title: Gene expression meta-analysis reveals immune response convergence on the IFNγ-STAT1-IRF1 axis and adaptive immune resistance mechanisms in lymphoma.
abstract: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Section of Experimental Haematology, Wellcome Trust Brenner Building, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, LS9 7TF, UK.|Bioinformatics Group, School of Molecular and Cellular Biology, University of Leeds, Leeds, UK.
authors.forename: Matthew A
authors.initials: MA
authors.lastname: Care
authors.affiliation: Bioinformatics Group, School of Molecular and Cellular Biology, University of Leeds, Leeds, UK.
authors.forename: David R
authors.initials: DR
authors.lastname: Westhead
authors.affiliation: Section of Experimental Haematology, Wellcome Trust Brenner Building, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, LS9 7TF, UK. r.tooze@leeds.ac.uk.
authors.forename: Reuben M
authors.initials: RM
authors.lastname: Tooze
mesh_terms: D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D050856:Interferon Regulatory Factor-1 / Q000235:genetics*; D007371:Interferon-gamma / Q000235:genetics*; D016403:Lymphoma, Large B-Cell, Diffuse / Q000235:genetics* / Q000276:immunology*; D050794:STAT1 Transcription Factor / Q000235:genetics*
chemical_list: C494257:IRF1 protein, human; D050856:Interferon Regulatory Factor-1; D050794:STAT1 Transcription Factor; C494079:STAT1 protein, human; D007371:Interferon-gamma