2. Academic Validation
  3. AI-based classification of anticancer drugs reveals nucleolar condensation as a predictor of immunogenicity

AI-based classification of anticancer drugs reveals nucleolar condensation as a predictor of immunogenicity

  • Mol Cancer. 2024 Dec 20;23(1):275. doi: 10.1186/s12943-024-02189-3.
Giulia Cerrato 1 2 3,  Peng Liu 4 5 6,  Liwei Zhao 4 5 6,  Adriana Petrazzuolo 4 6 7,  Juliette Humeau 4 6 8,  Sophie Theresa Schmid 9 10,  Mahmoud Abdellatif 4 6 9 10,  Allan Sauvat # 11 12 13,  Guido Kroemer # 14 15 16 17 18
Affiliations

Affiliations

  • 1 Centre de Recherche des Cordeliers, Equipe Labellisée par la Ligue Contre le Cancer, Université de Paris, Institut Universitaire de France, Sorbonne Université, Inserm U1138, Paris, France. giulia.cerrato@gustaveroussy.fr.
  • 2 Onco-Pheno-Screen Platform, Centre de Recherche des Cordeliers, Paris, France. giulia.cerrato@gustaveroussy.fr.
  • 3 Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, Villejuif, France. giulia.cerrato@gustaveroussy.fr.
  • 4 Centre de Recherche des Cordeliers, Equipe Labellisée par la Ligue Contre le Cancer, Université de Paris, Institut Universitaire de France, Sorbonne Université, Inserm U1138, Paris, France.
  • 5 Onco-Pheno-Screen Platform, Centre de Recherche des Cordeliers, Paris, France.
  • 6 Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, Villejuif, France.
  • 7 International Centre for T1D, Pediatric Clinical Research Center Romeo ed Enrica Invernizzi, Department of Biomedical and Clinical Sciences, Università Degli Studi di Milano, Milan, Italy.
  • 8 Centre de Recherche en Cancérologie de Lyon (CRCL), Equipe Oncopharmacologie, Faculté Rockfeller, Lyon, France.
  • 9 Department of Cardiology, Medical University of Graz, Graz, Austria.
  • 10 BioTechMed Graz, Graz, Austria.
  • 11 Centre de Recherche des Cordeliers, Equipe Labellisée par la Ligue Contre le Cancer, Université de Paris, Institut Universitaire de France, Sorbonne Université, Inserm U1138, Paris, France. allan.sauvat@gustaveroussy.fr.
  • 12 Onco-Pheno-Screen Platform, Centre de Recherche des Cordeliers, Paris, France. allan.sauvat@gustaveroussy.fr.
  • 13 Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, Villejuif, France. allan.sauvat@gustaveroussy.fr.
  • 14 Centre de Recherche des Cordeliers, Equipe Labellisée par la Ligue Contre le Cancer, Université de Paris, Institut Universitaire de France, Sorbonne Université, Inserm U1138, Paris, France. kroemer@orange.fr.
  • 15 Onco-Pheno-Screen Platform, Centre de Recherche des Cordeliers, Paris, France. kroemer@orange.fr.
  • 16 Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, Villejuif, France. kroemer@orange.fr.
  • 17 Department of Biology, Institut du Cancer Paris CARPEM, Hôpital Européen Georges Pompidou, AP-HP, Paris, France. kroemer@orange.fr.
  • 18 Centre de Recherche des Cordeliers, 15 Rue de l'École de Médecine, Paris, 75006, France. kroemer@orange.fr.
  • # Contributed equally.
PMID: 39702289 DOI: 10.1186/s12943-024-02189-3
Abstract

Background: Immunogenic cell death (ICD) inducers are often identified in phenotypic screening campaigns by the release or surface exposure of various danger-associated molecular patterns (DAMPs) from malignant cells. This study aimed to streamline the identification of ICD inducers by leveraging cellular morphological correlates of ICD, specifically the condensation of nucleoli (CON).

Methods: We applied artificial intelligence (AI)-based imaging analyses to Cell Paint-stained cells exposed to drug libraries, identifying CON as a marker for ICD. CON was characterized using SYTO 14 fluorescent staining and holotomographic microscopy, and visualized by AI-deconvoluted transmitted light microscopy. A neural network-based quantitative structure-activity relationship (QSAR) model was trained to link molecular descriptors of compounds to the CON phenotype, and the classifier was validated using an independent dataset from the NCI-curated mechanistic collection of Anticancer agents.

Results: CON strongly correlated with the inhibition of DNA-to-RNA transcription. Cytotoxic drugs that inhibit RNA synthesis without causing DNA damage were as effective as conventional cytotoxicants in inducing ICD, as demonstrated by DAMPs release/exposure and vaccination efficacy in mice. The QSAR classifier successfully predicted drugs with a high likelihood of inducing CON.

Conclusions: 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.

Keywords

Artificial intelligence; Automated image analysis; Cancer chemotherapy; Immunogenic cell death; Integrated stress response; Neural network; Transcription inhibition.

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