Learning and actioning general principles of cancer cell drug sensitivity

Francesco Carli (francesco.carli@sns.it), Pierluigi Chiaro, Mariangela Morelli, Chakit Arora, Luisa Bisceglia, Natalia Oliveira Rosa, Alice Cortesi, Sara Franceschi, Francesca Lessi, Anna Luisa Stefano, Orazio Santo Santonocito, Francesco Pasqualetti, Paolo Aretini, Pasquale Miglionico, Giuseppe R. Diaferia, Fosca Giannotti, Pietro Liò, Miquel Duran-Frigola, Chiara Maria Mazzanti, Gioacchino Natoli and Francesco Raimondi (francesco.raimondi@sns.it)
Additional contact information
Francesco Carli: Scuola Normale Superiore
Pierluigi Chiaro: European Institute of Oncology IRCCS
Mariangela Morelli: Fondazione Pisana per la Scienza ONLUS
Chakit Arora: Scuola Normale Superiore
Luisa Bisceglia: Scuola Normale Superiore
Natalia Oliveira Rosa: Scuola Normale Superiore
Alice Cortesi: European Institute of Oncology IRCCS
Sara Franceschi: Fondazione Pisana per la Scienza ONLUS
Francesca Lessi: Fondazione Pisana per la Scienza ONLUS
Anna Luisa Stefano: Neurosurgical Department of Spedali Riuniti di Livorno
Orazio Santo Santonocito: Neurosurgical Department of Spedali Riuniti di Livorno
Francesco Pasqualetti: Azienda Ospedaliera Universitaria Pisana
Paolo Aretini: Fondazione Pisana per la Scienza ONLUS
Pasquale Miglionico: Scuola Normale Superiore
Giuseppe R. Diaferia: European Institute of Oncology IRCCS
Fosca Giannotti: Scuola Normale Superiore
Pietro Liò: University of Cambridge
Miquel Duran-Frigola: Can Sutirà
Chiara Maria Mazzanti: Fondazione Pisana per la Scienza ONLUS
Gioacchino Natoli: European Institute of Oncology IRCCS
Francesco Raimondi: Scuola Normale Superiore

Nature Communications, 2025, vol. 16, issue 1, 1-23

Abstract: Abstract High-throughput screening of drug sensitivity of cancer cell lines (CCLs) holds the potential to unlock anti-tumor therapies. In this study, we leverage such datasets to predict drug response using cell line transcriptomics, focusing on models’ interpretability and deployment on patients’ data. We use large language models (LLMs) to match drug to mechanisms of action (MOA)-related pathways. Genes crucial for prediction are enriched in drug-MOAs, suggesting that our models learn the molecular determinants of response. Furthermore, by using only LLM-curated, MOA-genes, we enhance the predictive accuracy of our models. To enhance translatability, we align RNAseq data from CCLs, used for training, to those from patient samples, used for inference. We validated our approach on TCGA samples, where patients’ best scoring drugs match those prescribed for their cancer type. We further predict and experimentally validate effective drugs for the patients of two highly lethal solid tumors, i.e., pancreatic cancer and glioblastoma.

Date: 2025
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DOI: 10.1038/s41467-025-56827-5

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