Medizinische Universität Graz - Research portal
Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
Cerrato, G; Liu, P; Zhao, L; Petrazzuolo, A; Humeau, J; Schmid, ST; Abdellatif, M; Sauvat, A; Kroemer, G.
AI-based classification of anticancer drugs reveals nucleolar condensation as a predictor of immunogenicity.
Mol Cancer. 2024; 23(1):275
Doi: 10.1186/s12943-024-02189-3
[OPEN ACCESS]
Web of Science
PubMed
FullText
FullText_MUG
- Co-authors Med Uni Graz
- Abdellatif Mahmoud
- Schmid Sophie Theresa
- Altmetrics:
- Dimensions Citations:
- Plum Analytics:
- Scite (citation analytics):
- 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.
- Find related publications in this database (using NLM MeSH Indexing)
- Humans - administration & dosage
- Antineoplastic Agents - pharmacology
- Animals - administration & dosage
- Cell Nucleolus - metabolism, drug effects
- Mice - administration & dosage
- Artificial Intelligence - administration & dosage
- Quantitative Structure-Activity Relationship - administration & dosage
- Immunogenic Cell Death - drug effects
- Cell Line, Tumor - administration & dosage
- Neoplasms - drug therapy, pathology, immunology
- Find related publications in this database (Keywords)
- Artificial intelligence
- Automated image analysis
- Neural network
- Cancer chemotherapy
- Immunogenic cell death
- Integrated stress response
- Transcription inhibition