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 comple-ment diagnostic marker-based identifi cation of mali...
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American Association for Cancer Research
2022
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ftethz:oai:www.research-collection.ethz.ch:20.500.11850/584166 2023-06-18T03:40:23+02:00 Deep Morphology Learning Enhances Ex Vivo Drug Profiling-Based Precision Medicine 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 2022-11 application/application/pdf https://hdl.handle.net/20.500.11850/584166 https://doi.org/10.3929/ethz-b-000584166 en eng American Association for Cancer Research info:eu-repo/semantics/altIdentifier/doi/10.1158/2643-3230.BCD-21-0219 info:eu-repo/semantics/altIdentifier/wos/000884784300001 info:eu-repo/grantAgreement/SNF/SNF-Förderungsprofessuren Stufe 2/163961 info:eu-repo/grantAgreement/SNF/SNF-Förderungsprofessuren: Fortsetzungsgesuche/194809 info:eu-repo/grantAgreement/SNF/Sinergia/193832 info:eu-repo/grantAgreement/EC/H2020/803063 http://hdl.handle.net/20.500.11850/584166 doi:10.3929/ethz-b-000584166 info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Blood Cancer Discovery, 3 (6) info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2022 ftethz https://doi.org/20.500.11850/58416610.3929/ethz-b-00058416610.1158/2643-3230.BCD-21-0219 2023-06-04T23:50:03Z 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 comple-ment diagnostic marker-based identifi cation 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 immunofl uorescence and DML doubled the fraction of patients achieving exceptional clinical responses. Thus, DML-enhanced ex vivo drug screening is a promising tool in the identifi cation of effective personalized treatments.SIGNIFICANCE: We have recently demonstrated that image-based drug screening in patient samples identifi es 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 workfl ow is robust, automatable, and compatible with clinical routine. ISSN:2643-3249 ISSN:2643-3230 Article in Journal/Newspaper DML ETH Zürich Research Collection |
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ETH Zürich Research Collection |
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ftethz |
language |
English |
description |
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 comple-ment diagnostic marker-based identifi cation 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 immunofl uorescence and DML doubled the fraction of patients achieving exceptional clinical responses. Thus, DML-enhanced ex vivo drug screening is a promising tool in the identifi cation of effective personalized treatments.SIGNIFICANCE: We have recently demonstrated that image-based drug screening in patient samples identifi es 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 workfl ow is robust, automatable, and compatible with clinical routine. ISSN:2643-3249 ISSN:2643-3230 |
format |
Article in Journal/Newspaper |
author |
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 |
spellingShingle |
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 Deep Morphology Learning Enhances Ex Vivo Drug Profiling-Based Precision Medicine |
author_facet |
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 |
author_sort |
Heinemann, Tim |
title |
Deep Morphology Learning Enhances Ex Vivo Drug Profiling-Based Precision Medicine |
title_short |
Deep Morphology Learning Enhances Ex Vivo Drug Profiling-Based Precision Medicine |
title_full |
Deep Morphology Learning Enhances Ex Vivo Drug Profiling-Based Precision Medicine |
title_fullStr |
Deep Morphology Learning Enhances Ex Vivo Drug Profiling-Based Precision Medicine |
title_full_unstemmed |
Deep Morphology Learning Enhances Ex Vivo Drug Profiling-Based Precision Medicine |
title_sort |
deep morphology learning enhances ex vivo drug profiling-based precision medicine |
publisher |
American Association for Cancer Research |
publishDate |
2022 |
url |
https://hdl.handle.net/20.500.11850/584166 https://doi.org/10.3929/ethz-b-000584166 |
genre |
DML |
genre_facet |
DML |
op_source |
Blood Cancer Discovery, 3 (6) |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1158/2643-3230.BCD-21-0219 info:eu-repo/semantics/altIdentifier/wos/000884784300001 info:eu-repo/grantAgreement/SNF/SNF-Förderungsprofessuren Stufe 2/163961 info:eu-repo/grantAgreement/SNF/SNF-Förderungsprofessuren: Fortsetzungsgesuche/194809 info:eu-repo/grantAgreement/SNF/Sinergia/193832 info:eu-repo/grantAgreement/EC/H2020/803063 http://hdl.handle.net/20.500.11850/584166 doi:10.3929/ethz-b-000584166 |
op_rights |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International |
op_doi |
https://doi.org/20.500.11850/58416610.3929/ethz-b-00058416610.1158/2643-3230.BCD-21-0219 |
_version_ |
1769005387869061120 |