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  • Clinical Implications
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Clinical Implication

Making sense of microarray data to classify cancer

The Pharmacogenomics Journal volume 3, pages 308–311 (2003)Cite this article

Profiling gene expression using DNA arrays has had a tremendous impact on biomedical research. From a disease investigation point of view, applications of DNA microarrays include uncovering unsuspected associations between genes and specific clinical features of disease, resulting in novel, molecular-based disease classifications. Cancer is a case in point. Most published studies of cancers using DNA microarrays have either examined a pathologically homogeneous set of tumors to identify clinically relevant subtypes, for example, responders vs nonresponders, or pathologically distinct subtypes of cancer of the same lineage, for example, high-stage vs low-stage tumors to identify molecular correlates, or tumors of different lineages to identify molecular signatures for each lineage. A study of cutaneous T-cell lymphoma by Kari et al,1 published recently, typifies both what one hopes to gain from disease investigations using DNA microarrays and the limitations of such studies.

Primary cutaneous lymphomas are a heterogeneous group of lymphomas of T- or B-cell origin that represent a relatively common type of lymphoma and their incidence appears to be increasing. The two predominant subtypes of cutaneous T-cell lymphomas are mycosis fungoides, a mostly indolent variety, and its leukemic counterpart the Sezary syndrome, an aggressive variety characterized by skin involvement, lymphadenopathy and circulating atypical lymphocytes, the so-called Sezary cells. Kari et al used cDNA microarrays to study gene expression patterns in peripheral blood mononuclear cells from patients with the leukemic form of cutaneous T-cell lymphoma. The goal of the study was to identify markers that may be useful for diagnosis or prognosis, or that might provide new targets for treating this disease. The approach was to uncover gene expression differences between cells from 18 patients with high Sezary cell counts and an appropriate (Th2-skewed) cell fraction from nine normal controls. The differences in gene expression observed reflected many of the observed characteristics of the disease. Overexpressed genes in disease samples included some genes required for Th2 differentiation characteristic of Sezary cells. The analysis, however, did not uncover changes consistent with the hypothesis of defective apoptotic pathways in this disease. An important objective of the study was to identify markers for cutaneous T-cell lymphoma given the paucity of such markers. A member of the plastin gene family and a chemokine (CX3CR1) inappropriately expressed represented such potential novel markers. Two genes found to have a high predictive power to classify patients and controls were STAT4 and GTPase RhoB. These two genes alone accurately classified the high Sezary cell patients and controls. A signature profile with 10 genes was uncovered that identified a class of patients who succumb to the disease early, irrespective of their tumor burden. The study therefore uncovered a wealth of findings that shed some light on the biology of this disease and uncovered markers that may have a practical utility.

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Authors and Affiliations

  1. Department of Pediatrics, University of Michigan, Ann Arbor, MI, USA

    S Hanash

  2. Bioinformatics Program, Ann Arbor, MI, USA

    C Creighton

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  1. S Hanash
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  2. C Creighton
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Correspondence to S Hanash.

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Hanash, S., Creighton, C. Making sense of microarray data to classify cancer. Pharmacogenomics J 3, 308–311 (2003). https://doi.org/10.1038/sj.tpj.6500209

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