IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v415y2002i6870d10.1038_415436a.html
   My bibliography  Save this article

Prediction of central nervous system embryonal tumour outcome based on gene expression

Author

Listed:
  • Scott L. Pomeroy

    (Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA)

  • Pablo Tamayo

    (AI Lab, Massachusetts Institute of Technology)

  • Michelle Gaasenbeek

    (AI Lab, Massachusetts Institute of Technology)

  • Lisa M. Sturla

    (Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA)

  • Michael Angelo

    (AI Lab, Massachusetts Institute of Technology)

  • Margaret E. McLaughlin

    (Massachusetts General Hospital, Harvard Medical School)

  • John Y. H. Kim

    (Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
    Dana-Farber Cancer Institute, Massachusetts General Hospital, Harvard Medical School)

  • Liliana C. Goumnerova

    (Massachusetts General Hospital, Harvard Medical School)

  • Peter M. Black

    (Massachusetts General Hospital, Harvard Medical School)

  • Ching Lau

    (Baylor College of Medicine)

  • Jeffrey C. Allen

    (Beth Israel Medical Center)

  • David Zagzag

    (New York University School of Medicine)

  • James M. Olson

    (Fred Hutchinson Cancer Research Center)

  • Tom Curran

    (St Jude Children's Research Hospital)

  • Cynthia Wetmore

    (St Jude Children's Research Hospital)

  • Jaclyn A. Biegel

    (The Children's Hospital of Philadelphia, University of Pennsylvania School of Medicine)

  • Tomaso Poggio

    (McGovern Institute, Center for Biological and Computational Learning, AI Lab, Massachusetts Institute of Technology)

  • Shayan Mukherjee

    (McGovern Institute, Center for Biological and Computational Learning, AI Lab, Massachusetts Institute of Technology)

  • Ryan Rifkin

    (McGovern Institute, Center for Biological and Computational Learning, AI Lab, Massachusetts Institute of Technology)

  • Andrea Califano

    (IBM Watson Research Center)

  • Gustavo Stolovitzky

    (IBM Watson Research Center)

  • David N. Louis

    (Massachusetts General Hospital, Harvard Medical School)

  • Jill P. Mesirov

    (AI Lab, Massachusetts Institute of Technology)

  • Eric S. Lander

    (AI Lab, Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

  • Todd R. Golub

    (Children's Hospital, Massachusetts General Hospital, Harvard Medical School
    Dana-Farber Cancer Institute, Massachusetts General Hospital, Harvard Medical School
    AI Lab, Massachusetts Institute of Technology)

Abstract

Embryonal tumours of the central nervous system (CNS) represent a heterogeneous group of tumours about which little is known biologically, and whose diagnosis, on the basis of morphologic appearance alone, is controversial. Medulloblastomas, for example, are the most common malignant brain tumour of childhood, but their pathogenesis is unknown, their relationship to other embryonal CNS tumours is debated1,2, and patients’ response to therapy is difficult to predict3. We approached these problems by developing a classification system based on DNA microarray gene expression data derived from 99 patient samples. Here we demonstrate that medulloblastomas are molecularly distinct from other brain tumours including primitive neuroectodermal tumours (PNETs), atypical teratoid/rhabdoid tumours (AT/RTs) and malignant gliomas. Previously unrecognized evidence supporting the derivation of medulloblastomas from cerebellar granule cells through activation of the Sonic Hedgehog (SHH) pathway was also revealed. We show further that the clinical outcome of children with medulloblastomas is highly predictable on the basis of the gene expression profiles of their tumours at diagnosis.

Suggested Citation

  • Scott L. Pomeroy & Pablo Tamayo & Michelle Gaasenbeek & Lisa M. Sturla & Michael Angelo & Margaret E. McLaughlin & John Y. H. Kim & Liliana C. Goumnerova & Peter M. Black & Ching Lau & Jeffrey C. Alle, 2002. "Prediction of central nervous system embryonal tumour outcome based on gene expression," Nature, Nature, vol. 415(6870), pages 436-442, January.
  • Handle: RePEc:nat:nature:v:415:y:2002:i:6870:d:10.1038_415436a
    DOI: 10.1038/415436a
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/415436a
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1038/415436a?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Tianming Zhu & Jin-Ting Zhang, 2022. "Linear hypothesis testing in high-dimensional one-way MANOVA: a new normal reference approach," Computational Statistics, Springer, vol. 37(1), pages 1-27, March.
    2. Allison A. Appleton & Kevin C. Kiley & Lawrence M. Schell & Elizabeth A. Holdsworth & Anuoluwapo Akinsanya & Catherine Beecher, 2021. "Prenatal Lead and Depression Exposures Jointly Influence Birth Outcomes and NR3C1 DNA Methylation," IJERPH, MDPI, vol. 18(22), pages 1-15, November.
    3. Ghosh, Santu & Ayyala, Deepak Nag & Hellebuyck, Rafael, 2021. "Two-sample high dimensional mean test based on prepivots," Computational Statistics & Data Analysis, Elsevier, vol. 163(C).
    4. Michelle M. Kameda-Smith & Helen Zhu & En-Ching Luo & Yujin Suk & Agata Xella & Brian Yee & Chirayu Chokshi & Sansi Xing & Frederick Tan & Raymond G. Fox & Ashley A. Adile & David Bakhshinyan & Kevin , 2022. "Characterization of an RNA binding protein interactome reveals a context-specific post-transcriptional landscape of MYC-amplified medulloblastoma," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    5. Dong, Kai & Pang, Herbert & Tong, Tiejun & Genton, Marc G., 2016. "Shrinkage-based diagonal Hotelling’s tests for high-dimensional small sample size data," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 127-142.
    6. Outi Ruusunen & Marja Jalli & Lauri Jauhiainen & Mika Ruusunen & Kauko Leiviskä, 2022. "Identification of Optimal Starting Time Instance to Forecast Net Blotch Density in Spring Barley with Meteorological Data in Finland," Agriculture, MDPI, vol. 12(11), pages 1-16, November.
    7. Ayça Çakmak Pehlivanlı, 2016. "A novel feature selection scheme for high-dimensional data sets: four-Staged Feature Selection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(6), pages 1140-1154, May.

    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:nature:v:415:y:2002:i:6870:d:10.1038_415436a. 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.