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J Pathol Clin Res. 2015 Apr;1(2):83-94. doi: 10.1002/cjp2.9.

Gene Expression Profiling of Ewing Sarcoma Tumors Reveals the Prognostic Importance of Tumor-Stromal Interactions: A Report from the Children's Oncology Group.

The journal of pathology. Clinical research

Samuel L Volchenboum, Jorge Andrade, Lei Huang, Donald A Barkauskas, Mark Krailo, Richard B Womer, Andreas Ranft, Jenny Potratz, Uta Dirksen, Timothy J Triche, Elizabeth R Lawlor

Affiliations

  1. Center for Research Informatics, University of Chicago ; Department of Pediatrics, University of Chicago.
  2. Center for Research Informatics, University of Chicago.
  3. Department of Preventive Medicine, Keck School of Medicine of the University of Southern.
  4. Children's Hospital of Philadelphia and University of Pennsylvania.
  5. University Hospital Muenster, Department of Pediatrics and Pediatric Hematology and Oncology.
  6. Department of Pathology, Keck School of Medicine of the University of Southern California.
  7. Department of Pediatrics and Department of Pathology, University of Michigan.

PMID: 26052443 PMCID: PMC4457396 DOI: 10.1002/cjp2.9

Abstract

Relapse of Ewing sarcoma (ES) can occur months or years after initial remission and salvage therapy for relapsed disease is usually ineffective. Thus, there is great need to develop biomarkers that can predict which patients are at risk for relapse so that therapy and post-therapy evaluation can be adjusted accordingly. For the current study we performed whole genome expression profiling on two independent cohorts of clinically annotated ES tumors in an effort to identify and validate prognostic gene signatures. ES specimens were obtained from the Children's Oncology Group (COG) and whole genome expression profiling performed using Affymetrix Human Exon 1.0 ST arrays. Lists of differentially expressed genes between survivors and non-survivors were used to identify prognostic gene signatures. An independent cohort of tumors from the Euro-Ewing cooperative group was similarly analyzed as a validation cohort. Unsupervised clustering of gene expression data failed to segregate tumors based on outcome. Supervised analysis of survivors

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