TY - JOUR
TI - A Deep Learning Framework for Predicting Response to Therapy in Cancer
AU - Sakellaropoulos, T.
AU - Vougas, K.
AU - Narang, S.
AU - Koinis, F.
AU - Kotsinas, A.
AU - Polyzos, A.
AU - Moss, T.J.
AU - Piha-Paul, S.
AU - Zhou, H.
AU - Kardala, E.
AU - Damianidou, E.
AU - Alexopoulos, L.G.
AU - Aifantis, I.
AU - Townsend, P.A.
AU - Panayiotidis, M.I.
AU - Sfikakis, P.
AU - Bartek, J.
AU - Fitzgerald, R.C.
AU - Thanos, D.
AU - Mills Shaw, K.R.
AU - Petty, R.
AU - Tsirigos, A.
AU - Gorgoulis, V.G.
JO - Cell Reports Medicine
PY - 2019
VL - 29
TODO - 11
SP - 3367-3373.e4
PB - Elsevier B.V.
SN - null
TODO - 10.1016/j.celrep.2019.11.017
TODO - antineoplastic agent, area under the curve;  Article;  cancer cell line;  cancer therapy;  data base;  deep neural network;  drug response;  drug sensitivity;  gene expression;  intermethod comparison;  machine learning;  personalized medicine;  pharmacogenomics;  prediction;  priority journal;  random forest;  workflow;  drug resistance;  genetics;  human;  metabolism;  neoplasm;  procedures;  survival analysis;  tumor cell line, Cell Line, Tumor;  Deep Learning;  Drug Resistance, Neoplasm;  Humans;  Neoplasms;  Precision Medicine;  Survival Analysis
TODO - 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. © 2019 The Author(s)
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. © 2019 The Author(s)
ER -