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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
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    Citations

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    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. 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.
    3. 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.
    4. 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.
    5. 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).
    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.

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