A gene expression-based method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma

Abstract

To classify cancer specimens by their gene expression profiles, we created a statistical method based on Bayes' rule that estimates the probability of membership in one of two cancer subgroups. We used this method to classify diffuse large B cell lymphoma (DLBCL) biopsy samples into two gene expression subgroups based on data obtained from spotted cDNA microarrays. The germinal center B cell-like (GCB) DLBCL subgroup expressed genes characteristic of normal germinal center B cells whereas the activated B cell-like (ABC) DLBCL subgroup expressed a subset of the genes that are characteristic of plasma cells, particularly those encoding endoplasmic reticulum and golgi proteins involved in secretion. We next used this predictor to discover these subgroups within a second set of DLBCL biopsies that had been profiled by using oligonucleotide microarrays [Shipp, M. A., et al. (2002) Nat. Med. 8, 68-74]. The GCB and ABC DLBCL subgroups identified in this data set had significantly different 5-yr survival rates after multiagent chemotherapy (62% vs. 26%; P < or = 0.0051), in accord with analyses of other DLBCL cohorts. These results demonstrate the ability of this gene expression-based predictor to classify DLBCLs into biologically and clinically distinct subgroups irrespective of the method used to measure gene expression.

Fig. 2.

Relationship of gene expression in normal B cell subpopulations to DLBCL subgroups. Relative gene expression in the indicated purified B cell subpopulations (see Methods) is depicted according to the color scale in Fig. 1. The P value of the difference in expression of these genes between the GCB and ABC DLBCL subgroups is shown, and the subgroup with the higher expression is indicated (blue, ABC DLBCL; orange, GCB DLBCL). (A) DLBCL subgroup distinction genes that are more highly expressed in germinal center B cells than at other B cell differentiation stages. (B) DLBCL subgroup distinction genes that are more highly expressed in plasma cells than at other B cell differentiation stages.

Fig. 1.

Performance of the DLBCL subgroup predictor using gene expression measurements from spotted cDNA microarrays. (A) The expression levels for the 27 genes in the subgroup predictor in 274 DLBCL samples (2) are depicted according to the color scale shown at the left. The 14 genes that were used to predict the DLBCL subgroups within the Affymetrix data set (Fig. 3) are indicated with asterisks. The probabilities that the DLBCL samples belong to the ABC or GCB subgroups are graphed at the top, and the DLBCL cases are arranged accordingly. The cases that belong to either the ABC or GCB DLBCL subgroups with ≥90% likelihood are indicated. (B) The assignments of the DLBCL cases to the ABC or GCB subgroups based on hierarchical clustering (2) vs. the subgroup predictor are compared within the training, validation, and total set of samples.

Fig. 3.

Prediction of DLBCL subgroups using gene expression measurements from oligonucleotide microarrays. The DLBCL subgroup predictor was used to discover ABC and GCB subgroups within 58 DLBCL biopsies that were profiled on Affymetrix microarrays (3). The probabilities that each sample belongs to the ABC or GCB subgroups are indicated. The differential expression of genes between the two subgroups is depicted according to the color scale in Fig. 1. Also shown for each gene are the P values derived from Wilcoxon rank-sum tests of the difference in expression between the two subgroups by using Affymetrix microarray data or Lymphochip microarray data, if available. N.S., P > 0.05. Asterisks indicate genes that were included in the DLBCL subgroup predictor.

Fig. 4.

Differences in survival between DLBCL subgroups. The Kaplan–Meier plots display the survival of patients in the GCB and ABC DLBCL subgroups defined by using gene expression data from Lymphochip (2) (Left) or Affymetrix (3) (Right) microarrays. A log-rank test was used to calculate the P values.