Clinical cancer research : an official journal of the American Association for Cancer Research · 2008
Predicting outcome in follicular lymphoma by using interactive gene pairs.
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LeBrun David, Baetz Tara, Foster Cheryl, Farmer Patricia, Sidhu Roger, Guo Hong, Harrison Karen, Somogyi Roland, Greller Larry D, Feilotter Harriet
Аннотация
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.
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.