Highly accurate two-gene classifier for differentiating gastrointestinal stromal tumors and leiomyosarcomas

Nathan D. Price, Jonathan Trent, Adel K. El-Naggar, David Cogdell, Ellen Taylor, Kelly K. Hunt, Raphael E. Pollock, Leroy Hood, Ilya Shmulevich, Wei Zhang

Research output: Contribution to journalArticlepeer-review


Gastrointestinal stromal tumor (GIST) has emerged as a clinically distinct type of sarcoma with frequent overexpression and mutation of the c-Kit oncogene and a favorable response to imatinib mesylate [also known as STI571 (Gleevec)] therapy. However, a significant diagnostic challenge remains in the differentiation of GIST from leiomyosarcomas (LMSs). To improve on the diagnostic evaluation and to complement the immunohistochemical evaluation of these tumors, we performed a whole-genome gene expression study on 68 well characterized tumor samples. Using bioinformatic approaches, we devised a two-gene relative expression classifier that distinguishes between GIST and LMS with an accuracy of 99.3% on the microarray samples and an estimated accuracy of 97.8% on future cases. We validated this classifier by using RT-PCR on 20 samples in the microarray study and on an additional 19 independent samples, with 100% accuracy. Thus, our two-gene relative expression classifier is a highly accurate diagnostic method to distinguish between GIST and LMS and has the potential to be rapidly implemented in a clinical setting. The success of this classifier is likely due to two general traits, namely that the classifier is independent of data normalization and that it uses as simple an approach as possible to achieve this independence to avoid overfitting. We expect that the use of simple marker pairs that exhibit these traits will be of significant clinical use in a variety of contexts.

Original languageEnglish (US)
Pages (from-to)3414-3419
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number9
StatePublished - Feb 27 2007
Externally publishedYes


  • Cancer
  • Classification
  • Diagnostic
  • Machine learning
  • Molecular signature

ASJC Scopus subject areas

  • General


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