Literature DB >> 11269644

DNA microarrays in pediatric cancer.

T J Triche1, D Schofield, J Buckley.   

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.

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Year:  2001        PMID: 11269644

Source DB:  PubMed          Journal:  Cancer J        ISSN: 1528-9117            Impact factor:   3.360


  6 in total

1.  Expression profiling of human tumors: the end of surgical pathology?

Authors:  M Ladanyi; W C Chan; T J Triche; W L Gerald
Journal:  J Mol Diagn       Date:  2001-08       Impact factor: 5.568

Review 2.  Microarray-based expression profiling of normal and malignant immune cells.

Authors:  Rheem D Medh
Journal:  Endocr Rev       Date:  2002-06       Impact factor: 19.871

3.  Materiomics for Oral Disease Diagnostics and Personal Health Monitoring: Designer Biomaterials for the Next Generation Biomarkers.

Authors:  Wenjun Zhang; Ming L Wang; Sammy Khalili; Steven W Cranford
Journal:  OMICS       Date:  2016-01

Review 4.  Gene translocations in musculoskeletal neoplasms.

Authors:  Balaji Krishnan; Gaurav Khanna; Denis Clohisy
Journal:  Clin Orthop Relat Res       Date:  2008-06-20       Impact factor: 4.176

Review 5.  Molecular genetics of pediatric soft tissue tumors: clinical application.

Authors:  Chung-Che Chang; Vinod B Shidham
Journal:  J Mol Diagn       Date:  2003-08       Impact factor: 5.568

6.  Evaluation of the similarity of gene expression data estimated with SAGE and Affymetrix GeneChips.

Authors:  Fred van Ruissen; Jan M Ruijter; Gerben J Schaaf; Lida Asgharnegad; Danny A Zwijnenburg; Marcel Kool; Frank Baas
Journal:  BMC Genomics       Date:  2005-06-14       Impact factor: 3.969

  6 in total

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