Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2019 |
| Outros Autores: | , , , , , , , , , , |
| Tipo de documento: | Artigo |
| Idioma: | eng |
| Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| Texto Completo: | http://hdl.handle.net/1822/60862 |
Resumo: | 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. |
| id |
RCAP_355a27d4dffacf68438342b87e029498 |
|---|---|
| oai_identifier_str |
oai:repositorium.sdum.uminho.pt:1822/60862 |
| network_acronym_str |
RCAP |
| network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| repository_id_str |
7160 |
| spelling |
Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screenCiências Médicas::Biotecnologia MédicaScience & TechnologyThe 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.We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194).info:eu-repo/semantics/publishedVersionSpringer NatureUniversidade do MinhoMenden, Michael P.Wang, DennisMason, Mike J.Szalai, BenceBulusu, Krishna C.Guan, YuanfangYu, ThomasAstraZeneca-Sanger Drug Combination DREAM ConsortiumBaptista, DeloraMachado, D.Rocha, Miguelet. al.2019-062019-06-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/60862engMenden, M. P., Wang, D., Mason, Baptista, Delora, B., Machado, D., Rocha, Miguel, et. al. (2019). Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. Nature communications, 10(1), 2674204117232041172310.1038/s41467-019-09799-231209238https://www.nature.com/articles/s41467-019-09799-2#article-infoinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:30:44Zoai:repositorium.sdum.uminho.pt:1822/60862Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:25:58.566464Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
| 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. Ciências Médicas::Biotecnologia Médica Science & Technology |
| 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 |
| author |
Menden, Michael P. |
| author_facet |
Menden, Michael P. Wang, Dennis Mason, Mike J. Szalai, Bence Bulusu, Krishna C. Guan, Yuanfang Yu, Thomas AstraZeneca-Sanger Drug Combination DREAM Consortium Baptista, Delora Machado, D. Rocha, Miguel et. al. |
| author_role |
author |
| author2 |
Wang, Dennis Mason, Mike J. Szalai, Bence Bulusu, Krishna C. Guan, Yuanfang Yu, Thomas AstraZeneca-Sanger Drug Combination DREAM Consortium Baptista, Delora Machado, D. Rocha, Miguel et. al. |
| author2_role |
author author author author author author author author author author author |
| dc.contributor.none.fl_str_mv |
Universidade do Minho |
| dc.contributor.author.fl_str_mv |
Menden, Michael P. Wang, Dennis Mason, Mike J. Szalai, Bence Bulusu, Krishna C. Guan, Yuanfang Yu, Thomas AstraZeneca-Sanger Drug Combination DREAM Consortium Baptista, Delora Machado, D. Rocha, Miguel et. al. |
| dc.subject.por.fl_str_mv |
Ciências Médicas::Biotecnologia Médica Science & Technology |
| topic |
Ciências Médicas::Biotecnologia Médica Science & Technology |
| 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 2019-06-01T00:00:00Z |
| dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/60862 |
| url |
http://hdl.handle.net/1822/60862 |
| dc.language.iso.fl_str_mv |
eng |
| language |
eng |
| dc.relation.none.fl_str_mv |
Menden, M. P., Wang, D., Mason, Baptista, Delora, B., Machado, D., Rocha, Miguel, et. al. (2019). Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. Nature communications, 10(1), 2674 20411723 20411723 10.1038/s41467-019-09799-2 31209238 https://www.nature.com/articles/s41467-019-09799-2#article-info |
| dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
Springer Nature |
| publisher.none.fl_str_mv |
Springer Nature |
| dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
| instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
| instacron_str |
RCAAP |
| institution |
RCAAP |
| reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
| repository.mail.fl_str_mv |
|
| _version_ |
1799132745436233728 |