Integration of clinical, pathological, radiological, and transcriptomic data improves prediction for first-line immunotherapy outcome in metastatic non-small cell lung cancer
Authors/Creators
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Captier, Nicolas
(Project member)1
- Lerousseau, Marvin (Project member)1
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Orlhac, Fanny
(Project member)1
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Hovhannisyan-Baghdasarian, Narinée
(Project member)1
- Luporsi, Marie (Project member)1
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Woff, Erwin
(Project member)2
- Lagha, Sarah (Project member)1
- Salamoun Feghali, Paulette (Project member)1
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Lonjou, Christine
(Project member)1
- Beaulaton, Clément (Project member)1
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Zinovyev, Andrei
(Project member)3
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Salmon, Hélène
(Project member)1
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Walter, Thomas
(Work package leader)1
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Buvat, Irène
(Work package leader)1
- Girard, Nicolas (Project leader)1
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Barillot, Emmanuel
(Project leader)1
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1.
Institut Curie
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2.
Université Libre de Bruxelles
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3.
Evotec (Germany)
Contributors
Project leaders:
Project member:
Work package leaders:
-
1.
Institut Curie
Description
This repository contains the radiomic, pathomic, and transcriptomic features used in: "Integration of clinical, pathological, radiological, and transcriptomic data improves prediction for first-line immunotherapy outcome in metastatic non-small cell lung cancer"
The cohort includes 317 metastatic non-small cell lung cancer (NSCLC) patients treated with first-line immunotherapy (pembrolizumab), with or without concomitant chemotherapy, at Institut Curie (Paris, France). At baseline, the following data were collected:
- Clinical information from routine care
- 18F-FDG PET/CT scans, from which radiomic features were extracted
- Digitized pathological slides, from which pathomic features were extracted
- Bulk RNA-seq profiles from solid biopsies, from which transcriptomic features were extracted
These features were used as inputs to multimodal machine learning pipelines designed to predict immunotherapy outcomes.
Clinical data availability: Due to patient privacy requirements, curated clinical data could not be shared in this repository. They are available upon request to Nicolas Girard and Emmanuel Barillot. Immunotherapy outcomes (i.e., OS, PFS, and best observed RECIST response) are available in this repository.
* Please refer to README.md for more details about each modality as well as contact information
* Associated journal article: Captier, N., Lerousseau, M., Orlhac, F. et al. Integration of clinical, pathological, radiological, and transcriptomic data improves prediction for first-line immunotherapy outcome in metastatic non-small cell lung cancer. Nat Commun 16, 614 (2025).
Files
Data_NSCLC_Immuno_Curie.zip
Files
(1.4 MB)
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Additional details
Related works
- Is source of
- Journal article: 10.1038/s41467-025-55847-5 (DOI)
Funding
- Fondation ARC pour la Recherche sur le Cancer
- SIGN’IT 2020—Signatures in Immunotherapy
- Agence Nationale de la Recherche
- Investissements d’avenir (PRAIRIE 3IA Institute) ANR-19-P3IA-0001
Dates
- Created
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2024-12-09
- Available
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2025-01-15End of embargo
Software
- Repository URL
- https://github.com/sysbio-curie/multipit
- Programming language
- Python
- Development Status
- Active