Deep Morphology Learning Enhances Ex Vivo Drug Profiling-Based Precision Medicine
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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 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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Several examples have documented the power of chemical screens in organoids or directly in patient material.
“To that end, several examples have documented the power of chemical screens in organoids175,176 or directly in patient material.177−179”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [179]
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- Authors
- Heinemann, T., Kornauth, C., Severin, Y., Vladimer, G. I., Pemovska, T., Hadzijusufovic, E., Agis, H., Krauth, M.-T., Sperr, W. R., Valent, P., Jäger, U., Simonitsch-Klupp, I., Superti-Furga, G., Staber, P. B., & Snijder, B.
- Affiliations
- ETH Zurich (CH)
- Medical University of Vienna (AT)
- Austrian Academy of Sciences (AT)
- Ludwig Boltzmann Institute for Hematology and Oncology (AT)
- 4Department of Medicine I, Division of Hematology and Hemostaseology, Medical University of Vienna, Vienna, Austria.
- 2Department of Pathology, Medical University of Vienna, Vienna, Austria.
- 3CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.
- Corresponding author
- Snijder, Berend
- ORCID
- Venue
- Blood Cancer Discovery
- Published
- 2022-09-13
- Volume
- 3
- Issue
- 6
- Pages
- 502-515
- ISSN
- 2643-3230 (ISSN-L)
- 2643-3249
- PMC
- PMC9894727
- OpenAlex
- W4296784527
- Cited by
- 28
- References
- 44
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 7 · 2023: 8 · 2022: 1
- FWCI
- 1.88
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Blood Cancer Discovery (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Repository for Publications and Research Data (ETH Zurich) (publishedVersion, cc-by-nc-nd)
- ETH Zürich Research Collection
- Funders
- Swiss National Science Foundation (PP00P3_163961)
- Deutsche Forschungsgemeinschaft (389640585)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Imaging for Blood Diseases
- Health Sciences › Medicine › Hematology › Acute Myeloid Leukemia Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- ex vivo drug testing, hematologic cancer, relapsed/refractory cancer, precision medicine, weakly supervised deep learning, cell morphology, batch effect correction, progression-free survival, immunofluorescence comparison, treatment guidelines
- MeSH terms
- Humans, Reproducibility of Results, Hematologic Neoplasms, Precision Medicine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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