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JNCI Journal of the National Cancer Institute 2003 95(8):598-605; doi:10.1093/jnci/95.8.598
© 2003 by Oxford University Press
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Journal of the National Cancer Institute, Vol. 95, No. 8, 598-605, April 16, 2003
© 2003 Oxford University Press

ARTICLE

Using Gene Expression Ratios to Predict Outcome Among Patients With Mesothelioma

Gavin J. Gordon, Roderick V. Jensen, Li-Li Hsiao, Steven R. Gullans, Joshua E. Blumenstock, William G. Richards, Michael T. Jaklitsch, David J. Sugarbaker, Raphael Bueno

Affiliations of authors: G. J. Gordon, W. G. Richards, M. T. Jaklitsch, D. J. Sugarbaker, R. Bueno, Division of Thoracic Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA; R. V. Jensen, J. E. Blumenstock, Department of Physics, Wesleyan University, Middletown, CT; L.-L. Hsiao (Department of Medicine), S. R. Gullans (Department of Neurology), Brigham and Women’s Hospital, Harvard Medical School, Cambridge, MA.

Correspondence to: Raphael Bueno, M.D., Brigham and Women’s Hospital, Division of Thoracic Surgery, 75 Francis St., Boston, MA 02115 (e-mail: rbueno{at}partners.org).

Background: We have recently demonstrated that simple ratios of the expression levels of selected genes in tumor samples can be used to distinguish among types of thoracic malignancies. We examined whether this technique could predict treatment-related outcome for patients with mesothelioma. Methods: We used gene expression profiling data previously collected from 17 mesothelioma patients with different overall survival times to define two outcome-related groups of patients and to train an expression ratio-based outcome predictor model. A Student’s t test was used to identify genes among the two outcome groups that had statistically significant, inversely correlated expression levels; those genes were used to form prognostic expression ratios. We used a combination of several highly accurate expression ratios and cross-validation techniques to assess the internal consistency of this predictor model, quantitative reverse transcription–polymerase chain reaction of tumor RNA to confirm the microarray data, and Kaplan–Meier survival analysis to validate the model among an independent set of 29 mesothelioma tumors. All statistical tests were two-sided. Results: We developed an expression ratio-based test capable of identifying 100% (17/17) of the samples used to train the model. This test remained highly accurate (88%, 15/17) after cross-validation. A four-gene expression ratio test statistically significantly (P = .0035) predicted treatment-related patient outcome in mesothelioma independent of the histologic subtype of the tumor. Conclusions: Gene expression ratio-based analysis accurately predicts treatment-related outcome in mesothelioma samples. This technique could impact the clinical treatment of mesothelioma by allowing the preoperative identification of patients with widely divergent prognoses.


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