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  • Clinical Implications
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Clinical Implication

Class struggle: expression profiling and categorizing cancer

The Pharmacogenomics Journal volume 3, pages 257–260 (2003)Cite this article

The classification of disease by characterization of the morphologic features of affected tissue is a conventional approach to establish a diagnosis. Upon inspection of a tissue sample under light microscopy, a pathologist describes the morphologic findings applying commonly used diagnostic terms. The NCI Thesaurus, which captures terminology in use by the National Cancer Institute (NCI), lists over 6000 neoplastic disease concepts with approximately 24 000 preferred names and synonyms.1 The NCI Metathesaurus, an even broader system that maps synonymy across many biomedical vocabulary sources, suggests that as many as 7000 concepts with 42 000 preferred names and synonyms for neoplastic disorders may be in use worldwide.2 While there is no doubt great diversity in the manifestation of neoplasms in the human population, this pool of terminology likely exceeds the necessary descriptive and explanatory complexity that might be needed to properly distinguish scientifically and clinically distinct diseases.

There is thus an increasing awareness of the need to consolidate and standardize disease terminology, and modernize the basis by which distinct cancer types are defined. The International Classification of Disease for Oncology (ICD-O) from World Health Organization (WHO) is perhaps the most comprehensive effort to organize descriptions of cancer into coded categories.3 The ICD-O organizes classifications into two major axes: morphology and topography. The third edition, ICD-O-3, includes a substantial update to the classification of hematologic malignancies. In addition to morphological characteristics, the criteria used to classify the hematologic disorders include cytogenetic abnormalities, immunophenotypic profiles, and clinical presentation. The ICD-O is intended for coded summarization of disease incidence reporting rather than for clinical practice. A volume that gives greater detail on the histopathologic, immunologic, and cytogenetic characteristics of each disease class is also available.4

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Acknowledgements

I thank F. Hartel, N. Sioutos, and M. Heiskanen for helpful comments on the manuscript, and J. Berman for healthy skepticism.

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  1. National Cancer Institute Center for Bioinformatics, 6116 Executive Boulevard, Rockville, MD, USA

    P A Covitz

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  1. P A Covitz
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Correspondence to P A Covitz.

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Covitz, P. Class struggle: expression profiling and categorizing cancer. Pharmacogenomics J 3, 257–260 (2003). https://doi.org/10.1038/sj.tpj.6500187

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