Comparative Gene Expression Profiling of Leukemia Cells in Peripheral Blood and Tissue Compartments Reveals a Prominent Role of the Microenvironment for CLL Cell Proliferation.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 355-355 ◽  
Author(s):  
Yair Herishanu ◽  
Patricia Perez-Gelen ◽  
Delong Liu ◽  
Angelique Biancotto ◽  
Berengere Vire ◽  
...  

Abstract Abstract 355 In-vitro studies suggest that chronic lymphocytic leukemia (CLL) cells depend on the tissue microenvironment. Different molecules and cell types have been reported to enhance the proliferation and survival of CLL cells. The presence of CLL cells in three distinct compartments: peripheral blood (PB), bone marrow (BM) and lymph node (LN), provides a unique opportunity to investigate the effects of the microenvironment on tumor cell biology in-vivo. To this effect, we used gene expression profiling (Affymetrix HU133 plus arrays) to compare purified CLL cells sampled from PB, BM, and/or LN from 24 previously untreated patients. Initially, an unsupervised hierarchical clustering of all samples appeared to be dominated by the effect of the individual patient. However, in 12 patients where all three sites had been sampled, we used a 3-level one-way ANOVA blocked by patients to estimate patient effect and tissue effect. Three principal components of the 36 samples revealed a clear separation of the tumor cells according to their compartment of origin. Furthermore, supervised analysis with a cutoff of >2-fold change and false discovery rate <0.2 identified 151 genes that discriminated between circulating and LN resident CLL cells (n=17), most of which were more highly expressed in LN, and 27 genes that were differentially expressed in BM as compared to PB cells (n=19). Among the genes upregulated in the lymph node many are readily recognized as related to cell proliferation (e.g. Cyclin D2 and c-MYC) or NF-κB signaling. However, to use an observer independent, unbiased discovery tool to query the gene list for the presence of functional gene signatures we used gene set enrichment analysis (GSEA) and identified several gene expression signatures that were preferentially expressed in LN resident cells: a proliferation signature characterized by E2F and c-MYC regulated genes, signatures related to B-cell receptor and NF-kB signaling were prominent (FDR for all <0.02, normalized enrichment scores 1.81-2.15). A gene expression based E2F score was highest in LN, followed by BM and weakest in PB. Increased nuclear accumulation of E2F1 and c-MYC in LN compared to PB CLL cells was confirmed by Western blotting in paired samples. In general these changes were more prominent in the IgVH unmutated CLL subtype as compared to IgVH mutated CLL cases. In particular, the proliferation E2F score was higher in LN biopsies of IgVH unmutated CLL than IgVH mutated CLL (P=0.04). The E2F score was also an excellent predictor of tumor progression measured as progression free survival (PFS) from diagnosis to treatment: patients with a high E2F score had a median PFS of 16.6 months compared to a PFS in excess of 10 years for patients with a low score (P=0.015). The acquired proliferation and activation signatures in CLL cells which are more prominent in LN resident CLL cells than in cells residing in the BM, suggests that the two microenvironment niches are not identical. Possible upstream cascades driving the signature of CLL cells in the tissue appear to be related to NF-kB and B-cell receptor activation. In conclusion: proliferation and cell activation signatures are acquired in the tissue and are more prominent in LN resident CLL cells than in the BM, suggesting that these two microenvironmental niches have different effects on tumor biology. The LN E2F proliferation signature was more prominent in IgVH unmutated CLL cells and correlated with clinical disease progression. Disclosures: No relevant conflicts of interest to declare.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 2277-2277
Author(s):  
Daruka Mahadevan ◽  
Catherine Spier ◽  
Kimiko Della Croce ◽  
Susan Miller ◽  
Benjamin George ◽  
...  

Abstract Background: WHO classifies NHL into B (~85%) and T (~15%) cell subtypes. Of the T-cell NHL, peripheral T-cell NHL (PTCL, NOS) comprises ~6–10% with an inferior response and survival to chemotherapy compared to DLBCL. Gene Expression Profiling (GEP) of DLBCL has provided molecular signatures that define 3 subclasses with distinct survival rates. The current study analyzed transcript profiling in PTCL (NOS) and compared and contrasted it to GEP of DLBCL. Methods : Snap frozen samples of 5 patients with PTCL (NOS) and 4 patients with DLBCL were analyzed utilizing the HG-U133A 2.0 Affymetrix array (~18,400 transcripts, 22,000 probe sets) after isolating and purifying total RNA (Qiagen, RNAeasy). The control RNA samples were isolated from normal peripheral blood (PB) B-cell (AllCell, CA), normal PB T-cell (AllCell, CA) and normal lymph node (LN). Immunohisto-chemistry (IHC) confirmed tumor lineage and quantitative real time RT-PCR was performed on selected genes to validate the microarray study. The GEP data were processed and analyzed utilizing Affymetrix MAS 5.0 and GeneSpring 5.0 software. Our data were analyzed in the light of the published GEP of DLBCL (lymphochip and affymtrix) and the validated 10 prognostic genes (by IHC and real time RT-PCR). Results : Data are represented as “robust” increases or decreases of relative gene expression common to all 5 PTCL or 4 DLBCL patients respectively. The table shows the 5 most over-expressed genes in PTCL or DLBCL compared to normal T-cell (NT), B-cell (NB) and lymph node (LN). PTCL vs NT PTCL vs LN DLVCL vs NB DLBCL vs LN COL1A1 CHI3L1 CCL18 CCL18 CCL18 CCL18 VNN1 IGJ CXCL13 CCL5 UBD VNN1 IGFBP7 SH2D1A LYZ CD52 RARRES1 NKG7 CCL5 MAP4K1 Of the top 20 increases, 3 genes were common to PTCL and DLBCL when compared to normal T and B cells, while 11 were common when compared to normal LN. Comparison of genes common to normal B-cell and LN Vs DLBCL or PTCL and normal T-cell and LN Vs PTCL or DLBCL identified sets of genes that are commonly and differentially expressed in PTCL and/or DLBCL. The 4 DLBCL patients analyzed express 3 of 10 prognostic genes compared to normal B-cells and 7 of 10 prognostic genes compared to normal LN and fall into the non-germinal center subtype. Quantitative real time RT-PCR on 10 functionally distinct common over-expressed genes in the 5 PTCL (NOS) patients (Lumican, CCL18, CD14, CD54, CD106, CD163, α-PDGFR, HCK, ABCA1 and Tumor endothelial marker 6) validated the microarray data. Conclusions: GEP of PTCL (NOS) and DLBCL in combination with quantitative real time RT-PCR and IHC have identified a ‘molecular signature’ for PTCL and DLBCL based on a comparison to normal (B-cell, T-cell and LN) tissue. The categorization of the GEP based on the six hallmarks of cancer identifies a ‘tumor profile signature’ for PTCL and DLBCL and a number of novel targets for therapeutic intervention.


Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 619-619
Author(s):  
Rita Shaknovich ◽  
Huimin Geng ◽  
Nathalie Johnson ◽  
Leandro Cerchietti ◽  
Maria E Figueroa ◽  
...  

Abstract Abstract 619 Diffuse Large B-cell Lymphoma (DLBCL) is a complex biological entity with heterogeneous genetic, biological and clinical features. Gene expression profiling studies have attempted to resolve some of this heterogeneity. For example, DLBCL patients harboring gene signatures associated with expression of germinal center B-cell genes (GCB) or activated B-cell genes (ABC) vary in their response rate to standard chemo-immunotherapy regimens. Since epigenetic gene regulation can play a fundamental role in determining the phenotype of normal and malignant tissues we asked whether ABC and GCB DLBCLs also display unique epigenetic signatures that might be clinically useful and further explain the biology of these tumors. For this purpose we examined the DNA methylation level of 50,000 cytosine residues distributed among 14,000 gene promoters in a cohort of 159 patients with DLBCL, all of whom were uniformly treated with R-CHOP, using the HELP assay and high-density oligonucleotide microarrays. For a subset of these patients (n=69), we also performed Affymetrix gene expression profiling. First, a Bayesian predictor of ABC/GCB subtypes was trained from a published expression profiling study of 203 DLBCL patients. The predictor was then applied to our cohort of 69 patients. At a probability cutoff of 0.9, 20 patients were classified as ABC, 40 were classified as GCB and 9 could not be classified (i.e. “type III DLBCL”). As expected from published studies, the differences in progression free survival (PFS) and overall survival (OS) of these ABC vs. GCB patients thus determined was highly significant, with p=0.0026 (log rank) and p=0.043 (log rank) respectively, with a much worse prognosis for ABC patients compared to GCB ones. We then asked whether the ABC and GCB subtypes could be predicted from the DNA methylation profiles of the same 69 patients. By performing a t-test we found that 239 genes were differentially methylated between ABC and GCB (p<0.0001) and also displayed >30% difference in methylation level. This DNA methylation signature was incorporated into a new Bayesian predictor, which we showed to predict ABC and GCB DLBCL subtypes from DNA methylation profiles with a 91% accuracy. Using a cross-validation procedure, we estimated that the classification performance on independent cases to be ∼87%. The predicted ABC and GCB cases retained the clinical predictive power of the gene expression profile when applied to the remaining 90 patients that did not have gene expression profiles, confirming its clinical relevance (difference in PFS p=0.0148, log rank). Gene set enrichment analysis showed that the ABC DNA methylation signature was enriched in genes involved in antigen dependent B and T-cell responses and in TNF inflammatory responses (p<1.01E-4 and <6.01E-4 respectively). A computational analysis of promoter DNA sequences of the genes involved in this signature revealed over-representation of binding sites for transcription factors including MYB, STAT5A, MAZ, and JUN and Sp1; many of these factors have a known role in B cell development and function. The role of Sp1 in these tumors is under further examination. Among the 239 genes that were differentially methylated and the 411 genes that were differentially expressed between ABC and GCB there was an overlap of 16 genes (greater than expected by chance Fisher Exact p=0.005). A predictor based on the methylation profiles of these 16 genes was on its own 92% accurate in identifying ABC vs. GCB cases among our cohort of DLBCLs. Although there was a general trend for inverse correlation in expression between the 239 differentially methylated genes, these 16 overlapping genes displayed marked and extreme inverse correlation. This was validated by single locus quantitative methylation sequencing (MassArray) and QPCR. The 16 genes included genes known to play critical roles in B-cell differentiation, proliferation and metabolism but not previously implicated in DLBCL. Gain and loss of function assays of a subset of these genes in ABC and GCB DLBCL cell lines show that they have tumor suppressor functions in DLBCL. Our results demonstrate for the first time that ABC and GCB DLBCLs are epigenetically distinct diseases; they identify new biological differences and candidate tumor suppressor genes between these tumors and demonstrate that a DNA methylation classifier can be used to distinguish GCB and ABC DLBCL subtypes. Disclosures: No relevant conflicts of interest to declare.


2010 ◽  
Vol 8 (3) ◽  
pp. 353-360 ◽  
Author(s):  
Wing C. Chan ◽  
James O. Armitage

The application of gene expression profiling to the study of lymphomas will significantly influence the way these tumors are diagnosed and treated. Diffuse large B-cell lymphoma is now known to consist of several different genetic entities with different clinical presentations and therapeutic outcomes. In both follicular and diffuse large B-cell lymphoma, these studies have shown that host–tumor interactions have a major impact on the clinical course. Findings of gene expression profiling in diffuse large B-cell lymphoma has indicated the frequent up-regulation of the nuclear factor-κB and B-cell receptor signaling pathways in the activated B-cell type. Drugs targeting these pathways may be effective in the treatment of these cases and clinical trials have been initiated based on these findings. Gene expression profiling may assist in the selection of treatments based on specific metabolic pathways shown to be active in a particular lymphoma. These techniques offer the promise of truly personalized medicine for patients with lymphoma.


Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 2375-2375
Author(s):  
Nicolas Blin ◽  
Celine Bossard ◽  
Jean-Luc Harousseau ◽  
Catherine Charbonnel ◽  
Wilfried Gouraud ◽  
...  

Abstract Gene expression profiling has provided new insights into the understanding of mature B cell neoplasms by relating each one to its normal counterpart, so that they can be to some extent classified according to the corresponding normal B-cell stage. Thus, diffuse large B cell (DLBCL) and follicular lymphoma (FL) have been related to a germinal center precursor whereas mantle cell lymphoma (MCL) or marginal zone lymphoma (MZL) are more likely to derive from naïve and memory B cell, respectively. However, little is still known about the physiopathology of B-cell lymphomas and particularly the deregulated pathways involved in their oncogenesis. To further investigate that point, we performed laser capture microdissection (LCM) of the three anatomic lymphoid compartments (i.e germinal center, mantle zone and marginal zone) taken from nine normal spleens and lymph nodes and magnetic cell separation of the four normal B cell subpopulations (i.e naïve B cells, centroblasts, centrocytes and memory B cells) purified from twelve normal tonsils for gene expression profiling by cDNA microarray. These molecular profiles have been compared to those of the four most frequent mature B cell neoplasms in adult (i.e DLBCL, FL, MZL and MCL), each one isolated from five previously untreated patients. Unsupervised analysis by hierarchical clustering of the normal anatomic and cellular populations could discriminate the germinal from the extra-germinal populations by genes involved in cell proliferation (e.g. E2F5, CCNB2, BUB1B and AURKB), DNA repair (e.g. PCNA and EXO1), cytokine secretion (e.g. IL8, IL10RB, IL4R and TGFBI) and apoptosis (e.g. CASP8, CASP10, BCL2 and FAS). Supervised analysis of the comparison between each B-cell lymphoma and its anatomic and cellular physiologic equivalent identified molecular deregulations concerning several genes’families characterizing the different histologic subtypes. Genes associated with cellular adhesion and ubiquitin cycle were significantly up-regulated in MCL (FCGBP, ITGAE, USP7, VCAM1) and MZL (CTGF, CDH1, ITGAE) whereas germinal center derived lymphomas (i.e. DLBCL and FL) mainly showed up-regulation of genes involved in cell proliferation (TNFRSF17, SEPT8) and immune response (FCER1G, XBP1, IL1RN). Few deregulated genes were common to the four subtypes, principally associated with cell proliferation (CYR61, GPNMB), cytosqueleton organization (EPB41L3) and carbohydrates metabolism (GNPDA1), suggesting potential similar oncogenic pathways. Those preliminary results are compatible with both subtype-specific and overall mechanisms of lympomagenesis and should be verified in a wider range of samples to confirm the oncogenic events involved in this heterogeneous disease.


Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 3013-3013
Author(s):  
Ruth M de Tute ◽  
Sharon Barrans ◽  
Andy C. Rawstron ◽  
Peter W.M. Johnson ◽  
Andrew J Davies ◽  
...  

Abstract Clonal B-cell populations with either a CLL or a non-CLL phenotype are a common finding in normal individuals but uncertainty remains about how this relates to the development of clinically significant disease. The aim of this study was to investigate the frequency of peripheral blood clonal B-cell populations and B-cell subset abnormalities in newly presenting DLBCL patients and to determine whether the incidence of these abnormalities differed between the GCB and ABC subtypes, which are regarded as having distinct pathogenesis. The study was carried out using peripheral blood samples collected from patients entered in the UK-REMoDL-B trial. This trial is testing the hypothesis that the ABC subtype of DLBCL responds preferentially to R-CHOP- Bortezomib. Gene expression profiling is performed on the diagnostic tissue biopsy (FFPE) using the Illumina WG-DASL assay prior to randomisation classified as GCB, ABC or unclassified (UN). The availability of GEP data allows meaningful comparison with the phenotype of clonal populations detected by flow cytometry. Peripheral blood taken prior to first treatment was analysed using multi-colour flow cytometry. Following red cell lysis with ammonium chloride, samples were incubated with a panel of antibodies comprising of a CD19 and CD20 backbone, with Kappa, Lambda, CD5, CD45, CD49d, LAIR-1, CXCR5, CD31, CD95, CD38 and CD10, supplemented in some cases by CD81, CD79b, and CD43. A minimum of 500,000 events were acquired on a FacsCanto II flow cytometer (Becton Dickinson). B-cells were enumerated and any monoclonal populations identified were classified as CLL, germinal centre (GC), non-GC or not otherwise specified (NOS) where the phenotype was indeterminate. 358 samples were eligible for inclusion from patients with an average age of 62.2years (range 22.9-86.1). Abnormalities were detected in 52% of cases (B-lymphopenia ((<0.06 x 109/l) 33%, B-lymphocytosis (>1 x 109/l) 2.8%, CLL clone 3.6%, GC clone 9.8%, non-GC clone 9.8%, clonal population NOS 2.2%). Gene expression profiling results were available for 278 individuals; 51% GCB, 32% ABC and 17% unclassified. The relationship between peripheral blood B-cell findings and the GEP determined phenotype of the tumour is shown in the table:TableB-lymphopeniaCLL CloneMonoclonal GC typeMonoclonalNon-GC typeMonoclonal NOSNormalB-cellGCB n=14241/142 (29%)5/142 (3.5%)21/142 (15%)8/142 (5.6%)2/142 (1%)72/142 (51%)ABC n=8927/89 (30%)2/89 (2%)2/89 (2%)12/89 (13.5%)2/89 (2%)49/89 (55%)Unclassified n=4726/47 (55%)0/50 (0%)2/47 (4%)6/47 (12%)6/47 (5%)14/47 (30%) In patients where clonal populations were detected in the peripheral blood there was striking concordance between the phenotype of the clone and the GEP of the underlying tumour. Presence of a GC-population by flow was highly predictive of GCB GEP (84% GC–type populations detected were in GCB cases). The number of discordant cases and the number of CLL clones detected approximate to the numbers that would be expected in a normal population of a similar age. It is, therefore, likely that in most cases circulating tumour cells or a closely related precursor clone are being detected. The similarity between the results of the ABC and unclassified GEP groups suggest that these are biologically related. An unexpected finding in this study was the high incidence of B-lymphopenia at a level that might be expected to be associated with increased risk of infection. This may reflect suppression of normal B-cells by the neoplastic clone or be a marker of underlying immune dysfunction that may predispose to the development of the tumour. Immuosuppression has a role in the pathogenesis of DLBCL in the elderly and this study suggests that this may also be a factor in the wider patient population. These results may have implications for prognostic assessment and may offer opportunities for early diagnosis and possibly response assessment in some patients. The impact on outcome will be assessed in the course of the trial. Disclosures: Jack: Roche /Genentech: Research Funding.


Blood ◽  
2007 ◽  
Vol 109 (9) ◽  
pp. 3989-3997 ◽  
Author(s):  
Laurent D. Vallat ◽  
Yuhyun Park ◽  
Cheng Li ◽  
John G. Gribben

Abstract Gene expression in cells is a dynamic process but is usually examined at a single time point. We used gene expression profiling over time to build temporal models of gene transcription after B-cell receptor (BCR) signaling in healthy and malignant B cells and chose this as a model since BCR cross-linking induces both cell proliferation and apoptosis, with increased apoptosis in chronic lymphocytic leukemia (CLL) compared to healthy B cells. To determine the basis for this, we examined the global temporal gene expression profile for BCR stimulation and developed a linear combination method to summarize the effect of BCR simulation over all the time points for all patients. Functional learning identified common early events in healthy B cells and CLL cells. Although healthy and malignant B cells share a common genetic pattern early after BCR signaling, a specific genetic program is engaged by the malignant cells at later time points after BCR stimulation. These findings identify the molecular basis for the different functional consequences of BCR cross-linking in healthy and malignant B cells. Analysis of gene expression profiling over time may be used to identify genes that might be rational targets to perturb these pathways.


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 26-26
Author(s):  
Manishkumar S. Patel ◽  
Ellen K. Kendall ◽  
Sarah Ondrejka ◽  
Agrima Mian ◽  
Yazeed Sawalha ◽  
...  

Background Diffuse large B cell lymphoma (DLBCL) is curable in ~60-70% of patients using standard chemoimmunotherapy, but the prognosis is poor for relapsed/refractory (R/R) DLBCL. Therefore, understanding the underlying molecular mechanisms will facilitate early prediction and effective management of resistance to therapy. Recent studies of paired diagnostic-relapse biopsies from patients have relied on a single "omics" approach, examining either gene expression or epigenetic evolution. Here we present a combined analysis of gene expression and DNA methylation profiles of paired diagnostic-relapse DLBCL biopsies to identify changes responsible for relapse after R-CHOP. Methods Biopsies from 23 DLBCL patients were obtained at the time of diagnosis and relapse following frontline R-CHOP chemoimmunotherapy. The cohort had 18 (78.3%) male patients with median age of 62 (range, 35-86) years and median IPI of 2.5 (range, 1-5). The median time from diagnosis to relapse was 7 (range, 0-57) months. DNA and RNA were extracted simultaneously from formalin-fixed paraffin embedded (FFPE) biopsy samples. DNA methylation levels were measured through Illumina 850k Methylation Array for 22 pairs of diagnostic-relapse biopsies. RNA from diagnostic-relapse paired biopsies from 6 patients was sequenced using Illumina HiSeq4000. Differentially methylated probes were identified using the DMRcate package, and differentially expressed genes were identified using the DESeq2 package. Gene set enrichment analysis was performed using canonical pathway gene sets from MSigDB. Pearson's correlation with a Bonferroni correction to the p-value was used to calculate the correlation between regularized log transformed gene expression counts and methylation beta values. Results In a pairwise comparison of gene expression between diagnostic and R/R biopsy pairs, we found 14 differentially expressed genes (FDR&lt;0.1 & Log2FC&gt;|1|) consistent across all pairs. Compared to gene expression at diagnosis, five genes (CYP1B1, LGR4, ATXN1, CTSC, ZMAT3) were downregulated, and eight genes (ERBB3, CD19, CARD11, MT-RNR2, IGHG3, CCDC88C, ATP2A3, CENPE, and PCNT) were up-regulated in the R/R samples. Many of these genes have been previously implicated in oncogenesis, such as ERBB3, a member of the epidermal growth receptor family. Importantly, some of these genes have known roles in DLBCL biology, such as CD19, a member of the B-cell receptor complex, and CARD11, a gene in which several oncogenic mutations have been identified in DLBCL as a mediator of NF-KB activation. Gene set enrichment analysis revealed overexpression of immune signatures such as cytokine-cytokine receptor interaction, chemokine receptor-chemokine binding, and the IL-12-STAT4 pathway at diagnosis. At relapse, cell cycle, B-cell receptor, and NOTCH signaling pathways were overexpressed. Interestingly, in a pairwise comparison of methylation between diagnostic and R/R biopsy pairs, there were no differentially methylated probes (FDR&lt;0.05), suggesting no coordinated epigenetic evolution between diagnostic and R/R pairs. For biopsy pairs that had both gene expression and methylation data (5 pairs), we correlated gene expression and methylation values. We found that none of the differentially expressed genes between the diagnostic and R/R biopsies were significantly correlated with methylation status (adjusted p-value&lt;0.05). Conclusions By analyzing paired diagnostic and relapse DLBCL biopsies, we found that at the time of relapse, there are significant transcriptomic changes but no significant epigenetic changes when compared to diagnostic biopsies. Activation of B-cell receptor and NOTCH signaling, as well as the loss of immune signaling at relapse, cannot be attributed to coordinated epigenetic changes in methylation. As the epigenetic profile of the biopsies did not consistently evolve, these data emphasize the need for better understanding of the baseline methylation profiles at the time of diagnosis, as well as acquired somatic mutations that may contribute to the emergence of therapeutic resistance. Future studies are needed to focus on how activation of signaling pathways triggered by genomic alterations can be targeted in relapsed/refractory DLBCL. Disclosures Hsi: Seattle Genetics: Consultancy, Honoraria; Miltenyi: Consultancy, Honoraria; Abbvie: Research Funding; Eli Lilly: Research Funding; CytomX: Consultancy, Honoraria. Hill:Takeda: Research Funding; Genentech: Consultancy, Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria, Research Funding; Pharmacyclics: Consultancy, Honoraria, Research Funding; Beigene: Consultancy, Honoraria, Research Funding; AstraZenica: Consultancy, Honoraria, Research Funding; Kite, a Gilead Company: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria; BMS: Consultancy, Honoraria, Research Funding.


Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 819-819
Author(s):  
Mohit Aggarwal ◽  
Raquel Villuendas ◽  
Fatima Al-Shahrour ◽  
Abel S. Aguilera ◽  
Nerea Martinez ◽  
...  

Abstract B-cell lymphomas are presently diagnosed according to the WHO criteria based on morphologic, immunophenotype and cytogenetic findings. However, the precise distinction among common lymphoma types is frequently a difficult task, and there are areas of overlapping and heterogeneity between them. Here we have analyzed whether gene expression profiling (GEP) data, solely considered, could be used to validate the currently used B-cell lymphoma classification, or proposing new lymphoma types, and for identifying functional signatures or genes defining these GEP-based lymphoma classification. To this aim, we collected Gene Expression Profiling (GEP) for 173 cases of B-cell NHL, including BL (9), DLBCL (36), MALT (3), MCL (20), CLL (38), FL (33), MZL (6) and SMZL (29). The gene expression data for lymphoma cases was normalized against an average gene expression of reactive lymph nodes, except the SMZL which was normalized against normal spleen (3 cases). The analysis of the cases was done using Cluster Accuracy Analysis Tool (CAAT) (Cunningham P., 2005), that enabled us not only to compare gene expression between each node starting from root but also to identify new classes within existing lymphoma diagnosis defined by an internal validation method called Silhouette Width index (Julia Handl. et al, 2005). Using this approach each cluster could be represented by so called silhouette, which is based on the comparison of its tightness and separation. The average silhouette width could be applied for evaluation of clustering validity and can also be used to decide how good the number of selected clusters is. Using this approach, we obtained the following categorization of lymphoma cases: Figure Figure Using T-Rex (Herrero J & Dopazo J., 2005) to compare differential expression between the categories obtained by CAAT, and FatiScan analysis (Al-Shahrour, F., 2006), we identified genes that were differentially expressed between molecular categories of lymphoma types, assigning them to Gene Ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes)-defined pathways. The functional signatures that were identified as distinguishing between these lymphoma types were defining cell cycle, cytokine-cytokine receptor interaction, T-cell receptor, B-cell receptor, cell adhesion, NF-kB activation, and other significant interactions. Comparison between these lymphoma clusters following this definition yielded large number of genes distinguishing them, this list including already known genes and a large number of new potential markers.


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