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 (Lead / Corresponding author)

Research output: Contribution to journalArticlepeer-review

106 Citations (Scopus)
102 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)3367-3373.e4
Number of pages12
JournalCell Reports
Issue number11
Early online date10 Dec 2019
Publication statusPublished - 10 Dec 2019


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

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)


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