Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
- Autores
- Menden, Michael P.; Wang, Dennis; Mason, Mike J.; Szalai, Bence; Bulusu, Krishna C.; Guan, Yuanfang; Yu, Thomas; Kang, Jaewoo; Jeon, Minji; Wolfinger, Russ; Nguyen, Tin; Zaslavskiy, Mikhail; Chernomoretz, Ariel; Jang, In Sock; Ghazoui, Zara; Ahsen, Mehmet Eren; Vogel, Robert; Neto, Elias Chaibub; Norman, Thea; Tang, Eric K. Y.; Garnett, Mathew J.; Di Veroli, Giovanni Y.; Fawell, Stephen; Stolovitzky, Gustavo; Guinney, Justin; Dry, Jonathan R.; Saez-Rodriguez, Julio
- Año de publicación
- 2019
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.
Fil: Menden, Michael P.. Helmholtz Zentrum München - German Research Center for Environmental Health. Institute of Computational Biology; Alemania. AstraZeneca; Reino Unido. European Molecular Biology Laboratory; Reino Unido
Fil: Wang, Dennis. AstraZeneca; Reino Unido. University of Sheffield; Reino Unido
Fil: Mason, Mike J.. Sage Bionetworks; Estados Unidos
Fil: Szalai, Bence. Semmelweis University; Hungría. RWTH Aachen University; Alemania
Fil: Bulusu, Krishna C.. Astrazeneca; Reino Unido
Fil: Guan, Yuanfang. University of Michigan; Estados Unidos
Fil: Yu, Thomas. Sage Bionetworks; Estados Unidos
Fil: Kang, Jaewoo. Korea University; Corea del Norte
Fil: Jeon, Minji. Korea University; Corea del Norte
Fil: Wolfinger, Russ. Sas Institute, Inc.; Estados Unidos
Fil: Nguyen, Tin. University of Nevada; Estados Unidos
Fil: Zaslavskiy, Mikhail. Owkin, Inc.; Estados Unidos
Fil: Chernomoretz, Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina. Fundación Instituto Leloir; Argentina
Fil: Jang, In Sock. Sage Bionetworks; Estados Unidos
Fil: Ghazoui, Zara. AstraZeneca; Reino Unido
Fil: Ahsen, Mehmet Eren. IBM Research. Thomas J. Watson Research Center; Estados Unidos
Fil: Vogel, Robert. IBM Research. Thomas J. Watson Research Center; Estados Unidos
Fil: Neto, Elias Chaibub. Sage Bionetworks; Estados Unidos
Fil: Norman, Thea. Sage Bionetworks; Estados Unidos
Fil: Tang, Eric K. Y.. AstraZeneca; Reino Unido
Fil: Garnett, Mathew J.. Wellcome Sanger Institute; Reino Unido
Fil: Di Veroli, Giovanni Y.. AstraZeneca; Reino Unido
Fil: Fawell, Stephen. AstraZeneca; Reino Unido
Fil: Stolovitzky, Gustavo. IBM Research. Thomas J. Watson Research Center; Estados Unidos. Icahn School of Medicine at Mount Sinai; Estados Unidos
Fil: Guinney, Justin. Sage Bionetworks; Estados Unidos
Fil: Dry, Jonathan R.. AstraZeneca; Reino Unido
Fil: Saez-Rodriguez, Julio. European Molecular Biology Laboratory; Reino Unido. RWTH Aachen University; Alemania - Materia
-
Drug synergy
Biomarkers
Cancer treatment - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by/2.5/ar/
- Repositorio
.jpg)
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/148271
Ver los metadatos del registro completo
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Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screenMenden, Michael P.Wang, DennisMason, Mike J.Szalai, BenceBulusu, Krishna C.Guan, YuanfangYu, ThomasKang, JaewooJeon, MinjiWolfinger, RussNguyen, TinZaslavskiy, MikhailChernomoretz, ArielJang, In SockGhazoui, ZaraAhsen, Mehmet ErenVogel, RobertNeto, Elias ChaibubNorman, TheaTang, Eric K. Y.Garnett, Mathew J.Di Veroli, Giovanni Y.Fawell, StephenStolovitzky, GustavoGuinney, JustinDry, Jonathan R.Saez-Rodriguez, JulioDrug synergyBiomarkersCancer treatmenthttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Fil: Menden, Michael P.. Helmholtz Zentrum München - German Research Center for Environmental Health. Institute of Computational Biology; Alemania. AstraZeneca; Reino Unido. European Molecular Biology Laboratory; Reino UnidoFil: Wang, Dennis. AstraZeneca; Reino Unido. University of Sheffield; Reino UnidoFil: Mason, Mike J.. Sage Bionetworks; Estados UnidosFil: Szalai, Bence. Semmelweis University; Hungría. RWTH Aachen University; AlemaniaFil: Bulusu, Krishna C.. Astrazeneca; Reino UnidoFil: Guan, Yuanfang. University of Michigan; Estados UnidosFil: Yu, Thomas. Sage Bionetworks; Estados UnidosFil: Kang, Jaewoo. Korea University; Corea del NorteFil: Jeon, Minji. Korea University; Corea del NorteFil: Wolfinger, Russ. Sas Institute, Inc.; Estados UnidosFil: Nguyen, Tin. University of Nevada; Estados UnidosFil: Zaslavskiy, Mikhail. Owkin, Inc.; Estados UnidosFil: Chernomoretz, Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina. Fundación Instituto Leloir; ArgentinaFil: Jang, In Sock. Sage Bionetworks; Estados UnidosFil: Ghazoui, Zara. AstraZeneca; Reino UnidoFil: Ahsen, Mehmet Eren. IBM Research. Thomas J. Watson Research Center; Estados UnidosFil: Vogel, Robert. IBM Research. Thomas J. Watson Research Center; Estados UnidosFil: Neto, Elias Chaibub. Sage Bionetworks; Estados UnidosFil: Norman, Thea. Sage Bionetworks; Estados UnidosFil: Tang, Eric K. Y.. AstraZeneca; Reino UnidoFil: Garnett, Mathew J.. Wellcome Sanger Institute; Reino UnidoFil: Di Veroli, Giovanni Y.. AstraZeneca; Reino UnidoFil: Fawell, Stephen. AstraZeneca; Reino UnidoFil: Stolovitzky, Gustavo. IBM Research. Thomas J. Watson Research Center; Estados Unidos. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Guinney, Justin. Sage Bionetworks; Estados UnidosFil: Dry, Jonathan R.. AstraZeneca; Reino UnidoFil: Saez-Rodriguez, Julio. European Molecular Biology Laboratory; Reino Unido. RWTH Aachen University; AlemaniaNature Publishing Group2019-06-17info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/148271Menden, Michael P.; Wang, Dennis; Mason, Mike J.; Szalai, Bence; Bulusu, Krishna C.; et al.; Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen; Nature Publishing Group; Nature Communications; 10; 1; 17-6-2019; 1-172041-1723CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1038/s41467-019-09799-2info:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41467-019-09799-2info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:51:07Zoai:ri.conicet.gov.ar:11336/148271instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:51:07.471CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
| dc.title.none.fl_str_mv |
Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen |
| title |
Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen |
| spellingShingle |
Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen Menden, Michael P. Drug synergy Biomarkers Cancer treatment |
| title_short |
Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen |
| title_full |
Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen |
| title_fullStr |
Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen |
| title_full_unstemmed |
Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen |
| title_sort |
Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen |
| dc.creator.none.fl_str_mv |
Menden, Michael P. Wang, Dennis Mason, Mike J. Szalai, Bence Bulusu, Krishna C. Guan, Yuanfang Yu, Thomas Kang, Jaewoo Jeon, Minji Wolfinger, Russ Nguyen, Tin Zaslavskiy, Mikhail Chernomoretz, Ariel Jang, In Sock Ghazoui, Zara Ahsen, Mehmet Eren Vogel, Robert Neto, Elias Chaibub Norman, Thea Tang, Eric K. Y. Garnett, Mathew J. Di Veroli, Giovanni Y. Fawell, Stephen Stolovitzky, Gustavo Guinney, Justin Dry, Jonathan R. Saez-Rodriguez, Julio |
| author |
Menden, Michael P. |
| author_facet |
Menden, Michael P. Wang, Dennis Mason, Mike J. Szalai, Bence Bulusu, Krishna C. Guan, Yuanfang Yu, Thomas Kang, Jaewoo Jeon, Minji Wolfinger, Russ Nguyen, Tin Zaslavskiy, Mikhail Chernomoretz, Ariel Jang, In Sock Ghazoui, Zara Ahsen, Mehmet Eren Vogel, Robert Neto, Elias Chaibub Norman, Thea Tang, Eric K. Y. Garnett, Mathew J. Di Veroli, Giovanni Y. Fawell, Stephen Stolovitzky, Gustavo Guinney, Justin Dry, Jonathan R. Saez-Rodriguez, Julio |
| author_role |
author |
| author2 |
Wang, Dennis Mason, Mike J. Szalai, Bence Bulusu, Krishna C. Guan, Yuanfang Yu, Thomas Kang, Jaewoo Jeon, Minji Wolfinger, Russ Nguyen, Tin Zaslavskiy, Mikhail Chernomoretz, Ariel Jang, In Sock Ghazoui, Zara Ahsen, Mehmet Eren Vogel, Robert Neto, Elias Chaibub Norman, Thea Tang, Eric K. Y. Garnett, Mathew J. Di Veroli, Giovanni Y. Fawell, Stephen Stolovitzky, Gustavo Guinney, Justin Dry, Jonathan R. Saez-Rodriguez, Julio |
| author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author |
| dc.subject.none.fl_str_mv |
Drug synergy Biomarkers Cancer treatment |
| topic |
Drug synergy Biomarkers Cancer treatment |
| purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
| dc.description.none.fl_txt_mv |
The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. Fil: Menden, Michael P.. Helmholtz Zentrum München - German Research Center for Environmental Health. Institute of Computational Biology; Alemania. AstraZeneca; Reino Unido. European Molecular Biology Laboratory; Reino Unido Fil: Wang, Dennis. AstraZeneca; Reino Unido. University of Sheffield; Reino Unido Fil: Mason, Mike J.. Sage Bionetworks; Estados Unidos Fil: Szalai, Bence. Semmelweis University; Hungría. RWTH Aachen University; Alemania Fil: Bulusu, Krishna C.. Astrazeneca; Reino Unido Fil: Guan, Yuanfang. University of Michigan; Estados Unidos Fil: Yu, Thomas. Sage Bionetworks; Estados Unidos Fil: Kang, Jaewoo. Korea University; Corea del Norte Fil: Jeon, Minji. Korea University; Corea del Norte Fil: Wolfinger, Russ. Sas Institute, Inc.; Estados Unidos Fil: Nguyen, Tin. University of Nevada; Estados Unidos Fil: Zaslavskiy, Mikhail. Owkin, Inc.; Estados Unidos Fil: Chernomoretz, Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina. Fundación Instituto Leloir; Argentina Fil: Jang, In Sock. Sage Bionetworks; Estados Unidos Fil: Ghazoui, Zara. AstraZeneca; Reino Unido Fil: Ahsen, Mehmet Eren. IBM Research. Thomas J. Watson Research Center; Estados Unidos Fil: Vogel, Robert. IBM Research. Thomas J. Watson Research Center; Estados Unidos Fil: Neto, Elias Chaibub. Sage Bionetworks; Estados Unidos Fil: Norman, Thea. Sage Bionetworks; Estados Unidos Fil: Tang, Eric K. Y.. AstraZeneca; Reino Unido Fil: Garnett, Mathew J.. Wellcome Sanger Institute; Reino Unido Fil: Di Veroli, Giovanni Y.. AstraZeneca; Reino Unido Fil: Fawell, Stephen. AstraZeneca; Reino Unido Fil: Stolovitzky, Gustavo. IBM Research. Thomas J. Watson Research Center; Estados Unidos. Icahn School of Medicine at Mount Sinai; Estados Unidos Fil: Guinney, Justin. Sage Bionetworks; Estados Unidos Fil: Dry, Jonathan R.. AstraZeneca; Reino Unido Fil: Saez-Rodriguez, Julio. European Molecular Biology Laboratory; Reino Unido. RWTH Aachen University; Alemania |
| description |
The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. |
| publishDate |
2019 |
| dc.date.none.fl_str_mv |
2019-06-17 |
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info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
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article |
| status_str |
publishedVersion |
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http://hdl.handle.net/11336/148271 Menden, Michael P.; Wang, Dennis; Mason, Mike J.; Szalai, Bence; Bulusu, Krishna C.; et al.; Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen; Nature Publishing Group; Nature Communications; 10; 1; 17-6-2019; 1-17 2041-1723 CONICET Digital CONICET |
| url |
http://hdl.handle.net/11336/148271 |
| identifier_str_mv |
Menden, Michael P.; Wang, Dennis; Mason, Mike J.; Szalai, Bence; Bulusu, Krishna C.; et al.; Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen; Nature Publishing Group; Nature Communications; 10; 1; 17-6-2019; 1-17 2041-1723 CONICET Digital CONICET |
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eng |
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eng |
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Nature Publishing Group |
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Nature Publishing Group |
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