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

Pan-cancer detection of driver genes at the single-patient resolution

View ORCID ProfileJoel Nulsen, Hrvoje Misetic, View ORCID ProfileChristopher Yau, View ORCID ProfileFrancesca D. Ciccarelli
doi: https://doi.org/10.1101/2020.06.12.147983
Joel Nulsen
1Cancer Systems Biology Laboratory, The Francis Crick Institute, London NW1 1AT, UK
2School of Cancer and Pharmaceutical Sciences, King’s College London, London SE11UL, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Joel Nulsen
Hrvoje Misetic
1Cancer Systems Biology Laboratory, The Francis Crick Institute, London NW1 1AT, UK
2School of Cancer and Pharmaceutical Sciences, King’s College London, London SE11UL, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Christopher Yau
3School of Health Sciences, University of Manchester, Manchester M13 9PL, UK
4The Alan Turing Institute, London NW1 2DB, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Christopher Yau
Francesca D. Ciccarelli
1Cancer Systems Biology Laboratory, The Francis Crick Institute, London NW1 1AT, UK
2School of Cancer and Pharmaceutical Sciences, King’s College London, London SE11UL, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Francesca D. Ciccarelli
  • For correspondence: francesca.ciccarelli@crick.ac.uk
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

ABSTRACT

Identifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. Established methods for driver detection focus mostly on genes that are recurrently altered across cohorts of cancer patients. However, mapping these genes back to patients leaves a sizeable fraction with few or no driver events, hindering our understanding of cancer mechanisms and limiting the choice of therapeutic interventions. Here we present sysSVM2, a tool based on machine learning that integrates somatic alteration data with systems-level gene properties to predict drivers in individual patients. We develop sysSVM2 for pan-cancer applicability, demonstrating robust performance on real and simulated cancer data. We benchmark its performance against other driver detection methods and show that sysSVM2 has a lower false positive rate and superior patient driver coverage. Applying sysSVM2 to 7,646 samples from 34 cancer types, we find that predicted drivers are often rare or patient-specific. However, they converge to disrupt well-known cancer-related processes including DNA repair, chromatin organisation and the cell cycle. sysSVM2 is a resource to enhance personalised predictions of cancer driver events with possible use in research and clinical settings. Code to implement sysSVM2 and the trained models in simulated cancer-agnostic data as well as in 34 cancer types are available at https://github.com/ciccalab/sysSVM2.

Competing Interest Statement

The authors have declared no competing interest.

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 4.0 International license.
Back to top
PreviousNext
Posted June 12, 2020.
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.
Pan-cancer detection of driver genes at the single-patient resolution
(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
Pan-cancer detection of driver genes at the single-patient resolution
Joel Nulsen, Hrvoje Misetic, Christopher Yau, Francesca D. Ciccarelli
bioRxiv 2020.06.12.147983; doi: https://doi.org/10.1101/2020.06.12.147983
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Pan-cancer detection of driver genes at the single-patient resolution
Joel Nulsen, Hrvoje Misetic, Christopher Yau, Francesca D. Ciccarelli
bioRxiv 2020.06.12.147983; doi: https://doi.org/10.1101/2020.06.12.147983

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

  • Cancer Biology
Subject Areas
All Articles
  • Animal Behavior and Cognition (4113)
  • Biochemistry (8815)
  • Bioengineering (6518)
  • Bioinformatics (23460)
  • Biophysics (11789)
  • Cancer Biology (9207)
  • Cell Biology (13322)
  • Clinical Trials (138)
  • Developmental Biology (7436)
  • Ecology (11409)
  • Epidemiology (2066)
  • Evolutionary Biology (15150)
  • Genetics (10436)
  • Genomics (14043)
  • Immunology (9171)
  • Microbiology (22153)
  • Molecular Biology (8812)
  • Neuroscience (47567)
  • Paleontology (350)
  • Pathology (1428)
  • Pharmacology and Toxicology (2491)
  • Physiology (3730)
  • Plant Biology (8079)
  • Scientific Communication and Education (1437)
  • Synthetic Biology (2221)
  • Systems Biology (6037)
  • Zoology (1253)