Literature DB >> 11807556

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

Scott L Pomeroy1, 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 Allen, David Zagzag, James M Olson, Tom Curran, Cynthia Wetmore, Jaclyn A Biegel, Tomaso Poggio, Shayan Mukherjee, Ryan Rifkin, Andrea Califano, Gustavo Stolovitzky, David N Louis, Jill P Mesirov, Eric S Lander, Todd R Golub.   

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 debated, and patients' response to therapy is difficult to predict. 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.

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Year:  2002        PMID: 11807556     DOI: 10.1038/415436a

Source DB:  PubMed          Journal:  Nature        ISSN: 0028-0836            Impact factor:   49.962


  519 in total

1.  Identification of genes expressed with temporal-spatial restriction to developing cerebellar neuron precursors by a functional genomic approach.

Authors:  Qing Zhao; Alvin Kho; Anna Marie Kenney; Dong-in Yuk Di; Isaac Kohane; David H Rowitch
Journal:  Proc Natl Acad Sci U S A       Date:  2002-04-16       Impact factor: 11.205

Review 2.  Gene expression profiling to analyze embryonal tumors of the central nervous system.

Authors:  Roger J Packer
Journal:  Curr Neurol Neurosci Rep       Date:  2003-03       Impact factor: 5.081

3.  Validated genomic approach to study differentially expressed genes in complex tissues.

Authors:  Elisa Wurmbach; Javier González-Maeso; Tony Yuen; Barbara J Ebersole; Jason W Mastaitis; Charles V Mobbs; Stuart C Sealfon
Journal:  Neurochem Res       Date:  2002-10       Impact factor: 3.996

4.  Expression profiling with oligonucleotide arrays: technologies and applications for neurobiology.

Authors:  Timothy J Sendera; David Dorris; Ramesh Ramakrishnan; Allen Nguyen; Dionisios Trakas; Abhijit Mazumder
Journal:  Neurochem Res       Date:  2002-10       Impact factor: 3.996

5.  YMD: a microarray database for large-scale gene expression analysis.

Authors:  Kei-Hoi Cheung; Kevin White; Janet Hager; Mark Gerstein; Valerie Reinke; Kenneth Nelson; Peter Masiar; Ranjana Srivastava; Yuli Li; Ju Li; Hongyu Zhao; Jinming Li; David B Allison; Michael Snyder; Perry Miller; Kenneth Williams
Journal:  Proc AMIA Symp       Date:  2002

6.  Network-based Prediction of Cancer under Genetic Storm.

Authors:  Ahmet Ay; Dihong Gong; Tamer Kahveci
Journal:  Cancer Inform       Date:  2014-10-15

7.  N-myc alters the fate of preneoplastic cells in a mouse model of medulloblastoma.

Authors:  Jessica D Kessler; Hiroshi Hasegawa; Sonja N Brun; Brian A Emmenegger; Zeng-Jie Yang; John W Dutton; Fan Wang; Robert J Wechsler-Reya
Journal:  Genes Dev       Date:  2009-01-15       Impact factor: 11.361

8.  Pediatric rhabdoid tumors of kidney and brain show many differences in gene expression but share dysregulation of cell cycle and epigenetic effector genes.

Authors:  Diane K Birks; Andrew M Donson; Purvi R Patel; Alexandra Sufit; Elizabeth M Algar; Christopher Dunham; B K Kleinschmidt-DeMasters; Michael H Handler; Rajeev Vibhakar; Nicholas K Foreman
Journal:  Pediatr Blood Cancer       Date:  2013-02-04       Impact factor: 3.167

9.  Machine learning-based receiver operating characteristic (ROC) curves for crisp and fuzzy classification of DNA microarrays in cancer research.

Authors:  Leif E Peterson; Matthew A Coleman
Journal:  Int J Approx Reason       Date:  2008-01       Impact factor: 3.816

Review 10.  stepwiseCM: An R Package for Stepwise Classification of Cancer Samples Using Multiple Heterogeneous Data Sets.

Authors:  Askar Obulkasim; Mark A van de Wiel
Journal:  Cancer Inform       Date:  2014-01-02
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