A Deep Learning Framework for Predicting Response to Therapy in Cancer

Theodore Sakellaropoulos; Konstantinos Vougas; Sonali Narang; Filippos Koinis; Athanassios Kotsinas; Alexander Polyzos; Tyler J. Moss; Sarina Piha-Paul; Hua Zhou; Eleni Kardala; Eleni Damianidou; Leonidas G. Alexopoulos; Iannis Aifantis; Paul A. Townsend; Mihalis I. Panayiotidis; Petros Sfikakis; Jiri Bartek; Rebecca C. Fitzgerald; Dimitris Thanos; Kenna R. Mills Shaw; Russell Petty; Aristotelis Tsirigos; Vassilis G. Gorgoulis

doi:10.1016/j.celrep.2019.11.017

A Deep Learning Framework for Predicting Response to Therapy in Cancer

Theodore Sakellaropoulos, Konstantinos Vougas, Sonali Narang, Filippos Koinis, Athanassios Kotsinas, Alexander Polyzos, Tyler J. Moss, Sarina Piha-Paul, Hua Zhou, Eleni Kardala, Eleni Damianidou, Leonidas G. Alexopoulos, Iannis Aifantis, Paul A. Townsend, Mihalis I. Panayiotidis, Petros Sfikakis, Jiri Bartek, Rebecca C. Fitzgerald, Dimitris Thanos, Kenna R. Mills ShawRussell Petty, Aristotelis Tsirigos, Vassilis G. Gorgoulis (Lead / Corresponding author)

Molecular and Clinical Medicine

Research output: Contribution to journal › Article › peer-review

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Abstract

A major challenge in cancer treatment is predicting clinical response to anti-cancer drugs on a personalized basis. Using a pharmacogenomics database of 1,001 cancer cell lines, we trained deep neural networks for prediction of drug response and assessed their performance on multiple clinical cohorts. We demonstrate that deep neural networks outperform the current state in machine learning frameworks. We provide a proof of concept for the use of deep neural network-based frameworks to aid precision oncology strategies.

Original language	English
Pages (from-to)	3367-3373.e4
Number of pages	12
Journal	Cell Reports
Volume	29
Issue number	11
Early online date	10 Dec 2019
DOIs	https://doi.org/10.1016/j.celrep.2019.11.017
Publication status	Published - 10 Dec 2019

Keywords

deep neural networks
DNN
drug response prediction
machine learning
precision medicine

ASJC Scopus subject areas

Biochemistry, Genetics and Molecular Biology(all)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.celrep.2019.11.017Licence: CC BY

Final Published VersionFinal published version, 3.21 MBLicence: CC BY

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Sakellaropoulos, T., Vougas, K., Narang, S., Koinis, F., Kotsinas, A., Polyzos, A., Moss, T. J., Piha-Paul, S., Zhou, H., Kardala, E., Damianidou, E., Alexopoulos, L. G., Aifantis, I., Townsend, P. A., Panayiotidis, M. I., Sfikakis, P., Bartek, J., Fitzgerald, R. C., Thanos, D., ... Gorgoulis, V. G. (2019). A Deep Learning Framework for Predicting Response to Therapy in Cancer. Cell Reports, 29(11), 3367-3373.e4. https://doi.org/10.1016/j.celrep.2019.11.017

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title = "A Deep Learning Framework for Predicting Response to Therapy in Cancer",

abstract = "A major challenge in cancer treatment is predicting clinical response to anti-cancer drugs on a personalized basis. Using a pharmacogenomics database of 1,001 cancer cell lines, we trained deep neural networks for prediction of drug response and assessed their performance on multiple clinical cohorts. We demonstrate that deep neural networks outperform the current state in machine learning frameworks. We provide a proof of concept for the use of deep neural network-based frameworks to aid precision oncology strategies.",

keywords = "deep neural networks, DNN, drug response prediction, machine learning, precision medicine",

author = "Theodore Sakellaropoulos and Konstantinos Vougas and Sonali Narang and Filippos Koinis and Athanassios Kotsinas and Alexander Polyzos and Moss, {Tyler J.} and Sarina Piha-Paul and Hua Zhou and Eleni Kardala and Eleni Damianidou and Alexopoulos, {Leonidas G.} and Iannis Aifantis and Townsend, {Paul A.} and Panayiotidis, {Mihalis I.} and Petros Sfikakis and Jiri Bartek and Fitzgerald, {Rebecca C.} and Dimitris Thanos and {Mills Shaw}, {Kenna R.} and Russell Petty and Aristotelis Tsirigos and Gorgoulis, {Vassilis G.}",

note = "Financial support to V.G.G. and his team is from the European Union{\textquoteright}s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant 722729 (SYNTRAIN); the Welfare Foundation for Social & Cultural Sciences (KIKPE), Greece; Pentagon Biotechnology, UK; DeepMed IO, UK; and NKUA-SARG grants 70/3/9816 and 70/3/12128. J.B. is financially supported by the Novo Nordisk Foundation grant 16584, the Danish Cancer Society (R204-A12617), and the Swedish Cancerfonden (170176). Financial support to P.A.T. is from the Medical Research Council – MRC (Confidence in Concept to support Dr. Tom Jackson for early pilot data and a subsequent DTP studentship to P.A.T. and V.G.G.) and the infrastructure of Manchester Cancer Research Centre and CRUK Manchester Institute. Support to D.T. is from the KMW offsets program. A.T. and the NYU Applied Bioinformatics Laboratories (ABL) are partially supported by the Cancer Center Support Grant P30CA016087 at the Laura and Isaac Perlmutter Cancer Center. On behalf of the OCCAMS Consortium, we acknowledge that infrastructure was supported by Cancer Research UK (CRUK).",

year = "2019",

month = dec,

day = "10",

doi = "10.1016/j.celrep.2019.11.017",

language = "English",

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Sakellaropoulos, T, Vougas, K, Narang, S, Koinis, F, Kotsinas, A, Polyzos, A, Moss, TJ, Piha-Paul, S, Zhou, H, Kardala, E, Damianidou, E, Alexopoulos, LG, Aifantis, I, Townsend, PA, Panayiotidis, MI, Sfikakis, P, Bartek, J, Fitzgerald, RC, Thanos, D, Mills Shaw, KR, Petty, R, Tsirigos, A & Gorgoulis, VG 2019, 'A Deep Learning Framework for Predicting Response to Therapy in Cancer', Cell Reports, vol. 29, no. 11, pp. 3367-3373.e4. https://doi.org/10.1016/j.celrep.2019.11.017

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T1 - A Deep Learning Framework for Predicting Response to Therapy in Cancer

AU - Sakellaropoulos, Theodore

AU - Vougas, Konstantinos

AU - Narang, Sonali

AU - Koinis, Filippos

AU - Kotsinas, Athanassios

AU - Polyzos, Alexander

AU - Moss, Tyler J.

AU - Piha-Paul, Sarina

AU - Zhou, Hua

AU - Kardala, Eleni

AU - Damianidou, Eleni

AU - Alexopoulos, Leonidas G.

AU - Aifantis, Iannis

AU - Townsend, Paul A.

AU - Panayiotidis, Mihalis I.

AU - Sfikakis, Petros

AU - Bartek, Jiri

AU - Fitzgerald, Rebecca C.

AU - Thanos, Dimitris

AU - Mills Shaw, Kenna R.

AU - Petty, Russell

AU - Tsirigos, Aristotelis

AU - Gorgoulis, Vassilis G.

N1 - Financial support to V.G.G. and his team is from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant 722729 (SYNTRAIN); the Welfare Foundation for Social & Cultural Sciences (KIKPE), Greece; Pentagon Biotechnology, UK; DeepMed IO, UK; and NKUA-SARG grants 70/3/9816 and 70/3/12128. J.B. is financially supported by the Novo Nordisk Foundation grant 16584, the Danish Cancer Society (R204-A12617), and the Swedish Cancerfonden (170176). Financial support to P.A.T. is from the Medical Research Council – MRC (Confidence in Concept to support Dr. Tom Jackson for early pilot data and a subsequent DTP studentship to P.A.T. and V.G.G.) and the infrastructure of Manchester Cancer Research Centre and CRUK Manchester Institute. Support to D.T. is from the KMW offsets program. A.T. and the NYU Applied Bioinformatics Laboratories (ABL) are partially supported by the Cancer Center Support Grant P30CA016087 at the Laura and Isaac Perlmutter Cancer Center. On behalf of the OCCAMS Consortium, we acknowledge that infrastructure was supported by Cancer Research UK (CRUK).

PY - 2019/12/10

Y1 - 2019/12/10

N2 - A major challenge in cancer treatment is predicting clinical response to anti-cancer drugs on a personalized basis. Using a pharmacogenomics database of 1,001 cancer cell lines, we trained deep neural networks for prediction of drug response and assessed their performance on multiple clinical cohorts. We demonstrate that deep neural networks outperform the current state in machine learning frameworks. We provide a proof of concept for the use of deep neural network-based frameworks to aid precision oncology strategies.

AB - A major challenge in cancer treatment is predicting clinical response to anti-cancer drugs on a personalized basis. Using a pharmacogenomics database of 1,001 cancer cell lines, we trained deep neural networks for prediction of drug response and assessed their performance on multiple clinical cohorts. We demonstrate that deep neural networks outperform the current state in machine learning frameworks. We provide a proof of concept for the use of deep neural network-based frameworks to aid precision oncology strategies.

KW - deep neural networks

KW - DNN

KW - drug response prediction

KW - machine learning

KW - precision medicine

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DO - 10.1016/j.celrep.2019.11.017

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SN - 2211-1247

VL - 29

SP - 3367-3373.e4

JO - Cell Reports

JF - Cell Reports

IS - 11

ER -

A Deep Learning Framework for Predicting Response to Therapy in Cancer

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Keywords

ASJC Scopus subject areas

UN SDGs

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Petty, Russell

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A Deep Learning Framework for Predicting Response to Therapy in Cancer

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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Fingerprint

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Petty, Russell

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