Cell Reports (Dec 2019)
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
Affiliations
- Theodore Sakellaropoulos
- Department of Pathology, NYU School of Medicine, New York, NY 10016, USA; Laura and Isaac Perlmutter Cancer Center, NYU School of Medicine, New York, NY 10016, USA
- Konstantinos Vougas
- Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou Str., Athens 11527, Greece; Molecular Carcinogenesis Group, Department of Histology and Embryology, School of Medicine, National and Kapodistrian University of Athens, 75 Mikras Asias Str., Athens 11527, Greece; Corresponding author
- Sonali Narang
- Department of Pathology, NYU School of Medicine, New York, NY 10016, USA; Laura and Isaac Perlmutter Cancer Center, NYU School of Medicine, New York, NY 10016, USA
- Filippos Koinis
- Molecular Carcinogenesis Group, Department of Histology and Embryology, School of Medicine, National and Kapodistrian University of Athens, 75 Mikras Asias Str., Athens 11527, Greece
- Athanassios Kotsinas
- Molecular Carcinogenesis Group, Department of Histology and Embryology, School of Medicine, National and Kapodistrian University of Athens, 75 Mikras Asias Str., Athens 11527, Greece
- Alexander Polyzos
- Sanford I. Weill Department of Medicine, Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA
- Tyler J. Moss
- Sheikh Khalifa Bin Zayed al Nahyan Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
- Sarina Piha-Paul
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
- Hua Zhou
- Applied Bioinformatics Laboratories, NYU School of Medicine, New York, NY 10016, USA
- Eleni Kardala
- Molecular Carcinogenesis Group, Department of Histology and Embryology, School of Medicine, National and Kapodistrian University of Athens, 75 Mikras Asias Str., Athens 11527, Greece
- Eleni Damianidou
- Molecular Carcinogenesis Group, Department of Histology and Embryology, School of Medicine, National and Kapodistrian University of Athens, 75 Mikras Asias Str., Athens 11527, Greece
- Leonidas G. Alexopoulos
- School of Mechanical Engineering, National Technical University of Athens, Zografou 15780, Greece
- Iannis Aifantis
- Department of Pathology, NYU School of Medicine, New York, NY 10016, USA; Laura and Isaac Perlmutter Cancer Center, NYU School of Medicine, New York, NY 10016, USA
- Paul A. Townsend
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, Manchester Cancer Research Centre, NIHR Manchester Biomedical Research Centre, University of Manchester, Manchester M20 4GJ, UK
- Mihalis I. Panayiotidis
- Department of Applied Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK; Department of Electron Microscopy & Molecular Pathology, Cyprus Institute of Neurology & Genetics, Nicosia, 2371, Cyprus
- Petros Sfikakis
- 1st Department of Propaedeutic Internal Medicine, Medical School, Laikon Hospital, National and Kapodistrian University of Athens, 75 Mikras Asias Str., Athens 11527, Greece; Center for New Biotechnologies and Precision Medicine, Medical School, National and Kapodistrian University of Athens, 75 Mikras Asias Str., Athens 11527, Greece
- Jiri Bartek
- Genome Integrity Unit, Danish Cancer Society Research Centre, Strandboulevarden 49, Copenhagen 2100, Denmark; Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University, Hněvotínská, Olomouc 1333/5 779 00, Czech Republic; Science for Life Laboratory, Division of Genome Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm SE-171 77, Sweden
- Rebecca C. Fitzgerald
- Medical Research Council Cancer Unit, Hutchison/Medical Research Council Research Centre, University of Cambridge, Cambridge CB2 0XZ, UK
- Dimitris Thanos
- Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou Str., Athens 11527, Greece
- Kenna R. Mills Shaw
- Sheikh Khalifa Bin Zayed al Nahyan Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
- Russell Petty
- Division of Molecular and Clinical Medicine, Ninewells Hospital and School of Medicine, University of Dundee, Dundee DD1 9SY, UK
- Aristotelis Tsirigos
- Department of Pathology, NYU School of Medicine, New York, NY 10016, USA; Laura and Isaac Perlmutter Cancer Center, NYU School of Medicine, New York, NY 10016, USA; Applied Bioinformatics Laboratories, NYU School of Medicine, New York, NY 10016, USA; Corresponding author
- Vassilis G. Gorgoulis
- Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou Str., Athens 11527, Greece; Molecular Carcinogenesis Group, Department of Histology and Embryology, School of Medicine, National and Kapodistrian University of Athens, 75 Mikras Asias Str., Athens 11527, Greece; Division of Cancer Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, Manchester Cancer Research Centre, NIHR Manchester Biomedical Research Centre, University of Manchester, Manchester M20 4GJ, UK; 1st Department of Propaedeutic Internal Medicine, Medical School, Laikon Hospital, National and Kapodistrian University of Athens, 75 Mikras Asias Str., Athens 11527, Greece; Corresponding author
- Journal volume & issue
-
Vol. 29,
no. 11
pp. 3367 – 3373.e4
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
