IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v9y2018i1d10.1038_s41467-018-06921-8.html
   My bibliography  Save this article

Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival

Author

Listed:
  • Daniele Ramazzotti

    (Stanford University
    Stanford University)

  • Avantika Lal

    (Stanford University)

  • Bo Wang

    (Stanford University)

  • Serafim Batzoglou

    (Stanford University
    Illumina Mission Bay)

  • Arend Sidow

    (Stanford University
    Stanford University)

Abstract

Outcomes for cancer patients vary greatly even within the same tumor type, and characterization of molecular subtypes of cancer holds important promise for improving prognosis and personalized treatment. This promise has motivated recent efforts to produce large amounts of multidimensional genomic (multi-omic) data, but current algorithms still face challenges in the integrated analysis of such data. Here we present Cancer Integration via Multikernel Learning (CIMLR), a new cancer subtyping method that integrates multi-omic data to reveal molecular subtypes of cancer. We apply CIMLR to multi-omic data from 36 cancer types and show significant improvements in both computational efficiency and ability to extract biologically meaningful cancer subtypes. The discovered subtypes exhibit significant differences in patient survival for 27 of 36 cancer types. Our analysis reveals integrated patterns of gene expression, methylation, point mutations, and copy number changes in multiple cancers and highlights patterns specifically associated with poor patient outcomes.

Suggested Citation

  • Daniele Ramazzotti & Avantika Lal & Bo Wang & Serafim Batzoglou & Arend Sidow, 2018. "Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival," Nature Communications, Nature, vol. 9(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-06921-8
    DOI: 10.1038/s41467-018-06921-8
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-018-06921-8
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-018-06921-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Avantika Lal & Keli Liu & Robert Tibshirani & Arend Sidow & Daniele Ramazzotti, 2021. "De novo mutational signature discovery in tumor genomes using SparseSignatures," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-24, June.
    2. Mark Bustoros & Shankara Anand & Romanos Sklavenitis-Pistofidis & Robert Redd & Eileen M. Boyle & Benny Zhitomirsky & Andrew J. Dunford & Yu-Tzu Tai & Selina J. Chavda & Cody Boehner & Carl Jannes Neu, 2022. "Genetic subtypes of smoldering multiple myeloma are associated with distinct pathogenic phenotypes and clinical outcomes," Nature Communications, Nature, vol. 13(1), pages 1-10, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-06921-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.