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Predicting Outcome in Follicular Lymphoma by Using Interactive Gene Pairs

Authors' Affiliations: ¹ Department of Pathology and Molecular Medicine, ² Cancer Research Institute, Departments of ³ Oncology, ⁴ Internal Medicine, ⁵ Computing, and ⁶ Biology, Queen's University, Kingston, and ⁷ Biosystemix Ltd., Sydenham, Ontario, Canada

Requests for reprints: Harriet Feilotter, Department of Pathology and Molecular Medicine, Richardson Laboratory, Queen's University, Kingston, Ontario, Canada K7L 3N6. Phone: 613-548-1302; E-mail: feilotth{at}kgh.kari.net.

Purpose: Follicular lymphoma is a common lymphoma of adults. Although its course is often indolent, a substantial proportion of patients have a poor prognosis, often due to rapid progression or transformation to a more aggressive lymphoma. Currently available clinical prognostic scores, such as the follicular lymphoma international prognostic index, are not able to optimally predict transformation or poor outcome.

Experimental Design: Gene expression profiling was done on primary lymphoma biopsy samples.

Results: Using a statistically conservative approach, predictive interaction analysis, we have identified pairs of interacting genes that predict poor outcome, measured as death within 5 years of diagnosis. The best gene pair performs >1,000-fold better than any single gene or the follicular lymphoma international prognostic index in our data set. Many gene pairs achieve outcome prediction accuracies exceeding 85% in extensive cross-validation and noise sensitivity computational analyses. Many genes repeatedly appear in top-ranking pairs, suggesting that they reproducibly provide predictive capability.

Conclusions: The evidence reported here may provide the basis for an expression-based, multi-gene test for predicting poor follicular lymphoma outcomes.