LUNG PREDICT : Stade 4 NSCLC (Non-Small Cell Lung Cancer)

Dendritic cells spatial organization shapes tumor microenvironment and impact immunotherapy response in Non-Small-Cell lung cancer

Mains objective: we investigated the spatial organization of DC subsets in NSCLC and how this organization is able to shape the surrounding tumor microenvironment and transcription factor signaling, driving the immunotherapy response.

Figure 1. A schematic overview of the paper analysis

Data used in the analysis

• 116 samples with bulk RNAseq
• 68 samples with spatial
• 60 samples with bulk RNAseq and spatial
• 58 samples analyzed

Project organization

input: Files used for the analysis

• Deconvolution_Gobbini: Cell type deconvolution matrix
• MegaClusters_area: DC Megaclusters area information
• MegaClusters_density: DC subsets density information
• MegaClusters_entropy: DC Megaclusters CD8 enrichment information
• Spatial_densities.RData: DC subsets densities after preprocessing
• signatures/: DC signatures from scRNAseq used for cell type deconvolution

output: Output files

• Patients_groups.RData: Patients groups assignations
• group_marker_genesets.rds: TFs sets fromm differential analysis across groups
• TFs_modules.csv: TFs module composition

Scripts: Codes used for analysis.

• Spatial_densities: Preprocessing of spatial data
• Cell_densities_analysis: Analysis of DC subsets densities across response information and survival
• Megaclusters_analysis: Analysis of megaclusters density and CD8 enrichment across responders and patient stratification
• RNAseq_analysis: Bulk RNAseq analysis of patient groups to characterize them with transcriptomic features (pathways, TFs modules, immunescores, chemokines, deconvolution)
• Signature_projection: Validation of TF signature in independent cohort

Results: All results from analysis including those used in the paper.

Figures: Figures for the paper.

Specifications

Analysis was done using R version 4.3.1 with the OS Ubuntu 22.04.3 LTS.

Reproducibility

If you would like to reproduce the analysis done here, we invite you to use our provided r-environment. Setting it up will install all the neccessary packages, along with their specific versions in an isolated environment.

For this, open the project LP_MOSAIC.Rproj inside the scripts/ folder and in the R console run:

# Download renv package (if not installed)
install.packages('renv')
# To activate the R environment 
renv::activate()
# To download and install all the require libraries and packages 
renv::restore() 

Note that this is an once-step only when running the repository for the first time. For the following times, you will only need to open the LP_MOSAIC.Rproj and you are ready to go!

Once all packages have been installed, you can start reproducing the analysis using the scripts inside the scripts/ folder.

Make sure to run renv::deactivate() when finishing, to avoid conflicts whenever you start a different R project.

For more information about how R-environments work, visit the main page of the tool renv.

Citation

Gobbini, E.* , Duplouye, P.*, Hurtado, M *. et al. Specific dendritic cells spatial organization is associated to ICB Response in Non–Small-Cell Lung Cancer. 2026. doi: 10.64898/2026.05.04.720587

Contributing

If you are interested or have questions about the analysis done in this project, we invite you to open an issue in https://github.com/VeraPancaldiLab/LungPredict_DC_paper/issues or contact Marcelo Hurtado (marcelo.hurtado@inserm.fr) for more information.

Authors

• Marcelo Hurtado
• Vera Pancaldi
• Elisa Gobbini
• Jenny Valladeau
• Pierre Duplouye