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Molecular classification of cutaneous malignant melanoma by gene expression profiling

Author

Listed:
  • M. Bittner

    (Cancer Genetics Branch, National Human Genome Research Institute, NIH)

  • P. Meltzer

    (Cancer Genetics Branch, National Human Genome Research Institute, NIH)

  • Y. Chen

    (Cancer Genetics Branch, National Human Genome Research Institute, NIH)

  • Y. Jiang

    (Cancer Genetics Branch, National Human Genome Research Institute, NIH)

  • E. Seftor

    (Department of Anatomy and Cell Biology University of Iowa Cancer Center)

  • M. Hendrix

    (Department of Anatomy and Cell Biology University of Iowa Cancer Center)

  • M. Radmacher

    (National Cancer Institute, DCTDC, NIH)

  • R. Simon

    (National Cancer Institute, DCTDC, NIH)

  • Z. Yakhini

    (Chemical and Biological Systems Department Agilent Laboratories)

  • A. Ben-Dor

    (Chemical and Biological Systems Department Agilent Laboratories
    Computer Science and Engineering Department University of Washington)

  • N. Sampas

    (Chemical and Biological Systems Department Agilent Laboratories)

  • E. Dougherty

    (Department of Electrical Engineering Texas A & M University)

  • E. Wang

    (National Cancer Institute, Surgery Branch, NIH)

  • F. Marincola

    (National Cancer Institute, Surgery Branch, NIH)

  • C. Gooden

    (Cancer Genetics Branch, National Human Genome Research Institute, NIH)

  • J. Lueders

    (Cancer Genetics Branch, National Human Genome Research Institute, NIH)

  • A. Glatfelter

    (Cancer Genetics Branch, National Human Genome Research Institute, NIH)

  • P. Pollock

    (Queensland Institute of Medical Research)

  • J. Carpten

    (Cancer Genetics Branch, National Human Genome Research Institute, NIH)

  • E. Gillanders

    (Cancer Genetics Branch, National Human Genome Research Institute, NIH)

  • D. Leja

    (Cancer Genetics Branch, National Human Genome Research Institute, NIH)

  • K. Dietrich

    (Cancer Genetics Branch, National Human Genome Research Institute, NIH)

  • C. Beaudry

    (Neuro-Oncology Laboratory, Barrow Neurological Institute)

  • M. Berens

    (Neuro-Oncology Laboratory, Barrow Neurological Institute)

  • D. Alberts

    (Arizona Cancer Center, University of Arizona)

  • V. Sondak

    (University of Michigan)

  • N. Hayward

    (Queensland Institute of Medical Research)

  • J. Trent

    (Cancer Genetics Branch, National Human Genome Research Institute, NIH)

Abstract

The most common human cancers are malignant neoplasms of the skin1,2. Incidence of cutaneous melanoma is rising especially steeply, with minimal progress in non-surgical treatment of advanced disease3,4. Despite significant effort to identify independent predictors of melanoma outcome, no accepted histopathological, molecular or immunohistochemical marker defines subsets of this neoplasm2,3. Accordingly, though melanoma is thought to present with different ‘taxonomic’ forms, these are considered part of a continuous spectrum rather than discrete entities2. Here we report the discovery of a subset of melanomas identified by mathematical analysis of gene expression in a series of samples. Remarkably, many genes underlying the classification of this subset are differentially regulated in invasive melanomas that form primitive tubular networks in vitro, a feature of some highly aggressive metastatic melanomas5. Global transcript analysis can identify unrecognized subtypes of cutaneous melanoma and predict experimentally verifiable phenotypic characteristics that may be of importance to disease progression.

Suggested Citation

  • M. Bittner & P. Meltzer & Y. Chen & Y. Jiang & E. Seftor & M. Hendrix & M. Radmacher & R. Simon & Z. Yakhini & A. Ben-Dor & N. Sampas & E. Dougherty & E. Wang & F. Marincola & C. Gooden & J. Lueders &, 2000. "Molecular classification of cutaneous malignant melanoma by gene expression profiling," Nature, Nature, vol. 406(6795), pages 536-540, August.
    • Handle: RePEc:nat:nature:v:406:y:2000:i:6795:d:10.1038_35020115
    DOI: 10.1038/35020115
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    Citations

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    Cited by:

    1. Rocci, Roberto & Vichi, Maurizio, 2008. "Two-mode multi-partitioning," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1984-2003, January.
    2. Francesca Martella & Maurizio Vichi, 2012. "Clustering microarray data using model-based double K -means," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(9), pages 1853-1869, April.
    3. Alessio Farcomeni, 2009. "Robust Double Clustering: A Method Based on Alternating Concentration Steps," Journal of Classification, Springer;The Classification Society, vol. 26(1), pages 77-101, April.
    4. Maureen Stone & Xiaofeng Liu & Hegang Chen & Jerry L. Prince, 2010. "A preliminary application of principal components and cluster analysis to internal tongue deformation patterns," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 13(4), pages 493-503.
    5. Teresa Maria Rosaria Noviello & Anna Maria Giacomo & Francesca Pia Caruso & Alessia Covre & Roberta Mortarini & Giovanni Scala & Maria Claudia Costa & Sandra Coral & Wolf H. Fridman & Catherine Sautès, 2023. "Guadecitabine plus ipilimumab in unresectable melanoma: five-year follow-up and integrated multi-omic analysis in the phase 1b NIBIT-M4 trial," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    6. Miles C. Andrews & Junna Oba & Chang-Jiun Wu & Haifeng Zhu & Tatiana Karpinets & Caitlin A. Creasy & Marie-Andrée Forget & Xiaoxing Yu & Xingzhi Song & Xizeng Mao & A. Gordon Robertson & Gabriele Roma, 2022. "Multi-modal molecular programs regulate melanoma cell state," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    7. Tritchler, David & Fallah, Shafagh & Beyene, Joseph, 2005. "A spectral clustering method for microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 49(1), pages 63-76, April.

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