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

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 myelo

SUBMITTER: Ianevski A 

PROVIDER: S-EPMC11450203 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature