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

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 malign...

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Bibliographic Details
Published in:Blood Cancer Discovery
Main Authors: Heinemann, Tim, Kornauth, Christoph, Severin, Yannik, Vladimer, Gregory I., Pemovska, Tea, Hadzijusufovic, Emir, Agis, Hermine, Krauth, Maria-Theresa, Sperr, Wolfgang R., Valent, Peter, Jäger, Ulrich, Simonitsch-Klupp, Ingrid, Superti-Furga, Giulio, Staber, Philipp B., Snijder, Berend
Format: Text
Language:English
Published: American Association for Cancer Research 2022
Subjects:
Research Briefs
DML
Online Access:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9894727/
http://www.ncbi.nlm.nih.gov/pubmed/36125297
https://doi.org/10.1158/2643-3230.BCD-21-0219
Description
Summary: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 choice–based treatments. Treatments recommended by both immunofluorescence and DML doubled the fraction of patients achieving exceptional clinical responses. Thus, DML-enhanced ex vivo drug screening is a promising tool in the identification of effective personalized treatments. SIGNIFICANCE: We have recently demonstrated that image-based drug screening in patient samples identifies effective treatment options for patients with advanced blood cancers. Here we show that using deep learning to identify malignant and nonmalignant cells by morphology improves such screens. The presented workflow is robust, automatable, and compatible with clinical routine. This article is highlighted in the In This Issue feature, p. 476

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