<img height="1" width="1" style="display:none" src="https://www.facebook.com/tr?id=1052243361488264&amp;ev=PageView&amp;noscript=1">

Unlocking the Mechanisms of T Cell Exhaustion for the Advancement of Immune-Based Therapies

Posted on: January 06, 2021

Over the last decade there has been a revolution in the development of immune-based therapies and personalized medicine for the treatment of a wide array of cancers. Many of these cutting-edge therapies have been accelerated by immunology research using advanced flow cytometry techniques that enable researchers to decipher the underlying pathways and cell types associated with the therapeutic strategy. Cancer immunotherapy leverages the fact that many cancer cells express tumor specific surface antigens that can be detected by our immune system.  Effector T-cells play a key role in this process because they produce substances that kill the pathogenic cells, however prolonged exposure to the antigen, can result in exhaustion of these T-cells. This T cell exhaustion represents a state of dysfunction that is associated with a loss of effector function and proliferative capacity that arises during chronic infection and cancer. 

Our understanding of the molecular mechanisms associated with T cell exhaustion have been pivotal to the development of many of these therapies, but this research has also revealed that reversing exhaustion using checkpoint inhibitors can have unexpected consequences that hold both therapeutic promise as well as clinical challenges.

The Path to T Cell Exhaustion

Naïve T cells have the potential to be activated in response to infection and can rapidly proliferate and differentiate into a variety of effector T cell subsets. Activation can be triggered by exposure to antigens from infectious agents or vaccines. Our CD4+ and CD8+ T cells are armed with an arsenal of molecular weapons that make them suited to fight infection and have the potential to then transition into memory T cells. The majority of effector CD8+ T cells die off during the contraction phase of T cell proliferation when antigen exposure or infection diminishes, but a subset of these cells express CD127 and can differentiate into memory T cells that produce IL-2, IFN-γ and TNF-α and retain the potential to expand upon re-exposure to the same antigen (Angelosanto et. al. 2012).

If antigen exposure persists, such as during chronic infection or from exposure to a tumor antigen, effector CD8+ T cells continue to be stimulated and lose effector function, thus entering a state of unresponsiveness or “exhaustion”.  CD4+ T cells can also become exhausted, particularly during chronic viral infections, and produce lower levels of TNF-α and IFN-γ and higher levels of IL-10 and IL-21, which may also contribute to CD8+  T cell exhaustion. 

The concept that chronic infections exhaust the T-cell population was formulated from studies in murine models with persistent Lymphocytic choriomeningitis virus (LCMV) (Moskophidis et. al. 1993); continuous infection resulting in severely diminished pathogen-specific T-cell responses, with substantial loss in effector functions, and the observation that the immune system was unable to clear the virus. However, we now understand that antigen-specific T cells in persisting infections are quite different from those found in response to acute infections, and rather than representing an inert non-functional population, these so called exhausted T-cells fulfill unique immune control functions that may be controlled by external regulatory mechanisms involving PD-1 signaling.

Defining Exhausted T Cells

So called ‘exhausted’ CD4+ or CD8+ T cells display changes in their transcriptional programs, which leads to overexpression of multiple inhibitory receptors, most notably, PD-1, CTLA-4, Lag-3, and Tim-3 (Crawford et. al. 2014).  However, activated T cells can also express these inhibitory receptors, and so phenotypic confirmation of exhaustion should be included in order to effectively differentiate from effector and memory T cells.  

Under normal conditions, these inhibitory receptors are critical checkpoints of T cell function that can protect against autoimmunity. CTLA4 binds with high affinity to CD28 resulting in obstruction of the co-stimulatory signaling pathway for T-cell activation, resulting in an increase in the threshold for activation. Tumor antigens are typically weakly stimulatory, and therefore under increased CTLA4- conditions, T-cell exposed to these antigens may remain unstimulated.  The PD-1 pathway is affected very differently; the interaction between PD-1 and PD-L1/2 acts to suppress effector function and promote T-cell apoptosis.  

Phenotypic distinction of exhausted T cells can also be challenging due to their significant heterogeneity, including variations of populations with progenitor-progeny relationships or populations with varying degrees of exhaustion or homeostatic potential. Since these exhausted T cell subsets can have clinical implications on intervention strategies and clinical outcome, combining the exhaustion-directed phenotype, with reduced effector functional profiling has become a central focus for immune monitoring of patients during cancer immunotherapy.  

In exhausted T cells, overexpression of these inhibitory receptors and engagement with multiple ligands on antigen-presenting cells trigger multiple intracellular signaling pathways that alter expression of transcription factors, including T-bet, NFAT, and EOMES. The combination of changes in transcription factor expression in the context of chronic antigen exposure leads to a shift toward exhaustion (Wherry et. al. 2007), including changes in cytokine expression (Riches et. al. 2013), functional impairment (Almadzadeh et. al. 2009), metabolic and epigenetic changes (Franco et. al. 2020), and an inability to proliferate or persist.  This contributes to the overall failure of the immune system in chronic infections like HIV and for the unchecked proliferation of tumor cells. (Fig.1). 

Recently, University of Pennsylvania researchers discovered that a protein known as TOX controls the evolution of exhausted T cells. The level of TOX expression in a T cell appears to coordinate the body’s response to both infection and cancer through the control of effector and exhausted T cell activity. Although the underlying mechanisms are not fully understood, it is believed that TOX affects enzymes involved in opening and closing chromatin structure resulting in changes in epigenetic profiles and downstream gene expression. All of this might appear to make TOX an exciting target for therapeutic intervention, however it is important to note that TOX works closely in coordination with several other transcription factors including TOX2, the NR4A family of transcription factors as well as NFAT. In order to leverage these findings in the clinic, studies are now underway to engineer CAR-T cells lacking TOX, TOX2 and NR4A in order to establish more effective CAR-T responses (Chen et. al. 2019), and initial clinical trial data, particularly with CAR-T knockouts of NR4A, are very promising.

Exhaustion Marker

Predicted Expression versus

Tnaive, Teff, Tmem

Functional Role

Minimum Exhaustion Panel Component

2B4

UP

Co-regulatory receptor

CD39

UP

Ectoenzyme

CD127

DOWN

Interleukin Receptor

CTLA-4

UP

Co-regulatory receptor

CXCR5

UP

Chemokine Receptor

Eomes

UP

Transcription Factor

PD-1

UP

Co-regulatory Receptor

TCF-1

DOWN

Transcription Factor

TIGIT

UP

Co-regulatory Receptor

TOX

UP

Transcription Factor

CCL3

UP

Chemokine

 

CCR7

DOWN

Chemokine Receptor

 

CD38

UP

Ecotenzyme

 

CD7

UP

Co-regulatory Receptor

 

CD73

DOWN

Ectoenzyme

 

CXCL10

UP

Chemokine

 

Granzyme K

UP

Cytotoxic molecule

 

IL-2

DOWN

Cytokine

 

IL-10

UP

Cytokine

 

IL-21

UP

Cytokine

 

Lag-3

UP

Co-regulatory Receptor

 

Ptger2

UP

Prostaglandin Receptor

 

Table 1. Summary of Biomarkers that have been used to Profile T cell Exhaustion. Transcriptome analyses have revealed regulated exhaustion-specific genotypes, and many of these markers have also been confirmed phenotypically using flow cytometry to track expression profiles. Flow cytometry has been instrumental to our understanding of the heterogeneous exhausted T cell population, and for monitoring treatments that can reverse this state. Distinction of these cells can be achieved using multiparameter flow cytometry panels that can discern up to 20 surface and intracellular markers and discriminate exhausted T cells from other subsets with similar phenotypes, including regulatory T cells, follicular helper T cells, and effector memory T cells (Kaech et. al. 2012). 

t-cell-exhuastion-inhibitory-costimulatory-reseptors-ligands

Fig. 1.  Inhibitory/costimulatory receptors and their corresponding ligands. Schematic overview of inhibitory/costimulatory receptors expressed by T cells interacting with their counterpart on antigen-presenting cells or tumor cells. Additionally, various blocking antibodies against inhibitory receptors or their ligands in clinical trials are depicted with the aim of reversing T cell exhaustion. 

Published in Cell Communication and Signaling 2016 T cell exhaustion: from pathophysiological basics to tumor immunotherapy Kemal Catakovic, Eckhard Klieser, Daniel Neureiter, Roland Geisberger

The Role of the Tumor Microenvironment in T-Cell Exhaustion

The tumor microenvironment (TME) is a highly complex matrix of cancer-, inflammatory-, and stromal cells along with an array of secreted cytokines (Fig.2). Collectively, this network limits T-cell activation and induces T-cell exhaustion by chronic tumor antigen exposure. The TME appears to play a critical role in driving T-cell dysfunction, including production of immunosuppressive soluble mediators by myeloid-derived suppressor cells (MDSCs) and tumor-associated macrophages (Peranzoni et. al. 2010; Mantovani et. al. 2010).

Exhausted T cells in tumors, also defined as tumor-infiltrating lymphocytes (TILs), express higher levels of inhibitory receptors that can bind to their respective ligands expressed on tumor cells or antigen-presenting cells, which contributes to impaired anti-tumor responses (Fourcade et. al. 2010; Chauvin et. al. 2015; Woo et. al. 2012).  As an area of intense research driven by breakthroughs using flow cytometry techniques, several studies have shown that T cell exhaustion induced by tumor antigens may be different than that triggered by chronic viral infection (Speiser et. al. 2014). 

One innovative application of this area of research involves so-called Tumor-Infiltrating Lymphocyte Therapy. The premise for this is that the TILs population may potentially be enriched in T cells that have already recognized and responded to tumor-specific antigens. Tumor-induced immunosuppression mediated by the TME may have prevented these T-cells from eradicating the tumor, however, ex vivo expansion of these T-cells may hold the potential for increasing the frequency and therefore efficacy of these TILs. This type of autologous TIL transfer, coupled with IL-2 therapy pre-dates CAR-T therapy, but has shown moderate success in the treatment of very challenging cancers including recurrent malignant gliomas (Quanttrocchi et. al. (1999) and metastatic ovarian cancer (Rosenberg et. al. 1986). Unfortunately, there are hurdles to advancing this as a widely accessible treatment. The extraction and expansion of TILs is not easy or cost effective in most treatment modalities and IL-2 infusion can induce serious side-effects and may require specialized clinical expertise. For this reason, the use of engineered T-cells, so called CAR-T cells, has gained more popularity and adoption. CAR-T provides patients with lymphocytes expressing both the T-cell receptor specific to the tumor antigen, but also a costimulatory ‘activation’ signal that enables these cells to bypass the immunosuppression of the TME or host immune system.

t-cell-exhuastion-immunoregulatory-pathways-tme-strategies

Fig. 2. Summary of the immunoregulatory pathways in the TME and strategies to reverse tumor-induced T-cell exhaustion.

Checkpoint Blockade Breakthroughs

Since the discovery that blocking the inhibitory receptors can restore function to exhausted T cells, checkpoint inhibitors have been a focus of many immuno-oncology trials. The concept was first described with the blockade of PD-1, which restored T cell function during chronic viral infection or improved anti-tumor responses (Hirano et. al. 2005). This “checkpoint blockade” was first tested clinically as a cancer treatment with the development of anti-CTLA-4 and anti-PD-1 monoclonal antibodies (Topalian et. al. 2012).  With the approval of these therapies by the FDA in recent years, checkpoint blockade has yielded impressive results for the treatment of previously devastating cancers including metastatic melanoma and non-small cell lung cancer. The use of humanized anti-CTLA4 antibody, ipilimumab by metastatic melanoma cancer patients has doubled the 10-year survival rates (Hodi et. al 2010)

However, only a subset of patients derive clinical benefit, and therefore the application of flow cytometry to stratify patients and their responses to checkpoint blockade has been essential to the success of preclinical and clinical studies that leverage immune function for the treatment of cancers. 

The efficacy of immune checkpoint inhibition is affected by a range of factors including the genomics of the tumor, PD1 and PDL1 levels and the genetics of the host germline including HLA class. Tumor Mutation Burden (TMB) appears to influence the odds of a tumor generating an immunogenic peptide and therefore may highly influence the therapeutic success of checkpoint blockade, although not for all types of cancers. The complexity of tumor-immune interactions means that simple static biomarkers may not be enough to strategy responding patients, calling now the need for so called ‘dynamic biomarkers’ (Lesterhuis et. al. 2017). Using algorithms to track these dynamic biomarkers has the potential to provide early indications of tipping points that would more effectively stratify responding and non-responding patients. 

The widespread use of checkpoint blockade has also revealed the limitations of this approach, particularly the emergence of adverse events associated with reversing T cell exhaustion or the development of treatment-refractory tumors (Hargadon et. al. 2018).  Current clinical trials are exploring the effectiveness of combining different checkpoint inhibitors or using them with other treatment modalities to enhance the effectiveness of this therapeutic approach and limit adverse events. Although immune checkpoint inhibitors generally have fewer toxic side effects than chemotherapy, there are also still some patients who experience severe immune related adverse events (irAE). Females with higher baseline IL-6 levels were found to be at higher risk of irAE in a study of melanoma patients treated with ipilimumab, whereas other risk factors including baseline antibody signatures have also been reported. 

So how do PD-1 and CTLA-4 Mediate the Attenuation of T-cell Activity?

The primary biological function of PD1 is believed to be the maintenance of T-cell responses within a ‘normal’ physiological range, forming a feedback loop that helps attenuate local T-cell responses through TCR signaling, to minimize damage to tissue. Both PD-1 and CTLA4 appear to act through molecular mechanisms that downregulate CD28-mediated co-stimulation. This suggests that modulating CD28 signaling could represent a functional convergence point for CTLA-4 and PD-1 mediation of T-cell regulation.

t-cell-exhuastion-molecular-mechanisms-ctla-4-pd-1-blockade

Fig. 3 summary of the molecular mechanisms of action for CTLA-4 and PD-1 blockade. 

  • Represents the activation, attenuation, and therapeutic intervention of anti-CTLA-4 and anti-PD1 antibodies. 
  • Several mechanisms contribute to the efficacy of these intervention strategies- including the antibody mediated depletion of Tregs, the enhancement of T-cell positive co-stimulation within the TME, the blocking of non-tumor, host-derived PD-1 signals and the blockade interactions between PD-L1 and B7-1.  

Interestingly, the PD-1 blockade does not appear to alter the underlying epigenetic state of exhausted T cells to enable restoration of their effector functions, rather, checkpoint blockade induces a robust proliferation and differentiation of the progenitor exhausted T cells into terminally differentiated exhausted T-cells. Recent findings confirm that CD28 costimulation is required for PD-1 blockade responses resulting in viral clearance and tumor rejection.  

The PD-1+, TCF1+ progenitor cells have been shown to display characteristics of both memory and stem cell-like since they are able to proliferate, provide rapid recall responses, self-renew and generate terminally differentiated PD1+, TCF1+, TIM3low,  GZMB+ cells that display partial effector function after vaccination or PD1 checkpoint blockade. 

CTLA-4 blockade works through a number of mechanisms to induce tumor rejection. Firstly, the generation of unrestrained CD28-mediated positive co-stimulation through the direct blockade of CTLA-4 competition for B7-1 and B7-2 costimulatory ligands (Fehlings et. al. 2017). In addition, anti-CTLA-4 enables specific expansion of tumor-neoantigen-specific CD8 T-cell within the TME, specifically a subset of phenotypically exhausted CD8 T cells and a PD-1+, iCOS+, TBET+ Th1-like CD4 effector T cell population (Wei et. al. 2017).

Beyond CTLA-4 and PD-1 

There are many T-cell costimulatory proteins that are now being investigated for their therapeutic potential. These include CD40 and CD27, as well as LAG3, TIM3, TIGIT, VISTA, iCOS, OX40, GITR and 4-1BB. A clearer understanding of these molecules, their precise molecular mechanism and interactions is central to the development of new immune checkpoint blockade therapies. 

The discovery of T cell exhaustion and the development of checkpoint inhibitors has greatly advanced the field of immuno-oncology and continues to drive basic research and preclinical development. Flow cytometry technology has also propelled this research through the development of hardware and reagents that can detect more colors and improved data analysis methods, like t-SNE, that enable the visualization of high-dimensional datasets. The impact of this research was further validated by the 2018 Nobel Prize in Physiology or Medicine that honored James P. Allison and Tasuku Honjo “for their discover of cancer therapy by inhibition of negative immune regulation” 

https://www.nobelprize.org/prizes/medicine/2018/press-release

There are certain to be surprises and complexities along the way, but the field of immuno-oncology is evolving daily, and high-complexity cellular analyses such as flow cytometry and functional proteomics are truly spearheading these advances. 

Authored by: Dr. Julie Bick

 

 


Dr. Julie Bick is a medicinal biochemist who has spent close to 7 years with FlowMetric Life Sciences. After receiving her doctorate in Biochemistry at Southampton University in the UK, she began her career as Associate Professor at Rutgers University, NJ, before moving to the west coast to perform biomedical research with Syngenta and Novartis at the Torrey Mesa Research Institute in San Diego. Dr. Bick specializes in biomedical engineering of cells and proteins in order to provide innovative therapeutic and diagnostic solutions. She brings to FlowMetric a clinical expertise across a wide range of therapeutic areas from autoimmunity to oncology and chronic inflammatory conditions, acquired over 25 years of research experience in academic, biotechnology and pharmaceutical laboratories. In leading FlowMetric Life Sciences’ innovation initiatives, Dr. Bick has been collaborating with BurstIQ to implement Block Chain solutions into the company’s Contract Research Organization division, with a focus on enhanced big data analytics and process control solutions in the regulated clinical environment. Dr. Bick is committed to working with local Community Colleges to support STEM programs for the next generation of scientists.

References

  1. Angelosanto, J. M. et.al. (2012) Progressive loss of memory T cell potential and commitment to exhaustion during chronic viral infection. J. Virol.;86:8161-8170.
    https://www.ncbi.nlm.nih.gov/pubmed/22623779

  2. Crawford, A. et. Al. (2014) Molecular and transcriptional basis of CD4+  T cell dysfunction during chronic infection. Immunity. 40:289-302.
    https://www.ncbi.nlm.nih.gov/pubmed/24530057

  3. Wherry,E. J.  et.al. (2007). Molecular signature of CD8+ T cell exhaustion during chronic viral infection. Immunity. 27:670-684
    https://www.ncbi.nlm.nih.gov/pubmed/17950003

  4. Riches, J. C. et. al. (2003)  T cells from CLL  patients exhibit features of T-cell exhaustion but retain capacity for cytokine production. Blood 121(9):1612-21.
    https://www.ncbi.nlm.nih.gov/pubmed/23247726

  5. Ahmadzadeh, M. et, al. (2009) Tumor antigen-specific CD8 T cells infiltrating the tumor express high levels of PD-1 and are functionally impaired. Blood 114(8):1573-44.
    https://www.ncbi.nlm.nih.gov/pubmed/19423728

  6. Chauvin, J. M. et.al. (2015) TIGIT and PD-1 impair tumor antigen-specific CD8+  cells  in melanoma patients. J. Clin Invest. 125:2046-2058.
    https://www.ncbi.nlm.nih.gov/pubmed/25866972

  7. Woo, S. R. et. al. (2012) Immune inhibitory molecules LAG-3 and PD-1 synergistically regulate T-cell function to promote tumoral immune escape. Cancer Res. 72:917-927.
    https://www.ncbi.nlm.nih.gov/pubmed/22186141

  8. Fourcade, J. et. al (2010) Upregulation of Tim-3 and PD-1 expression is associated with tumor antigen-specific CD8+ T cell dysfunction in melanoma patients. J Exp Med. 207:2175-2186. https://www.ncbi.nlm.nih.gov/pubmed/20819923

  9. Speiser, D. E. et.al. (2014) T cell differentiation in chronic infection and cancer; functional adaptation or exhaustion? Nat Rev Immunol. 14:768-774.
    https://www.ncbi.nlm.nih.gov/pubmed/25257362

  10. Peranzoni, E. et.al. (2010) Myeloid-derived suppressor cell heterogeneity and subset definition. Curr Opin Immunol. 22:238-244.
    https://www.ncbi.nlm.nih.gov/pubmed/20171075

  11. Mantovani, A. et.al. (2010) Macrophages, innate immunity and cancer: balance, tolerance, and diversity. Curr Opin Immunol. 22:231-237.
    https://www.ncbi.nlm.nih.gov/pubmed/20144856

  12. Kaech, S. M.  et, al. (2012) Transcriptional control of effector and memory CD8+ T cell differentiation. Nat Rev Immunol.12:749-761.
    https://www.ncbi.nlm.nih.gov/pubmed/23080391

  13. Hirano, F. et. al. (2005) Blockade of B7-H1 and PD-1 by monoclonal antibodies potentiates cancer therapeutic immunity. Cancer Res.65(3):1089-96.
    https://www.ncbi.nlm.nih.gov/pubmed/15705911

  14. Topalian, S. L. et. al. (2012) Safety, activity and immune correlates of anti-PD-1 antibody in cancer. N Engl J Med. 366(26):2443-54.
    https://www.NEJM.org/doi/full/10.1056/NEJMoa1200690

  15. Hargadon, K. M. et. al. (2018) Immune checkpoint blockade therapy for cancer: an overview of FDS-approved immune checkpoint inhibitors. Int. Immunopharm.62:29-39.
    https://www.ncbi.nlm.nih.gov/pubmed/29990692

  16. Franco, F., Jaccard, A., Romero, P., Yu, Y, Ho, C. (2020) Metabolic and Epigenetic Regulation of T-cell Exhaustion. Nature Metabolism. Sept 2020.

  17. Moskophidis D, Lechner F, Pircher H, Zinkernagel RM. (1993) Virus persistence in acutely infected immunocompetent mice by exhaustion of antiviral cytotoxic effector T cells. Nature 362:758–61. doi: 10.1038/362758a0

  18. Chen., J. et. al. (2019) NR4A transcription factors limit CAR T cell function in solid tumours. Nature Mar (7749): 530-534. PMID: 30814732 PMCID: PMC6546093 DOI: 10.1038/s41586-019-0985-x

  19. Quattrocchi KB, et al. (1999) Pilot study of local autologous tumor infiltrating lymphocytes for the treatment of recurrent malignant gliomas. J Neuro-Oncol. 45:141–157

  20. Rosenberg SA, Spiess P, Lafreniere R. (1986) A new approach to the adoptive immunotherapy of cancer with tumor-infiltrating lymphocytes. Science. 233:1318–21

  21. Hodi, F. S. et al. (2010) Improved survival with ipilimumab in patients with metastatic melanoma. N. Engl. J. Med 363, 711–723.

  22. Lesterhuis, W. J. et al. (2017) Dynamic versus static biomarkers in cancer immune checkpoint blockade: unravelling complexity. Nat. Rev. Drug Discov 16, 264–272.

  23. Fehlings, M. et. al. (2017) Checkpoint blockade immunotherapy reshapes the high-dimensional phenotypic heterogeneity of murine intratumoural neoantigen-specific CD8 T cells. Nat. Commun. 8:562

  24. Wei, S. C. et. al. (2017) Distinct cellular mechanisms underlie anti-CTLA-4 and anti PD-1 checkpoint blockade. Cell 170:1120-33