Deep Morphology Learning Enhances Ex Vivo Drug Profiling-Based Precision Medicine.

ABSTRACT: Drug testing in patient biopsy-derived cells can identify potent treatments for patients suffering from relapsed or refractory hematologic cancers. Here we investigate the use of weakly supervised deep learning on cell morphologies (DML) to complement diagnostic marker-based identification of malignant and nonmalignant cells in drug testing. Across 390 biopsies from 289 patients with diverse blood cancers, DML-based drug responses show improved reproducibility and clustering of drugs with the same mode of action. DML does so by adapting to batch effects and by autonomously recognizing disease-associated cell morphologies. In a post hoc analysis of 66 patients, DML-recommended treatments led to improved progression-free survival compared with marker-based recommendations and physician's cho

SUBMITTER: Heinemann T 

PROVIDER: S-EPMC9894727 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature