Outcomes With First-line Immune Checkpoint Inhibitor–Based Therapy in Patients With Advanced Non–Small-Cell Lung Cancer. JAMA Oncology. (2021 May 5);
18. Liu C, Zheng S, Jin R, Wang X, Wang F, Zang R, et al. The superior efficacy of anti-PD-1/PD-L1 immunotherapy in KRAS-mutant non-small cell lung cancer that correlates with an inflammatory phenotype and increased immunogenicity. Cancer Lett. (2020);470:95–105.
19. Sanchez-Vega F, Mina M, Armenia J, Chatila WK, Luna A, La KC, et al. Oncogenic Signaling Pathways in The Cancer Genome Atlas. Cell. (2018 Apr 5);173(2):321-337.e10.
20. East P, Kelly GP, Biswas D, Marani M, Hancock DC, Creasy T, et al. RAS oncogenic activity predicts response to chemotherapy and outcome in lung adenocarcinoma. Nat Commun. (2022);13(1):5632.

Research Environment & Training

Supervision arrangements

PhD director: Prof. Fabrice Barlesi, MD, PhD (HDR)
PhD supervisor: Jr Prof. Sophie de Carné, PhD
PhD supervisor: Philip East

Scientific and material conditions of the research project

The PhD candidate will benefit from an environment at the interface of a cancer biology research lab, bioinformatics experts, and close collaboration with clinical teams. They will be co-supervised by Sophie de Carné, cell biology scientist and head of the Inflammation and Cancer Plasticity laboratory, and Philip East, bioinformatician and head of Clinical Discovery Bioinformatics, with support from the Gustave Roussy Bioinformatics platform, led by Marc Deloger. In addition to support from these three teams, the candidate will also play an active role in the rich bioinformatics ecosystem at Gustave Roussy, with regular opportunities to present their work and access state-of-the-art computational infrastructure. The Inflammation and Cancer Plasticity and Clinical Discovery Bioinformatics teams are both supported by the IHU PRISM (Institut Hospitalo Universitaire – National PRecISion Medicine Centre in Oncology). The IHU PRISM is a multi-disciplinary national flagship institute, uniting Gustave Roussy, Inserm, Université Paris-Saclay and CentraleSupélec to advance precision oncology by combining state-of-the-art profiling technologies, advanced data science and innovative clinical trials to redefine how cancer is diagnosed and treated. The inflammation and Cancer Plasticity team is also part of Inserm U981. Experimental and data costs are supported by the Inflammation and Cancer Plasticity team, and the candidate will need to apply for a PhD fellowship to cover salary.

Objectives for the dissemination of the work

Presentation at international conferences (at least once over a period of three years). Aiming for at least one publication as a first author in a high-impact factor journal (QU1, top 20% journals in the field).

Funding

Application for PhD fellowships (e.g., Doctoral School)

Candidate Profile & Application Process

Profile and skills

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

How to apply

https://adum.fr/as/ed/voirproposition.pl?site=adumR&matricule_prop=71447#version

Contact

• Sophie de Carné – sophie.de-carne@gustaveroussy.fr
• Phil East – philip.east@gustaveroussy.fr