Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.06.26.546571v1?rss=1
Authors: Ianevski, A., Nader, K., Bulava, D., Giri, A. K., Ruokoranta, T., Kuusanmaki, H., Ikonen, N., Sergeev, P., Vaha-Koskela, M., Vaharautio, A., Kontro, M., Porkka, K., Heckman, C. A., Wennerberg, K., Aittokallio, T.
Abstract:
Intratumoral cellular heterogeneity necessitates multi-targeting therapies for improved clinical benefits in patients with 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 scarce patient cells. Here, we developed scTherapy, a machine learning model that leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors.
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