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

Division of Cancer Sciences (L5)

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

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
Journal	Cell Reports
Volume	29
Issue number	11
DOIs	https://doi.org/10.1016/j.celrep.2019.11.017
Publication status	Published - 10 Dec 2019

Research Beacons, Institutes and Platforms

Manchester Cancer Research Centre

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

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10.1016/j.celrep.2019.11.017

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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

@article{b1560876d61e42e283878a574fa291c5,

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.",

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}",

year = "2019",

month = dec,

day = "10",

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

language = "English",

volume = "29",

pages = "3367--3373.e4",

journal = "Cell Reports",

issn = "2211-1247",

publisher = "Cell Press",

number = "11",

}

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

TY - JOUR

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

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.

U2 - 10.1016/j.celrep.2019.11.017

DO - 10.1016/j.celrep.2019.11.017

M3 - Article

C2 - 31825821

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

Abstract

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