Sensitive Gene Expression Profiling of Human T Cell Subsets Reveals Parallel Post-Thymic Differentiation for CD4+ and CD8+ Lineages

Abstract Abstract 2395 Human T-cell lymphoblastic lymphomas (T-LBLs) are neoplasms of immature T-cells and constitute a group of rare, heterogeneous and clinically very aggressive tumors. The molecular pathogenesis that contributes to T-LBL development is not fully elucidated. Since murine T-LBLs are histopathologically and phenotypically comparable to human T-LBLs, mouse models of T-LBLs are ideal to obtain additional insight into the mechanism of T-LBL development in humans. When injected into newborn mice of the NMRI inbred strain, the SL3-3 murine leukemia virus (MLV) induces various types of hematological malignancies, including T-LBLs. The oncogenic effects of the SL3-3 MLV are caused by integration of the viral genome into the host cell DNA through multiple rounds of infection, and subsequent deregulation of nearby cellular genes – a process defined as insertional mutagenesis. If the integration occurs near or within a gene of importance for cancer development, the cell in which the virus has integrated, may gain a growth advantage, eventually leading to malignant transformation and development of a full blown tumor. Screening the murine genome for resulting integration sites in the end-stage tumors, is therefore an efficient method for identifying genes involved in murine and potentially also human T-cell lymphomagenic processes. In a search for genes and pathways implicated in T-cell lymphoblastic lymphoma (T-LBL) development, we used a murine lymphoma model, where mice of the NMRI inbred strain were inoculated with mutants of SL3-3 MLV. The mutants were affected in the glucocorticoid response element and an overlapping E-box of the viral enhancer in the long termial releat. By performing integration analysis on 19 and global gene expression profiling on 22 of the resulting T-LBL tumors, we determined both the effect of the retroviral integrations on the summarized expression of the nearby genes, and the deregulated pathways in the tumors. Fifty two different genes were identified within a 10 kb distance of the retroviral integrations, whereof 15 were specifically involved in G1/S phase transition. Gene expression dot-plots showed an activating effect of the retrovirus on Mr1, Stx6, Cask and Sh3gl3. Gene expression profiling identified increased expression of genes involved in the minichromosome maintenance (Mcm) and origin of recognition (Orc) pathway as well as downregulation in negative regulators of G1/S transition, indicating that murine T-LBLs have increased S-phase initiation. In conclusion, both the integration analysis and patterns of mRNA expression identified by gene expression profiling in the mouse models of T-LBL strongly indicate that genes involved in G1/S phase transition and/or S-phase initiation are deregulated suggesting similar mechanisms to be of importance in human T-LBL pathogenesis. Disclosures: No relevant conflicts of interest to declare.

Download Full-text

New insights on human T cell development by quantitative T cell receptor gene rearrangement studies and gene expression profiling

Journal of Experimental Medicine ◽

10.1084/jem.20042524 ◽

2005 ◽

Vol 201 (11) ◽

pp. 1715-1723 ◽

Cited By ~ 221

Author(s):

Willem A. Dik ◽

Karin Pike-Overzet ◽

Floor Weerkamp ◽

Dick de Ridder ◽

Edwin F.E. de Haas ◽

...

Keyword(s):

Gene Expression ◽

T Cell ◽

Gene Expression Profiling ◽

T Cell Receptor ◽

Expression Profiling ◽

Gene Rearrangement ◽

T Cell Development ◽

Cell Receptor ◽

Cell Development ◽

Human T Cell

To gain more insight into initiation and regulation of T cell receptor (TCR) gene rearrangement during human T cell development, we analyzed TCR gene rearrangements by quantitative PCR analysis in nine consecutive T cell developmental stages, including CD34+ lin− cord blood cells as a reference. The same stages were used for gene expression profiling using DNA microarrays. We show that TCR loci rearrange in a highly ordered way (TCRD-TCRG-TCRB-TCRA) and that the initiating Dδ2-Dδ3 rearrangement occurs at the most immature CD34+CD38−CD1a− stage. TCRB rearrangement starts at the CD34+CD38+CD1a− stage and complete in-frame TCRB rearrangements were first detected in the immature single positive stage. TCRB rearrangement data together with the PTCRA (pTα) expression pattern show that human TCRβ-selection occurs at the CD34+CD38+CD1a+ stage. By combining the TCR rearrangement data with gene expression data, we identified candidate factors for the initiation/regulation of TCR recombination. Our data demonstrate that a number of key events occur earlier than assumed previously; therefore, human T cell development is much more similar to murine T cell development than reported before.

Download Full-text

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

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.10030 ◽

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.

Download Full-text

Genome-wide Gene Expression Profiling of SCID Mice with T-cell-mediated Colitis

Scandinavian Journal of Immunology ◽

10.1111/j.1365-3083.2009.02243.x ◽

2009 ◽

Vol 69 (5) ◽

pp. 437-446 ◽

Cited By ~ 3

Author(s):

D. Brudzewsky ◽

A. E. Pedersen ◽

M. H. Claesson ◽

M. Gad ◽

N. N. Kristensen ◽

...

Keyword(s):

Gene Expression ◽

T Cell ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Scid Mice ◽

Genome Wide ◽

Genome Wide Gene Expression

Download Full-text

Addendum to: “Egr2-dependent gene expression profiling and ChIP-Seq reveal novel biologic targets in T cell anergy” [Mol. Immunol. 55 (2013) 283–291]

Molecular Immunology ◽

10.1016/j.molimm.2013.05.229 ◽

2013 ◽

Vol 56 (4) ◽

pp. 530 ◽

Cited By ~ 1

Author(s):

Yan Zheng ◽

Yuanyuan Zha ◽

Robbert M. Spaapen ◽

Rebecca Mathew ◽

Kenneth Barr ◽

...

Keyword(s):

Gene Expression ◽

T Cell ◽

Gene Expression Profiling ◽

Expression Profiling ◽

T Cell Anergy ◽

Cell Anergy

Download Full-text

CD8+ T cell gene expression profiling to determine inflammatory bowel disease (IBD) prognosis

Science-Business eXchange ◽

10.1038/scibx.2011.1133 ◽

2011 ◽

Vol 4 (40) ◽

pp. 1133-1133

Keyword(s):

Gene Expression ◽

Inflammatory Bowel Disease ◽

T Cell ◽

Gene Expression Profiling ◽

Bowel Disease ◽

Expression Profiling ◽

Cd8 T Cell ◽

Cell Gene Expression ◽

Inflammatory Bowel ◽

Cell Gene

Download Full-text

Gene expression profiling reveals intrinsic differences between T-cell acute lymphoblastic leukemia and T-cell lymphoblastic lymphoma

Pediatric Blood & Cancer ◽

10.1002/pbc.20550 ◽

2006 ◽

Vol 47 (2) ◽

pp. 130-140 ◽

Cited By ~ 90

Author(s):

Elizabeth A. Raetz ◽

Sherrie L. Perkins ◽

Deepa Bhojwani ◽

Kristi Smock ◽

Mary Philip ◽

...

Keyword(s):

Gene Expression ◽

Acute Lymphoblastic Leukemia ◽

T Cell ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Lymphoblastic Leukemia ◽

Lymphoblastic Lymphoma ◽

Cell Acute Lymphoblastic Leukemia

Download Full-text

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

Blood ◽

10.1182/blood.v104.11.2277.2277 ◽

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.

Download Full-text