AI-based classification of anticancer drugs reveals nucleolar condensation as a predictor of immunogenicity
We developed AI-based algorithms for predicting CON-inducing drugs based on molecular descriptors and their validation using automated micrographs analysis, offering a new approach for screening ICD inducers with minimized adverse effects in cancer therapy.
Authors
- Giulia Cerrato (ORCID: https://orcid.org/0000-0003-2755-2860)
- Allan Sauvat (ORCID: https://orcid.org/0000-0001-7076-8638)
- Juliette Humeau (ORCID: https://orcid.org/0000-0002-1991-8001)
- Mahmoud Abdellatif (ORCID: https://orcid.org/0000-0002-5042-9054)
- Peng Liu (ORCID: https://orcid.org/0000-0002-1682-9222)
- Liwei Zhao
- Sophie Theresa Schmid
- Adriana Petrazzuolo
- Guido Kroemer
Institutions
- Inserm (FR)
- Institut Universitaire de France (FR)
- University of Milan (IT)
- Medical University of Graz (AT)
- Université Paris Cité (FR)
- Institut Gustave Roussy (FR)
- Sorbonne Université (FR)
- Centre de Recherche des Cordeliers (FR)
- BioTechMed-Graz (AT)
- Centre de Recherche en Cancérologie de Lyon (FR)
Publication Details
- Journal
- Molecular Cancer
- Published
- 2024-12-20
- DOI
- https://doi.org/10.1186/s12943-024-02189-3
- Citations
- 10
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 6.62
Funders
- Association pour la Recherche sur le Cancer
- Seerave Foundation
- Agence Nationale de la Recherche
- Fondation pour la Recherche Médicale
- Institut Universitaire de France
- Cancéropôle Ile de France
- Horizon 2020
- Labex Immuno-Oncology