DNA microarrays in pediatric cancer

Cancer J. 2001 Jan-Feb;7(1):2-15.

Abstract

Childhood cancer, like all cancer, is at heart a genetic disease. Consequently, fundamental understanding of the oncogenic process is likely to be beneficially addressed by genetic methodology. Current methods have largely focused on single-gene defects, like chimeric genes, which are present in many sarcomas and leukemias. Real understanding is more likely to derive from a genome-wide analysis of these malignancies. Recent technologic advances have made it possible to simultaneously assess the entire expressed gene profile, or transcriptome, of a given cancer. Foremost among these methods is gene expression profiling using DNA microarrays. Two basic approaches predominate: spotted arrays and photolithography arrays. Regardless of the method, the resulting information can be used to create disease profiles, but only if appropriate bioinformatic solutions are employed. Common analytic approaches include two-way expression comparisons, or scatter analyses; outlier gene analysis, to identify significantly dysregulated genes; dendrogram analyses, as pioneered by Eisen; cluster analyses to identify diagnostic or biologic groups; and various forms of functional analyses to identify relevant genes and biologic pathways. Studies of both adult and pediatric cancer have demonstrated the feasibility of such analyses to identify both diagnostic and prognostic groups of tumors. Acute childhood leukemias have been grouped into myelogenous and lymphoid, and even B- and T-cell subsets. Breast cancer prognostic groups have been identified on the basis of a small subset of expressed genes. In addition, preliminary data on childhood sarcomas appear to identify both diagnostic and prognostic subsets. Specifically, embryonal rhabdomyosarcoma could be distinguished from alveolar rhabdomyosarcoma, and even morphologically mixed embryonal and alveolar rhabdomyosarcoma showed similar gene expression profiles in both histologies. Further, collaborative studies using clustering analyses appear to identify prognostic groups of diverse sarcomas. Larger institutional and cooperative group studies are currently underway to validate these preliminary findings.

Publication types

  • Review

MeSH terms

  • Animals
  • Bone Neoplasms / diagnosis
  • Bone Neoplasms / genetics
  • Child
  • Child, Preschool
  • Computational Biology
  • Gene Expression Profiling / methods*
  • Humans
  • Leukemia / diagnosis*
  • Leukemia / genetics
  • Oligonucleotide Array Sequence Analysis / methods*
  • Osteosarcoma / diagnosis
  • Osteosarcoma / genetics
  • Precursor Cell Lymphoblastic Leukemia-Lymphoma / diagnosis
  • Precursor Cell Lymphoblastic Leukemia-Lymphoma / genetics
  • Prognosis
  • Rhabdomyosarcoma, Alveolar / diagnosis
  • Rhabdomyosarcoma, Alveolar / genetics
  • Rhabdomyosarcoma, Embryonal / diagnosis
  • Rhabdomyosarcoma, Embryonal / genetics
  • Sarcoma / diagnosis*
  • Sarcoma / genetics
  • Statistics as Topic