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

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.

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Publication Details

Journal
Nature Communications
Published
2024-10-03
DOI
https://doi.org/10.1038/s41467-024-52980-5
Citations
39
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
8.89

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article

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

Esa Pitkänen, Anil K. Giri, Wojciech Senkowski, Tero A. Aittokallio et al.
39 citations
Nature Communications
Single-cell and spatial transcriptomics
8.89
article

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

Esa Pitkänen, Anil K. Giri, Wojciech Senkowski, Tero A. Aittokallio, Nemo Ikonen, Markus Vähä‐Koskela, Lidia Moyano‐Galceran, Philipp Sergeev, Tanja Ruokoranta, Kimmo Porkka, Heikki Kuusanmäki, Caroline A. Heckman, Daria Bulanova, Mika Kontro, Krister Wennerberg, Aleksandr Ianevski, Anna Vähärautio, Kristen Nader, Kyriaki Driva
article en
39 citations

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.

Nature CommunicationsVol. 15(1)
University of Copenhagen (DK), Oslo University Hospital (NO), University of Helsinki (FI), University of Oslo (NO), Helsinki University Hospital (FI), Institute for Molecular Medicine Finland (FI)
Blood Disease Research Foundation, Helsingin Yliopisto, Suomen Lääketieteen Säätiö, Innovationsfonden, Finnish Cancer Institute, European Commission, Novo Nordisk, NordForsk, Sigrid Juséliuksen Säätiö, Syöpäjärjestöt, Instrumentariumin Tiedesäätiö, Novo Nordisk Fonden, Syöpäsäätiö, Biocenter Finland, Helsinki Institute of Life Science, Helsingin Yliopisto, HORIZON EUROPE Framework Programme, H2020 Marie Skłodowska-Curie Actions
Good health and well-being
Openalex Percentile: Top 1%
Single-cell and spatial transcriptomics
8.89
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