Tumor Immunotherapy: The mechanism of action of anti-CTLA-4 antibodies requires FcgR-dependant TREG depletion

Ipilimumab is an anti-CTLA-4 antibody used for treatment of metastatic melanoma, one of only two cancer immunotherapeutic drugs approved to date by the FDA.  CTLA-4 is a negative regulatory molecule expressed by activated T cells as well as by negative regulatory T cells (TREGs).  CTLA-4 is related to the T cell co-stimulatory receptor CD28, and acts to suppress T cell function by competing with CD28 for binding to CD80 and CD86 on antigen presenting cells and recruiting inhibitory molecules into the TCR signaling synapse.  Thus, the mechanism of action of Ipilimumab has been presumed to involve releasing anti-tumor effector T cells from CTLA-4-inhibition and/or limiting TREG activity in the tumor and therefore resulting in an increase in the ratio of effector T cells/ TREGs within the tumor.   However, two recent articles demonstrate that Ipilimumab has an additional mechanism of action: FcgR-dependant depletion of intra-tumoral TREGs.

Fcg receptors are a multi-family class of immunoglobulin (IgG)-binding receptors that initiate either activating or inhibitory signals when engaged.  Activating receptors contain cytoplasmic immunoreceptor tyrosine-based activation motifs (ITAM) and activate the FcgR-expressing cell to mediate functions including antibody-dependant cell mediated cytotoxicity (ADCC) and phagocytosis of the antibody-labeled target cell.  FcgRIIB is the single inhibitory Fcg receptor in mice and humans and contains a cytoplasmic immunoreceptor tyrosine-based inhibitory motif (ITIM) which instead downregulates cellular responses.  There are four classes of IgG molecules in both humans and mice, and each bind to different Fcg receptors with varying affinity.  Thus differential affinities of IgG subclasses to functionally different Fcg receptors are thought to mediate the variation in clinical effectiveness of different antibodies targeting the same antigen.

Ipilimumab functions to increase the ratio of effector T cells to TREGS in the tumor microenvironment and has been shown to require binding to both types of T cells for maximal anti-tumor effectiveness.  However, how Ipilimumab differentially modulates these cell types remains to be understood.  A recent study published in The Journal of Experimental Medicine by Simpson et. al sought to clarify the mechanism by which Ipilimumab functions to alter the ratio of effector T cells/ TREGs in a murine tumor model.   Interestingly, the effects of Ipilimumab were found to be tissue-dependant.  In tumors which had high levels of infiltrating CD11b+ macrophages expressing the ADCC-activating FcgRIV, TREGS were selectively depleted in an FcgR-dependant manner, while effector T cells were instead expanded.  In lymph nodes lacking significant levels of these macrophages, frequencies of both effector T cells and TREGS were increased.  Tumor-associated TREGS expressedhigher levels of CTLA-4 than their effector T cell counterparts, or than TREGS present in the lymph node, indicating that higher CTLA-4 expression levels mediate ADCC via macrophages in the tumor.  Furthermore, the presence of FcgRs and hence TREG depletion was required for Ipilimumab’s effects.   Thus is appears that the mechanism of action of Ipilimumab on the effector T cell compartment is two-fold: directly targeting effector T cells to release inhibition via blocking CTLA4 activity, as well as by ADCC-mediated depletion of TREGS.

A second article in the same issue of The Journal of Experimental Medicine by Bulliard et al also explored the role of FcgR engagement on the effects of Ipilimumab as well as an agonistic antibody (DTA-1) targeting the T cell activating receptor GITR (TNFR glucocorticoid-induced TNFR-related protein), which is also expressed on both activated T cells and TREGs.  This study concluded that a major mechanism of action for both antibodies involved engagement of activating FcgRs leading to ADCC-mediated TREG depletion from the tumor.  Even though GITR-activation in effector T cells promotes activities including cytokine production and proliferation, the agonistic properties of this antibody alone were not effective in the absence of activating FcgR engagement.  Thus, even for functionally different (antagonistic versus agonistic) immunotherapeutic antibodies targeting these same T cell populations, FcgR-mediated ADCC of TREGs appears to be a critical mechanism for anti-tumor effects.

These studies highlight several important principles for the field of tumor immunotherapeutics.  Antibody targeting can elicit multiple effects, dependant on expression levels of the target, the isotype of the antibody, and the FcgR-expressing cell types present in the tissue.  Thus knowing the nature of the immune populations present in various types of tumors that are able to mediate ADCC, the FcgRs expressed by these cells, and the expression levels of the target molecule on immune populations that would be ideally targeted for elimination versus activation/inhibition will be critical areas for the forwarding of this field.  Furthermore, as FcgRs are polymorphous in humans, and affect IgG binding affinities, taking these genetic variations into account will be critical in the future of personalized medicine.

Further reading:

Fc-dependent depletion of tumor-infiltrating regulatory T cells co-defines the efficacy of anti-CTLA-4 therapy against melanoma.  Simpson TR, Li F, Montalvo-Ortiz W, Sepulveda MA, Bergerhoff K, Arce F, Roddie C, Henry JY, Yagita H, Wolchok JD, Peggs KS, Ravetch JV, Allison JP, Quezada SA. J Exp Med. 2013 Jul 29.

Activating Fc γ receptors contribute to the antitumor activities of immunoregulatory receptor-targeting antibodies.  Bulliard Y, Jolicoeur R, Windman M, Rue SM, Ettenberg S, Knee DA, Wilson NS, Dranoff G, Brogdon JL. J Exp Med. 2013 Jul 29.

Fcgamma receptors as regulators of immune responses.  Nimmerjahn F, Ravetch JV. Nat Rev Immunol. 2008 Jan;8(1):34-47.

PD-1 re-expression is differentially regulated by IL-12 vs IFNα in CD8 T cells

TUMOR_immunotherapyDownregulation of immune functions following responses to pathogen infections is critical for limiting damage to the host by the immune system.  T cell activity is known to be downregulated by a variety of negative regulatory mechanisms including negative checkpoint regulatory proteins, a family of CD28-related molecules.  PD-1 is one such molecule that is transiently expressed on activated T cells.   The ligands for PD-1 are PD-L1 and PD-L2, members of the B7 family of molecules which are upregulated on antigen presenting cells and tumor cells.  Interaction of PD-1 with its ligand leads to inhibition of TCR-mediated signaling via recruitment of SHP1 and SHP2 phosphatases to the TCR synapse.  In the August 2013 edition of The Journal of Immunology, Gerner et al., demonstrate that CD8 T cells initially activated in the presence of IL-12 and IFNα differentially re-express PD-1 upon antigen restimulation.

Cytokines play roles in regulation of nearly every aspect of immune responses.  The cytokine milieu present during T cell activation directs differentiation into the different functional classes of CD4 T helper or CD8 T cells.  This study sought to determine the differences in anti-tumor CD8 T cell effector functions mediated when T cells are activated in the presence of various cytokines.  IL-12 and IFNα activate both overlapping and distinct gene programs and promote cytotoxic CD8 T cell responses.  Thus, these cytokines were chosen for comparison in this study.

In this system, CD8+ OT-1 cells were activated ex-vivo in the presence of either IL-12 or IFNα, and transferred into B16-OVA tumor-bearing mice.  T cells activated in the presence of IL-12 were found to mediate tumor-growth inhibition significantly better than if they had been activated in the presence of IFNα.  Over time in tumor-bearing mice, transferred IFNα-matured OT-1 cells were observed to decline in number and lost the ability to produce IFNγ ex vivo upon restimulation, indicating these cells may be exhausted.

Because PD-1 is known to be a marker and mediator of T cell exhaustion, PD-1 expression was examined.  Initial induction levels of PD-1 were comparable on OT-1 cells following ex vivo activation with IFNα or IL-12.  Following transfer into tumor-bearing mice, PD-1 levels declined over time on both types of cells isolated from the spleen and on IL-12 matured cells isolated from the tumor.  However, PD-1 expression was high on transferred IFNα-matured cells when isolated from the tumor.  Similar results were seen when cells were transferred into mice that subsequently received an injection of the OVA peptide.  Thus, CD8+ T cells matured in the presence of IFNα appear to re-express significantly higher levels of PD-1 upon antigen restimulation than IL-12 matured T cells.

PD-1 and PD-L1 targeting with inhibitory antibodies have emerged as promising avenues in tumor immunotherapy.  In this study, anti-PD-1 antibody administration had no additional anti-tumor effect in mice that received IL-12-matured T cells, while in mice that received IFNα-matured T cells, anti-PD-1 antibodies led to inhibition of tumor-growth to a level similar to that in mice that had received IL-12-matured T cells.  Thus, the relatively poor ability of IFNα-matured T cells to efficiently inhibit tumor growth appears to be largely due to PD-1 upregulation.  Finally, when T cells were matured with both IL-12 and IFNα, the effect of IL-12 was dominant.

Many questions remain regarding the mechanisms mediating PD-1 re-expression in IFNα vs. IL-12 matured T cells.  However, since IL-12 activity was dominant over IFNα on regulating PD-1 expression, IL-12 administration during immunotherapy regimens may enhance anti-tumor T cell responses by blocking the mechanisms by which IFNα enhances PD-1 re-expression.

Further Reading:

Cutting Edge: IL-12 and Type I IFN Differentially Program CD8 T Cells for Programmed Death 1 Re-expression Levels and Tumor Control.  Gerner MY, Heltemes-Harris LM, Fife BT, Mescher MF. J Immunol. 2013 Aug 1;191(3):1011-5. doi: 10.4049/jimmunol.1300652. Epub 2013 Jun 26.

Upcoming Immunology Conferences: October – December, 2013

I previously posted on Immunology Conferences: July – September, 2013 and 2013 Conferences in Tumor Immunology and Cancer ImmunotherapyThis post lists upcoming Immunology-related conferences from October – December, 2013.

conferencesOctober
Aegean Conference: 6th International Conference on Autoimmunity: Mechanisms & Novel Treatments

October 2 – 7, 2013.

Corfu, Greece

Early registration deadline: July 25, 2013.

Abstract submission deadline: July 25, 2013.

Travel award application deadline: June 30, 2013.

 

IL-1-mediated inflammation and diabetes: From basic science to clinical applications

October 10 – 11, 2013.

Nijmegen, The Netherlands

Abstract submission deadline: August 1, 2013.

 

European Macrophage & Dendritic Cell Society Meeting: Myeloid Cells: Microenvironment, Microorganisms & Metabolism From Basic Science to Clinical Applications

October 10 – 12, 2013.

Erlangen, Germany

 

13th International Workshop on Langerhans Cells

October 10-13, 2013

Royal Tropical Institute, Amsterdam, The Netherlands

Early Registration deadline: August 1, 2013.

Abstract submission deadline: August 19, 2013.

 

The International Symposium on Immunotherapy

October 11-12, 2013

London, UK

Early Registration deadline: August 9, 2013.

Abstract submission deadline: August 9, 2013.

 

46th Annual Meeting of the Society for Leukocyte Biology

October 20-22, 2013

Newport Marriott, Newport, RI, USA

Late Breaking Abstract submission deadline: August 6, 2013.

Online Registration deadline: October 7, 2013.

 

16th Annual New York State Immunology Conference

October 20-23, 2013

Sagamore Resort and Conference Center, Bolton Landing, NY, USA

Abstract submission deadline: July 31, 2013.

Registration deadline: September 6, 2013.

 

Cold Spring Harbor Asia Conference: Tumour Immunology and Immunotherapy

October 28 – November 1, 2013.

Suzhou, China

Abstract submission deadline: August 16, 2013.

Early Registration deadline: August 16, 2013.

 

Keystone Symposium: Advancing Vaccines in the Genomics Era

October 31 – November 4, 2013.

Rio de Janeiro, Brazil

Abstract Deadline: July 30, 2013.

Early Registration Deadline: August 29, 2013.

 

November

The Lancet and Cell: What Will it Take to Achieve an AIDS-free World?

November 3–5, 2013.

San Francisco, California, USA.

Abstract submission deadline: July 26, 2013.

Early Registration Deadline: September 13, 2013.

 

International Primary Immunodeficiencies Congress

November 7–8, 2013.

Lisbon, Portugal

Early Registration deadline: July 19, 2013.

 

Asia Pacific Congress of Allergy, Asthma and Clinical Immunology

November 14–17, 2013.

Taipei City, Taiwan

Abstract Submission Deadline: July 31, 2013.

Early Registration: August 30, 2013.

Registration Deadline: October 25, 2013.

 

Cold Spring Harbor Asia Conference: Bacterial Infection and Host Defence

November 18–22, 2013.

Suzhou, China

Abstract submission deadline: September 6, 2013.

Early Registration Deadline: September 6, 2013.

 

6th Autoimmunity Congress Asia

November 20–22, 2013.

Hong Kong

Abstract Submission Deadline: July 20, 2013.

Early Registration deadline: August 6, 2013.

 

Harnessing Immunity to Prevent and Treat Disease

November 20–23, 2013.

Cold Spring Harbor Laboratory, New York, USA

Abstract submission deadline: September 6, 2013.

 

11th Annual UC Irvine Immunology Fair
November 21–22, 2013.

Irvine, California, USA

Abstract submission deadline: October 19, 2013.

Poster contest deadline: November 2, 2013.

 

December

EMBO Workshop: Complex Systems in Immunology

December 2–4, 2013.

Biopolis, Singapore

Abstract submission deadline: September 1, 2013.

Registration deadline: September 1, 2013.

 

British Society for Immunology Congress

December 2–5, 2013.

Liverpool, UK

Abstract submission deadline: September 6, 2013.

Early Registration deadline: September 30, 2013.

 

Annual Scientific Meeting of the Australasian Society for Immunology

December 2–5, 2013.

Wellington, New Zealand

Abstract submission deadline: September 1, 2013.

Early Registration deadline: September 1, 2013.

 

UK Primary Immunodeficiency Network Forum

December 6–7, 2013.

Liverpool, UK

Abstract submission deadline: September 6, 2013.

Early Registration deadline: September 30, 2013.

 

2013 American Society for Cell Biology Annual Meeting

December 14-18, 2013

New Orleans, LA, USA

Abstract submission deadline (Minisymposium Talk, ePoster Talk, and/or Poster Presentation): July 30, 2013.

Abstract submission deadline (Poster Presentation Only): September 4, 2013.

Early Registration deadline: October 10, 2013.

 

Websites that list upcoming Conferences & Events in Immunology, Tumor Immunology, and Cancer Immunotherapy:

The American Association of Immunologists (AAI) Meetings and Events Calendar

Nature Reviews Immunology’s list of conferences

Cancer Immunity Journal’s List of Conferences

FASEB Scientific Research Conferences Calendar

Using Mass Spectrometry for Mass T cell Epitope Discovery

Time of Flight Mass Cytometry (CyTOF) is a relatively new multiparametric technology that is far outpacing standard fluorescence-based flow cytometry in the number of parameters that can be simultaneously assessed on a single cell.  In CyTOF, rare transition element isotope-conjugated antibodies are used to label cellular antigens of interest, the magnitude of which is then quantitated by a time of flight mass cytometer, as discussed previously. Previous studies assessing 34 cell surface and intracellular proteins by this technology demonstrated the existence of high dimensional complexity in the heterogeneity of human bone marrow and CD8+ T cell populations.  In a July 2013 article in Nature Biotechonology, Newell et al., move CyTOF and the field of immunology another technological step forward by utilizing CyTOF to measure the frequencies of Rotavirus antigen-specific T cells in human peripheral blood mononuclear cells (PBMCs) and jejunal tissue with peptide-MHC tetramers.

In CyTOF, the theoretical maximum number of simultaneously assessable parameters is 100-200 depending on the instrument.  This vastly outnumbers the assessable parameters of standard fluorescence-based flow cytometry.  To date however, only approximately 40 metal ions have been utilized for antibody labeling, and the development of further metal-chelating technologies is awaited in order to utilize the maximum capacity of the CyTOF instrument.  In the current study, the authors circumvent this limitation by using a “bar-coding” methodology in which a variant combination of three out of ten metal ions are used for labeling each tetramer, allowing for up to 120 different metal combinations.

In this study, the authors sought to identify Rotavirus epitopes recognized by human CD8+ T cells in the context of the MHC class I allele, HLA-A*0201.  To date, only two Rotavirus epitopes recognized by T cells have been identified, and little is known about the phenotypic and functional diversity of antigen-specific T cells for any particular pathogen.  The technical difficulties in proper epitope prediction along with the limited number of cells attainable from human blood samples contribute to these issues.  Thus, this method represents a huge leap forward in the potential to identify significantly more antigen-specific T cell epitopes and to extensively classify these cells functionally.  Using an MHC-prediction algorithm, 77 possible Rotavirus peptides were identified that bound to HLA-A*0201.  An additional 32 positive and negative control tetramers were added for a total of 109 labeled tetramers used to stain each sample simultaneously.  This was further combined with 23-27 metal-chelated antibodies specific for cell surface and intracellular antigens to phenotypically characterize the T cells. A specialized Matlab script was used to analyze the high-dimensional data obtained following mass spectrometry of PBMC and jejunal samples.

On average, CD8 T cell populations specific for two Rotavirus-peptides plus 6-7 peptides from other viruses including influenza, EBV, and CMV, were identified on average across PBMCs from the 17 healthy donors analyzed.  These antigen-specific T cell populations were further phenotypically characterized by expression of surface and intracellular markers.   CD8 T cells specific for six Rotavirus epitopes that included the two previously identified epitopes, were recurrently detected in PBMCs from at least two individuals.  Of these, CD8 cells specific for a Rotavirus peptide from the VP3 protein were most common among healthy donor PBMCs and were phenotypically unique, being of the effector memory subtype compared with a central memory phenotype typical of the T cells specific for the other Rotavirus peptides.  VP3-specific T cells were also uniquely present in jejunal tissue obtained from obese patients that had undergone gastric bypass surgeries.  Thus, this methodology discovered at least 4 new Rotavirus peptides as well as unique characteristics of the different antigen-specific CD8 T cell populations.

In summary, this methodology of combining CyTOF technology with tetramer “bar-coding” paves the way for a vast expansion over fluorescent-based flow cytometry techniques for identifying antigen-specific T cell populations.  As vaccine strategies are an ongoing goal for treatment and prevention of infectious diseases and cancer, it is important to not only identify the peptides that can elicit T cell responses, but also functionally characterize these T cells in order to maximally promote desired immune responses.

Further  Reading:

Combinatorial tetramer staining and mass cytometry analysis facilitate T-cell epitope mapping and characterization.  Newell EW, Sigal N, Nair N, Kidd BA, Greenberg HB, Davis MM. Nat Biotechnol. 2013 Jul;31(7):623-9. doi: 10.1038/nbt.2593. Epub 2013 Jun 9.

Cracking the code of human T-cell immunity.  Harvey CJ, Wucherpfennig KW. Nat Biotechnol. 2013 Jul 9;31(7):609-10. doi: 10.1038/nbt.2626.

Cytometry by time-of-flight shows combinatorial cytokine expression and virus-specific cell niches within a continuum of CD8+ T cell phenotypes. Immunity. 2012 Jan 27;36(1):142-52. doi: 10.1016/j.immuni.2012.01.002.

Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum.  Bendall SC, Simonds EF, Qiu P, Amir el-AD, Krutzik PO, Finck R, Bruggner RV, Melamed R, Trejo A, Ornatsky OI, Balderas RS, Plevritis SK, Sachs K, Pe’er D, Tanner SD, Nolan GP. Science. 2011 May 6;332(6030):687-96. doi: 10.1126/science.1198704.

Whole Blood Phospho-flow: Direct Ex Vivo Measurement of Signaling in PBMC

I previously discussed phospho-flow cytometry, a method to study intracellular protein phosphorylation events in peripheral blood mononuclear cells (PBMC) at the single cell level.  In standard phospho-flow cytometry protocols, prior to performing assays, PBMCs are first isolated from blood using density gradient centrifugation methods such as Ficoll.  However, there may be times when it is advantageous to study signaling pathways in relatively unmanipulated cells directly ex vivo.  For this, Chow et al. have established a protocol for performing phospho-flow cytometry on PBMCs directly in whole blood.

phospho_flow_cytometry

There are many advantages to isolating and cryopreserving PBMCs with the intention of later studying signaling events by methods including standard phospho-flow cytometry.  In particular, when comparisons are desired between patient groups and healthy controls, there is likely to be less confounding contributions of experimental variability to the results if all of the comparative samples are assayed together.  However, as discussed by Chow et al., pharmacodynamic monitoring as well as evaluation of constitutively activated signaling pathways in PBMCs would be best studied on cells having undergone the least manipulation.  Some signaling pathway responses may be more robust in whole blood PBMCs as well.  For example, I have found in my own assays that signaling responses to IL-6 are strongest in whole blood PBMCs compared with PBMCs following Ficoll or culture in the incubator for any amount of time.  This method can also be used to study bone marrow immune cell signaling as well as expression of intracellular molecules that are exposed by the permeabilization method chosen. In addition, looking at signaling events in murine PBMCs is difficult to do if PBMCs need to be isolated first, given the very small amount of blood that can be obtained from a mouse.  In these cases, anti-coagulated whole blood phospho-flow cytometry should be considered.

Whole blood phospho-flow cytometry is a relatively easy method.  Using 100 ul of whole blood is enough for this assay, and the stimulus (cytokine or other activating signaling molecule) is added directly to the whole blood for the preferred amount of time.  PBMCs are then fixed with formaldehyde and a Triton X-100 based buffer is added to lyse the red blood cells and permeabilize the white blood cells.  This is followed with a few washes and finally the cells can be treated with methanol to unmask phospho-epitopes, similarly to the standard phospho-flow cytometry method by Nolan and colleagues.  Chow et al. include an optional step in which the PBMCs can be stored in a freezing buffer prior to methanol treatment.  However, I have successfully stored PBMCs in 90-100% methanol at -20 or -80 ºC until staining for flow cytometry, similarly to what is done for the standard phospho-flow cytometry method by Nolan and colleagues.

As with all protocols involving treatment of cells with reagents such as methanol or Triton X-100, some epitopes may be lost and thus will not be evaluable if staining is done following these treatments.  Thus, there is an alternate method included in the protocol to stain for some antigens up front.  As a reminder however, some fluorophores are sensitive to methanol, for instance V500, and thus cannot be used to stain PBMCs prior to such treatments.  Finally, in a prior article, Chow et al. (2005), tested different methods of fixation, permeabilization and alcohol unmasking, and I have included the link to that article below as an excellent reference in the case that modulation of the protocol is required for optimal assessment of your antigens of interest.

Further Reading:

Whole blood processing for measurement of signaling proteins by flow cytometry.  Chow S, Hedley D, Shankey TV. Curr Protoc Cytom. 2008 Oct;Chapter 9:Unit 9.27.

Whole blood fixation and permeabilization protocol with red blood cell lysis for flow cytometry of intracellular phosphorylated epitopes in leukocyte subpopulations.  Chow S, Hedley D, Grom P, Magari R, Jacobberger JW, Shankey TV. Cytometry A. 2005 Sep;67(1):4-17.

Single-cell phospho-protein analysis by flow cytometry. Schulz KR, Danna EA, Krutzik PO, Nolan GP.Curr Protoc Immunol. 2012 Feb;Chapter 8:Unit 8.17.1-20.

 

 

Computers meet T cells: in silico identification of mutated tumor antigens targeted by T cells

It is well accepted that T cells can recognize and kill tumors that arise in individuals but that tumor cells escape immune surveillance due to the immunosuppressive tumor microenvironment that renders these T cells dysfunctional is less understood.  Only a relatively small number of antigens that T cells recognize for tumor-killing have been identified, and the methods used to identify these antigens are quite cumbersome.  In a recent article in Nature Medicine, Robbins et al. utilize informatics methods to identify mutated tumor antigens in melanoma patients that allowed effective targeting by anti-tumor T cells.

Genome sequencing T cells

In an effort to identify clinically relevant mutated tumor cell epitopes recognized by T cells, Robbins et al. first performed whole-exome sequencing of tumor cells and matched normal cells from melanoma patients who demonstrated tumor regression following adoptive transfer of autologous tumor infiltrating lymphocytes (TILs).  Mutations in tumor cells that resulted in amino acid changes were identified and then screened using an MHC binding algorithm that predicts high affinity binding of peptide sequences to specific HLA alleles.  Candidate peptides of 9-10 amino acids in length were synthesized and pulsed with specific HLA-expressing target cell lines to load the peptides into the MHC complex.  Peptide-pulsed target cells or autologous tumor cell lines were then cultured with autologous TILs from the same donor and IFN-gamma production was assessed as a read out of T cell activation.

Three metastatic melanoma patients were assessed using this methodology.  The first patient was homozygous for HLA-A*0201, and thus mutated melanoma cell line peptides predicted to bind to the HLA-A*0201 allele were identified by the MHC-binding algorithm.  From this donor, 4 out of 55 candidate peptides elicited IFN-gamma responses from autologous T cells cultured with peptide-pulsed target cells.  Two of these mutated peptides were found to correspond to the casein kinase1α1 protein (CSNK1A1), one peptide was mapped to the growth arrest specific 7 gene (GAS7) gene, and the fourth was a fragment of the HAUS augmin-like complex, subunit 3 (HAUS3) protein.  The wild-type versions of each of these peptides bound very poorly (100-10,000 fold less) or not at all to the HLA and were not recognized by T cells.  Two other donors were assessed for predicted binding of mutated peptides to HLA-A*0101 and HLA-A*1101.  Autologous T cell responses were found to be activated in response to mutated peptides from pleckstrin homology domain containing, family M member 2 (PLEKHM2), protein phosphatase 1 regulatory subunit 3B (PPP1R3B), matrilin 2 (MATN2), and cyclin-dependent kinase 12 (CDK12) genes, but not their wild-type counterparts.  Furthermore, tumor lines were validated to express these mutated proteins.

Finally, the authors compared the reactivity of peripheral blood mononuclear cells (PBMCs) drawn before and after adoptive TIL transfer into two of these patients to determine if anti-tumor reactive T cell clones persisted in vivo.  T cells that recognized the same tumor antigens as the TILs were identified post-adoptive transfer at greater levels than prior to adoptive transfer.  Thus, T cells that recognize mutated tumor epitopes may play a clinically relevant role in mediating tumor regression.  Many questions remain, including a direct demonstration that such tumor-reactive TILs are responsible for mediating the observed tumor regression in these patients, and whether further mutation of these residues might facilitate immune escape later it the course of disease. 

Mining exomic sequencing data to identify mutated antigens recognized by adoptively transferred tumor-reactive T cells.  Robbins PF, Lu YC, El-Gamil M, Li YF, Gross C, Gartner J, Lin JC, Teer JK, Cliften P, Tycksen E, Samuels Y, Rosenberg SA. Nat Med. 2013 May 5. doi: 10.1038/nm.3161.

NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence.  Nielsen M, Lundegaard C, Blicher T, Lamberth K, Harndahl M, Justesen S, Røder G, Peters B, Sette A, Lund O, Buus S. PLoS One. 2007 Aug 29;2(8):e796.

A bifunctional FoxP3+ regulatory T cell subset converts to pro-inflammatory helper T cells

Recently a number of studies have arisen characterizing Tregulatory cellvarious functional subsets of CD4+ FoxP3+ regulatory T cells (TREGS), as well as their plasticity and ability to differentiate into other TH subtypes.  For instance, TREGS that express RORγt were found to be the specific TREG subset that promotes pro-tumor immune functions in colorectal cancer patients.  In a recent article in Immunity, Sharma et al. identify another TREG subset: FoxP3+ TREGS that loose expression of Eos convert to a pro-inflammatory helper subtype that promotes naïve CD8+ T cells differentiation into potent effectors.

Eos is a transcription factor in the Ikaros family, and acts as an obligate co-repressor in complex with FoxP3 to inhibit expression of FoxP3-repressed genes.  In a quest to understand why TREGS in inflammatory environments were observed to become pro-inflammatory without losing FoxP3 expression, Sharma et al. examined the expression of Eos in FoxP3+ TREGS under inflammatory conditions.

Conversion of FoxP3+ TREGS into an inflammatory phenotype was demonstrated by acquired expression of IL-2, IL-17, and CD40L in the draining lymph nodes of a vaccination site compared with FoxP3+ TREGS at distant lymph nodes that did not gain this function.  In these converted inflammatory FoxP3+ TREGS, expression of Eos was rapidly lost.  IL-6 was required for downregulation of Eos, as TREGS in mice lacking IL-6 did not lose Eos expression under the same conditions.  However, IL-6 alone was insufficient for Eos downregulation, which also required interactions with MHC class II on activated dendritic cells.  Loss of Eos expression was furthermore shown to be required for acquisition of the pro-inflammatory phenotype, as TREGS with forced overexpression of Eos did not undergo this conversion.

Interestingly, not all FoxP3+ TREGS were equivalent in their propensity to lose Eos expression and become pro-inflammatory.  Thymic FoxP3+ TREGS were assessed for stability of Eos under treatment with cyclohexamide. CD38+CD69+CD103 TREGS were “Eos-labile” and specifically lost Eos expression within one hour of cyclohexamide treatment, while CD38CD69CD103+ TREGS maintained Eos expression.  Expression of other markers associated with FoxP3+ TREGS including CD25 and CTLA-4 were equivalent between these two phenotypes highlighting the inability of using these TREG markers to discriminate between these populations.  When these FoxP3+ TREGS were sorted into CD38+CD103and CD38CD103+ subsets and transferred into mice, followed by the vaccination schema, only CD38+CD103 TREGS lost Eos expression and gained CD40L and IL-2 expression. The Eos-labile TREGS do however have characteristic suppressive functions when examined in several models including protection from colitis in a Rag-deficient CD45RBHI effector cell-driven autoimmune colitis model and in vitro suppression of T cell proliferation driven by anti-CD3.

Because the Eos-labile subset was observed in the thymus as part of the natural TREG repertoire, the authors examined the signals required for development of this subset.  Again, IL-6 was required as this subset did not arise in IL-6-/- mice.  Epigenetic analysis of DNA methylation patterns comparing these FoxP3+ TREGS subsets revealed distinctive patterns of methylation yet these subsets were still much more closely related to each other as compared with FoxP3 CD4+ T cells.  Future studies will be needed to determine the nature of these epigenetic differences and which signals are controlled by IL-6.

Interestingly, the authors explored the functional contribution of the Eos-labile pro-inflammatory TREGS subset on CD8+ priming in the vaccination model.  Depletion of TREGS resulted in loss of CD8+ T cell proliferation and granzyme B expression as well as loss of CD86 upregulation on DCs, while adding back just the Eos-labile subset or IL-2 plus CD40-agonist antibodies rescued these defects.  The Eos-labile subset did not however, contribute to reactivation of memory CD4+ T cells, and thus these cells appear to play a specific role in the initial priming stages of naïve T cell activation.  Thus, despite having regulatory activity, these cells are critical in priming CD8+ T cell responses by supplying IL-2 and CD40L signals.

However, indoleamine 2,3-dioxygenase (IDO) was able to block Eos downregulation and acquisition of IL-2, IL-17, and CD40L expression.  Importantly, in a murine tumor vaccination model, blocking IDO was important for FoxP3+ inflammatory TREG induction and acquisition of anti-tumor effector CD8+ T cell responses.  The mechanism of IDO inhibition of Eos downregulation was found to be at least in part, dependent on the antagonization of the IL-6-STAT3 pathway by IDO-mediated production of kynurenine-pathway metabolites which activate the aryl hydrocarbon receptor (AhR).  Interestingly, different AhR ligands have been previously shown to differentially regulate induction of TH17 cells vs. TREGS (Quintana et al.), and kyenurine was a TREG inducing AhR ligand (Mezrich et al.).  Additionally, the contrasting effects of IL-6 and IDO will be an important factor in priming immune cell responses.

Overall, this thorough investigation identified the mechanisms that induce and inhibit this newly defined Eos-labile TREG subset that maintains FoxP3 expression and has typical suppressive TREG activity, yet is critically important in priming effector T cell immune responses.  Future studies will be needed to address how these cells balance regulatory and priming activities as well as the relationships between this subset and the many other TREG subsets described.


An inherently bifunctional subset of foxp3(+) T helper cells is controlled by the transcription factor eos.   Sharma MD, Huang L, Choi JH, Lee EJ, Wilson JM, Lemos H, Pan F, Blazar BR, Pardoll DM, Mellor AL, Shi H, Munn DH. Immunity. 2013 May 23;38(5):998-1012. doi: 10.1016/j.immuni.2013.01.013. Epub 2013 May 16.

Eos, goddess of treg cell reprogramming.  Rieder SA, Shevach EM. Immunity. 2013 May 23;38(5):849-50. doi: 10.1016/j.immuni.2013.05.001.

Control of T(reg) and T(H)17 cell differentiation by the aryl hydrocarbon receptor.  Quintana FJ, Basso AS, Iglesias AH, Korn T, Farez MF, Bettelli E, Caccamo M, Oukka M, Weiner HL. Nature. 2008 May 1;453(7191):65-71. doi: 10.1038/nature06880. Epub 2008 Mar 23.

An interaction between kynurenine and the aryl hydrocarbon receptor can generate regulatory T cells.  Mezrich JD, Fechner JH, Zhang X, Johnson BP, Burlingham WJ, Bradfield CA. J Immunol. 2010 Sep 15;185(6):3190-8. doi: 10.4049/jimmunol.0903670. Epub 2010 Aug 18.

RORγt+ TREGS: A unique subset of TREGS that specifically promote Colorectal Cancer

tregThe role of CD4+ FoxP3+ regulatory T cells (TREGS) in colorectal cancer (CRC) has continued to be unclear.  TREGS act to suppress inflammatory mechanisms that are associated with tumor progression and can thus act to suppress the development of cancer.  However, TREGS also function to inhibit anti-tumor T cell responses, thereby promoting cancer escape from immune surveillance.  Many studies have been published on the frequencies of TREGS in the peripheral blood and tumors of CRC patients, but there is yet to be a consensus regarding the relationship between TREGS and disease outcome.  In a report by Blatner et. al, the expression of RORγt in a subset of CD4+ FoxP3+ T cells was found to specifically mediate pathogenic pro-tumor activity compared with RORγtCD4+FoxP3+ TREGS in CRC patients.

CD4+ FoxP3+ cells have been classified into three functional populations based on the expression of CD45RA and FoxP3: CD45RA+FoxP3int, CD45RAFoxP3int, and CD45RAFoxP3high.  The CD45RAFoxP3high population exhibits the most suppressive activity of these subsets.  In the study by Blatner et. al, the CD45RAFoxP3high population was found to be specifically expanded in peripheral blood mononuclear cells (PBMCs) and within the tumor of CRC patients and increased with cancer stage.  Because IL-17 expressing CD4+ FoxP3+ cells have been described in the gut and enhanced in patients with CRC and Crohn’s disease, the authors examined CD4+ FoxP3+ populations for expression of the TH17 transcription factor, RORγt. 

In CRC patients, a large fraction of all three subsets of TREGS in PBMCs and in the tumor were found to express RORγt.  Interestingly, when TREG populations were sorted from healthy donors versus CRC patients, CRC patient TREGS  retained suppressive activity over T cell proliferation but had lost their ability to suppress mast cell degranulation.  Expression of IL-17 was also found in a large percentage of CRC TREGS, in a fashion mutually exclusive from IL-10 expression.

To further explore the role of RORγt in CRC, APC∆468 polyposis-prone mice were crossed with mice deficient in RORγt.  RORγt-/-APC∆468 mice were highly resistant to polyp development, had reduced expansion of splenic proinflammatory macrophages, myeloid-derived suppressor cells (MDSCs) and polyp-associated mast cells, compared with RORγt+APC∆468 mice.  Interestingly, the effect of RORγt deficiency in APC∆468 mice was not phenocopied by the loss of IL-17.  Instead, although IL-17 deficiency reduced the frequency of polyps, mast cell recruitment to polyps was enhanced, and ultimately IL-17-/-APC∆468 mice developed invasive lesions.

Overall, this study revealed several fascinating points: CD4+FoxP3+RORγt+ cells appear to be a pathogenic TREG subset that have lost their anti-inflammatory properties and are specifically expanded in CRC patients where they assist in disease progression.  The function of RORγt was not synonymous with IL-17 in TREGS, indicating that other effects of RORγt contribute to the role of these cells in tumor pathogenesis.  Thus, the roles and relationships between FoxP3, RORγt, and IL-17 in TREGS deserve further attention in CRC pathogenesis.  Hopefully, a clearer understanding of this newly identified subset of RORγt+  TREGS and their role in CRC progression will enable much improved methodology for targeting specific TREGS populations in CRC and other disease settings.

Further Reading:

Expression of RORγt marks a pathogenic regulatory T cell subset in human colon cancer.  Blatner NR, Mulcahy MF, Dennis KL, Scholtens D, Bentrem DJ, Phillips JD, Ham S, Sandall BP, Khan MW, Mahvi DM, Halverson AL, Stryker SJ, Boller AM, Singal A, Sneed RK, Sarraj B, Ansari MJ, Oft M, Iwakura Y, Zhou L, Bonertz A, Beckhove P, Gounari F, Khazaie K. Sci Transl Med. 2012 Dec 12;4(164):164ra159. doi: 10.1126/scitranslmed.3004566.

Translational mini-review series on Th17 cells: induction of interleukin-17 production by regulatory T cells.  Afzali B, Mitchell P, Lechler RI, John S, Lombardi G. Clin Exp Immunol. 2010 Feb;159(2):120-30. doi: 10.1111/j.1365-2249.2009.04038.x. Epub 2009 Nov 11.

Inflammation-driven reprogramming of CD4+ Foxp3+ regulatory T cells into pathogenic Th1/Th17 T effectors is abrogated by mTOR inhibition in vivo.  Yurchenko E, Shio MT, Huang TC, Da Silva Martins M, Szyf M, Levings MK, Olivier M, Piccirillo CA. PLoS One. 2012;7(4):e35572. doi: 10.1371/journal.pone.0035572. Epub 2012 Apr 24.

In colorectal cancer mast cells contribute to systemic regulatory T-cell dysfunction.  Blatner NR, Bonertz A, Beckhove P, Cheon EC, Krantz SB, Strouch M, Weitz J, Koch M, Halverson AL, Bentrem DJ, Khazaie K. Proc Natl Acad Sci U S A. 2010 Apr 6;107(14):6430-5. doi: 10.1073/pnas.0913683107. Epub 2010 Mar 22.

 

Using Application Settings to Standardize Flow Cytometry Results Across Experiments and Instruments

While many fluorescence-based flow flow cytometrycytometry assays can be run without concern for hitting the exact same fluorescence intensity target values across different experiments, there are assays that require standardization such that assays run on different days, or even different flow cytometers will render results that can be directly compared.  BD Biosciences has developed a protocol for enabling “Application Settings” on machines running the FACSDiva V6 software, to create settings that allow for standardized consistent fluorescence intensity target values to be obtained across experiments run on different days or instruments.

The ability to collect replicable fluorescence intensity values across assays is invaluable when assays require directly comparable results.  This can easily be envisioned for use in the clinical setting where specifically defined expression levels of a given marker may be used for prognostic or diagnostic indications or therapeutic responses.  Other examples include assays where the number of samples is too large for an experiment to be logically performed on a single day, or time series experiments where samples will be assayed for the same parameters and assessed for their changes over time.

Creating an optimized Application Settings for a given antibody staining panel requires several steps and the understanding of several principles of flow cytometry, and a thorough reading of the protocol referenced at the end of the article is recommended.  Here I will discuss the basics for generating Application Settings and using them on the same cytometer.  For a more detailed explanation of the procedure and how to apply this to additional instruments which have the exact same laser and detector configurations, please refer to BD’s protocol.

In the first step of this protocol, the user runs the standard Cytometer Setup and Tracking (CS&T) software using the CS&T beads.  This will determine the optimized photomultiplier (PMT) detector voltages for the CS&T beads to minimize electronic noise while maintaining the brightest fluorescence staining in the linear range of the detector.  However, these voltages are optimized for these beads and not for cells.  Thus, once the CS&T report is generated, the next step is to optimize the settings for the cells and stains of interest.

In the next step of this protocol, the objective is to adjust the voltage settings to optimize the balance between the electronic noise and the linear range for each detector using the same cells and fluorescent antibody stains for which the application settings are being created.  The negatively stained cell populations are desired to be located above the noise on the low end, and the positively stained cell populations need to be within the linear range of the measurement.

The electronic noise robust standard deviation (rSDEN) is determined by the CS&T software and indicated in the CS&T report for each detector.  To determine the optimal location between the noise and background for the negatively stained cell populations, a calculation of 2.5 x rSDEN is made for each detector.  Then the voltages are adjusted while running the negatively stained cells to place them at this median fluorescence intensity (MFI) value for every detector being used.

After adjusting for all of the negative populations, the positively stained cells must be assessed to ensure that the cells are still within the detector’s linear range.  The CS&T report also generates a linearity max channel value for each detector.  Thus, the MFI of the positively stained population should be below this value, and allow for any anticipated increases in expression to remain below this value.  Remaining within the linear range is more important than having the negative populations at the 2.5 x rSDEN levels.  Thus, when there is very bright staining, this is important to keep in mind and the voltage should be lowered accordingly.

Finally, after the user has adjusted the voltages to the optimal settings for each detector, the application settings can be saved using the FACSDiva V6 software under: Cytometer Settings -> Application Settings -> Save.

Once the application settings have been created, they can be applied in any future experiments.  To use them, following daily CS&T cytometer setup and performance checks, open a new experiment.  Apply the Use CS&T Settings selection.  Then open the application settings under: Cytometer Settings -> Application Settings -> Apply.  Now consistent MFIs should be obtained for cells run on different experimental days.  One final note is that additional considerations need to be taken if the baseline target values change over time, or a new lot of CS&T beads is used for the daily cytometer setup.  The BD protocol discusses what to do in these instances.

Protocol:

Standardizing Application Setup Across Multiple Flow Cytometers Using BD FACSDiva™ Version 6 Software.  Ellen Meinelt, Mervi Reunanen, Mark Edinger, Maria Jaimes, Alan Stall, Dennis Sasaki, Joe Trotter.

Upcoming Immunology Conferences: July – September, 2013

I previously posted on  Immunology Conferences: March- June, 2013 and 2013 Conferences in Tumor Immunology and Cancer Immunotherapy

This listing will include upcoming Immunology-related conferences from July – September, 2013.

 

July:

Frontiers in Immunology Researchdescribe the image

July 1 – 4, 2013.

Monte Carlo, Monaco

 

14th International TNF Conference

July 7 – 10, 2013.

Loews Le Concorde, Quebec, Canada.

Travel grant application deadline: April 5, 2013.

Early Registration deadline: April 5, 2013.

 

British Society of Allergy & Clinical Immunology Annual Meeting: Allergy Across the Ages

July 8 – 10, 2013.

The International Centre, Telford, UK.

Abstract submission deadline: April 8, 2013.

BSACI membership deadline: June 10, 2013.

Travel fellowship deadline: May 20, 2013.

Early Registration deadline: May 26, 2013.

 

FASEB Conference: Autoimmunity

July 7 – 12, 2013

Saxtons River, Vermont, USA.

Early Registration deadline: June 3, 2013.

 

AAI Introductory Course in Immunology

July 13 – 18, 2013.

University of Pennsylvania, Philadelphia, PA, USA.

An intensive introductory immunology course.

Registration deadline: June 28, 2013.

 

FASEB Conference: Molecular Mechanisms of Lymphocyte Development and Function

July 14 – 19, 2013.

Steamboat Springs, Colorado, USA.

Early Registration deadline: June 3, 2013.

 

16th International Congress of Mucosal Immunology (ICMI 2013)

July 17 – 20, 2013.

Westin Bayshore Vancouver, Vancouver, Canada.

Late Breaking Abstract Submission Deadline: April 15, 2013.

 

The American Society for Virology 32nd Annual Scientific Meeting

July 20 – 24, 2013.

Pennsylvania State University, State College, PA, USA.

Early Registration Deadline: May 31, 2013.

T Follicular Helper Cells: Basic Discoveries and Clinical Applications

July 21 – 26, 2013.

The Chinese University of Hong Kong, Hong Kong, China.

Short-talk abstracts deadline: May 15, 2013.

Application deadline: June 23, 2013.

AAI Advanced Course in Immunology
July 28 – August 2, 2013.

Seaport World Trade Center, Boston, MA, USA.

Registration deadline: July 12, 2013.

 

August:

FASEB Conference: Gastrointestinal Tract XV: Epithelia, Microbes, Inflammation and Cancer

August 11–15, 2013.

Steamboat Springs, Colorado, USA

Registration deadline: July 3, 2013.

 

14th European Meeting on Complement in Human Disease (EMCHD)

August 17-21, 2013.

Jena, Germany.

Early registration deadline: June 21, 2013.

 

15th International Congress of Immunology

August 22-27, 2013.

MiCo – Milano Congressi, Milan, Italy.

Late Abstract Deadline: June 30, 2013.

Early Registration Deadline: April 15, 2013.

 

Immune-related Pathologies: Understanding Leukocyte Signalling and Emerging therapies (IMPULSE 2013)

August 31– September 3, 2013.

Mátraháza, Hungary

Abstract Submission Deadline: June 15, 2013.

Early Registration Deadline: June 15, 2013.

 

September:

ESF-EMBO Symposium: B Cells From Bedside To Bench And Back Again

September 2–7, 2013.

Pultusk, Poland.

Application Deadline: June 3, 2013.

 

7th Leukocyte signal Transduction Conference

September 8 – 13, 2013.

Grecotel Kos Imperial Hotel, Kos, Greece.

Early Registration Deadline: June 15, 2013.

Abstract Submission Deadline: June 15, 2013.

Travel Award Application Deadline: June 30, 2013.

2nd International Conference on ImmunoMetabolism: Molecular and Cellular Immunology of Metabolism

September 15–20, 2013.

Sheraton Conference Center, Rhodes, Greece.

Abstract Submission Deadline: June 15, 2013.

Early Registration Deadline: June 15, 2013.

Travel Award Application Deadline: June 30, 2013.

 

Cytokines 2013: From Molecular Mechanisms to Human Disease

September 29 – October 3, 2013

Hyatt Regency San Francisco, San Francisco, CA, USA.

Early Registration Deadline: May 7, 2013.

Abstract Submission Deadline: May 7, 2013.

A free iPhone/iPad App for the meeting is now available!

The Android version will be out very soon.

 

3rd International Lymphoid Tissue Meeting

September 15–17, 2013.

Rotterdam, The Netherlands.

Abstract Submission Deadline: July 1, 2013.

Early Registration Deadline: July 15, 2013.

 

Websites that list upcoming Conferences & Events in Immunology, Tumor Immunology, and Cancer Immunotherapy:

The American Association of Immunologists (AAI) Meetings and Events Calendar

Nature Reviews Immunology’s list of conferences

Cancer Immunity Journal’s List of Conferences

FASEB Scientific Research Conferences Calendar