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Epstein-Barr virus (EBV)-driven B cell neoplasms arise from the reactivation of latently infected B cells. In a subset of patients, EBV was seen to drive a polymorphous lymphoproliferative disorder (LPD) in which B cell differentiation was retained. In this work, spontaneous EBV reactivation following B cell mitogen stimulation was shown to provide a potential model of polymorphic EBV-driven LPD. Here, we developed an in vitro model of plasma cell (PC) differentiation from peripheral blood memory B cells. To assess the frequency and phenotypes of EBV-associated populations derived during differentiation, we analysed eight differentiations during the PC stage with a targeted single-cell gene expression panel. We identified subpopulations of EBV-gene expressing cells with PC and/or B cell expression features in differentiations from all tested donors. EBV-associated cells varied in frequency, ranging from 3–28% of cells. Most EBV-associated cells expressed PC genes such as XBP1 or MZB1, and in all samples these included a quiescent PC fraction that lacked cell a cycle gene expression. With increasing EBV-associated cells, populations with B cell features became prominent, co-expressing a germinal centre (GC) and activating B cell gene patterns. The presence of highly proliferative EBV-associated cells was linked to retained MS4A1/CD20 expression and IGHM and IGHD co-expression, while IGHM class-switched cells were enriched in quiescent PC fractions. Thus, patterns of gene expression in primary EBV reactivation were shown to include features related to GC B cells, which was also observed in EBV-transformed lymphoblastoid cell lines. This suggests a particular association between spontaneously developing EBV-expansions and IgM+ IgD+ non-switched B cells.

Histone deacetylase (HDAC) inhibitors have proven useful therapeutic agents for certain hematologic cancers. However, HDAC inhibition causes diverse cellular outcomes, and identification of cancer-relevant pathways within these outcomes remains unresolved. In this study, we utilized an unbiased loss-of-function screen and identified the Toll-like receptor (TLR) adaptor protein MYD88 as a key regulator of the antiproliferative effects of HDAC inhibition. High expression of MYD88 exhibited increased sensitivity to HDAC inhibitors; conversely, low expression coincided with reduced sensitivity. MYD88-dependent TLR signaling controlled cytokine levels, which then acted via an extracellular mechanism to maintain cell proliferation and sensitize cells to HDAC inhibition. MYD88 activity was directly regulated through lysine acetylation and was deacetylated by HDAC6. MYD88 was a component of a wider acetylation signature in the ABC subgroup of diffuse large B-cell lymphoma, and one of the most frequent mutations in MYD88, L265P, conferred increased cell sensitivity to HDAC inhibitors. Our study defines acetylation of MYD88, which, by regulating TLR-dependent signaling to cytokine genes, influences the antiproliferative effects of HDAC inhibitors. Our results provide a possible explanation for the sensitivity of malignancies of hematologic origin to HDAC inhibitor–based therapy. Cancer Res; 76(23); 6975–87. ©2016 AACR.

High-resolution heat map of top gene signatures and GO terms enriched for top MYD88 correlated genes in ABC DLBCL

MYD88 expression and IL-6 sensitises cells to HDAC inhibition

Supplemental figures 1-5 legends

MYD88 expression coincides with an acetylation signature at ABC subgroup-specific GO gene clusters

High levels of MYD88 are apparent in tissue microarray biopsies from patients with DLBCL, but not prostate and colorectal cancer.

HDAC6 is a key regulator of MYD88 acetylation and somatic mutation in MYD88 influences HDI sensitivity.

Supplemental figures 1-5 legends

Upon encounter with Ag, B cells undergo a sequential process of differentiation to become Ab-secreting plasma cells. Although the key drivers of differentiation have been identified, research has been limited by the lack of in vitro models recapitulating the full process for murine B cells. In this study, we describe methodology using BCR or TLR ligation to obtain plasma cells that are phenotypically mature, have exited cell cycle and express a gene signature concordant with long-lived plasma cells. Dependent on the initial stimuli, the transcriptomes also show variation including the enhanced expression of matrisome components after BCR stimulation, suggestive of unique functional properties for the resultant plasma cells. Moreover, using the new culture conditions we demonstrate that alternative promoter choice regulating the expression of the master transcription factor Blimp-1/Prdm1 can be observed; when the canonical B cell promoter for Prdm1 is deleted, differentiating B cells exhibit flexibility in the choice of promoter, dictated by the initiating stimulus, with preferential maintenance of expression following exposure to TLR ligation. Thus our system provides a readily tractable model for furthering our understanding of plasma cell biology.

Ab-secreting cells survive in niche microenvironments, but cellular responses driven by particular niche signals are incompletely defined. The TNF superfamily member a proliferation-inducing ligand (APRIL) can support the maturation of transitory plasmablasts into long-lived plasma cells. In this study, we explore the biological programs established by APRIL in human plasmablasts. Under conditions allowing the maturation of ex vivo– or in vitro–generated plasmablasts, we find that APRIL drives activation of ERK, p38, and JNK, accompanied by a classical NF-κB response and activation of the AKT/FOXO1 pathway. Time-course gene expression data resolve coordinated transcriptional responses propagated via immediate early genes and NF-κB targets and converging onto modules of genes enriched for MYC targets and metabolism/cell growth–related pathways. This response is shared between APRIL and an alternate TNF superfamily member CD40L but is not a feature of alternative niche signals delivered by IFN-α or SDF1. However, APRIL and CD40L responses also diverge. CD40L drives expression of genes related to the activated B cell state whereas APRIL does not. Thus, APRIL establishes a broad foundation for plasma cell longevity with features of cellular refueling while being uncoupled from support of the B cell state.

Antibody secreting cells (ASCs) survive in niche microenvironments, but cellular responses driven by particular niche signals are incompletely defined. The TNF superfamily member APRIL provides a niche signal that can support the transition of transitory plasmablasts into long-lived plasma cells. Here we explore how APRIL helps to establish the biological programs that promote life in the niche, by studying the initial response of primary human plasmablast to APRIL. Under conditions allowing the maturation of ex vivo or in vitro generated plasmablasts, we find that APRIL drives activation of ERK, p38 and JNK. This is accompanied by a classical NFκB response. Under these conditions induction of AKT phosphorylation is also observed with similar kinetics, paralleled by FOXO1 phosphorylation and nuclear exclusion. Time course gene expression data resolve the downstream co-ordinated transcriptional response. The APRIL-signal propagates via immediate early genes and classical NFκB responsive targets to converge onto modules of MYC- and OCT2-regulated gene expression linked to cell growth, as well as leading to enhanced expression of ICAM1 and SQSTM1 associated with adhesion and metabolic/stress responses. Thus, APRIL drives a combination of multiple transcriptional programs that co-ordinate cell growth, stress response and adhesion in human ASCs, providing a broad foundation to support plasma cell longevity.

Cancers converge onto shared patterns that arise from constraints placed by the biology of the originating cell lineage and microenvironment on programs driven by oncogenic events. Here we define consistent expression modules reflecting this structure in colon and breast cancer by exploiting expression data resources and a new computationally efficient approach that we validate against other comparable methods. This approach, Parsimonious Gene Correlation Network Analysis (PGCNA), allows comparison of network structures between these cancer types identifying shared modules of gene co-expression reflecting: cancer hallmarks, functional and structural gene batteries, copy number variation and biology of originating lineage. These networks along with the mapping of outcome data at gene and module level provide an interactive resource that generates context for relationships between genes within and between such modules. Assigning module expression values (MEVs) provides a tool to summarize network level gene expression in individual cases illustrating potential utility in classification and allowing analysis of linkage between module expression and mutational state. Exploiting TCGA data thus defines both recurrent patterns of association between module expression and mutation at data-set level, and exemplifies the polarization of mutation patterns with the leading edge of module expression at individual case level. We illustrate the scalable nature of the approach within immune response related modules, which in the context of breast cancer demonstrates the selective association of immune subsets, in particular mast cells, with the underlying mutational pattern. Together our analyses provide evidence for a generalizable framework to enhance molecular stratification in cancer., Gene expression networks: radical pruning enhances resolution To control cell behavior in physiology and disease, genes are expressed in co-ordinated networks, but the sheer number of connections between genes challenges interpretation of this structure. Care, Westhead and Tooze—University of Leeds, UK—show how retaining only the 3 nearest neighbors of any gene improves information content of expression networks. This new approach, PGCNA, outperforms alternatives and can be used to integrate multiple sets of data. With breast and colon cancer as examples the team demonstrate how such networks can be used to derive bar-codes that summarize cancer gene expression and link to underlying gene mutation states. This has potential application in precision medicine and establishes a scalable, efficient, approach to enhance network analysis.

Cell-of-origin subclassification of diffuse large B cell lymphoma (DLBCL) into activated B cell-like (ABC), germinal centre B cell-like (GCB) and unclassified (UNC) or type III by gene expression profiling is recommended in the latest update of the World Health Organization's classification of lymphoid neoplasms. There is, however, no accepted gold standard method or dataset for this classification. Here, we compare classification results using gene expression data for 68 formalin-fixed paraffin-embedded DLBCL samples measured on four different gene expression platforms (Illumina wG-DASLTM arrays, Affymetrix PrimeView arrays, Illumina TrueSeq RNA sequencing and the HTG EdgeSeq DLBCL Cell of Origin Assay EU using an established platform agnostic classification algorithm (DAC) and the classifier native to the HTG platform, which is CE marked for in vitro diagnostic use (CE-IVD). Classification methods and platforms show a high level of concordance, with agreement in at least 80% of cases and rising to much higher levels for classifications of high confidence. Our results demonstrate that cell-of-origin classification by gene expression profiling on different platforms is robust, and that the use of the confidence value alongside the classification result is important in clinical applications.

Upon encounter with antigen, B cells undergo a sequential process of differentiation to become antibody-secreting plasma cells. While the key drivers of differentiation have been identified, research has been limited by the lack of in vitro models recapitulating the full process for murine B cells. Here we describe methodology using BCR or TLR ligation to obtain plasma cells that are phenotypically mature, have exited cell cycle and express a gene signature concordant with long-lived plasma cells. Dependent on the initial stimuli, the transcriptomes also show variation including the enhanced expression of matrisome components after BCR stimulation, suggestive of unique functional properties for the resultant plasma cells. Moreover, using the new culture conditions we demonstrate that alternative promoter choice regulating the expression of the master transcription factor Blimp-1/Prdm1 can be observed; when the canonical B cell promoter for Prdm1 is deleted differentiating B cells exhibit flexibility in the choice of promoter, dictated by the initiating stimulus, with preferential maintenance of expression following exposure to TLR ligation. Thus, our novel system provides a readily tractable model for furthering our understanding of plasma cell biology.

As human B-cells differentiate, IRF4 switches occupancy and acquires CTCF colocalisation and IRF4 regulatory output diverts from cell activation to plasma cell state genes in conjunction with XBP1, whereas BLIMP1 represses activation., The activated B-cell (ABC) to plasmablast transition encompasses the cusp of antibody-secreting cell (ASC) differentiation. We explore this transition with integrated analysis in human cells, focusing on changes that follow removal from CD40-mediated signals. Within hours of input signal loss, cell growth programs shift toward enhanced proliferation, accompanied by ER-stress response, and up-regulation of ASC features. Clustering of genomic occupancy for IRF4, BLIMP1, XBP1, and CTCF with histone marks identifies a dichotomy: XBP1 and IRF4 link to induced but not repressed gene modules in plasmablasts, whereas BLIMP1 links to modules of ABC genes that are repressed, but not to activated genes. Between ABC and plasmablast states, IRF4 shifts away from AP1/IRF composite elements while maintaining occupancy at IRF and ETS/IRF elements. This parallels the loss of BATF expression, which is identified as a potential BLIMP1 target. In plasmablasts, IRF4 acquires an association with CTCF, a feature maintained in plasma cell myeloma lines. Thus, shifting occupancy links IRF4 to both ABC and ASC gene expression, whereas BLIMP1 occupancy links to repression of the activation state.

The activated B-cell (ABC) to plasmablast transition is the cusp of antibody secreting cell (ASC) differentiation but is incompletely defined. We apply expression time-courses, parsimonious gene correlation network analysis, and ChIP-seq to explore this in human cells. The transition initiates with input signal loss leading within hours from cell growth dominant programs to enhanced proliferation, accompanied from 24h by ER-stress response, secretory optimization and upregulation of ASC features. Clustering of genomic occupancy for ASC transcription factors (TFs) IRF4, BLIMP1 and XBP1 with CTCF and histone marks defines distinct patterns for each factor in plasmablasts. Integrating TF-associated clusters and modular gene expression identifies a dichotomy: XBP1 and IRF4 significantly link to gene modules induced in plasmablasts, but not to modules of repressed genes, while BLIMP1 links to modules of ABC genes repressed in plasmablasts but is not significantly associated with modules induced in plasmablasts. Pharmacological inhibition of the G9A (EHMT2) histone-methytransferase, a BLIMP1 co-factor that catalyzes repressive H3K9me2 marks, leaves functional ASC differentiation intact but de-represses ABC-state genes. Thus, in human plasmablasts IRF4 and XBP1 emerge as the dominant association with ASC gene expression, while BLIMP1 links to repressed modules with particular focus in repression of the B-cell activation state.

Waldenström macroglobulinemia (WM) is a rare malignancy in which clonal B cells infiltrate the bone marrow and give rise to a smaller compartment of neoplastic plasma cells that secrete monoclonal immunoglobulin M paraprotein. Recent studies into underlying mutations in WM have enabled a much greater insight into the pathogenesis of this lymphoma. However, there is considerably less characterization of the way in which WM B cells differentiate and how they respond to immune stimuli. In this study, we assess WM B-cell differentiation using an established in vitro model system. Using T-cell–dependent conditions, we obtained CD138+ plasma cells from WM samples with a frequency similar to experiments performed with B cells from normal donors. Unexpectedly, a proportion of the WM B cells failed to upregulate CD38, a surface marker that is normally associated with plasmablast transition and maintained as the cells proceed with differentiation. In normal B cells, concomitant Toll-like receptor 7 (TLR7) activation and B-cell receptor cross-linking drives proliferation, followed by differentiation at similar efficiency to CD40-mediated stimulation. In contrast, we found that, upon stimulation with TLR7 agonist R848, WM B cells failed to execute the appropriate changes in transcriptional regulators, identifying an uncoupling of TLR signaling from the plasma cell differentiation program. Provision of CD40L was sufficient to overcome this defect. Thus, the limited clonotypic WM plasma cell differentiation observed in vivo may result from a strict requirement for integrated activation.

Using a Burkitt lymphoma-like gene expression signature, we recently defined a high-risk molecular high-grade (MHG) group mainly within germinal centre B-cell like diffuse large B-cell lymphomas (GCB-DLBCL), which was enriched for MYC/BCL2 double-hit (MYC/BCL2-DH). The genetic basis underlying MHG-DLBCL and their aggressive clinical behaviour remain unknown. We investigated 697 cases of DLBCL, particularly those with MYC/BCL2-DH (n = 62) by targeted sequencing and gene expression profiling. We showed that DLBCL with MYC/BCL2-DH, and those with BCL2 translocation, harbour the characteristic mutation signatures that are associated with follicular lymphoma and its high-grade transformation. We identified frequent MYC hotspot mutations that affect the phosphorylation site (T58) and its adjacent amino acids, which are important for MYC protein degradation. These MYC mutations were seen in a subset of cases with MYC translocation, but predominantly in those of MHG. The mutations were more frequent in double-hit lymphomas with IG as the MYC translocation partner, and were associated with higher MYC protein expression and poor patient survival. DLBCL with MYC/BCL2-DH and those with BCL2 translocation alone are most likely derived from follicular lymphoma or its precursor lesion, and acquisition of MYC pathogenic mutations may augment MYC function, resulting in aggressive clinical behaviour.

Recent findings from our group, and the wider community, show that standard treatment does not impose an apparent bottleneck on the clonal evolution of adult glioblastoma (GBM), implying a lack of direct therapeutic opportunity. This does not negate the possibility that multiple treatment-resistance mechanisms co-exist in tumours, repeated across patients, making a combination of targeted therapies a potentially effective approach. We investigated whether treatment resistance may be driven by selection of cellular properties conferred above the level of the genome. Differential expression analysis was performed on 23 pairs of primary and recurrent tumours from patients who received standard treatment and had a local recurrence treated by surgery and second line chemotherapy. This revealed a treatment-induced shift in cell states linked to normal neurodevelopment. The latter is orchestrated by cascades of transcription factors. We, therefore, applied a bespoke gene set enrichment analysis to our paired expression data to investigate whether any factors were implicated in co-regulation of the genes that were altered through therapy. This identified a specific chromatin remodelling machinery, instrumental in normal neurogenesis. We validated our results in an independent cohort of 22 paired GBM samples. Our results suggest that the chromatin remodelling machinery is responsible for determining transcriptional hierarchies in GBM, shown elsewhere to have different treatment sensitivities such that their relative abundances are altered through treatment.

BACKGROUND Glioblastoma (GBM) tumours recur following standard treatment in almost all cases. We use ‘omics technologies to simultaneously profile pairs of primary and matched recurrent GBM to specifically identify and characterise the cells that resisted treatment, with the aim of determining how to more effectively kill them. MATERIAL AND METHODS We have analysed high coverage RNAseq data from pairs of GBM tumours: primary de novo tumour and matched local recurrence from patients that underwent standard therapy. Our original cohort constituted 23 pairs and our validation cohort was an additional 22 pairs. We also cultured two plates of spheroids directly from a patient’s GBM, treating one with radiation and temozolomide. We monitored growth and captured and sequenced RNA from single cells at two time-points: one week post-treatment when the deviation between untreated and treated spheroid growth curves was most pronounced; and three weeks post-treatment when the growth rate of treated spheroids had recovered. We investigated differential gene expression between primary and recurrent pairs, and single cells pre- and post-treatment, and performed a bespoke per patient gene set enrichment analysis. RESULTS Differential gene expression analysis in 23 tumour pairs indicated a treatment-induced shift in cell states linked to normal neurogenesis and prompted us to develop a novel gene set enrichment analysis approach to identify gene regulatory factors that may orchestrate such a shift. This revealed the significant and universal dysregulation of genes, through therapy, that are targeted by a specific chromatin remodeling machinery. This finding was validated in an independent cohort of 22 further GBM pairs. To understand the therapeutic potential of this finding we must determine whether genes are dysregulated through therapy owing to a) their fixed expression in inherently treatment resistance cells in the primary tumour which get selected during therapy to increase the signal of that profile, or b) changes in expression during the process of cells acquiring treatment resistance. To inspect this, we analysed single cell gene expression data from GBM spheroids pre- and post-treatment. We found that there was significant dysregulation of the genes associated with the chromatin remodeling complex but only at the three-week post-treatment time-point. CONCLUSION Our results indicate that GBM cells are being transcriptionally reprogrammed in response to treatment; the mechanism of which may represent a therapeutic opportunity.

BackgroundGlioblastoma (GBM) is a fatal and incurable brain cancer with a dismal prognosis. In order to impact on this disease, we need to understand how infiltrating, non resectable tumour cells resist chemoradiation and facilitate disease recurrence. To this end, we generated or acquired bulk tumour RNA sequencing data from 45 paired primary and locally recurrent GBM tumours (split into original and validation cohorts) from patients that received standard treatment. We also generated DNA methylation profiles for 9 pairs and sequenced RNA from single cells isolated from a patient derived GBM spheroid model at different timepoints following in vitro chemoradiation.ResultsWe have identified a set of genes with Jumonji and AT-Rich Interacting Domain 2 (JARID2) binding sites in their promoters that are universally dysregulated in post-standard treatment recurrent GBMs compared to the primary tumour. The direction of dysregulation is patient-dependent and not associated with differential promoter DNA methylation. Our in vitro experiments suggest that this dysregulation occurs dynamically following treatment as opposed to resulting from selection of cells with specific expression profiles.ConclusionJARID2 is an accessory protein to a chromatin remodeling complex, responsible for histone modifications observed during cell state transitions in both normal brain and GBM. We propose that JARID2 facilitates GBM recurrence following treatment by indirect transcriptional reprogramming of surviving cells in whichever manner is needed to reproduce the phenotypic heterogeneity required for tumour regrowth in vivo. The mechanism of this reprogramming may present a therapeutic vulnerability for more effective treatment of GBM.

The Epstein-Barr virus (EBV) is found almost exclusively in the activated B-cell (ABC) subtype of diffuse large B-cell lymphoma (DLBCL), yet its contribution to this tumour remains poorly understood. We have focused on the EBV-encoded latent membrane protein-1 (LMP1), a constitutively activated CD40 homologue expressed in almost all EBV-positive DLBCLs and which can disrupt germinal centre (GC) formation and drive lymphomagenesis in mice. Comparison of the transcriptional changes that follow LMP1 expression with those that follow transient CD40 signalling in human GC B cells enabled us to define pathogenic targets of LMP1 aberrantly expressed in ABC-DLBCL. These included the down-regulation of S1PR2, a sphingosine-1-phosphate (S1P) receptor that is transcriptionally down-regulated in ABC-DLBCL, and when genetically ablated leads to DLBCL in mice. Consistent with this, we found that LMP1-expressing primary ABC-DLBCLs were significantly more likely to lack S1PR2 expression than were LMP1-negative tumours. Furthermore, we showed that the down-regulation of S1PR2 by LMP1 drives a signalling loop leading to constitutive activation of the phosphatidylinositol-3-kinase (PI3-K) pathway. Finally, core LMP1-PI3-K targets were enriched for lymphoma-related transcription factors and genes associated with shorter overall survival in patients with ABC-DLBCL. Our data identify a novel function for LMP1 in aggressive DLBCL. Copyright (c) 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Recurrent mutational activation of the MAP kinase pathway in plasma cell myeloma implicates growth factor-like signaling responses in the biology of antibody secreting cells (ASCs). Physiological ASCs survive in niche microenvironments, but how niche signals are propagated and integrated is poorly understood. Here we dissect such a response in human ASCs using an in vitro model. Applying time course expression data and parsimonious gene correlation networking analysis (PGCNA), we map expression changes that occur during the maturation of proliferating plasmablast to quiescent plasma cell under survival conditions including the potential niche signal TGFB3. This analysis demonstrates a convergent pattern of differentiation, linking UPR/ER stress to secretory optimization, co-ordinated with cell cycle exit. TGFB3 supports ASC survival while having a limited effect on gene expression including up-regulation of CXCR4. This is associated with a significant shift in response to SDF1 in ASCs with amplified ERK1/2 activation, growth factor-like immediate early gene regulation and EGR1 protein expression. Similarly, ASCs responding to survival conditions initially induce partially overlapping sets of immediate early genes, without sustaining the response. Thus, in human ASCs growth factor-like gene regulation is transiently imposed by niche signals but is not sustained during subsequent survival and maturation.

Histone deacetylase (HDAC) inhibitors have proven useful therapeutic agents for certain hematologic cancers. However, HDAC inhibition causes diverse cellular outcomes, and identification of cancer-relevant pathways within these outcomes remains unresolved. In this study, we utilized an unbiased loss-of-function screen and identified the Toll-like receptor (TLR) adaptor protein MYD88 as a key regulator of the antiproliferative effects of HDAC inhibition. High expression of MYD88 exhibited increased sensitivity to HDAC inhibitors; conversely, low expression coincided with reduced sensitivity. MYD88-dependent TLR signaling controlled cytokine levels, which then acted via an extracellular mechanism to maintain cell proliferation and sensitize cells to HDAC inhibition. MYD88 activity was directly regulated through lysine acetylation and was deacetylated by HDAC6. MYD88 was a component of a wider acetylation signature in the ABC subgroup of diffuse large B-cell lymphoma, and one of the most frequent mutations in MYD88, L265P, conferred increased cell sensitivity to HDAC inhibitors. Our study defines acetylation of MYD88, which, by regulating TLR-dependent signaling to cytokine genes, influences the antiproliferative effects of HDAC inhibitors. Our results provide a possible explanation for the sensitivity of malignancies of hematologic origin to HDAC inhibitor–based therapy. Cancer Res; 76(23); 6975–87. ©2016 AACR.

The unfolded protein response (UPR) and activation of XBP1 is necessary for high secretory efficiency and functional differentiation of antibody secreting cells (ASCs). The UPR additionally includes a branch in which membrane-bound transcription factors, exemplified by ATF6, undergo intramembrane-proteolysis by the sequential action of site-1 (MBTPS1/S1P) and site-2 proteases (MBTPS2/S2P) and release of the cytoplasmic domain as an active transcription factor. Such regulation is shared with a family of CREB3-related transcription factors and sterol regulatory element-binding proteins (SREBPs). Of these, we identify that the CREB3 family member CREB3L2 is strongly induced and activated during the transition from B-cell to plasma cell state. Inhibition of site-1 protease leads to a profound reduction in plasmablast number linked to induction of autophagy. Plasmablasts generated in the presence of site-1 protease inhibitor segregated into CD38high and CD38low populations, the latter characterized by a marked reduction in the capacity to secrete IgG. Site-1 protease inhibition is accompanied by a distinctive change in gene expression associated with amino acid, steroid and fatty acid synthesis pathways. These results demonstrate that transcriptional control of metabolic programs necessary for secretory activity can be targeted via site-1 protease inhibition during ASC differentiation.

Cancers converge onto shared patterns that arise from constraints placed by the biology of the originating cell lineage and microenvironment on recurrent programs driven by oncogenic events. This structure should be transferable to molecular stratification. We exploit expression data resources and a parsimonious and computationally efficient network analysis method to define consistent expression modules in colon and breast cancer. Comparison between cancer types identifies principles of gene co-expression: cancer hallmarks, functional and structural gene batteries, copy number variation and biology of originating lineage. Mapping outcome data at gene and module level onto these networks generates a detailed interactive resource. Testing the utility of the resulting modules in TCGA data defines specific associations of module expression with mutation state, identifying striking associations such as mast cell gene expression and mutation pattern in breast cancer. These analyses provide evidence for a generalizable framework to enhance molecular stratification in cancer.

Interferon regulatory factor 4 (IRF4) is central to the transcriptional network of activated B-cell-like diffuse large B-cell lymphoma (ABC-DLBCL), an aggressive lymphoma subgroup defined by gene expression profiling. Since cofactor association modifies transcriptional regulatory input by IRF4, we assessed genome occupancy by IRF4 and endogenous cofactors in ABC-DLBCL cell lines. IRF4 partners with SPIB, PU.1 and BATF genome-wide, but SPIB provides the dominant IRF4 partner in this context. Upon SPIB knockdown IRF4 occupancy is depleted and neither PU.1 nor BATF acutely compensates. Integration with ENCODE data from lymphoblastoid cell line GM12878, demonstrates that IRF4 adopts either SPIB- or BATF-centric genome-wide distributions in related states of post-germinal centre B-cell transformation. In primary DLBCL high-SPIB and low-BATF or the reciprocal low-SPIB and high-BATF mRNA expression links to differential gene expression profiles across nine data sets, identifying distinct associations with SPIB occupancy, signatures of B-cell differentiation stage and potential pathogenetic mechanisms. In a population-based patient cohort, SPIBhigh/BATFlow-ABC-DLBCL is enriched for mutation of MYD88, and SPIBhigh/BATFlow-ABC-DLBCL with MYD88-L265P mutation identifies a small subgroup of patients among this otherwise aggressive disease subgroup with distinct favourable outcome. We conclude that differential expression of IRF4 cofactors SPIB and BATF identifies biologically and clinically significant heterogeneity among ABC-DLBCL.

Plasma cells (PCs), the terminal effectors of humoral immunity, are short-lived unless supported by niche environments in which they may persist for years. No model system has linked B cell activation with niche function to allow the in vitro generation of long-lived PCs. Thus, the full trajectory of B cell terminal differentiation has yet to be investigated in vitro. In this article, we describe a robust model for the generation of polyclonal long-lived human PCs from peripheral blood B cells. After a proliferative plasmablast phase, PCs persist in the absence of cell division, with viability limited only by elective culture termination. Conservative predictions for PC life expectancy are 300 d, but with the potential for significantly longer life spans for some cells. These long-lived PCs are preferentially derived from memory B cells, and acquire a CD138high phenotype analogous to that of human bone marrow PCs. Analysis of gene expression across the system defines clusters of genes with related dynamics and linked functional characteristics. Importantly, genes in these differentiation clusters demonstrate a similar overall pattern of expression for in vitro and ex vivo PCs. In vitro PCs are fully reprogrammed to a secretory state and are adapted to their secretory load, maintaining IgG secretion of 120 pg/cell/day in the absence of XBP1 mRNA splicing. By establishing a set of conditions sufficient to allow the development and persistence of mature human PCs in vitro, to our knowledge, we provide the first platform with which to sequentially explore and manipulate each stage of human PC differentiation.

Chronic lymphocytic leukaemia (CLL) is the most common clonal B-cell disorder characterized by clonal diversity, a relapsing and remitting course, and in its aggressive forms remains largely incurable. Current front-line regimes include agents such as fludarabine, which act primarily via the DNA damage response pathway. Key to this is the transcription factor p53. Mutations in the TP53 gene, altering p53 functionality, are associated with genetic instability, and are present in aggressive CLL. Furthermore, the emergence of clonal TP53 mutations in relapsed CLL, refractory to DNA-damaging therapy, suggests that accurate detection of sub-clonal TP53 mutations prior to and during treatment may be indicative of early relapse. In this study, we describe a novel deep sequencing workflow using multiple polymerases to generate sequencing libraries (MuPol-Seq), facilitating accurate detection of TP53 mutations at a frequency as low as 0.3%, in presentation CLL cases tested. As these mutations were mostly clustered within the regions of TP53 encoding DNA-binding domains, essential for DNA contact and structural architecture, they are likely to be of prognostic relevance in disease progression. The workflow described here has the potential to be implemented routinely to identify rare mutations across a range of diseases.

Plasma cells (PCs) as effectors of humoral immunity produce Igs to match pathogenic insult. Emerging data suggest more diverse roles exist for PCs as regulators of immune and inflammatory responses via secretion of factors other than Igs. The extent to which such responses are preprogrammed in B-lineage cells or can be induced in PCs by the microenvironment is unknown. In this study, we dissect the impact of IFNs on the regulatory networks of human PCs. We show that core PC programs are unaffected, whereas PCs respond to IFNs with distinctive transcriptional responses. The IFN-stimulated gene 15 (ISG15) system emerges as a major transcriptional output induced in a sustained fashion by IFN-α in PCs and linked both to intracellular conjugation and ISG15 secretion. This leads to the identification of ISG15-secreting plasmablasts/PCs in patients with active systemic lupus erythematosus. Thus, ISG15-secreting PCs represent a distinct proinflammatory PC subset providing an Ig-independent mechanism of PC action in human autoimmunity.

The t(8;21) translocation fuses the DNA-binding domain of the hematopoietic master regulator RUNX1 to the ETO protein. The resultant RUNX1/ETO fusion protein is a leukemia-initiating transcription factor that interferes with RUNX1 function. The result of this interference is a block in differentiation and, finally, the development of acute myeloid leukemia (AML). To obtain insights into RUNX1/ETO-dependant alterations of the epigenetic landscape, we measured genome-wide RUNX1- and RUNX1/ETO-bound regions in t(8;21) cells and assessed to what extent the effects of RUNX1/ETO on the epigenome depend on its continued expression in established leukemic cells. To this end, we determined dynamic alterations of histone acetylation, RNA Polymerase II binding and RUNX1 occupancy in the presence or absence of RUNX1/ETO using a knockdown approach. Combined global assessments of chromatin accessibility and kinetic gene expression data show that RUNX1/ETO controls the expression of important regulators of hematopoietic differentiation and self-renewal. We show that selective removal of RUNX1/ETO leads to a widespread reversal of epigenetic reprogramming and a genome-wide redistribution of RUNX1 binding, resulting in the inhibition of leukemic proliferation and self-renewal, and the induction of differentiation. This demonstrates that RUNX1/ETO represents a pivotal therapeutic target in AML.

Human NK cell deficiencies are rare yet result in severe and often fatal disease, particularly as a result of viral susceptibility. NK cells develop from hematopoietic stem cells, and few monogenic errors that specifically interrupt NK cell development have been reported. Here we have described biallelic mutations in IRF8, which encodes an interferon regulatory factor, as a cause of familial NK cell deficiency that results in fatal and severe viral disease. Compound heterozygous or homozygous mutations in IRF8 in 3 unrelated families resulted in a paucity of mature CD56dim NK cells and an increase in the frequency of the immature CD56bright NK cells, and this impairment in terminal maturation was also observed in Irf8–/–, but not Irf8+/–, mice. We then determined that impaired maturation was NK cell intrinsic, and gene expression analysis of human NK cell developmental subsets showed that multiple genes were dysregulated by IRF8 mutation. The phenotype was accompanied by deficient NK cell function and was stable over time. Together, these data indicate that human NK cells require IRF8 for development and functional maturation and that dysregulation of this function results in severe human disease, thereby emphasizing a critical role for NK cells in human antiviral defense.

DLBL subtypes identified by patterns of gene expression correspond to germinal center (GCB) or activated (ABC) B‐cells like. The latter demonstrate dysregulation of the NF‐KB pathway. Outcomes of treatment with R‐CHOP are inferior for ABC DLBL in retrospective series. This study investigated whether adding bortezomib (B), a putative NF‐KB inhibitor, can reverse this phenotype.

*† ‡ SUMMARY Despite recent advances, accurate gene function prediction remains an elusive goal, with very few methods directly applicable to the plant Arabidopsis thaliana. In this study, we present GO-At (gene ontology prediction in A. thaliana), a method that combines five data types (co-expression, sequence, phylogenetic profile, interaction and gene neighbourhood) to predict gene function in Arabidopsis. Using a simple, yet powerful two-step approach, GO-At first generates a list of genes ranked in descending order of probability of functional association with the query gene. Next, a prediction score is automatically assigned to each function in this list based on the assumption that functions appearing most frequently at the top of the list are most likely to represent the function of the query gene. In this way, the second step provides an effective alternative to simply taking the ‘best hit’ from the first list, and achieves success rates of up to 79%. GO-At is applicable across all three GO categories: molecular function, biological process and cellular component, and can assign functions at multiple levels of annotation detail. Furthermore, we demonstrate GO-At’s ability to predict functions of uncharacterized genes by identifying ten putative golgins/Golgi-associated proteins amongst 8219 genes of previously unknown cellular component and present independent evidence to support our predictions. A web-based implementation of GO-At (http://www.bioinformatics.leeds.ac.uk/goat) is available, providing a unique resource for plant researchers to make predictions for uncharacterized genes and predict novel functions in Arabidopsis.

The moss Physcomitrella patens is a model for the study of plant cell biology and, by virtue of its basal position in land plant phylogeny, for comparative analysis of the evolution of plant gene function and development. It is ideally suited for ‘reverse genetic’ analysis by virtue of its outstanding ability to undertake targeted transgene integration by homologous recombination. However, gene identification through mutagenesis and map-based cloning has hitherto not been possible, due to the lack of a genetic linkage map. Using molecular markers [amplified fragment length polymorphisms (AFLP) and simple sequence repeats (SSR)] we have generated genetic linkage maps for Physcomitrella. One hundred and seventy-nine gene-specific SSR markers were mapped in 46 linkage groups, and 1574 polymorphic AFLP markers were identified. Integrating the SSR- and AFLP-based maps generated 31 linkage groups comprising 1420 markers. Anchorage of the integrated linkage map with gene-specific SSR markers coupled with computational prediction of AFLP loci has enabled its correspondence with the newly sequenced Physcomitrella genome. The generation of a linkage map densely populated with molecular markers and anchored to the genome sequence now provides a resource for forward genetic interrogation of the organism and for the development of a pipeline for the map-based cloning of Physcomitrella genes. This will radically enhance the potential of Physcomitrella for determining how gene function has evolved for the acquisition of complex developmental strategies within the plant kingdom.

Motivation: To predict which of the vast number of human single nucleotide polymorphisms (SNPs) are deleterious to gene function or likely to be disease associated is an important problem, and many methods have been reported in the literature. All methods require data sets of mutations classified as ‘deleterious’ or ‘neutral’ for training and/or validation. While different workers have used different data sets there has been no study of which is best. Here, the three most commonly used data sets are analysed. We examine their contents and relate this to classifiers, with the aims of revealing the strengths and pitfalls of each data set, and recommending a best approach for future studies. Results: The data sets examined are shown to be substantially different in content, particularly with regard to amino acid substitutions, reflecting the different ways in which they are derived. This leads to differences in classifiers and reveals some serious pitfalls of some data sets, making them less than ideal for non-synonymous SNP prediction. Availability: Software is available on request from the authors. Contact: d.r.westhead@leeds.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Background Classifiers based on molecular criteria such as gene expression signatures have been developed to distinguish Burkitt lymphoma and diffuse large B cell lymphoma, which help to explore the intermediate cases where traditional diagnosis is difficult. Transfer of these research classifiers into a clinical setting is challenging because there are competing classifiers in the literature based on different methodology and gene sets with no clear best choice; classifiers based on one expression measurement platform may not transfer effectively to another; and, classifiers developed using fresh frozen samples may not work effectively with the commonly used and more convenient formalin fixed paraffin-embedded samples used in routine diagnosis. Methods Here we thoroughly compared two published high profile classifiers developed on data from different Affymetrix array platforms and fresh-frozen tissue, examining their transferability and concordance. Based on this analysis, a new Burkitt and diffuse large B cell lymphoma classifier (BDC) was developed and employed on Illumina DASL data from our own paraffin-embedded samples, allowing comparison with the diagnosis made in a central haematopathology laboratory and evaluation of clinical relevance. Results We show that both previous classifiers can be recapitulated using very much smaller gene sets than originally employed, and that the classification result is closely dependent on the Burkitt lymphoma criteria applied in the training set. The BDC classification on our data exhibits high agreement (~95 %) with the original diagnosis. A simple outcome comparison in the patients presenting intermediate features on conventional criteria suggests that the cases classified as Burkitt lymphoma by BDC have worse response to standard diffuse large B cell lymphoma treatment than those classified as diffuse large B cell lymphoma. Conclusions In this study, we comprehensively investigate two previous Burkitt lymphoma molecular classifiers, and implement a new gene expression classifier, BDC, that works effectively on paraffin-embedded samples and provides useful information for treatment decisions. The classifier is available as a free software package under the GNU public licence within the R statistical software environment through the link http://www.bioinformatics.leeds.ac.uk/labpages/softwares/ or on github https://github.com/Sharlene/BDC. Electronic supplementary material The online version of this article (doi:10.1186/s13073-015-0187-6) contains supplementary material, which is available to authorized users.

Aggressive B-cell non-Hodgkin lymphomas are haematological malignancies that account for significant morbidity and mortality worldwide. They encompass a wide range of histological and clinical features: Diffuse large B-cell lymphoma (DLBCL) represents the most frequent subtype and Burkitt lymphoma (BL) occurs less frequently. Recent advances in molecular profiling have demonstrated considerable heterogeneity at the molecular level, and further classified DLBCL into germinal centre B-cell-like (GCB), activated B-cell-like (ABC), primary mediastinal B-cell lymphoma (PMBL) and type III subgroups, where ABC associates with the most unfavourable prognosis. Gene-expression profiling (GEP) also confirmed a subgroup with features intermediate between BL and DLBCL, and these cases particularly that have concurrent chromosomal rearrangements of MYC and BCL2are often associated with poor prognosis. Despite the progress made from GEP, the classification still has limited influence on clinical treatment decision-making. In fact, as more heterogeneity beyond the subtypes above has been discovered by recent next-generation sequencing studies the approach of dividing cases into limited subgroups makes less sense in clinical practice. It is clear that the subtypes overlap to an extent in the affected signalling and regulatory pathways, and that small groupings within subtypes exhibit clear mechanistic differences and treatment responses. We describe an alternative approach, where a large database of aggressive B-cell lymphomas is used in a similarity search to identify those cases most similar at a molecular level to a query case. The hypothesis is that the most similar cases provide the best guide to prognosis and treatment outcome in the query case, independent of any need to place the query case into a particular subtype. We used both large public datasets and data from our Haematological Malignancy Research Network (www.HMRN.org) to explore genes associated with pathogenic pathways and an unfavourable prognosis. We also defined similarity between cases according to their molecular features and treatment responses. We then trained the similarity search method, by employing a distance metric learning approach that has been successfully used in similar machine learning applications, to test our HMRN dataset which contains detailed clinical data with treatment and outcome information. The cross-validation result on the public dataset achieved nearly 90% accuracy in recognizing cases with similar overall survival, and initial test results on HMRN data also shows that over 80% cases can be correctly represented by similar cases. In summary, we present a similarity learning method as an alternative to the current sub-type classification method. This mathematical method is able to accurately recognise cases with similar molecular features and provides important information on predicted treatment response for any given query case. Disclosures Smith: Novartis: Research Funding; Celgene: Research Funding; Jansen Cilag: Research Funding; Amgen: Research Funding.

Purpose To assess whether comparative gene network analysis can reveal characteristic immune response signatures that predict clinical response in Diffuse large B-cell lymphoma (DLBCL). Background The wealth of available gene expression data sets for DLBCL and other cancer types provides a resource to define recurrent pathological processes at the level of gene expression and gene correlation neighbourhoods. This is of particular relevance in the context of cancer immune responses, where convergence onto common patterns may drive shared gene expression profiles. Where existing and novel immunotherapies harness the immune response for therapeutic benefit such responses may provide predictive biomarkers. Methods We independently analysed publically available DLBCL gene expression data sets and a wide compendium of gene expression data from diverse cancer types, and then asked whether common elements of cancer host response could be identified from resulting networks. Using 10 DLBCL gene expression data sets, encompassing 2030 cases, we established pairwise gene correlation matrices per data set, which were merged to generate median correlations of gene pairs across all data sets. Gene network analysis and unsupervised clustering was then applied to define global representations of DLBCL gene expression neighbourhoods. In parallel a diverse range of solid and lymphoid malignancies including; breast, colorectal, oesophageal, head and neck, non-small cell lung, prostate, pancreatic cancer, Hodgkin lymphoma, Follicular lymphoma and DLBCL were independently analysed using an orthogonal weighted gene correlation network analysis of gene expression data sets from which correlated modules across diverse cancer types were identified. The biology of resulting gene neighbourhoods was assessed by signature and ontology enrichment, and the overlap between gene correlation neighbourhoods and WGCNA derived modules associated with immune/host responses was analysed. Results Amongst DLBCL data, we identified distinct gene correlation neighbourhoods associated with the immune response. These included both elements of IFN-polarised responses, core T-cell, and cytotoxic signatures as well as distinct macrophage responses. Neighbourhoods linked to macrophages separated CD163 from CD68 and CD14. In the WGCNA analysis of diverse cancer types clusters corresponding to these immune response neighbourhoods were independently identified including a highly similar cluster related to CD163. The overlapping CD163 clusters in both analyses linked to diverse Fc-Receptors, complement pathway components and patterns of scavenger receptors potentially linked to alternative macrophage activation. The relationship between the CD163 macrophage gene expression cluster and outcome was tested in DLBCL data sets, identifying a poor response in CD163 -cluster high patients, which reached statistical significance in one data set (GSE10846). Notably, the effect of the CD163-associated gene neighbourhood which correlates with poor outcome post rituximab containing immunochemotherapy is distinct from the effect of IFNG-STAT1-IRF1 polarised cytotoxic responses. The latter represents the predominant immune response pattern separating cell of origin unclassifiable (Type-III) DLBCL from either ABC or GCB DLBCL subsets, and is associated with a trend toward positive outcome. Conclusion Comparative gene expression network analysis identifies common immune response signatures shared between DLBCL and other cancer types. Gene expression clusters linked to CD163 macrophage responses and IFNG-STAT1-IRF1 polarised cytotoxic responses are common patterns with apparent divergent outcome association. Disclosures Davies: CTI: Honoraria; GIlead: Consultancy, Honoraria, Research Funding; Mundipharma: Honoraria, Research Funding; Bayer: Research Funding; Takeda: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Roche: Honoraria, Research Funding; GSK: Research Funding; Pfizer: Honoraria; Celgene: Honoraria, Research Funding. Jack:Jannsen: Research Funding.

Introduction: DLBCL subtypes may be classified by gene expression corresponding to germinal centre (GCB) or activated peripheral blood (ABC) B-cells. Treatment outcomes with R-CHOP therapy were inferior for ABCs in retrospective series, and this study investigated whether adding bortezomib could reverse the adverse prognosis. The trial used gene expression profiling (GEP) to stratify cases, with adaptive design to analyse the outcome by subtypes at predefined timepoints. Methods: Newly diagnosed patients with DLBCL underwent staging and commenced standard R-CHOP. During cycle 1, formalin-fixed paraffin-embedded (FFPE) tissue was used to extract messenger RNA for GEP using the Illumina DASL array platform. Cases were allocated to GCB, ABC or Unclassifiable (Unc) type before starting cycle 2, using an established algorithm based upon 20 genes. Patients with successful GEP were randomised 1:1 to receive R-CHOP +/- bortezomib 1.6 mg/m2 s/c on days 1+8 in cycles 2-6. The study was powered to detect a difference in progression-free survival (PFS) of 10% with bortezomib, with a 2-sided significance, 5% and 90% power. The adaptive design allowed for closure of randomization for GCB cases if 1-year PFS was Results: Between 6/2011 and 5/2015 1132 patients were enrolled from 109 sites, with 1078 samples analysed. Of these, 157 (15%) biopsies had inadequate material for GEP, but the remaining 921 were classified as 246 (27%) ABC, 476 (52%) GCB and 199 (22%) Unc. Successful classification was possible from both surgical and needle core biopsies. Median laboratory turnaround time was 12 working days and all results were available prior to the scheduled administration of cycle 2. Characteristics of the patients of different subtypes are shown in the table. Following both interim analyses the DMEC recommended continued recruitment of patients with a GCB phenotype. Table. ABC GCB Unc Age (years): median 67 63 63 Age (years) : range 23 to 86 20 to 82 20 to 85 % performance status 0-1 88 88 90 % at least one extranodal site 53 54 62 % bone marrow involved 15 14 23 % LDH>ULN 69 76 79 % IPI score 0/1 29 27 26 % IPI score 2/3 57 55 55 % IPI score 4/5 15 19 19 % B symptoms 46 43 49 % Bulk>10cm 17 26 21 Conclusions: This study has demonstrated the feasibility of GEP at diagnosis to subsequently guide therapy in a large multicentre trial. Although patients with ABC type lymphoma were in general slightly older, they did not appear to have other adverse prognostic features at diagnosis vs GCB. All patients will have completed therapy by the time of the meeting, allowing the initial response and toxicity data to be available for presentation. Disclosures Davies: GIlead: Consultancy, Honoraria, Research Funding; Mundipharma: Honoraria, Research Funding; CTI: Honoraria; Takeda: Honoraria, Research Funding; Bayer: Research Funding; GSK: Research Funding; Janssen: Honoraria, Research Funding; Roche: Honoraria, Research Funding; Pfizer: Honoraria; Celgene: Honoraria, Research Funding. Off Label Use: The addition of bortezomib to R-CHOP chemotherapy in diffuse large B-cell lymphoma. Pocock:Janssen: Honoraria. Jack:Jannsen: Research Funding. Johnson:Takeda: Honoraria; Pfizer: Honoraria; Janssen: Research Funding.

Germinal Center (GCB) and activated (ABC) B-cell subtypes of DLBCL can be identified by Gene expression profiling (GEP). These subgroups are biologically distinct, harboring mutations in different pathways. Patients classified as ABC more often have mutations of the NF-kB pathway and an inferior response to standard R-CHOP therapy. The REMoDL-B trial utilised GEP to stratify patients for the addition of bortezomib to R-CHOP, based on the hypothesis that this agent may selectively improve the outcome of the ABC subtype. GEP was performed on RNA extracted from diagnostic formalin-fixed paraffin-embedded (FFPE) biopsies using Illumina WG-DASLTM during the 1st cycle of R-CHOP. Patients were classified as GCB, ABC or Unclassified before cycle 2 using the cross-platform DAC classifier (Care et al, PLOS ONE 2013) and randomised to continue R-CHOP+/-bortezomib. Work is underway to compare data generated on Affymetrix arrays and targeted RNA-seq (Illumina TRex), as well as validation by targeted mutational analysis of 18 genes associated with DLBCL (TNFAIP3, CARD11, CD79A, CD79B, MYD88, TRAF3, TNFRSF11A, PRDM1, TP53, FAS, B2M, CD58, EZH2, MLL2, MEF2B, EP300, CREBBP, KDM2B) using Fluidigm multiplex PCR and Illumina MiSeq on DNA also from the FFPE blocks. The trial closed to recruitment in May 2015 and 1147 samples have been analysed. One hundred and fifty three (13%) biopsies were unsuitable for GEP (for insufficient tumor tissue, inappropriate block sent). The remaining samples were classified as ABC (n=261, 23%), GCB (n=471, 44%) and Unclassified (n=214, 19%), with only 11 samples (1%) failing to yield a GEP result. GEP was successful in a range of sample types, including needle and endoscopic biopsies, bone marrow trephines and formal biopsies, with results obtained from as little as 40ng of total RNA, all from FFPE samples. Mutational data were available in 199 samples, with 73% of these having a mutation detectable in 1 or more genes (range 0-5) at a AAF (alternative allele frequency) cutoff at 10%. MYD88 was most commonly mutated (in 30% of ABC and 7% of GCB). EZH2 mutations were restricted to the GCB category (26%) and MYD88, CD79a/b and PRDM1 were more commonly associated with the ABC group. MYD88/PRDM1 were the most frequently associated events, with MYD88/CD79a/b and MYD88/NF-kB being mutually exclusive. Where MYD88 was seen in GCB cases, coexisting mutations imply an origin from transformed follicular lymphoma. B2M mutations were commonly identified across all subtypes (n=26), but specifically enriched in Type III (unclassified) cases (25%), which supports the hypothesis that mutational immune escape may be a feature of DLBCL, in common with other tumor types. Cross platform validation is highly concordant using Affymetrix arrays from a pilot series (27/27 gave the same classifier output, with correlating confidences also seen between platforms). RNA-seq analysis is ongoing, however initial analysis shows 86% concordance with the DASL output. Comprehensive cross platform comparison data will be available for presentation at the meeting. This study demonstrates the feasibility of GEP classification of DLBCL at diagnosis in a large international trial. The molecular classification can also be replicated using different technologies. Mutational analysis confirmed the association between DLBCL subtype and specific mutational hotspots. Disclosures Sharon: Johnson & Johnson: Other: Funded the laboratory work for the REMoDL-B trial (ISRCTN 51837425). Davies:Takeda: Honoraria; Seattle Genetics: Research Funding. Jack:Jannsen: Research Funding. Johnson:Johnson & Johnson: Other: Funded the laboratory work for the REMoDL-B trial (ISRCTN 51837425).

The transcriptional repressor B lymphocyte-induced maturation protein-1 (BLIMP1) regulates gene expression and cell fate. The DNA motif bound by BLIMP1 in vitro overlaps with that of interferon regulatory factors (IRFs), which respond to inflammatory/immune signals. At such sites, BLIMP1 and IRFs can antagonistically regulate promoter activity. In vitro motif selection predicts that only a subset of BLIMP1 or IRF sites is subject to antagonistic regulation, but the extent to which antagonism occurs is unknown, since an unbiased assessment of BLIMP1 occupancy in vivo is lacking. To address this, we identified an extended set of promoters occupied by BLIMP1. Motif discovery and enrichment analysis demonstrate that multiple motif variants are required to capture BLIMP1 binding specificity. These are differentially associated with CpG content, leading to the observation that BLIMP1 DNA-binding is methylation sensitive. In occupied promoters, only a subset of BLIMP1 motifs overlap with IRF motifs. Conversely, a distinct subset of IRF motifs is not enriched amongst occupied promoters. Genes linked to occupied promoters containing overlapping BLIMP1/IRF motifs (e.g. AIM2, SP110, BTN3A3) are shown to constitute a dynamic target set which is preferentially activated by BLIMP1 knock-down. These data confirm and extend the competitive model of BLIMP1 and IRF interaction.

Cell of origin classification of diffuse large B-cell lymphoma (DLBCL) identifies subsets with biological and clinical significance. Despite the established nature of the classification existing studies display variability in classifier implementation, and a comparative analysis across multiple data sets is lacking. Here we describe the validation of a cell of origin classifier for DLBCL, based on balanced voting between 4 machine-learning tools: the DLBCL automatic classifier (DAC). This shows superior survival separation for assigned Activated B-cell (ABC) and Germinal Center B-cell (GCB) DLBCL classes relative to a range of other classifiers. DAC is effective on data derived from multiple microarray platforms and formalin fixed paraffin embedded samples and is parsimonious, using 20 classifier genes. We use DAC to perform a comparative analysis of gene expression in 10 data sets (2030 cases). We generate ranked meta-profiles of genes showing consistent class-association using ≥6 data sets as a cut-off: ABC (414 genes) and GCB (415 genes). The transcription factor ZBTB32 emerges as the most consistent and differentially expressed gene in ABC-DLBCL while other transcription factors such as ARID3A, BATF, and TCF4 are also amongst the 24 genes associated with this class in all datasets. Analysis of enrichment of 12323 gene signatures against meta-profiles and all data sets individually confirms consistent associations with signatures of molecular pathways, chromosomal cytobands, and transcription factor binding sites. We provide DAC as an open access Windows application, and the accompanying meta-analyses as a resource.