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

Investigational Cancer Therapeutics

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

124 Scopus citations

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 language	English (US)
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
State	Published - Dec 10 2019

Keywords

DNN
deep neural networks
drug response prediction
machine learning
precision medicine

ASJC Scopus subject areas

General Biochemistry, Genetics and Molecular Biology

Access to Document

10.1016/j.celrep.2019.11.017

Cite this

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

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

@article{2d23673ec50545c6bef4a920bd7f333b,

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

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 = "DNN, deep neural networks, 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 = "Publisher Copyright: {\textcopyright} 2019 The Author(s)",

year = "2019",

month = dec,

day = "10",

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

language = "English (US)",

volume = "29",

pages = "3367--3373.e4",

journal = "Cell Reports",

issn = "2211-1247",

publisher = "Cell Press",

number = "11",

}

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 - 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.

AB - 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.

KW - DNN

KW - deep neural networks

KW - drug response prediction

KW - machine learning

KW - precision medicine

UR - http://www.scopus.com/inward/record.url?scp=85076028003&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85076028003&partnerID=8YFLogxK

U2 - 10.1016/j.celrep.2019.11.017

DO - 10.1016/j.celrep.2019.11.017

M3 - Article

C2 - 31825821

AN - SCOPUS:85076028003

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

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this