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. This paper describes scTherapy, a machine learning approach leveraging single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients. The approach reveals co-inhibitors of multiple biological pathways for AML and HGSC patients, with experimental validations confirming selective efficacy and low toxicity in most cases. Pan-cancer analysis shows a mix of shared and patient-specific treatments.

Aleksandr Lanevski, 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, Tero Aittokallio