Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Deep Learning and Association Rule Mining for Predicting Drug Response in Cancer. A Personalised Medicine Approach

View ORCID ProfileKonstantinos Vougas, Magdalena Krochmal, Thomas Jackson, Alexander Polyzos, Archimides Aggelopoulos, Ioannis S. Pateras, Michael Liontos, Anastasia Varvarigou, Elizabeth O. Johnson, Vassilis Georgoulias, Antonia Vlahou, Paul Townsend, Dimitris Thanos, Jiri Bartek, Vassilis G. Gorgoulis
doi: https://doi.org/10.1101/070490
Konstantinos Vougas
1Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou Str., Athens, GR-11527, Greece
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Konstantinos Vougas
Magdalena Krochmal
1Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou Str., Athens, GR-11527, Greece
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Thomas Jackson
2Faculty Institute of Cancer Sciences, University of Manchester, Manchester Academic Health Science Centre, Wilmslow Road, Manchester, M20 4QL, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Alexander Polyzos
1Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou Str., Athens, GR-11527, Greece
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Archimides Aggelopoulos
1Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou Str., Athens, GR-11527, Greece
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ioannis S. Pateras
3Molecular Carcinogenesis Group, Department of Histology and Embryology, School of Medicine, National & Kapodistrian University of Athens, 75 Mikras Asias Str, Athens, GR-11527, Greece
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Michael Liontos
3Molecular Carcinogenesis Group, Department of Histology and Embryology, School of Medicine, National & Kapodistrian University of Athens, 75 Mikras Asias Str, Athens, GR-11527, Greece
4Oncology Unit, Department of Clinical Therapeutics, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Anastasia Varvarigou
5Department of Pediatrics, University of Patras Medical School, Rio, Patras, Greece
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Elizabeth O. Johnson
6Laboratory for Education & Research in Neurosciences (LERNs), Department of Anatomy, School of Medicine, National & Kapodistrian University of Athens, 75 Mikras Asias Str, Athens, 11527, Greece
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Vassilis Georgoulias
7Laboratory of Tumour Cell Biology, School of Medicine, University of Crete, Heraklion, Crete, Greece
3Molecular Carcinogenesis Group, Department of Histology and Embryology, School of Medicine, National & Kapodistrian University of Athens, 75 Mikras Asias Str, Athens, GR-11527, Greece
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Antonia Vlahou
1Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou Str., Athens, GR-11527, Greece
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Paul Townsend
2Faculty Institute of Cancer Sciences, University of Manchester, Manchester Academic Health Science Centre, Wilmslow Road, Manchester, M20 4QL, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Dimitris Thanos
1Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou Str., Athens, GR-11527, Greece
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jiri Bartek
8Genome Integrity Unit, Danish Cancer Society Research Centre, Strandboulevarden 49, Copenhagen DK-2100, Denmark
9Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University, Hn ěvotínská, Olomouc 1333/5 779 00, Czech Republic
10Science for Life Laboratory, Division of Translational Medicine and Chemical Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm SE-171 77, Sweden
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: vgorg@med.uoa.gr jb@cancer.dk
Vassilis G. Gorgoulis
3Molecular Carcinogenesis Group, Department of Histology and Embryology, School of Medicine, National & Kapodistrian University of Athens, 75 Mikras Asias Str, Athens, GR-11527, Greece
2Faculty Institute of Cancer Sciences, University of Manchester, Manchester Academic Health Science Centre, Wilmslow Road, Manchester, M20 4QL, UK
1Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou Str., Athens, GR-11527, Greece
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: vgorg@med.uoa.gr jb@cancer.dk
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

ABSTRACT

A major challenge in cancer treatment is predicting the clinical response to anti-cancer drugs for each individual patient. For complex diseases such as cancer, characterized by high inter-patient variance, the implementation of precision medicine approaches is dependent upon understanding the pathological processes at the molecular level. While the “omics” era provides unique opportunities to dissect the molecular features of diseases, the ability to utilize it in targeted therapeutic efforts is hindered by both the massive size and diverse nature of the “omics” data. Recent advances with Deep Learning Neural Networks (DLNNs), suggests that DLNN could be trained on large data sets to efficiently predict therapeutic responses in cancer treatment. We present the application of Association Rule Mining combined with DLNNs for the analysis of high-throughput molecular profiles of 1001 cancer cell lines, in order to extract cancer-specific signatures in the form of easily interpretable rules and use these rules as input to predict pharmacological responses to a large number of anti-cancer drugs. The proposed algorithm outperformed Random Forests (RF) and Bayesian Multitask Multiple Kernel Learning (BMMKL) classification which currently represent the state-of-the-art in drug-response prediction. Moreover, the in silico pipeline presented, introduces a novel strategy for identifying potential therapeutic targets, as well as possible drug combinations with high therapeutic potential. For the first time, we demonstrate that DLNNs trained on a large pharmacogenomics data-set can effectively predict the therapeutic response of specific drugs in different cancer types. These findings serve as a proof of concept for the application of DLNNs to predict therapeutic responsiveness, a milestone in precision medicine.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted May 09, 2017.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Deep Learning and Association Rule Mining for Predicting Drug Response in Cancer. A Personalised Medicine Approach
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Deep Learning and Association Rule Mining for Predicting Drug Response in Cancer. A Personalised Medicine Approach
Konstantinos Vougas, Magdalena Krochmal, Thomas Jackson, Alexander Polyzos, Archimides Aggelopoulos, Ioannis S. Pateras, Michael Liontos, Anastasia Varvarigou, Elizabeth O. Johnson, Vassilis Georgoulias, Antonia Vlahou, Paul Townsend, Dimitris Thanos, Jiri Bartek, Vassilis G. Gorgoulis
bioRxiv 070490; doi: https://doi.org/10.1101/070490
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Deep Learning and Association Rule Mining for Predicting Drug Response in Cancer. A Personalised Medicine Approach
Konstantinos Vougas, Magdalena Krochmal, Thomas Jackson, Alexander Polyzos, Archimides Aggelopoulos, Ioannis S. Pateras, Michael Liontos, Anastasia Varvarigou, Elizabeth O. Johnson, Vassilis Georgoulias, Antonia Vlahou, Paul Townsend, Dimitris Thanos, Jiri Bartek, Vassilis G. Gorgoulis
bioRxiv 070490; doi: https://doi.org/10.1101/070490

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3589)
  • Biochemistry (7553)
  • Bioengineering (5498)
  • Bioinformatics (20742)
  • Biophysics (10305)
  • Cancer Biology (7962)
  • Cell Biology (11624)
  • Clinical Trials (138)
  • Developmental Biology (6596)
  • Ecology (10175)
  • Epidemiology (2065)
  • Evolutionary Biology (13586)
  • Genetics (9525)
  • Genomics (12824)
  • Immunology (7911)
  • Microbiology (19518)
  • Molecular Biology (7647)
  • Neuroscience (42014)
  • Paleontology (307)
  • Pathology (1254)
  • Pharmacology and Toxicology (2195)
  • Physiology (3260)
  • Plant Biology (7027)
  • Scientific Communication and Education (1294)
  • Synthetic Biology (1948)
  • Systems Biology (5420)
  • Zoology (1113)