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

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Abstract

Sakellaropoulos et al. designed a machine learning workflow to predict drug response and survival of cancer patients. All pipelines are trained on a large panel of cancer cell lines and tested in clinical cohorts. DNN outperforms other machine learning algorithms by capturing pathways that link gene expression with drug response.

Original languageEnglish
Pages (from-to)3367-3373.e4
Number of pages12
JournalCell Reports
Volume29
Issue number11
DOIs
Publication statusPublished - 10 Dec 2019

Fingerprint

Learning systems
Learning
Workflow
Gene expression
Pharmaceutical Preparations
Learning algorithms
Neoplasms
Pipelines
Cells
Gene Expression
Cell Line
Survival
Therapeutics
Machine Learning
Deep learning

Keywords

  • deep neural networks
  • DNN
  • drug response prediction
  • machine learning
  • precision medicine

Cite this

Sakellaropoulos, T., Vougas, K., Narang, S., Koinis, F., Kotsinas, A., Polyzos, A., ... 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
Sakellaropoulos, Theodore ; Vougas, Konstantinos ; Narang, Sonali ; Koinis, Filippos ; Kotsinas, Athanassios ; Polyzos, Alexander ; Moss, Tyler J. ; Piha-Paul, Sarina ; Zhou, Hua ; Kardala, Eleni ; Damianidou, Eleni ; Alexopoulos, Leonidas G. ; Aifantis, Iannis ; Townsend, Paul A. ; Panayiotidis, Mihalis I. ; Sfikakis, Petros ; Bartek, Jiri ; Fitzgerald, Rebecca C. ; Thanos, Dimitris ; Mills Shaw, Kenna R. ; Petty, Russell ; Tsirigos, Aristotelis ; Gorgoulis, Vassilis G. / A Deep Learning Framework for Predicting Response to Therapy in Cancer. In: Cell Reports. 2019 ; Vol. 29, No. 11. pp. 3367-3373.e4.
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abstract = "Sakellaropoulos et al. designed a machine learning workflow to predict drug response and survival of cancer patients. All pipelines are trained on a large panel of cancer cell lines and tested in clinical cohorts. DNN outperforms other machine learning algorithms by capturing pathways that link gene expression with drug response.",
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.}",
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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

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

In: Cell Reports, Vol. 29, No. 11, 10.12.2019, p. 3367-3373.e4.

Research output: Contribution to journalArticle

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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.

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PY - 2019/12/10

Y1 - 2019/12/10

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Sakellaropoulos T, Vougas K, Narang S, Koinis F, Kotsinas A, Polyzos A et al. A Deep Learning Framework for Predicting Response to Therapy in Cancer. Cell Reports. 2019 Dec 10;29(11):3367-3373.e4. https://doi.org/10.1016/j.celrep.2019.11.017