Germinal center B cell-like (GCB) and activated B cell-like (ABC) type of diffuse large B cell lymphoma (DLBCL): analysis of molecular predictors, signatures, cell cycle state and patient survival

Abstract

Aiming to find key genes and events, we analyze a large data set on diffuse large B-cell lymphoma (DLBCL) gene-expression (248 patients, 12196 spots). Applying the loess normalization method on these raw data yields improved survival predictions, in particular for the clinical important group of patients with medium survival time. Furthermore, we identify a simplified prognosis predictor, which stratifies different risk groups similarly well as complex signatures. We identify specific, activated B cell-like (ABC) and germinal center B cell-like (GCB) distinguishing genes. These include early (e.g. CDKN3) and late (e.g. CDKN2C) cell cycle genes. Independently from previous classification by marker genes we confirm a clear binary class distinction between the ABC and GCB subgroups. An earlier suggested third entity is not supported. A key regulatory network, distinguishing marked over-expression in ABC from that in GCB, is built by: ASB13, BCL2, BCL6, BCL7A, CCND2, COL3A1, CTGF, FN1, FOXP1, IGHM, IRF4, LMO2, LRMP, MAPK10, MME, MYBL1, NEIL1 and SH3BP5. It predicts and supports the aggressive behaviour of the ABC subgroup. These results help to understand target interactions, improve subgroup diagnosis, risk prognosis as well as therapy in the ABC and GCB DLBCL subgroups.

Keywords: cancer; gene expression; immunity; prognosis; regulation.

Figure 1

DLBCL splits into sub-groups independent of signatures. Optimal bipartitions of patients are calculated by ISIS based on optimal bipartition subsets of genes (50). Every column of the x-axis represents a patient. On the bottom, the DLBCL-type of the patient is labelled. On the y-axis every row shows the bipartitions ranked in increasing score of separation quality. The three best bipartitions show a very consistent and clear signal separating the ABC- from the GCB-patients. The unsupervised method ISIS reveals the ABC-GCB classification independent of proliferation signatures. No evidence for a previously suggested third group “Type 3” was found. Only a few patients are falsely assigned if compared to the DLBCL gene signature assignment.

Figure 2

Prognosis prediction applying a molecular predictor of 6 gene spots after improved normalization. Kaplan-Meier plots show large differences in the survival rate for all risk groups. They are estimated by a Cox-Regression Hazard model of the genes listed in Table 1. Normalization was improved applying the “loess” method. x-axis: time (years); y-axis: probability of survival, predicted for the risk groups “low”, “medium” and “high”.

Figure 3

Early and late cell cycle genes are overrepresented in the best separating cell cycle gene set. The density plot compares the distribution of different cell cycle gene sets. x-axis: cell cycle states (from 0 to 99; complete cell cycle). y-axis: relative frequencies. Black line: density of all mapped cell cycle genes of de Lichtenberg et al (de Lichtenberg et al. 2005) in the data set. The area under this line is coloured for easier comparison. Blue line: Optimal separating subset of cell cycle genes (77 spots). Two peaks in the early and late cell cycle states show cell cycle gene expression differences between the subgroups ABC and GCB.