A Deep Learning Framework for Predicting Response to Therapy in Cancer.,Cell Reports

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A Deep Learning Framework for Predicting Response to Therapy in Cancer.
Cell Reports ( IF 7.5 ) Pub Date : 2019-12-10 , DOI: 10.1016/j.celrep.2019.11.017
Theodore Sakellaropoulos ₁ , Konstantinos Vougas ₂ , Sonali Narang ₁ , Filippos Koinis ₃ , Athanassios Kotsinas ₃ , Alexander Polyzos ₄ , Tyler J Moss ₅ , Sarina Piha-Paul ₆ , Hua Zhou ₇ , Eleni Kardala ₃ , Eleni Damianidou ₃ , Leonidas G Alexopoulos ₈ , Iannis Aifantis ₁ , Paul A Townsend ₉ , Mihalis I Panayiotidis ₁₀ , Petros Sfikakis ₁₁ , Jiri Bartek ₁₂ , Rebecca C Fitzgerald ₁₃ , Dimitris Thanos ₁₄ , Kenna R Mills Shaw ₅ , Russell Petty ₁₅ , Aristotelis Tsirigos ₁₆ , Vassilis G Gorgoulis ₁₇

Affiliation

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.
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.
Molecular Carcinogenesis Group, Department of Histology and Embryology, School of Medicine, National and Kapodistrian University of Athens, 75 Mikras Asias Str., Athens 11527, Greece.
Sanford I. Weill Department of Medicine, Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA.
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.
Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA.
Applied Bioinformatics Laboratories, NYU School of Medicine, New York, NY 10016, USA.
School of Mechanical Engineering, National Technical University of Athens, Zografou 15780, 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.
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.
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.
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.
Medical Research Council Cancer Unit, Hutchison/Medical Research Council Research Centre, University of Cambridge, Cambridge CB2 0XZ, UK.
Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou Str., Athens 11527, Greece.
Division of Molecular and Clinical Medicine, Ninewells Hospital and School of Medicine, University of Dundee, Dundee DD1 9SY, UK.
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.
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.

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.

中文翻译：

用于预测癌症治疗反应的深度学习框架。

癌症治疗的一个主要挑战是个性化预测抗癌药物的临床反应。使用包含 1,001 个癌细胞系的药物基因组学数据库，我们训练了深度神经网络来预测药物反应，并评估了它们在多个临床队列中的表现。我们证明深度神经网络的性能优于机器学习框架的当前状态。我们提供了使用基于深度神经网络的框架来辅助精准肿瘤学策略的概念证明。

更新日期：2019-12-11

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