Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones

Aleksandr Ianevski, Kristen Nader, Kyriaki Driva, Wojciech Senkowski, Daria Bulanova, Lidia Moyano-Galceran, Tanja Ruokoranta, Heikki Kuusanmäki, Nemo Ikonen, Philipp Sergeev, Markus Vähä-Koskela, Anil K. Giri, Anna Vähärautio, Mika Kontro, Kimmo Porkka, Esa Pitkänen, Caroline A. Heckman, Krister Wennerberg and Tero Aittokallio ()
Additional contact information
Aleksandr Ianevski: University of Helsinki
Kristen Nader: University of Helsinki
Kyriaki Driva: University of Copenhagen
Wojciech Senkowski: University of Copenhagen
Daria Bulanova: University of Helsinki
Lidia Moyano-Galceran: University of Copenhagen
Tanja Ruokoranta: University of Helsinki
Heikki Kuusanmäki: University of Helsinki
Nemo Ikonen: University of Helsinki
Philipp Sergeev: University of Helsinki
Markus Vähä-Koskela: University of Helsinki
Anil K. Giri: University of Helsinki
Anna Vähärautio: Foundation for the Finnish Cancer Institute (FCI)
Mika Kontro: University of Helsinki
Kimmo Porkka: Helsinki University Hospital Comprehensive Cancer Center
Esa Pitkänen: University of Helsinki
Caroline A. Heckman: University of Helsinki
Krister Wennerberg: University of Copenhagen
Tero Aittokallio: University of Helsinki

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: Abstract Intratumoral cellular heterogeneity necessitates multi-targeting therapies for improved clinical benefits in advanced malignancies. However, systematic identification of patient-specific treatments that selectively co-inhibit cancerous cell populations poses a combinatorial challenge, since the number of possible drug-dose combinations vastly exceeds what could be tested in patient cells. Here, we describe a machine learning approach, scTherapy, which leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors. Patient-specific treatments reveal a wide spectrum of co-inhibitors of multiple biological pathways predicted for primary cells from heterogenous cohorts of patients with acute myeloid leukemia and high-grade serous ovarian carcinoma, each with unique resistance patterns and synergy mechanisms. Experimental validations confirm that 96% of the multi-targeting treatments exhibit selective efficacy or synergy, and 83% demonstrate low toxicity to normal cells, highlighting their potential for therapeutic efficacy and safety. In a pan-cancer analysis across five cancer types, 25% of the predicted treatments are shared among the patients of the same tumor type, while 19% of the treatments are patient-specific. Our approach provides a widely-applicable strategy to identify personalized treatment regimens that selectively co-inhibit malignant cells and avoid inhibition of non-cancerous cells, thereby increasing their likelihood for clinical success.

Date: 2024
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DOI: 10.1038/s41467-024-52980-5

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