Publications by authors named "Matthew A Care"

23 Publications

  • Page 1 of 1

Comparative analysis of gene expression platforms for cell-of-origin classification of diffuse large B-cell lymphoma shows high concordance.

Br J Haematol 2021 Feb 29;192(3):599-604. Epub 2020 Nov 29.

Haematological Malignancy Diagnostic Service, St James' University Hospital, Leeds, UK.

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-DASL 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.
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http://dx.doi.org/10.1111/bjh.17246DOI Listing
February 2021

A dichotomy of gene regulatory associations during the activated B-cell to plasmablast transition.

Life Sci Alliance 2020 10 25;3(10). Epub 2020 Aug 25.

Division of Immunology and Haematology, Leeds Institute of Medical Research, University of Leeds, Leeds, UK

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 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.
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http://dx.doi.org/10.26508/lsa.202000654DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7471511PMC
October 2020

TLR-mediated activation of Waldenström macroglobulinemia B cells reveals an uncoupling from plasma cell differentiation.

Blood Adv 2020 06;4(12):2821-2836

Division of Haematology and Immunology, Leeds Institute of Medical Research, University of Leeds, Leeds, United Kingdom; and.

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.
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http://dx.doi.org/10.1182/bloodadvances.2019001279DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7322944PMC
June 2020

Distinct genetic changes reveal evolutionary history and heterogeneous molecular grade of DLBCL with MYC/BCL2 double-hit.

Leukemia 2020 05 16;34(5):1329-1341. Epub 2019 Dec 16.

Department of Pathology, University of Cambridge, Cambridge, UK.

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.
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http://dx.doi.org/10.1038/s41375-019-0691-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7192846PMC
May 2020

Parsimonious Gene Correlation Network Analysis (PGCNA): a tool to define modular gene co-expression for refined molecular stratification in cancer.

NPJ Syst Biol Appl 2019 11;5:13. Epub 2019 Apr 11.

1Section of Experimental Haematology, Leeds Institute of Medical Research, University of Leeds, Leeds, LS9 7TF UK.

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.
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http://dx.doi.org/10.1038/s41540-019-0090-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6459838PMC
April 2020

Gene-expression profiling of bortezomib added to standard chemoimmunotherapy for diffuse large B-cell lymphoma (REMoDL-B): an open-label, randomised, phase 3 trial.

Lancet Oncol 2019 05 1;20(5):649-662. Epub 2019 Apr 1.

Cancer Research UK Centre, University of Southampton, Southampton, UK. Electronic address:

Background: Biologically distinct subtypes of diffuse large B-cell lymphoma can be identified using gene-expression analysis to determine their cell of origin, corresponding to germinal centre or activated B cell. We aimed to investigate whether adding bortezomib to standard therapy could improve outcomes in patients with these subtypes.

Methods: In a randomised evaluation of molecular guided therapy for diffuse large B-cell lymphoma with bortezomib (REMoDL-B), an open-label, adaptive, randomised controlled, phase 3 superiority trial, participants were recruited from 107 cancer centres in the UK (n=94) and Switzerland (n=13). Eligible patients had previously untreated, histologically confirmed diffuse large B-cell lymphoma with sufficient diagnostic material from initial biopsies for gene-expression profiling and pathology review; were aged 18 years or older; had ECOG performance status of 2 or less; had bulky stage I or stage II-IV disease requiring full-course chemotherapy; had measurable disease; and had cardiac, lung, renal, and liver function sufficient to tolerate chemotherapy. Patients initially received one 21-day cycle of standard rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone (R-CHOP; rituximab 375 mg/m, cyclophosphamide 750 mg/m, doxorubicin 50 mg/m, and vincristine 1·4 mg/m [to a maximum of 2 mg total dose] intravenously on day 1 of the cycle, and prednisolone 100 mg orally once daily on days 1-5). During this time, we did gene-expression profiling using whole genome cDNA-mediated annealing, selection, extension, and ligation assay of tissue from routine diagnostic biopsy samples to determine the cell-of-origin subtype of each participant (germinal centre B cell, activated B cell, or unclassified). Patients were then centrally randomly assigned (1:1) via a web-based system, with block randomisation stratified by international prognostic index score and cell-of-origin subtype, to continue R-CHOP alone (R-CHOP group; control), or with bortezomib (RB-CHOP group; experimental; 1·3 mg/m intravenously or 1·6 mg/m subcutaneously) on days 1 and 8 for cycles two to six. If RNA extracted from the diagnostic tissues was of insufficient quality or quantity, participants were given R-CHOP as per the control group. The primary endpoint was 30-month progression-free survival, for the germinal centre and activated B-cell population. The primary analysis was on the modified intention-to-treat population of activated and germinal centre B-cell population. Safety was assessed in all participants who were given at least one dose of study drug. We report the progression-free survival and safety outcomes for patients in the follow-up phase after the required number of events occurred. This study was registered at ClinicalTrials.gov, number NCT01324596, and recruitment and treatment has completed for all participants, with long-term follow-up ongoing.

Findings: Between June 2, 2011, and June 10, 2015, 1128 eligible patients were registered, of whom 918 (81%) were randomly assigned to receive treatment (n=459 to R-CHOP, n=459 to RB-CHOP), comprising 244 (26·6%) with activated B-cell disease, 475 (51·7%) with germinal centre B cell disease, and 199 (21·7%) with unclassified disease. At a median follow-up of 29·7 months (95% CI 29·0-32·0), we saw no evidence for a difference in progression-free survival in the combined germinal centre and activated B-cell population between R-CHOP and RB-CHOP (30-month progression-free survival 70·1%, 95% CI 65·0-74·7 vs 74·3%, 69·3-78·7; hazard ratio 0·86, 95% CI 0·65-1·13; p=0·28). The most common grade 3 or worse adverse event was haematological toxicity, reported in 178 (39·8%) of 447 patients given R-CHOP and 187 (42·1%) of 444 given RB-CHOP. However, RB-CHOP was not associated with increased haematological toxicity and 398 [87·1%] of 459 participants assigned to receive RB-CHOP completed six cycles of treatment. Grade 3 or worse neuropathy occurred in 17 (3·8%) patients given RB-CHOP versus eight (1·8%) given R-CHOP. Serious adverse events occurred in 190 (42·5%) patients given R-CHOP, including five treatment-related deaths, and 223 (50·2%) given RB-CHOP, including four treatment-related deaths.

Interpretation: This is the first large-scale study in diffuse large B-cell lymphoma to use real-time molecular characterisation for prospective stratification, randomisation, and subsequent analysis of biologically distinct subgroups of patients. The addition of bortezomib did not improve progression-free survival.

Funding: Janssen-Cilag, Bloodwise, and Cancer Research UK.
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http://dx.doi.org/10.1016/S1470-2045(18)30935-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6494978PMC
May 2019

Growth Factor-like Gene Regulation Is Separable from Survival and Maturation in Antibody-Secreting Cells.

J Immunol 2019 02 14;202(4):1287-1300. Epub 2019 Jan 14.

Section of Experimental Haematology, Leeds Institute of Medical Research, St. James's University Hospital, University of Leeds, Leeds LS9 7TF, United Kingdom;

Recurrent mutational activation of the MAP kinase pathway in plasma cell myeloma implicates growth factor-like signaling responses in the biology of Ab-secreting cells (ASCs). Physiological ASCs survive in niche microenvironments, but how niche signals are propagated and integrated is poorly understood. In this study, we dissect such a response in human ASCs using an in vitro model. Applying time course expression data and parsimonious gene correlation network analysis (PGCNA), a new approach established by our group, we map expression changes that occur during the maturation of proliferating plasmablast to quiescent plasma cell under survival conditions including the potential niche signal TGF-β3. This analysis demonstrates a convergent pattern of differentiation, linking unfolded protein response/endoplasmic reticulum stress to secretory optimization, coordinated with cell cycle exit. TGF-β3 supports ASC survival while having a limited effect on gene expression including upregulation 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.
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http://dx.doi.org/10.4049/jimmunol.1801407DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6360259PMC
February 2019

Molecular High-Grade B-Cell Lymphoma: Defining a Poor-Risk Group That Requires Different Approaches to Therapy.

J Clin Oncol 2019 01 3;37(3):202-212. Epub 2018 Dec 3.

1 University of Leeds, Leeds, United Kingdom.

Purpose: Biologic heterogeneity is a feature of diffuse large B-cell lymphoma (DLBCL), and the existence of a subgroup with poor prognosis and phenotypic proximity to Burkitt lymphoma is well known. Conventional cytogenetics identifies some patients with rearrangements of MYC and BCL2 and/or BCL6 (double-hit lymphomas) who are increasingly treated with more intensive chemotherapy, but a more biologically coherent and clinically useful definition of this group is required.

Patients And Methods: We defined a molecular high-grade (MHG) group by applying a gene expression-based classifier to 928 patients with DLBCL from a clinical trial that investigated the addition of bortezomib to standard rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) therapy. The prognostic significance of MHG was compared with existing biomarkers. We performed targeted sequencing of 70 genes in 400 patients and explored molecular pathology using gene expression signature databases. Findings were validated in an independent data set.

Results: The MHG group comprised 83 patients (9%), with 75 in the cell-of-origin germinal center B-cell-like group. MYC rearranged and double-hit groups were strongly over-represented in MHG but comprised only one half of the total. Gene expression analysis revealed a proliferative phenotype with a relationship to centroblasts. Progression-free survival rate at 36 months after R-CHOP in the MHG group was 37% (95% CI, 24% to 55%) compared with 72% (95% CI, 68% to 77%) for others, and an analysis of treatment effects suggested a possible positive effect of bortezomib. Double-hit lymphomas lacking the MHG signature showed no evidence of worse outcome than other germinal center B-cell-like cases.

Conclusion: MHG defines a biologically coherent high-grade B-cell lymphoma group with distinct molecular features and clinical outcomes that effectively doubles the size of the poor-prognosis, double-hit group. Patients with MHG may benefit from intensified chemotherapy or novel targeted therapies.
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http://dx.doi.org/10.1200/JCO.18.01314DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6338391PMC
January 2019

Site-1 protease function is essential for the generation of antibody secreting cells and reprogramming for secretory activity.

Sci Rep 2018 09 25;8(1):14338. Epub 2018 Sep 25.

Section of Experimental Haematology, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, LS9 7TF, United Kingdom.

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 CD38 and CD38 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.
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http://dx.doi.org/10.1038/s41598-018-32705-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6156501PMC
September 2018

Biallelic interferon regulatory factor 8 mutation: A complex immunodeficiency syndrome with dendritic cell deficiency, monocytopenia, and immune dysregulation.

J Allergy Clin Immunol 2018 06 8;141(6):2234-2248. Epub 2017 Nov 8.

Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom; Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom.

Background: The homozygous K108E mutation of interferon regulatory factor 8 (IRF8) is reported to cause dendritic cell (DC) and monocyte deficiency. However, more widespread immune dysfunction is predicted from the multiple roles ascribed to IRF8 in immune cell development and function.

Objective: We sought to describe the effect on hematopoiesis and immunity of the compound heterozygous R83C/R291Q mutation of IRF8, which is present in a patient with recurrent viral infection, granuloproliferation, and intracerebral calcification.

Methods: Variant IRF8 alleles were identified by means of exome sequencing, and their function was tested by using reporter assays. The cellular phenotype was studied in detail by using flow cytometry, functional immunologic assay transcriptional profiling, and antigen receptor profiling.

Results: Both mutations affected conserved residues, and R291Q is orthologous to R294, which is mutated in the BXH2 IRF8-deficient mouse. R83C showed reduced nuclear translocation, and neither mutant was able to regulate the Ets/IRF composite element or interferon-stimulated response element, whereas R291Q retained BATF/JUN interactions. DC deficiency and monocytopenia were observed in blood, dermis, and lung lavage fluid. Granulocytes were consistently increased, dysplastic, and hypofunctional. Natural killer cell development and maturation were arrested. T1, T17, and CD8 memory T-cell differentiation was significantly reduced, and T cells did not express CXCR3. B-cell development was impaired, with fewer memory cells, reduced class-switching, and lower frequency and complexity of somatic hypermutation. Cell-specific gene expression was widely disturbed in interferon- and IRF8-regulated transcripts.

Conclusions: This analysis defines the clinical features of human biallelic IRF8 deficiency, revealing a complex immunodeficiency syndrome caused by DC and monocyte deficiency combined with widespread immune dysregulation.
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http://dx.doi.org/10.1016/j.jaci.2017.08.044DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5986711PMC
June 2018

Biallelic mutations in IRF8 impair human NK cell maturation and function.

J Clin Invest 2017 01 28;127(1):306-320. Epub 2016 Nov 28.

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.
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http://dx.doi.org/10.1172/JCI86276DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5199714PMC
January 2017

TLR Adaptor Protein MYD88 Mediates Sensitivity to HDAC Inhibitors via a Cytokine-Dependent Mechanism.

Cancer Res 2016 12 12;76(23):6975-6987. Epub 2016 Oct 12.

Department of Oncology, University of Oxford, Oxford, United Kingdom.

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.
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http://dx.doi.org/10.1158/0008-5472.CAN-16-0504DOI Listing
December 2016

Network Analysis Identifies Proinflammatory Plasma Cell Polarization for Secretion of ISG15 in Human Autoimmunity.

J Immunol 2016 08 29;197(4):1447-59. Epub 2016 Jun 29.

Section of Experimental Haematology, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds LS9 7TF, United Kingdom;

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.
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http://dx.doi.org/10.4049/jimmunol.1600624DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4974491PMC
August 2016

Gene expression meta-analysis reveals immune response convergence on the IFNγ-STAT1-IRF1 axis and adaptive immune resistance mechanisms in lymphoma.

Genome Med 2015 Sep 11;7:96. Epub 2015 Sep 11.

Section of Experimental Haematology, Wellcome Trust Brenner Building, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, LS9 7TF, UK.

Background: Cancers adapt to immune-surveillance through evasion. Immune responses against carcinoma and melanoma converge on cytotoxic effectors and IFNγ-STAT1-IRF1 signalling. Local IFN-driven immune checkpoint expression can mediate feedback inhibition and adaptive immune resistance. Whether such coupled immune polarization and adaptive resistance is generalisable to lymphoid malignancies is incompletely defined. The host response in diffuse large B-cell lymphoma (DLBCL), the commonest aggressive lymphoid malignancy, provides an empirical model.

Methods: Using ten publicly available gene expression data sets encompassing 2030 cases we explore the nature of host response in DLBCL. Starting from the "cell of origin" paradigm for DLBCL classification, we use the consistency of differential expression to define polarized patterns of immune response genes in DLBCL, and derive a linear classifier of immune response gene expression. We validate and extend the results in an approach independent of "cell of origin" classification based on gene expression correlations across all data sets.

Results: T-cell and cytotoxic gene expression with polarization along the IFNγ-STAT1-IRF1 axis provides a defining feature of the immune response in DLBCL. This response is associated with improved outcome, particularly in the germinal centre B-cell subsets of DLBCL. Analysis of gene correlations across all data sets, independent of "cell of origin" class, demonstrates a consistent association with a hierarchy of immune-regulatory gene expression that places IDO1, LAG3 and FGL2 ahead of PD1-ligands CD274 and PDCD1LG2.

Conclusion: Immune responses in DLBCL converge onto the IFNγ-STAT1-IRF1 axis and link to diverse potential mediators of adaptive immune resistance identifying future therapeutic targets.
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http://dx.doi.org/10.1186/s13073-015-0218-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4566848PMC
September 2015

Transferring genomics to the clinic: distinguishing Burkitt and diffuse large B cell lymphomas.

Genome Med 2015 1;7(1):64. Epub 2015 Jul 1.

School of Molecular and Cellular Biology, Garstang Building, University of Leeds, Leeds, LS2 9JT UK.

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.
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http://dx.doi.org/10.1186/s13073-015-0187-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4512160PMC
July 2015

SPIB and BATF provide alternate determinants of IRF4 occupancy in diffuse large B-cell lymphoma linked to disease heterogeneity.

Nucleic Acids Res 2014 Jul 29;42(12):7591-610. Epub 2014 May 29.

Section of Experimental Haematology, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK Haematological Malignancy Diagnostic Service, Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK

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.
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http://dx.doi.org/10.1093/nar/gku451DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4081075PMC
July 2014

A microarray platform-independent classification tool for cell of origin class allows comparative analysis of gene expression in diffuse large B-cell lymphoma.

PLoS One 2013 12;8(2):e55895. Epub 2013 Feb 12.

Section of Experimental Haematology, Leeds Institute of Molecular Medicine, University of Leeds, Leeds, United Kingdom.

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.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0055895PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3570548PMC
August 2013

In vitro generation of long-lived human plasma cells.

J Immunol 2012 Dec 16;189(12):5773-85. Epub 2012 Nov 16.

Section of Experimental Haematology, Leeds Institute of Molecular Medicine, University of Leeds, Leeds LS9 7TF, United Kingdom.

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 CD138(high) 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.
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http://dx.doi.org/10.4049/jimmunol.1103720DOI Listing
December 2012

Whole genome expression profiling based on paraffin embedded tissue can be used to classify diffuse large B-cell lymphoma and predict clinical outcome.

Br J Haematol 2012 Nov 13;159(4):441-53. Epub 2012 Sep 13.

Haematological Malignancy Diagnostic Service, St. James's Institute of Oncology, Leeds, UK.

This study tested the validity of whole-genome expression profiling (GEP) using RNA from formalin-fixed, paraffin-embedded (FFPE) tissue to sub-classify Diffuse Large B-cell Lymphoma (DLBCL), in a population based cohort of 172 patients. GEP was performed using Illumina Whole Genome cDNA-mediated Annealing, Selection, extension & Ligation, and tumours were classified into germinal centre (GCB), activated B-cell (ABC) and Type-III subtypes. The method was highly reproducible and reliably classified cell lines of known phenotype. GCB and ABC subtypes were each characterized by unique gene expression signatures consistent with previously published data. A significant relationship between subtype and survival was observed, with ABC having the worst clinical outcome and in a multivariate survival model only age and GEP class remained significant. This effect was not seen when tumours were classified by immunohistochemistry. There was a significant association between age and subtype (mean ages ABC - 72·8 years, GC - 68·4 years, Type-III - 64·5 years). Older patients with ABC subtype were also over-represented in patients who died soon after diagnosis. The relationship between prognosis and subtype improved when only patients assigned to the three categories with the highest level of confidence were analysed. This study demonstrates that GEP-based classification of DLBCL can be applied to RNA extracted from routine FFPE samples and has potential for use in stratified medicine trials and clinical practice.
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http://dx.doi.org/10.1111/bjh.12045DOI Listing
November 2012

An extended set of PRDM1/BLIMP1 target genes links binding motif type to dynamic repression.

Nucleic Acids Res 2010 Sep 26;38(16):5336-50. Epub 2010 Apr 26.

Section of Experimental Haematology, Leeds Institute of Molecular Medicine, University of Leeds, Leeds LS9 7TF, UK.

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.
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http://dx.doi.org/10.1093/nar/gkq268DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2938208PMC
September 2010

GO-At: in silico prediction of gene function in Arabidopsis thaliana by combining heterogeneous data.

Plant J 2010 Feb 27;61(4):713-21. Epub 2009 Nov 27.

Institute of Molecular and Cellular Biology, University of Leeds, Leeds, LS2 9JT, UK.

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.
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http://dx.doi.org/10.1111/j.1365-313X.2009.04097.xDOI Listing
February 2010

Deleterious SNP prediction: be mindful of your training data!

Bioinformatics 2007 Mar 18;23(6):664-72. Epub 2007 Jan 18.

Institute of Molecular and Cellular Biology, University of Leeds, Leeds, LS2 9JT, UK and School of Computing, University of Leeds, Leeds, LS2 9JT, UK.

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.
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http://dx.doi.org/10.1093/bioinformatics/btl649DOI Listing
March 2007

Predicting the effect of missense mutations on protein function: analysis with Bayesian networks.

BMC Bioinformatics 2006 Sep 6;7:405. Epub 2006 Sep 6.

School of Computing, University of Leeds, Leeds, LS2 9JT, UK.

Background: A number of methods that use both protein structural and evolutionary information are available to predict the functional consequences of missense mutations. However, many of these methods break down if either one of the two types of data are missing. Furthermore, there is a lack of rigorous assessment of how important the different factors are to prediction.

Results: Here we use Bayesian networks to predict whether or not a missense mutation will affect the function of the protein. Bayesian networks provide a concise representation for inferring models from data, and are known to generalise well to new data. More importantly, they can handle the noisy, incomplete and uncertain nature of biological data. Our Bayesian network achieved comparable performance with previous machine learning methods. The predictive performance of learned model structures was no better than a naïve Bayes classifier. However, analysis of the posterior distribution of model structures allows biologically meaningful interpretation of relationships between the input variables.

Conclusion: The ability of the Bayesian network to make predictions when only structural or evolutionary data was observed allowed us to conclude that structural information is a significantly better predictor of the functional consequences of a missense mutation than evolutionary information, for the dataset used. Analysis of the posterior distribution of model structures revealed that the top three strongest connections with the class node all involved structural nodes. With this in mind, we derived a simplified Bayesian network that used just these three structural descriptors, with comparable performance to that of an all node network.
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http://dx.doi.org/10.1186/1471-2105-7-405DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1586217PMC
September 2006