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, 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
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: DNN; deep neural networks; drug response prediction; machine learning; precision medicine.
Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.
Source: PubMed