Basic Mechanisms of Immunotherapy

Basic mechanisms of Immunotherapy Making it in a simple Lenguage Dr. Ada G. Blidner Instute of Biology and Experimental Medicine, Buenos Aires, Argenna [email protected] Who, Where, When? Innate and Adaptive Immune Response Fast Slow Recognizes Recognizes Pathogen specific associated angens paterns Very efficient in Capable of eliminang eliminang infec8ons infec8ons In the Lymph Ready to nodes, act, in the needs IIR ssues ac8vaon to migrate to Interacts ssues and with AIR Dranoff, Nature Reviews, 2004 act Antigen presentation and Priming Koichi, Nature Reviews, 2012 Huppa, Nature Reviews, 2003 Integrating the Immune Response What are checkpoints good for? PD-1 PD-1 CD4 CTLA-4 Treg CTLA-4 BTLA LAG3 BTLA LAG3 PD-1 CD8 CTLA-4 HVEM BTLA LAG3 HVEM PD-L1 B VISTA PD-L1 BTLA LAG3 Immune homeostasis CTLA-4 Activation Sustained Activation Immunosuppression CD4 CD4 Treg CD8 CD8 CTLA-4 migration CTLA-4 Expression Antigen presentation inhibition Ligand Competition Deactivation of proliferation signals PD-1 PD1 CD4 PD-1 is expressed PD1 following T cel activation CD8 PD-L1 is expressed in PD1 lymphoid and non-lymphoid B PD-L1 tissues. PD-L2 mainly in PD-L2 APCs PD1 Treg PD-L1 PD-L1 is expressed on Endothelial cells, which PD1 inhibit T cell activity in SLO PD-L1 PD-L2 PD-1 is expressed in PD1 Exhausted T cells TFh PD-1 Exhausted T Cell PD-1/PD-L1 interaction inhibits TCR mediated T cell activation and Proliferation T cell ON/OFF Co-stimulatory molecules Co-inhibitory molecules Activation Anergy Inflammation Homeostasis Autoimmunity Cancer How do Tumors take advantage of IC? I.S. wins The I.S. selects resistant clones Tumor wins How do Tumors take advantage of IC? EQUILIBRIUM PHASE PD-1 LAG 3 CD4 PD-1 CTLA-4 BTLA Treg CD8 CTLA-4 PD-1 CTLA-4 PD-L1 PD-L2 MDSCs GM-CSF IL-1B IL-12 PD-L1 IFN-γ PD-L2 IDO HVEM CTLA-4 blockade broadens T cell repertoire Robert et al. Page 18 NIH-PA Author Manuscript NIH-PA Author Manuscript Figure 5. Changes in number of unique TCR V-beta CDR3 sequences related to toxicity during the first 3 months of therapy with tremelimumab. NIH-PA Author Manuscript Clin Cancer Res. Author manuscript; available in PMC 2015 May 01. Please cite this article in press as: Wei et al., Distinct Cellular Mechanisms Underlie Anti-CTLA-4 and Anti-PD-1 Checkpoint Blockade, Cell (2017), http://dx.doi.org/10.1016/j.cell.2017.07.024 A B C Please cite this article in press as: Wei et al., Distinct Cellular Mechanisms Underlie Anti-CTLA-4 and Anti-PD-1 Checkpoint Blockade, Cell (2017), http://dx.doi.org/10.1016/j.cell.2017.07.024 Please cite this article in press as: Wei et al., Distinct Cellular Mechanisms Underlie Anti-CTLA-4 and Anti-PD-1 Checkpoint Blockade, Cell (2017), http://dx.doi.org/10.1016/j.cell.2017.07.024 ABC Differences between CTLA-4 and PD-1 blockade ABCD F D E Fife & Bluestone T-cell tolerance mediated by CTLA-4 and PD-1 E Á D F Peripheral Tolerance Tissue SpecificG Inhibition Pancreatic Pancreas Lymph Node Tissue Specific Inhibition T cell Activation T cell T cell Treg Activated Immune E T cell Trafficking Islets (β cells) DC DC CTLA-4 - B7-1/B7-2 PD-1 - PD-L1 (legend on next page) PD-1 Inhibition CTLA-4 Inhibition B7-1/B7-2 8 Cell 170, 1–14, September 7, 2017 PD-L1/PD-L2 Ligand Concentration Fig. 1. Peripheral tolerance mediated by negative regulators. This figure represents the differential effects for CTLA-4 and PD-1 for limiting T-cell G function during T-cell activation and the maintenance of peripheral tolerance. CTLA-4 and PD-1 are rapidly expressed following T-cell receptor engagement and CD28 co-stimulatory signals. B7-1 and B7-2 are constitutively expressed in the lymph nodes at low levels on antigen-presenting cells and upon activation are highly upregulated. PD-L1 and PD-L2 are constitutively expressed in peripheral tissue sites and upregulated in response to inflammatory signals. The temporal and spatial patterns for ligand expression may help to explain the distinct differences between these two inhibitory molecules. CTLA-4 blockade prevents tolerance induction and therefore may act in the earliest stages of immunity, particularly those within the lymphatic system. PD-1 blockade acts in tissue sites to release anergic T cells and cause autoimmunity,suggesting the inhibitory functions are within the target tissue. Therefore, PD-1 and CTLA-4 can play critical yet non-redundant roles in the maintenance of peripheral tolerance. administer these types of reagents as therapeutics during balance of positive and negative signals establishing mechan- autoimmune disease progression. isms for T-cell expansion and control. CTLA-4 is important for limiting T-cell activity for cells early during the immune Concluding remarks responses. PD-1, on the other hand, limits T-cell activity in PD-1 and CTLA-4 are critical regulators of T-cell function in peripheral tissues and may be the last chance to prevent T-cell diverse processes ranging from autoimmunity, transplantation destruction of self-tissue resulting in autoimmunity (Fig. 1). tolerance, chronic viral infections, and tumor immunity. In vitro PD-1 and PD-L1 are also expressed on Tregs and may help and in vivo animal models as well as clinical trials have control their suppression of effector T cells. Recent studies demonstrated great therapeutic opportunities by effectively indicate that PD-L1 and PD-L2 can signal bi-directionally by targeting these pathways. T-cell activation is a complicated engaging PD-1 on T cells. PD-L1/PD-L2 signaling may aid in biological process with several checkpoints and fail-safe pro- protection from apoptosis and promote tumor cell growth. The cesses built in to limit aberrant or unfaithful T-cell activation, local environment of cytokines and costimulatory molecule including autoimmunity. T-cell signaling through CD28, expression on professional APCs will be vital for dictating T- CTLA-4, and PD-1 controls complex but critical checkpoints helper outcomes. These pathways offer exciting new therapeu- (legend on next page) for T-cell activation and homeostasis. CD28 signaling induces a tic possibilities to selectively silence destructive T cells or 2 Cell 170178, 1–14, September 7,r 2008 2017 The Authors Journal compilation r 2008 Blackwell Munksgaard Immunological Reviews 224/2008 (legend on next page) 2 Cell 170, 1–14, September 7, 2017 Please cite this article in press as: Thorsson et al., The Immune Landscape of Cancer, Immunity (2018), https://doi.org/10.1016/j.immuni.2018.03.023 Please cite this article in press as: Thorsson et al., The Immune Landscape of Cancer, Immunity (2018), https://doi.org/10.1016/j.immuni.2018.03.023 Please cite this article in press as: Thorsson et al., The Immune Landscape of Cancer, Immunity (2018), https://doi.org/10.1016/j.immuni.2018.03.023 Th1/Th17 responses correlate with good prognosis Figure 3. Immune Response and Prognostics AB Figure 3. Immune Response and Prognostics AB (A) Overall survival (OS) by immune subtype. (A) Overall survival (OS) by immune subtype. (B) Concordance index (CI) for five characteristic (B) Concordance index (CI) for five characteristic AB Figureimmuneimmune 3. Immune expression expression Response signature and signature Prognostics scores scores (Figure ( 1FigureA) in 1A) in (A) Overallrelation survival to (OS) OS, by immune for immune subtype. subtypes and TCGA tu- (B)relation Concordance to OS, index for (CI) immune for five subtypes characteristic and TCGA tu- immunemormor types. expression types. Red signature denotes Red denotes scores higher (Figure higher and 1A) blue in and lower blue risk, lower risk, relationwithwith an to OS, increase an for increase immune in the subtypes in signature the and signature TCGA score. tu- score. mor(C) types. CI(C) for Red CI T for helper denotes T helper cellhigher scores and cell blue scores in lower relation risk, in relation to OS within to OS within with an increase in the signature score. (C)immune CI forimmune T helper subtypes. cell subtypes. scores in relation to OS within immune(D) Risk(D) subtypes. Risk stratification stratification from elastic from elastic net modeling net modeling of of (D)immune Risk stratification features. from Tumor elastic samples net modeling were of divided into immuneimmune features. Tumor features. samples Tumor were divided samples into were divided into discoverydiscoverydiscovery and and validation validation and sets, validation and sets, an elastic and sets, net an and elastic an elastic net net modelmodelmodel was was optimized was optimized on optimized the discovery on the on set discovery the using discovery set using set using CDimmuneimmune gene gene signatures, signatures, TCR/BCR TCR/BCR richness, and richness, and CDneoantigenimmune counts. gene Kaplan-Meier signatures, plot shows TCR/BCR the richness, and CDneoantigen counts. Kaplan-Meier plot shows the high (red)neoantigen and low (blue) riskcounts. groups from Kaplan-Meier this model plot shows the ashigh applied (red) to andthe validation low (blue) set, prisk < 0.0001 groups (G-rho from this model high (red) and low (blue) risk groups from this model familyas applied of tests, Harrington to the validation and Fleming). set, p < 0.0001 (G-rho (E) Predictionas applied versus outcome to the from validation elastic net model set, p < 0.0001 (G-rho infamily validation of set tests, data (from Harrington D). Top: Patient and outcomesFleming). for(E) each Predictionfamily sample of (black, versus tests, survival; outcomeHarrington red, death) from and plotted elastic Fleming). net model with vertical(E) Prediction jitter, alongthe versus sample’s outcome model pre- from elastic net model The increase in Th17 correlates in validation set data (from D).

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