Splenic marginal zone lymphoma: comprehensive analysis of gene expression and miRNA profiling

Abstract Splenic Marginal Zone Lymphoma (MZL) is described as an indolent lymphoma with a long term survival. However classical prognostic factors can not distinguish between patients who are likely to have good or poor outcomes. Moreover histological progression occurs in 10–20% of cases at the time of recurrence, but may be present at diagnosis, inducing a shorter survival with a median around 2 years. New model based on molecular understanding are needed to better discriminate these patients for their prognosis and disease evolution. Biopsy samples of splenic MZL from 43 patients treated in one institution and 8 with transformed splenic MZL were examined for gene expression using a nylon cDNA microarray consisting of 7, 000 human genes. Two of them came from a group of matched pair of splenic MZL and the transformed counterpart. An additional group comprising 4 transformed follicular lymphomas and 1 transformed small lymphocytic lymphoma were also analysed. Hierarchical clustering realized with all samples allowed to visualized patterns of gene expression already described in our previous work1 and corresponding to precise functions (proliferation, early response...), cell or tissue subtype (T cells, stroma …) or lymphoma subtype (MZL, MCL, SLL). The ability of individual genes to distinguish 1/ MZL and transformed-MZL subtypes, and 2/ alive patients and dead patients was calculated using a supervised method (discriminating score and bootstrap resampling). This allowed us to construct molecular predictors of survival and histological transformation. Transformation discriminating genes regrouped 73 genes related to cell proliferation such as CDC10, CDK4, PRKDC, PLCG2, LDHA, MCL2, and NPM1. P53 was down-expressed in all the transformed samples. Survival discriminating genes regrouped 26 genes from a unique cluster related to cell proliferation such as LDHA, NME1, MYC, ENO1. Although these genes seem to participate to the same function, only one gene was found discriminating the 2 parameters (LDHA). Gene ontology analysis using GOminer showed that the histological transformation was more related to cyclin-dependent protein kinase and that the survival was more related to metabolism. We used 26 genes to construct a predictor for survival in MZL patients. This gene-based predictor allowed predicting survival (p <0.00001) in MZL patients better than with classical prognostic factors (age, performance status, stage, LDH, monoclonal component, IPI…), but also in transformed MZL patients. In conclusion, genes involved in histological transformation and survival in MZL patients are implicated in independent but complementary functions leading to cell proliferation.

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Gene Expression Profiling of Post-Follicular B-Cell Lymphoma

Blood ◽

10.1182/blood.v112.11.617.617 ◽

2008 ◽

Vol 112 (11) ◽

pp. 617-617

Author(s):

Wee-Joo Chng ◽

Gaofeng Huang ◽

Paul J. Kurtin ◽

Ahmet Dogan ◽

Ellen Remstein

Keyword(s):

Gene Expression ◽

B Cell ◽

Marginal Zone ◽

Cell Lymphoma ◽

B Cell Lymphoma ◽

Marginal Zone Lymphoma ◽

Gene Set Enrichment Analysis ◽

Splenic Marginal Zone Lymphoma ◽

Major Cluster ◽

Significant Enrichment

Abstract Although classified as marginal zone lymphomas under the WHO classification, the molecular relationship between splenic marginal zone lymphoma (SMZL), extranodal marginal zone B-cell lymphoma of mucosa-associated lymphoid tissue (MALT), and nodal marginal zone lymphoma (NMZL) has not been clarified. Furthermore, lymphoplasmacytic lymphoma (LPL) can show clinical morphologic and immunophenotypic overlap with these entities and may present a diagnostic challenge. In this analysis of gene expression data generated using the Affymetrix U133plus 2.0 chip from 32 SMZL, 25 MALTs (GI, salivary gland and lung), 23 NMZL and 25 LPL, we aim to identify the molecular relationship between these pathological entities using unsupervised methods, identify disease specific signatures and markers using supervised methods and also the functional implications of these signatures using a modified gene-set enrichment analysis. Using hierarchical clustering, MALT lymphoma forms a tight cluster regardless of tumor site. In addition, SMZL and NMZL form another large cluster. LPLs are divided into 2 main clusters with occasional samples interspersed amongst the SMZL and NMZL. There is no correlation between the percentage of CD20-positive B-cells, CD3-positive T-cells or CD138- positive plasma cells or tissue origin of the tumor and the way the samples are clustered. However, the separation of the LPLs into 2 major cluster correspond to the presence and absence of underlying Waldenstrom Macroglobulinaemia (WM), suggesting that the genes distinguishing the 2 clusters are potential markers for differentiating WM LPL from non-WM LPL. Next, using supervised analysis we identified a cluster of genes including MMP7, LTF and SFRP2 with high signals specific to MALTs, while other genes including PRDM1 (BLIMP1), XBP1 and TNFRSF17 (BCMA) were specifically over-expressed in LPL. These may therefore represent novel diagnostic marker differentiating these entities. Several of these are further validated at the protein level using immunohistochemistry on a tissue microarray. Consistent with the unsupervised analysis, SMZL and NMZL have little difference and share the over-expression of CD22 and WNT3 among other genes. Clustering of these samples based on the pathways and genesets that are enriched in the individual tumors as compared to their normal tissue counterpart showed a mutually exclusive pattern with significant enrichment of NFKB-related genesets and genes in LPL and MALT, and significant enrichment of B-cell receptor signaling genesets and genes in SMZL and NMZL. Our analysis, for the first time, describes the molecular relationship between these closely related lymphomas. In the process, we identified novel diagnostic markers that may differentiate these conditions and also new insights into molecular pathways that are differentially activated in the different conditions. These may represent potential therapeutic targets

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