Predicting Outcome in Follicular Lymphoma by Using Interactive Gene Pairs

Abstract

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.

A pluripotency signature predicts histologic transformation and influences survival in follicular lymphoma patients

“…Other approaches may be better suited to deriving optimal predictors of FL survival. 11,42 However, it is important that our model validates in an unbiased FL patient population consisting entirely of diagnostic samples, 11 to alleviate the possibility that our analysis is affected by selection bias in the training data, 7 which contain a high proportion of FL-pt. The fact that MYC and SOX2, 2 key drivers of iPS, were generally more highly expressed in DLBCL-t relative to FL whereas PAX5 was generally repressed, is provocative.…”

Sense-antisense gene-pairs in breast cancer and associated pathological pathways

“…In the cases of gene pairs, the method may be improved and/or specified by analysing gene pairs. Specialised statistical and computational methods are required to reliably identify gene pairs (e.g., the predictive interaction analysis (PIA) [ 79 , 80 ] or the 2-dimensional data-driven grouping procedure (2-D DDg) [ 29 , 34 ]). Due to the sample size, cohort variation and the computerized implementation of a mathematical model, the selection of unbiased and high-confidence gene pairs was not a trivial task.…”

New developments in the pathology of malignant lymphoma: a review of the literature published from January to April 2008

“…For follicular lymphoma, a gene expression profile was determined by Lebrun et al that predicts poor outcome [43]. Diaz-Alderete et al showed that clinical features are related to the presence of bcl2 and bcl6 translocations [44].…”