A strategy for full interrogation of prognostic gene expression patterns: exploring the biology of diffuse large B cell lymphoma

Abstract

Background: Gene expression profiling yields quantitative data on gene expression used to create prognostic models that accurately predict patient outcome in diffuse large B cell lymphoma (DLBCL). Often, data are analyzed with genes classified by whether they fall above or below the median expression level. We sought to determine whether examining multiple cut-points might be a more powerful technique to investigate the association of gene expression with outcome.

Methodology/principal findings: We explored gene expression profiling data using variable cut-point analysis for 36 genes with reported prognostic value in DLBCL. We plotted two-group survival logrank test statistics against corresponding cut-points of the gene expression levels and smooth estimates of the hazard ratio of death versus gene expression levels. To facilitate comparisons we also standardized the expression of each of the genes by the fraction of patients that would be identified by any cut-point. A multiple comparison adjusted permutation p-value identified 3 different patterns of significance: 1) genes with significant cut-point points below the median, whose loss is associated with poor outcome (e.g. HLA-DR); 2) genes with significant cut-points above the median, whose over-expression is associated with poor outcome (e.g. CCND2); and 3) genes with significant cut-points on either side of the median, (e.g. extracellular molecules such as FN1).

Conclusions/significance: Variable cut-point analysis with permutation p-value calculation can be used to identify significant genes that would not otherwise be identified with median cut-points and may suggest biological patterns of gene effects.

Figure 1. Graphs for each of the 13 genes with a significant logrank statistic (Z-value).

On the Y-axis, an unadjusted score statistic of 2 corresponds to a p-value of approximately 0.05. On the X-axis, a value of 0.1 corresponds to the 10^th percentile of gene expression, 0.2 to the 20^th percentile, and etc up to the 90^th percentile of expression. Different cut-point values assessed for each gene are represented by the dots along the connected line of chi-square values. The solid horizontal line represents the 90^th percentile of the permutation distribution of the maximal score statistics. The range on the x-axis is from 10% to 90% of the distribution of the gene expression variable. An overall p-value adjusted for the permutation analysis is shown along the right sided Y-axis.

Figure 2. Hazard regression functions for the 13 genes with significant cut-points.

The Y-axis shows the log of the hazard ratio of death. The X-axis shows the quantile of gene expression. The thin lines show the 90% confidence intervals.