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.

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.

B-Cell Scaffold Protein with Ankyrin Repeats (BANK1) Is Differentially Expressed in ZAP-70 Negative Compared to ZAP-70 Positive Chronic Lymphocytic Leukemia Cells.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 2938-2938
Author(s):  
Frank Dicker ◽  
Susanne Schnittger ◽  
Claudia Schoch ◽  
Alexander Kohlmann ◽  
Wei-Min Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cells ◽  
Chronic Lymphocytic Leukemia ◽  
B Cell ◽  
Gene Expression Profiling ◽  
Differentially Expressed Genes ◽  
Expression Profiling ◽  
Lymphocytic Leukemia ◽  
Differentially Expressed ◽  
Cell Scaffold

Abstract The lack of somatic mutations of the immunoglobulin variable heavy chain (IgVH) gene has been established as poor prognostic marker for chronic lymphocytic leukemia (CLL) patients at early stage disease. Expression of the non receptor tyrosine kinase zeta chain associated protein (ZAP-70) was proposed as a surrogate marker for an unmutated IgVH, however, up to 30% discordant samples have been reported depending on the respective study. B cell receptor (BCR) mediated signaling is enhanced by ZAP-70 expression in CLL cells in vitro and ZAP-70 expression also tends to decrease the time from diagnosis to treatment irrespective of the IgVH status. Therefore, we wanted to identify differentially expressed genes between the ZAP-70 positive and negative CLLs by gene expression profiling of peripheral blood mononuclear cells (PBMCs) using Affymetrix microarrays (HG-U133 Plus 2.0). ZAP-70 expression was analyzed by quantitative real time PCR of CD19 purified (purity > 99%) PBMCs (n=62) using a LightCycler instrument. Expression of ZAP-70 mRNA was normalized against the housekeeping gene ABL and a relative quantitation against Jurkat T cells as a calibrator was performed. Results are expressed as normalized ratio and a cut-off of 0.5 normalized ratio gave the best correlation to the IgVH status with 77% concordant samples between ZAP-70 expression and the IgVH status. The discordant samples consisted of 5 unmutated IgVHs in the ZAP-70 negative group and 9 mutated in the ZAP-70 positive group. In a second step PBMCs of the same samples were analyzed by gene expression profiling and differentially expressed genes were identified by t-test. Among the two best genes that could be used in a classification algorithm (SVM) to distinguish between the 2 subsets with 92% accuracy were ZAP-70 and B cell scaffold protein with ankyrin repeats (BANK1). The expression of BANK1 was increased 3–4-fold in the ZAP-70 negative compared to the ZAP-70 positive CLL subset (P = 0,001). In the literature, BANK1 has been identified in human BCR expressing B cells and seems to be B cell restricted. In B cells the scaffolding protein BANK1 enhances BCR-mediated Ca2+-signaling, a signaling pathway that is also enhanced by ZAP-70 expression in CLL B cells. Based on these data we show that increased BANK1 expression correlates with a ZAP-70 negative status in CLL B cells. The functional consequences of BANK1 expression in the ZAP-70 negative subset of CLL B cells, which are usually associated with a more favorable prognosis, still need to be established further.

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.

New Pathogenetic Insights Into Classical Hodgkin Lymphoma Revealed by Gene Expression Profiling of Microdissected Hodgkin/Reed-Sternberg Cells.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 266-266 ◽  
Author(s):  
Enrico Tiacci ◽  
Verena Brune ◽  
Susan Eckerle ◽  
Wolfram Klapper ◽  
Ines Pfeil ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cells ◽  
B Cell ◽  
Gene Expression Profiling ◽  
Hodgkin Lymphoma ◽  
Cell Lines ◽  
Expression Profiling ◽  
Expression Profiles ◽  
B Cell Lymphoma ◽  
Cell Phenotype

Abstract Abstract 266 Background. Previous gene expression profiling studies on cHL have been performed on whole tissue sections (mainly reflecting the prominent reactive background in which the few HRS cells are embedded), or on cHL cell lines. However, cultured HRS cells do not likely reflect primary HRS cells in all aspects, being derived from end-stage patients and from sites (e.g. pleural effusions or bone marrow) which are not typically involved by cHL and where HRS cells lost their dependence on the inflammatory microenvironment of the lymph node. Methods. ∼1000–2000 neoplastic cells were laser-microdissected from hematoxylin/eosin-stained frozen sections of lymph nodes taken at disease onset from patients with cHL (n=16) or with various B-cell lymphomas (n=35), including primary mediastinal B-cell lymphoma (PMBL) and nodular lymphocyte-predominant Hodgkin lymphoma (nLPHL). After two rounds of in vitro linear amplification, mRNA was hybridized to Affymetrix HG-U133 Plus 2.0 chips. Expression profiles were likewise generated from sorted cHL cell lines and several normal mature B-cell populations. Results. Primary and cultured HRS cells, although sharing hallmark cHL signatures such as high NF-kB transcriptional activity and lost B-cell identity, showed considerable transcriptional divergence in chemokine/chemokine receptor activity, extracellular matrix remodeling and cell adhesion (all enriched in primary HRS cells), as well as in proliferation (enriched in cultured HRS cells). Unsupervised and supervised analyses indicated that microdissected HRS cells of cHL represent a transcriptionally unique lymphoma entity, overall closer to nLPHL than to PMBL but with differential behavior of the cHL histological subtypes, being HRS cells of the lymphocyte-rich and mixed-cellularity subtypes close to nLPHL cells while HRS cells of NS and LD exhibited greater similarity to PMBL cells. HRS cells downregulated a large number of genes involved in cell cycle checkpoints and in the maintenance of genomic integrity and chromosomal stability, while upregulating gene and gene signatures involved in various oncogenic signaling pathways and in cell phenotype reprogramming. Comparisons with normal B cells highlighted the lack of consistent transcriptional similarity of HRS cells to bulk germinal center (GC) B cells or plasma cells and, interestingly, a more pronounced resemblance to CD30+ GC B cells and CD30+ extrafollicular B cells, two previously uncharacterized subsets that are transcriptionally distinct from the other mature B-cell types. Conclusions. Gene expression profiling of primary HRS cells provided several new insights into the biology and pathogenesis of cHL, its relatedness to other lymphomas and normal B cells, and its enigmatic phenotype. Disclosures: No relevant conflicts of interest to declare.

scholarly journals De NovoTranscriptome Assembly and Differential Gene Expression Profiling of ThreeCapra hircusSkin Types during Anagen of the Hair Growth Cycle

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Teng Xu ◽  
Xudong Guo ◽  
Hui Wang ◽  
Xiaoyuan Du ◽  
Xiaoyu Gao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
De Novo ◽  
Growth Cycle ◽  
Hair Shaft ◽  
Hair Follicles ◽  
Differentially Expressed ◽  
Kegg Pathway Database ◽  
Anagen Phase

Despite that goat is one of the best nonmodel systems for villus growth studies and hair biology, limited gene resources associated with skin or hair follicles are available. In the present study, using Illumina/Solexa sequencing technology, wede novoassembled 130 million mRNA-Seq reads into a total of 49,115 contigs. Searching public databases revealed that about 45% of the total contigs can be annotated as known proteins, indicating that some of the assembled contigs may have previously uncharacterized functions. Functional classification by KOG and GO showed that activities associated with metabolism are predominant in goat skin during anagen phase. Many signaling pathways was also created based on the mapping of assembled contigs to the KEGG pathway database, some of which have been previously demonstrated to have diverse roles in hair follicle and hair shaft formation. Furthermore, gene expression profiling of three skin types identified ~6,300 transcript-derived contigs that are differentially expressed. These genes mainly enriched in the functional cluster associated with cell cycle and cell division. The large contig catalogue as well as the genes which were differentially expressed in different skin types provide valuable candidates for further characterization of gene functions.

scholarly journals Evidence for Selective Transformation of Autoreactive Immature Plasma Cells in Mice Deficient in Fasl

2004 ◽  
Vol 200 (11) ◽  
pp. 1467-1478 ◽  
Author(s):  
Jian Qiao Zhang ◽  
Cheryl Okumura ◽  
Thomas McCarty ◽  
Min Sun Shin ◽  
Partha Mukhopadhyay ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cells ◽  
B Cell ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Gene Repertoire ◽  
Altered Expression ◽  
Autoreactive B Cells ◽  
Systemic Autoimmunity ◽  
The Relationship

Germline mutations in Fas and Fasl induce nonmalignant T cell hyperplasia and systemic autoimmunity and also greatly increase the risk of B cell neoplasms. B lymphomas occurring in Fasl mutant (gld) mice usually are immunoglobulin (Ig) isotype switched, secrete Ig, and are plasmacytoid in appearance but lack Myc translocations characteristic of other plasma cell (PC) neoplasms. Here, we explore the relationship between B cell autoreactivity and transformation and use gene expression profiling to further classify gld plasmacytoid lymphomas (PLs) and to identify genes of potential importance in transformation. We found that the majority of PLs derive from antigen-experienced autoreactive B cells producing antinuclear antibody or rheumatoid factor and exhibit the skewed Ig V gene repertoire and Ig gene rearrangement patterns associated with these specificities. Gene expression profiling revealed that both primary and transplanted PLs share a transcriptional profile that places them at an early stage in PC differentiation and distinguishes them from other B cell neoplasms. In addition, genes were identified whose altered expression might be relevant in lymphomagenesis. Our findings provide a strong case for targeted transformation of autoreactive B cells in gld mice and establish a valuable model for understanding the relationship between systemic autoimmunity and B cell neoplasia.

Characterization of Early Human B Cell Development by Gene Expression Profiling.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 1352-1352
Author(s):  
Marit E. Hystad ◽  
Trond H. Bo ◽  
Edith Rian ◽  
June H. Myklebust ◽  
Einar Sivertsen ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cells ◽  
B Cell ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Cell Development ◽  
B Cell Development ◽  
Cell Populations ◽  
Early Human ◽  
Human B Cell

Abstract B cells develop from hematopoietic stem cells (HSC) in the bone marrow (BM) through a number of distinct stages before they migrate to the periphery as naïve mature B lymphocytes. These developmental stages can be identified by expression of cell surface antigens and Ig gene rearrangement status. The aim of this study was to characterize the earliest steps of normal human B cell development by gene expression profiling. Immunomagnetic selection and subsequent fluorescence-activated cell sorting (FACS) were used to isolate five populations from adult human BM: CD34+CD38− (HSC), CD34+CD10+CD19− early lymphoid progenitor cells (ELP), CD34+CD10+CD19+IgM− progenitor B cells (pro-B), CD34−CD10+CD19+IgM− precursor B cells (pre-B) and CD34−CD10+CD19+IgM+ immature B cells (IM). Total RNA was extracted from the purified cell populations, amplified and hybridized to Lymphochip cDNA microarrays. Six independent experiments from different donors were performed for each cell population. Expression of the genes encoding the selection markers confirmed the validity of the approach. Interestingly, genes necessary for the V(D)J-recombination such as RAG-1, RAG-2, TdT and ADA showed higher gene expression in the ELP population than in the HSC. In contrast, the transcription factors E2A, EBF, and Pax-5, which are all essential for early B-cell development, were first turned on in pro-B cells, in accordance with the B-cell lineage commitment. The ELP did not express B, T or NK lineage markers, except for a higher expression level of CD2 in the ELP population than in the four other cell populations. Taken together, the expression pattern of CD2 and the V(D)J-recombination genes in the ELP population, indicate that these cells have developed a lymphocyte potential, but are not fully committed to B-lineage cells. Hierarchical cluster analysis of the 758 differentially expressed genes (differences in relative expression by a factor of two or more and with maximum10% FDR) revealed a pattern that clearly separated the five consecutive cell populations. Furthermore, we created expression signatures based on information from Gene Ontology (GO) http://source.stanford.edu/cgi-bin/source/sourceSearch. One of the clearest distinctions between the gene expressions of the five developmental populations involved genes associated with proliferation, and showed that the HSC and IM populations are relatively indolent while the pro-B and pre-B populations comprised high expression levels of nearly all the proliferation associated genes. Finally, we examined in further detail the transitions between HSC, ELP and pro B cells. We found 25 genes to be differently expressed in the ELP population in comparison to the HSC and pro-B populations, including IGJ, BCL2 and BLNK. To identify combinations of markers that could better discriminate the ELP population, we also performed a gene pair class separation test. This resulted in 68 gene pairs with score above 10 that were denoted very good discriminators. For several of the markers the differences in gene expression were verified at the protein level by five colour FACS analysis. Taken together, these results provide new insight into the molecular processes that take place in the early human B cell differentiation, and in particular provide new information regarding expression of genes in the ELP population.

Cell of Origin Determination in Diffuse Large B-Cell Lymphoma: Performance of Immunohistochemical (IHC) Algorithms and Ability to Predict Outcome

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 950-950 ◽  
Author(s):  
Brian T. Hill ◽  
Angela M.B. Collie ◽  
Tomas Radivoyevitch ◽  
Eric D. Hsi ◽  
John Sweetenham
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
B Cell ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
De Novo ◽  
Progression Free Survival ◽  
B Cell Lymphoma ◽  
Cell Of Origin ◽  
Free Survival

Abstract Abstract 950 INTRODUCTION: Diffuse large B-cell lymphoma (DLBCL) can be categorized by its cell of origin (CoO) as either being derived from a germinal center B-cell (GCB) or activated B-cell (ABC). Primary mediastinal DLBCL represents a third, distinct entity. This classification was initially defined by gene expression profiling (GEP), which remains the gold standard for such determination. Determination of CoO will likely become the basis for patient selection for clinical trials of targeted therapies. Several algorithms and methods have been developed that use immunohistochemistry (IHC) to differentiate GCB-DLBCL from non-GCB DLBCL. These include the Hans algorithm (utilizes staining for CD10, Bcl-6 and Mum1), the Choi algorithm (utilizes additional staining for GCET1 and FoxP1) as well as the Tally method (does not use Bcl-6 and utilizes LMO2 as a tie-breaker stain for otherwise equivocal cases). Recently, it has been recognized that IHC approaches to assign CoO may not be reproducible even at highly experienced laboratories. We sought to determine the performance of these IHC assays in our laboratory as a necessary step in developing trials based on CoO stratification. METHODS: We reviewed 108 adult (age ≥18) cases of de novo DLBCL, the majority of which were treated with chemoimmunotherapy (R-CHOP or R-CVP) at the Cleveland Clinic from 2000–2010. Diagnostic biopsies were available for all cases. IHC staining was performed on tissue microarrays (TMAs), and published algorithms (Hans, Choi and Tally) were applied to categorize cases as GCB or non-GCB. In addition, gene expression profiling was completed in a subset of these cases, for which frozen tissue was available. A linear predictor score for gene expression profiling (GEP) was used to assign cases in 31 of 33 cases with 2 technical failures at the array stage (overall success rate 84.8%). Clinical details including age, sex, International Prognostic Index (IPI) stage at diagnosis, treatment, progression free survival (PFS) and overall survival (OS) were captured for 69 of the 108 patients. Actuarial survival analysis was performed according to the Kaplan and Meier method, and the curves compared by the log-rank test. RESULTS: For the 69 patients with adequate clinical follow-up, the median age was 64 years old (range 18–88). There were 49% males and 51% females. The distribution of patients with stage I, II, III, and IV disease at the time of diagnosis was 20%, 14%, 20%, and 32% (14% had unknown stage). The 5-year overall survival of patients was 88%. Results of the Hans algorithm, Choi algorithm and Tally method were interpretable in 98 (90.7%), 95 (87.9%) and 88 (81.5%) of 108 cases, respectively. Inability to assign subtypes was due to suboptimal staining of the TMA (tissue loss or poor staining of an individual core). Using GEP to assign CoO, 42% of cases were classified as GCB, 42% as ABC and 14% were unclassifiable. The sensitivities of the Hans, Choi and Tally approaches to identify the CoO predicted by GEP were 0.83, 0.83, and 0.58 for correctly identifying GCB cases, respectively, and were 0.70, 0.70 and 0.80 for identifying non-GCB cases, respectively. The positive predictive values of the Hans, Choi and Tally approaches were 0.83, 0.83, and 1.0 for GCB and 0.78, 0.78, and 0.89 for non-GCB. As shown in the figure, 5-year overall survival was significantly superior for GCB relative to ABC cases using GEP (100% vs. 58.9%, P < 0.001) and for GCB vs. non-GCB cases for the algorithms of Hans (100% vs. 82.3%, P = 0.0197) and Choi (95.6% vs. 78.0%, P = 0.0482). The Tally method was not predictive of outcome, possibly due to insufficient power (5-year OS 94.4% for GCB vs. 80.7% for non-GCB, P = 0.1725). Similar findings were observed for progression-free survival. CONCLUSIONS: The Hans and Choi algorithms are reasonable methods for identifying PFS and OS differences based on CoO for de novo DLBCL treated with chemoimmunotherapy. The positive predictive value is universally high for all algorithms tested, but the sensitivity of IHC for identifying CoO was fair, particularly for the Tally method. IHC represents a valid biomarker to identify non-GCB cases. Clinical trials of DLBCL that stratify patients by IHC are feasible provided the performance characteristics of the algorithms are taken into consideration during study design. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Immune Profiling of Diagnostic DLBCL Biopsies Dramatically Improves upon Cell-of-Origin Risk Stratification

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 719-719
Author(s):  
Michael Nissen ◽  
Xuehai Wang ◽  
Clementine Sarkozy ◽  
Aixiang Jiang ◽  
Daisuke Ennishi ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
T Cell ◽  
B Cell ◽  
Gene Expression Profiling ◽  
Patient Outcomes ◽  
Expression Profiling ◽  
Research Funding ◽  
Cell Of Origin ◽  
Discovery Cohort

Abstract Background: Diffuse large B cell lymphoma (DLBCL) is an aggressive malignancy of mature B cells. The disease has traditionally been subdivided into cell-of-origin (COO) subtypes - germinal centre B cell-like (GCB) or activated B cell-like (ABC) - as determined by expression profiling or immunohistochemistry of the tumor cells. However the role of the immune microenvironment, and how the tumor and immune system interact to influence patient outcomes, remains to be fully investigated. Methods: In this project, we used mass cytometry (CyTOF) to deeply profile both tumor cell phenotypes and the immune microenvironments, alongside ABC/GCB classification and mutation profiling, in a discovery cohort of 54 DLBCL cases. As well, a validation cohort of 129 DLBCL patients were immunologically profiled by high-dimensional conventional flow cytometry, and their immune profiles alongside ABC/GCB classification, mutation profiling, and RNAseq data, were correlated with patient outcomes as measured by progression-free survival (PFS). Results: Analysis of the CyTOF/discovery cohort demonstrated that DLBCL tumor cells are phenotypically unique to each patient, with a small number of samples displaying distinct sub-clonal structure, often distinguished by differential expression of immune-related proteins like MHC-II. ABC/GCB classifications could be recapitulated based on tumor cell phenotypes, demonstrating that while COO was a robust feature, a great deal of heterogeneity exists within these established subtypes. Immunological profiling of the CyTOF/discovery cohort revealed that DLBCL samples could be divided into three distinct groups which roughly correlated with abundances of naïve, activated, or terminally differentiated T cells, respectively. This profiling schema was extended to the validation cohort of 129 patients which in turn led to identification of a subset of patients with a very high risk of disease progression (5-year PFS; 30% high risk vs. 80% low risk, p&lt;0.0001). This final classifier was based on a combination of ABC-DLBCL designation, combined with the presence of an immune microenvironment dominated by terminally differentiated (CD57+) T cells. We performed a limited series of functional studies using primary DLBCL biopsy samples to characterize further these CD57+ T cells as clonally restricted and incapable of responding to antigenic challenge. Interestingly, traditional immune markers of T cell exhaustion such as PD-1, TIM3, LAG3 and TIGIT were not correlated with patient outcomes. Conclusions: Overall, this study demonstrates the utility of immune profiling in risk stratification based on initial diagnostic biopsy material and highlights a subset of DLBCL patients who may benefit from immune-based therapies to rejuvenate the anti-tumor T cell response. We conclude that T cell senescence, rather than exhaustion, is the more relevant feature in DLBCL disease biology and highlights an alternate target for immunomodulatory therapy. Figure 1 Figure 1. Disclosures Craig: Bayer: Consultancy. Slack: Seagen: Consultancy, Honoraria. Scott: Abbvie: Consultancy; AstraZeneca: Consultancy; Celgene: Consultancy; NanoString Technologies: Patents & Royalties: Patent describing measuring the proliferation signature in MCL using gene expression profiling.; BC Cancer: Patents & Royalties: Patent describing assigning DLBCL COO by gene expression profiling--licensed to NanoString Technologies. Patent describing measuring the proliferation signature in MCL using gene expression profiling. ; Rich/Genentech: Research Funding; Janssen: Consultancy, Research Funding; Incyte: Consultancy. Steidl: Epizyme: Research Funding; Bayer: Consultancy; Curis Inc.: Consultancy; Seattle Genetics: Consultancy; AbbVie: Consultancy; Trillium Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding.

scholarly journals BTG1 Mutation Promotes Aggressive Lymphoma Development By Lowering the Threshold to MYC Activation and Generating "Super-Competitor" B Cells

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 359-359
Author(s):  
Coraline Mlynarczyk ◽  
Matthew Teater ◽  
Juhee Pae ◽  
Theinmozhi Arulraj ◽  
Christopher R Chin ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cells ◽  
B Cell ◽  
Gene Expression Profiling ◽  
Mrna Stability ◽  
Expression Profiling ◽  
Research Funding ◽  
Target Genes ◽  
S Phase

Abstract Somatic missense mutations of BTG1 are exclusive to germinal center (GC)-derived B cell lymphomas (~12% of DLBCLs) and are most prevalent in ABC-DLBCL (p=0.0184 vs GCB-DLBCL), particularly in the MCD/cluster 5 subtype, which features extranodal dissemination and unfavorable outcome. However, the relevance, mechanism of action and biological contribution of BTG1 mutations have not been studied. Using a rigorous genomic covariate analysis, we identified BTG1 mutations as a top genetic driver in DLBCL. Furthermore, molecular dynamics simulations indicated that BTG1 recurrent mutations, including the most frequent Q36H, disrupted the protein structure, with likely deleterious functional consequences. To investigate the effect of BTG1 mutation in GC B cells, we generated a conditional Btg1Q36H knock in mouse crossed to the B cell specific Cd19Cre line. Surprisingly, there was no apparent phenotype in GC B cells or other B cell populations. However, placing Btg1 Q36H and WT GC B cells in competition within the same mouse through adoptive transfer revealed a dramatic competitive advantage of Btg1 Q36H cells, virtually taking over the GC reaction. To gain further insight into this striking fitness advantage, we performed RNAseq in Btg1 Q36H GCs, which showed marked enrichment for genes induced in positively selected GC B cells, including MYC targets and biosynthetic pathways. The same genes were also enriched in BTG1 mutant DLBCL patients in 2 independent cohorts. Furthermore, Btg1 Q36H GC B cells displayed greater RNA content and cell size, reflecting increased fitness. Positive selection normally triggers a brief Myc pulse in GC B cells. We therefore crossed our Btg1Q36H mice to the MycGFPprotein fusion reporter and observed higher proportion of Myc GFP+ cells in Btg1 Q36H GCs. For mechanistic studies, we generated isogenic BTG1 Q36H or BTG1 WT human DLBCL cell lines. BTG1 Q36H cells exhibited enrichment for the same positively selected GC B and MYC target genes, as well as greater RNA content and cell size. BTG1 family members were suggested to interact with RNA. Performing RNA-immunoprecipitation, we discovered that ~800 transcripts associated with BTG1 WT, but not BTG1 Q36H. Notably, these corresponded to the same positively selected GC B and MYC target genes, including MYC itself. BTG1 was shown to regulate mRNA stability in other cell types. However, BTG1 Q36H did not alter MYC mRNA stability and instead facilitated MYC protein synthesis, thus disrupting a novel GC context-specific checkpoint mechanism, whereby BTG1 normally attenuates spurious MYC translation to tightly restrict fitness potential. In GC B cells, Myc induction coincides with S phase entry, but G2/M progression requires re-entry into the proliferative dark zone. To characterize GC dynamics in vivo, we performed targeted single cell RNAseq in competing Btg1 Q36H and WT GC B cells and noted earlier and higher proportion of positively selected Btg1 Q36H GC B cells having committed to G2/M and the proliferative program. We confirmed faster S phase completion in competing Btg1 Q36H GC B cells by in vivo EdU/BrdU labelling and greater re-entry into the proliferative dark zone by in vivo antigen delivery to synchronize GC B cells at the time of positive selection. Given that MCD-DLBCLs express high levels of BCL2, we crossed our Btg1Q36H mice to the VavP-Bcl2 model. As compared to VavP-Bcl2, VavP-Bcl2+Btg1 Q36H mice displayed shorter survival (p=0.0005), earlier onset of lymphoma, dysplastic B cell infiltration into non lymphoid organs and they contained highly mutated, selected and clonal tumor B cells. Moribund VavP-Bcl2+Btg1 Q36H mice uniquely featured sheets of large, immunoblastic lymphoma cells, characteristic of ABC-DLBCLs. Most notably, examining ABC-DLBCLs from 5 independent cohorts showed inferior clinical outcome for BTG1 mutant patients (p=0.0011) and independent association of BTG1 mutation with inferior overall survival by multivariable Cox regression (p=0.0190). Collectively, we find that BTG1 mutations mediate lymphomagenesis through an entirely novel mechanism of action that recapitulates the embryonic MYC-dependent "super-competitive" phenotype originally described in Drosophila imaginal disc cells. In the GC, "super-competition" is provided by BTG1 mutation via a subtle acceleration of MYC induction and GC dynamics, conferring dramatic fitness and the potential to transform into aggressive lymphomas. Disclosures Hoehn: Prellis Biologics: Consultancy. Elemento: Janssen: Research Funding; Freenome: Consultancy, Other: Current equity holder in a privately-held company; Volastra Therapeutics: Consultancy, Other: Current equity holder, Research Funding; Owkin: Consultancy, Other: Current equity holder; Champions Oncology: Consultancy; One Three Biotech: Consultancy, Other: Current equity holder; Eli Lilly: Research Funding; AstraZeneca: Research Funding; Johnson and Johnson: Research Funding. Scott: NanoString Technologies: Patents & Royalties: Patent describing measuring the proliferation signature in MCL using gene expression profiling.; BC Cancer: Patents & Royalties: Patent describing assigning DLBCL COO by gene expression profiling--licensed to NanoString Technologies. Patent describing measuring the proliferation signature in MCL using gene expression profiling. ; AstraZeneca: Consultancy; Abbvie: Consultancy; Celgene: Consultancy; Incyte: Consultancy; Janssen: Consultancy, Research Funding; Rich/Genentech: Research Funding. Melnick: Constellation: Consultancy; Epizyme: Consultancy; Daiichi Sankyo: Research Funding; Sanofi: Research Funding; Janssen Pharmaceuticals: Research Funding; KDAC Pharma: Membership on an entity's Board of Directors or advisory committees.

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