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A Deep Learning Framework for Predicting Response to Therapy in Cancer

Authors :
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
Source :
Cell Reports, Vol 29, Iss 11, Pp 3367-3373.e4 (2019)
Publication Year :
2019
Publisher :
Elsevier, 2019.

Abstract

Summary: 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. : 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: drug response prediction, precision medicine, machine learning, deep neural networks, DNN

Subjects

Subjects :
Biology (General)
QH301-705.5

Details

Language :
English
ISSN :
22111247
Volume :
29
Issue :
11
Database :
Directory of Open Access Journals
Journal :
Cell Reports
Publication Type :
Academic Journal
Accession number :
edsdoj.2f093439d55c431e845ff5db7f56e938
Document Type :
article
Full Text :
https://doi.org/10.1016/j.celrep.2019.11.017