A cancer drug atlas enables synergistic targeting of independent drug vulnerabilities

Ravi S. Narayan; Piet Molenaar; Jian Teng; Fleur M. G. Cornelissen; Irene Roelofs; Renee Menezes; Rogier Dik; Tonny Lagerweij; Yoran Broersma; Naomi Petersen; Jhon Alexander Marin Soto; Eelke Brands; Philip van Kuiken; Maria C. Lecca; Kristiaan J. Lenos; Sjors G. J. G. in ‘t Veld; Wessel van Wieringen; Frederick F. Lang; Erik Sulman; Roel Verhaak; Brigitta G. Baumert; Lucas J. A. Stalpers; Louis Vermeulen; Colin Watts; David Bailey; Ben J. Slotman; Rogier Versteeg; David Noske; Peter Sminia; Bakhos A. Tannous; Tom Wurdinger; Jan Koster; Bart A. Westerman

doi:https://doi.org/10.1038/s41467-020-16735-2

A cancer drug atlas enables synergistic targeting of independent drug vulnerabilities

Ravi S. Narayan, Piet Molenaar, Jian Teng, Fleur M. G. Cornelissen, Irene Roelofs, Renee Menezes, Rogier Dik, Tonny Lagerweij, Yoran Broersma, Naomi Petersen, Jhon Alexander Marin Soto, Eelke Brands, Philip van Kuiken, Maria C. Lecca, Kristiaan J. Lenos, Sjors G. J. G. in ‘t Veld, Wessel van Wieringen, Frederick F. Lang, Erik Sulman, Roel VerhaakBrigitta G. Baumert, Lucas J. A. Stalpers, Louis Vermeulen, Colin Watts, David Bailey, Ben J. Slotman, Rogier Versteeg, David Noske, Peter Sminia, Bakhos A. Tannous, Tom Wurdinger, Jan Koster, Bart A. Westerman

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Abstract

Personalized cancer treatments using combinations of drugs with a synergistic effect is attractive but proves to be highly challenging. Here we present an approach to uncover the efficacy of drug combinations based on the analysis of mono-drug effects. For this we used dose-response data from pharmacogenomic encyclopedias and represent these as a drug atlas. The drug atlas represents the relations between drug effects and allows to identify independent processes for which the tumor might be particularly vulnerable when attacked by two drugs. Our approach enables the prediction of combination-therapy which can be linked to tumor-driving mutations. By using this strategy, we can uncover potential effective drug combinations on a pan-cancer scale. Predicted synergies are provided and have been validated in glioblastoma, breast cancer, melanoma and leukemia mouse-models, resulting in therapeutic synergy in 75% of the tested models. This indicates that we can accurately predict effective drug combinations with translational value.

Original language	English
Article number	2935
Journal	Nature communications
Volume	11
Issue number	1
DOIs	https://doi.org/10.1038/s41467-020-16735-2
Publication status	Published - 1 Dec 2020

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Narayan, R. S., Molenaar, P., Teng, J., Cornelissen, F. M. G., Roelofs, I., Menezes, R., Dik, R., Lagerweij, T., Broersma, Y., Petersen, N., Marin Soto, J. A., Brands, E., van Kuiken, P., Lecca, M. C., Lenos, K. J., in ‘t Veld, S. G. J. G., van Wieringen, W., Lang, F. F., Sulman, E., ... Westerman, B. A. (2020). A cancer drug atlas enables synergistic targeting of independent drug vulnerabilities. Nature communications, 11(1), Article 2935. https://doi.org/10.1038/s41467-020-16735-2

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title = "A cancer drug atlas enables synergistic targeting of independent drug vulnerabilities",

abstract = "Personalized cancer treatments using combinations of drugs with a synergistic effect is attractive but proves to be highly challenging. Here we present an approach to uncover the efficacy of drug combinations based on the analysis of mono-drug effects. For this we used dose-response data from pharmacogenomic encyclopedias and represent these as a drug atlas. The drug atlas represents the relations between drug effects and allows to identify independent processes for which the tumor might be particularly vulnerable when attacked by two drugs. Our approach enables the prediction of combination-therapy which can be linked to tumor-driving mutations. By using this strategy, we can uncover potential effective drug combinations on a pan-cancer scale. Predicted synergies are provided and have been validated in glioblastoma, breast cancer, melanoma and leukemia mouse-models, resulting in therapeutic synergy in 75% of the tested models. This indicates that we can accurately predict effective drug combinations with translational value.",

author = "Narayan, {Ravi S.} and Piet Molenaar and Jian Teng and Cornelissen, {Fleur M. G.} and Irene Roelofs and Renee Menezes and Rogier Dik and Tonny Lagerweij and Yoran Broersma and Naomi Petersen and {Marin Soto}, {Jhon Alexander} and Eelke Brands and {van Kuiken}, Philip and Lecca, {Maria C.} and Lenos, {Kristiaan J.} and {in {\textquoteleft}t Veld}, {Sjors G. J. G.} and {van Wieringen}, Wessel and Lang, {Frederick F.} and Erik Sulman and Roel Verhaak and Baumert, {Brigitta G.} and Stalpers, {Lucas J. A.} and Louis Vermeulen and Colin Watts and David Bailey and Slotman, {Ben J.} and Rogier Versteeg and David Noske and Peter Sminia and Tannous, {Bakhos A.} and Tom Wurdinger and Jan Koster and Westerman, {Bart A.}",

year = "2020",

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Narayan, RS, Molenaar, P, Teng, J, Cornelissen, FMG, Roelofs, I, Menezes, R, Dik, R, Lagerweij, T, Broersma, Y, Petersen, N, Marin Soto, JA, Brands, E, van Kuiken, P, Lecca, MC, Lenos, KJ, in ‘t Veld, SGJG, van Wieringen, W, Lang, FF, Sulman, E, Verhaak, R, Baumert, BG, Stalpers, LJA , Vermeulen, L, Watts, C, Bailey, D, Slotman, BJ , Versteeg, R , Noske, D, Sminia, P, Tannous, BA, Wurdinger, T , Koster, J & Westerman, BA 2020, 'A cancer drug atlas enables synergistic targeting of independent drug vulnerabilities', Nature communications, vol. 11, no. 1, 2935. https://doi.org/10.1038/s41467-020-16735-2

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T1 - A cancer drug atlas enables synergistic targeting of independent drug vulnerabilities

AU - Narayan, Ravi S.

AU - Molenaar, Piet

AU - Teng, Jian

AU - Cornelissen, Fleur M. G.

AU - Roelofs, Irene

AU - Menezes, Renee

AU - Dik, Rogier

AU - Lagerweij, Tonny

AU - Broersma, Yoran

AU - Petersen, Naomi

AU - Marin Soto, Jhon Alexander

AU - Brands, Eelke

AU - van Kuiken, Philip

AU - Lecca, Maria C.

AU - Lenos, Kristiaan J.

AU - in ‘t Veld, Sjors G. J. G.

AU - van Wieringen, Wessel

AU - Lang, Frederick F.

AU - Sulman, Erik

AU - Verhaak, Roel

AU - Baumert, Brigitta G.

AU - Stalpers, Lucas J. A.

AU - Vermeulen, Louis

AU - Watts, Colin

AU - Bailey, David

AU - Slotman, Ben J.

AU - Versteeg, Rogier

AU - Noske, David

AU - Sminia, Peter

AU - Tannous, Bakhos A.

AU - Wurdinger, Tom

AU - Koster, Jan

AU - Westerman, Bart A.

PY - 2020/12/1

Y1 - 2020/12/1

N2 - Personalized cancer treatments using combinations of drugs with a synergistic effect is attractive but proves to be highly challenging. Here we present an approach to uncover the efficacy of drug combinations based on the analysis of mono-drug effects. For this we used dose-response data from pharmacogenomic encyclopedias and represent these as a drug atlas. The drug atlas represents the relations between drug effects and allows to identify independent processes for which the tumor might be particularly vulnerable when attacked by two drugs. Our approach enables the prediction of combination-therapy which can be linked to tumor-driving mutations. By using this strategy, we can uncover potential effective drug combinations on a pan-cancer scale. Predicted synergies are provided and have been validated in glioblastoma, breast cancer, melanoma and leukemia mouse-models, resulting in therapeutic synergy in 75% of the tested models. This indicates that we can accurately predict effective drug combinations with translational value.

AB - Personalized cancer treatments using combinations of drugs with a synergistic effect is attractive but proves to be highly challenging. Here we present an approach to uncover the efficacy of drug combinations based on the analysis of mono-drug effects. For this we used dose-response data from pharmacogenomic encyclopedias and represent these as a drug atlas. The drug atlas represents the relations between drug effects and allows to identify independent processes for which the tumor might be particularly vulnerable when attacked by two drugs. Our approach enables the prediction of combination-therapy which can be linked to tumor-driving mutations. By using this strategy, we can uncover potential effective drug combinations on a pan-cancer scale. Predicted synergies are provided and have been validated in glioblastoma, breast cancer, melanoma and leukemia mouse-models, resulting in therapeutic synergy in 75% of the tested models. This indicates that we can accurately predict effective drug combinations with translational value.

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U2 - https://doi.org/10.1038/s41467-020-16735-2

DO - https://doi.org/10.1038/s41467-020-16735-2

M3 - Article

C2 - 32523045

SN - 2041-1723

VL - 11

JO - Nature communications

JF - Nature communications

IS - 1

M1 - 2935

ER -

A cancer drug atlas enables synergistic targeting of independent drug vulnerabilities

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