Gene Expression Profiling of Peripheral T-Cell Lymphoma (PTCL, NOS) and Comparison to Diffuse Large B-Cell Lymphoma (DLBCL).

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 2277-2277
Author(s):  
Daruka Mahadevan ◽  
Catherine Spier ◽  
Kimiko Della Croce ◽  
Susan Miller ◽  
Benjamin George ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lymph Node ◽  
T Cell ◽  
B Cells ◽  
B Cell ◽  
Gene Expression Profiling ◽  
Real Time ◽  
Expression Profiling ◽  
Cell Lymphoma ◽  
Rt Pcr

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.

Gene Expression Profiling Comparison between the Major Mature B-Cell Neoplasms and Their Normal Cellular and Anatomic Counterparts: Identification of Candidate Genes Potentially Involved in Lymphomagenesis.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 2375-2375
Author(s):  
Nicolas Blin ◽  
Celine Bossard ◽  
Jean-Luc Harousseau ◽  
Catherine Charbonnel ◽  
Wilfried Gouraud ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Proliferation ◽  
B Cells ◽  
B Cell ◽  
Gene Expression Profiling ◽  
Germinal Center ◽  
Expression Profiling ◽  
Marginal Zone ◽  
Cell Lymphoma ◽  
B Cell Lymphoma

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.

Gene Expression Profiling of Highly Purified Malignant and Non-Malignant Cells: Characterization of the Tumor-Microenvironment Cellular Synapse in De Novo Follicular Lymphoma (FL).

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 355-355 ◽  
Author(s):  
Karin Tarte ◽  
Céline Pangault ◽  
John de Vos ◽  
Philippe Ruminy ◽  
Fabienne Sauvee ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
B Cells ◽  
B Cell ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
De Novo ◽  
Differentially Expressed ◽  
Malignant Cells ◽  
Activated T Cell

Abstract Genetic and functional studies have demonstrated that FL cells retain the major features of normal germinal center (GC)-derived B cells, in particular the dependency on an active crosstalk with their specialized microenvironment. In agreement, microarray analyses have recently revealed that FL patient outcome is primarily predicted by molecular characteristics of tumor-infiltrating immune cells instead of tumor cells. However, our knowledge of the crucial interactions between malignant and non-malignant cells in FL remains limited by the use of whole biopsy specimen to perform gene expression profiling (GEP). We thus conducted GEP on both CD19pos B cells and CD19negCD22neg non-B cells purified from lymph nodes of 17 patients with de novo FL & 4 normal donors (CD20pos >94.5%, median=98.2%) and 9 de novo FL patients & 5 normal donors (CD20pos<6.7%, median=0.5%), respectively. Biotinylated cRNA were amplified according to the small sample labelling protocol and hybridized onto HGU133 Plus 2.0 arrays (Affymetrix). Raw data were normalized using GC-RMA methodology (ArrayAssist, Stratagene) and finally, based on a CV>80, 10870 probesets were selected for further analyses. Unsupervised hierarchical clustering (Eisen’s software) allowed the correct classification of the 35 samples into the 4 groups: FL B-cell, Normal B-cells, FL non-B cells, and Normal non-B cells. Supervised analyzes were done using asymptotic non-parametric Mann-Whitney U-test (fold change ≥2, P<0.01) and confirmed by permutation analysis (500 permutations, false discovery rate <5%) using SAM software. We first established the list of the 841 probesets that were differentially expressed between FL and normal B-cells containing, 355 probesets overexpressed in malignant B cells including genes involved in GC B-cell biology (BCL6, MTA3, ID2, CD80, SDC4) and oncogenes as well (BCL2, AURK2) and conversely, 486 probesets downregulated in malignant B cells involving several interferon-stimulated genes for example. We then looked for the FL-specific microenvironment signature and pointed out the 1206 probesets that were differentially expressed between FL and normal non-B cells. Interestingly, all these genes were upregulated in the lymphoma context. Among them, we identified a striking follicular helper T-cell (TFH) signature (CXCR5, ICOS, CXCL13, CD200, PDCD1, SH2D1A) and an activated T-cell signature (IFNG, FASLG, GZMA, ZAP70, CD247). Notably, the TFH and activated T-cell signatures were not merely a surrogate for the number of T cells since many standard T-cell genes (i.e. CD2, CD4, CD7, LEF1, CD8A) were not induced in the FL microenvironment. Finally, in order to draw an overview of the FL-specific synapse between B and non-B cell compartments, we isolated a group of 2323 probesets that were differentially expressed between both compartments in FL and not in normal context. Using Ingenuity Pathway Analysis software we then identified among them FL-specific functional networks, including an IL-4- & an IL-15-centered pathway. Altogether, these data shed new light on our understanding of FL biology and could be a source of new therapeutics targeting the interplay between B cells and their microenvironment.

Resolving driver events in MLL-r negative high-risk infant ALL.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 10030-10030
Author(s):  
Jennifer Seelisch ◽  
Matthew Zatzman ◽  
Federico Comitani ◽  
Fabio Fuligni ◽  
Ledia Brunga ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
High Risk ◽  
B Cell ◽  
Gene Expression Profiling ◽  
Rna Sequencing ◽  
Clinical Features ◽  
Expression Profiling ◽  
High Risk Infant ◽  
Childhood All

10030 Background: Infant acute lymphoblastic leukemia (ALL) is the only subtype of childhood ALL whose outcome has not improved over the past two decades. The most important prognosticator is the presence of rearrangements in the Mixed Lineage Leukemia gene (MLL-r), however, many patients present with high-risk clinical features but without MLL-r. We recently identified two cases of infant ALL with high-risk clinical features resembling MLL-r, but were negative for MLL-r by conventional diagnostics. RNA sequencing revealed a partial tandem duplication in MLL (MLL-PTD). We thus aimed to determine if MLL-PTD, other MLL abnormalities, or other genetic or transcriptomic features were driving this subset of high-risk infant ALL without MLL-r. Methods: We obtained 19 banked patient samples from the Children’s Oncology Group (COG) infant ALL trial (AALL0631) from MLL wildtype patients as determined by FISH and cytogenetics. Utilizing deep RNA-sequencing, we manually inspected the MLL gene for MLL-PTD, while also performing automated fusion detection and gene expression profiling in search of defining features of these tumors. Results: 3 additional MLL-PTDs were identified, all in patients with infant T-cell ALL, whereas both index cases were in patients with infant B-cell ALL. Gene expression profiling analysis revealed that all five MLL-PTD infants clustered together. Eight infants (7 with B-cell ALL) were found to have Ph-like expression. Five of these 8 infants were also found to have an IKZF1/JAK2 expression profile; one of these five had a PAX5-JAK2 fusion detected. Two infants (including the one noted above) had novel PAX5 fusions, known drivers of B-cell leukemia. Additional detected fusions included TCF3-PBX1 and TCF4-ZNF384. Conclusions: MLL-PTDs were found in both B- and T-cell infant ALL. Though Ph-like ALL has been described in adolescents and young adults, we found a substantial frequency of Ph-like expression among MLL-WT infants. Further characterization of these infants is ongoing. If replicated in other infant cohorts, these two findings may help explain the poor prognosis of MLL-WT ALL when compared to children with standard risk ALL, and offer the possibility of targeted therapy for select infants.

Cell-of-Origin in Diffuse Large B-Cell Lymphoma: Are the Assays Ready for the Clinic?

2015 ◽  
pp. e458-e466 ◽  
Author(s):  
David W. Scott
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Practical Implementation ◽  
Cell Of Origin ◽  
Large B Cell Lymphoma ◽  
Large B Cell

Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoma worldwide and consists of a heterogeneous group of cancers classified together on the basis of shared morphology, immunophenotype, and aggressive clinical behavior. It is now recognized that this malignancy comprises at least two distinct molecular subtypes identified by gene expression profiling: the activated B-cell-like (ABC) and the germinal center B-cell-like (GCB) groups—the cell-of-origin (COO) classification. These two groups have different genetic mutation landscapes, pathobiology, and outcomes following treatment. Evidence is accumulating that novel agents have selective activity in one or the other COO group, making COO a predictive biomarker. Thus, there is now a pressing need for accurate and robust methods to assign COO, to support clinical trials, and ultimately guide treatment decisions for patients. The “gold standard” methods for COO are based on gene expression profiling (GEP) of RNA from fresh frozen tissue using microarray technology, which is an impractical solution when formalin-fixed paraffin-embedded tissue (FFPET) biopsies are the standard diagnostic material. This review outlines the history of the COO classification before examining the practical implementation of COO assays applicable to FFPET biopsies. The immunohistochemistry (IHC)-based algorithms and gene expression–based assays suitable for the highly degraded RNA from FFPET are discussed. Finally, the technical and practical challenges that still need to be addressed are outlined before robust gene expression–based assays are used in the routine management of patients with DLBCL.

scholarly journals Constitutive Nuclear Factor κB Activity Is Required for Survival of Activated B Cell–like Diffuse Large B Cell Lymphoma Cells

2001 ◽  
Vol 194 (12) ◽  
pp. 1861-1874 ◽  
Author(s):  
R. Eric Davis ◽  
Keith D. Brown ◽  
Ulrich Siebenlist ◽  
Louis M. Staudt
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Nuclear Factor ◽  
Large B Cell Lymphoma ◽  
Dlbcl Subtype ◽  
Large B Cell

Gene expression profiling has revealed that diffuse large B cell lymphoma (DLBCL) consists of at least two distinct diseases. Patients with one DLBCL subtype, termed activated B cell–like (ABC) DLBCL, have a distinctly inferior prognosis. An untapped potential of gene expression profiling is its ability to identify pathogenic signaling pathways in cancer that are amenable to therapeutic attack. The gene expression profiles of ABC DLBCLs were notable for the high expression of target genes of the nuclear factor (NF)-κB transcription factors, raising the possibility that constitutive activity of the NF-κB pathway may contribute to the poor prognosis of these patients. Two cell line models of ABC DLBCL had high nuclear NF-κB DNA binding activity, constitutive IκB kinase (IKK) activity, and rapid IκBα degradation that was not seen in cell lines representing the other DLBCL subtype, germinal center B-like (GCB) DLBCL. Retroviral transduction of a super-repressor form of IκBα or dominant negative forms of IKKβ was toxic to ABC DLBCL cells but not GCB DLBCL cells. DNA content analysis showed that NF-κB inhibition caused both cell death and G1-phase growth arrest. These findings establish the NF-κB pathway as a new molecular target for drug development in the most clinically intractable subtype of DLBCL and demonstrate that the two DLBCL subtypes defined by gene expression profiling utilize distinct pathogenetic mechanisms.

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