Sunday, February 5, 2017

Set Points vs Check Points

There has been a multiplicity of applications of such therapeutics as monoclonal antibodies which are used as "checkpoint inhibitors" to treat a variety of cancers. These therapeutics are proteins which can block the action of inhibitors on T cells which if activated and prevent the T cell from attacking the cancer cell. We have seen the proliferation of these therapeutic approaches in melanoma, lung cancer and even prostate cancer. In a recent White Paper we examine the concept of "Check Points" and "Set Points".[1]

The main driver for this analysis is the recent work of Chen and Mellman. In a recent paper these two authors state:

Immunotherapy is proving to be an effective therapeutic approach in a variety of cancers. But despite the clinical success of antibodies against the immune regulators CTLA4 and PD-L1/PD-1, only a subset of people exhibit durable responses, suggesting that a broader view of cancer immunity is required. Immunity is influenced by a complex set of tumour, host and environmental factors that govern the strength and timing of the anticancer response. Clinical studies are beginning to define these factors as immune profiles that can predict responses to immunotherapy. In the context of the cancer immunity cycle, such factors combine to represent the inherent immunological status — or ‘cancer–immune set point’ — of an individual.

The concept of a "set point" is in our opinion rather poorly used. The construct, if properly understood, means that there is some point at which T cell activators and inhibitors either permit activation and effective T cell immunotherapeutic action or inhibit that. Namely there is some set of activations less inhibitions which all T cells to perform and under that "set point" they no longer function.

If such a concept has physical meaning, then the authors state:

Although largely conceptual, the idea of a set point provides a framework to help organize the torrent of clinical and biomarker data that will emerge over the coming months and years. The number of targets that could prove effective for cancer immunotherapy is great; the number of potential combinations of therapeutic agents that are directed against these targets (or combinations of such agents with conventional standard-of-care agents) is even greater. The development of some cancer therapies may be largely empirical, but it can be guided by considering, even in general terms, the elements that comprise cancer immunity

Thus, our objective in this paper is to examine this set point concept and explore its dimensions. Specifically, we examine how it may be used for therapeutic uses.

To best understand some of the principles we examine some simplistic model.

Let us begin with a simplistic but representative set of examples. We know that a CD8 T cell has a receptor, the TCR, T cell receptor, and it examines an antigen presenting cell, APV, which presents an antigen on its MHC I protein. This process is essentially an activator process. If that were all which was needed, then the T cell would be activated and sent out its cytokines and destroy the cell. However, there are also inhibitor ligands which activate inhibition in the T cell. Now if the T cell is activated and the inhibition is not then we get the T cell sending out cytokines and killing the offending cell.

However, if the T cell has an inhibitor also connected then the inhibitor sends out an internal T cell signal which stops the release. The presenting cell survives.

Now in reality the T cells do not have just one receptor. It may have a multiplicity over the surface. Thus, there are a multiplicity of activators and inhibitors. It is a multiplicity amongst the same type as well a multiplicity of types. In fact, the T cell may be just covered with receptors searching out antigens.

Thus, the first layer of complexity of the immune response is not a simple activator/inhibitor complex but a mass of receptors and ligands interacting in a complex manner. The question then is; at what point does the T cell go from active to inactive and back again? In fact, we may ask if there is some hysteresis effect. If so can we therapeutically take advantage of it.

Ligands and receptors are basically two separate chemical elements. The binding of them is also essentially a chemical process whereby the ligand finds the correct location on the receptor to bind. It is in many ways like any chemical reaction. As such there is a reaction rate whereby the ligand and receptor combine, but equally there is the reverse reaction, the breaking apart of a bond.

In the work by Stone et al the authors note:

The interaction between the T-cell receptor (TCR) and its peptide–major histocompatibility complex (pepMHC) ligand plays a critical role in determining the activity and specificity of the T cell. The binding properties associated with these interactions have now been studied in many systems, providing a framework for a mechanistic understanding of the initial events that govern T-cell function. There have been various other reviews that have described the structural and biochemical features of TCR: pepMHC interactions.  Here we provide an overview of four areas that directly impact our understanding of T-cell function, as viewed from the perspective of the TCR: pepMHC interaction: (1) relationships between T-cell activity and TCR: pepMHC binding parameters, (2) TCR affinity, avidity and clustering, (3) influence of coreceptors on pepMHC binding by TCRs and T-cell activity, and (4) impact of TCR binding affinity on antigenic peptide specificity.

Now they conclude:

The binding properties of TCRs for their pepMHC ligands are critically important in the function of T cells, leading to outcomes that can involve T-cell selection in the thymus or full peripheral T-cell responsiveness or homeostatic T-cell proliferation in the periphery. The processes are even more complicated because the same TCR could interact with multiple pepMHC ligands on the same antigen-presenting cell, each with heterogeneous binding properties. These reactions would result in a complex integration of signals that ultimately determine the nature of the T-cell response. While there have been numerous studies to elucidate the precise binding parameters that correlate with different T-cell activities, various questions remain unanswered (in part because of the technical difficulties associated with performing binding experiments on low-affinity reactions). Further understanding of the TCR binding properties that generate defined signals is important, not only from a basic science perspective but also toward developing optimal strategies that improve T-cell responses to foreign antigens and tumour antigens.

Thus, one must be careful in developing an immune set point theory to be cautious about the affinity issues as discussed above.

We have examined the complex process fundamentally as a build. Specifically:

1. Activation: When an antibody binds with the TCR we expect a response.

2. Inhibitor: When there is an inhibitor, however, it may be possible to block the pathway leading to the activation.

3. Notwithstanding the above, the cell actually has a multiplicity of the previous two and thus there may be some race with a finish line defined by what has been called a "set point", or simply some collection of activators and inhibitors seeing which one dominates.

4. There are not just one possible activator and inhibitor. For a T cell, we have the TCR but we may have well more than a PD-1. New inhibitors are arising each day.

5. The internal machinations of the cellular pathways may also effect the net result. Thus, genetic changes can affect what happens.

6. The kinetics of the binding can and often do play a significant role. Binding is not a one-way street, and the result may be loss of tumor control.

7. Exogeneous Factors: The human biome is often a driving factor to the efficacy of immunotherapy. 

As Chen and Mellman note:

Factors that are extrinsic to the tumour or host genomes may also affect the immune profile of tumors. Chief among these is the gut microbiome, which has an important role not only in influencing the initiation of some cancers, but also in the response to chemotherapy and immunotherapy…mice bearing subcutaneous syngeneic tumors do not respond to chemotherapy if sterilized by prior treatment with antibiotics or when raised in germ-free conditions. The effect was attributed to the ability of commensal bacteria to activate the innate immune system of the host following chemotherapy, possibly by causing symbiosis and penetration of commensal bacteria into the gut lamina propria. Subsequent work established an even clearer link between T-cell responses and an intact microbiota1. Fecal transfer or co-housing experiments in mice demonstrated that defined species of gut bacteria enabled antitumor responses after treatment with anti-PD-L1/PD-1 or anti-CTLA4 therapies. Furthermore, the gut microbiota even influenced spontaneous antitumor responses, which correlated with the degree of T-cell infiltration into tumors before any therapy had been administered.

Each of these elements can be considered as a therapeutic target for immunotherapy. We summarize some of these below.

Thus, understanding the specifics may be a useful approach in guiding therapeutic development using the immune system.

The previous brief summary lays out some of the issues inherent in the Chen and Mellman paper. To better understand let us now return to the ideas of Chen and Mellman. Specifically, their definition of a "set point". They state:

The cancer–immune set point is the threshold that must be overcome to generate effective cancer immunity. The set point can be understood as a balance between the stimulatory factors (Fstim) minus the inhibitory factors (Finhib), which together must be equal to or greater than 1, over the summation of all T-cell antigen receptor (TCR) signals for tumour antigens. The cancer–immune set point is shown here:

∫ (Fstim) − ∫ (Finhib) ≥ 1 ∕ Σ n=1, y (TCRaffinity × frequency)

The set point is defined by the summation of the frequency of peptide–MHC–TCR interactions and TCR signalling in all anticancer CD8+ T-cell clones (mainly, the TCR affinity for the antigen–MHC class I complex) against antigens present in the cancer cells, including neoantigens and cancer-associated antigens, and the endogenous balance of the positive and negative immune regulators that are inherent to each host or patient.

Now just what this means is somewhat open for debate because it is written by a biologist not a physical scientist and definitely not an engineer. Permit me to attempt an interpretation. First let us try to be specific about a definition. Namely some definition of a variable which is measurable.

Let us try to first understand the F terms.

Fstim: This is a stimulatory factor. What is it? One could guess it is some cell with an MHC I presenting some antigen Ag to a T cell receptor TCR. Should we examine cell by cell? Should we look at every possible T cell, namely ones that say are CD 8 T cells, or how about other immune cells. Why not include NK cells as well? Should we look at stem cells only, do we know what they are? Do we then count these for every T cell, for a mass of T cells, for what?

Finhib: We know some of these we believe. There is PD-1 and CTLA-4. They can block the T cell from attacking. We also suspect that there are many others we have yet to find. So, let us simplistically assume we can model with the two mentioned. But what are we measuring? Are we measuring a single cell, a collection of cells, the totality of all cells? Are we measuring all stimulatory factors or just a few? Are we measuring all inhibitory factors or just the ones we know? Are we weighting some differently than others or the same?

This if we have two single cells and it has say 50 T cell receptors and 45 PD-1 receptors, then we can have activated say 35 of the TCR and have activate say 22 of the PD-1. Now what happens? Is activation by each TCR the same and can a TCR being activated be inhibited by an activated PD-1 on a one to one basis?

The above still has no physical meaning. Now let us consider T Cell Affinity. As Nicholich et al state:

Affinity refers to the steady-state association constant between a monovalent receptor and its ligand, in this case a single T-cell receptor (TCR) and peptide–MHC (pMHC) complex. Structural avidity is the steady-state association constant between multiple cell-bound receptors and ligands and is determined by the direct binding affinities of multiple TCRs to their pMHC complexes. Functional avidity depends on the relative kinetics of signalling that translate into measurable biological functions such as proliferation, cytokine production or cytolytic function. APC, antigen-presenting cell.

Now as Hsieh et al note:

TCR affinity: The strength of interaction between the T cell receptor and a single peptide–MHC complex.

As an abstraction that may be fine but as something used in a measurement and equation it is highly deficient.

Now as Daniels et al note:

To estimate the TCR affinity of the ligands comprising the selection boundary, we measured tetramer binding; which correlates with monomeric TCR–pMHC affinities, is performed on live cells and involves the participation of CD8. The binding characteristics of tetramers were determined on pre-selection OT-I double positive thymocytes at 37 uC. The dissociation constant (Kd) was calculated by nonlinear regression analysis and confirmed by homologous competition experiments. The tetramer binding curves for Q4R7 (weakest negative selector), T4 (border ligand) and Q4H7 (strongest positive selector) overlapped. Their Kd values (Q4R7, 4869.5 nM; T4, 55610.1 nM; Q4H7, 5169.1 nM; n57, P50.455) and their half-lives (t1/2) were not significantly different (Table 1). However, heterologous competition assays showed that Q4R7 was more efficient than Q4H7 at inhibiting the binding of OVA tetramers.

or perhaps they mean something akin to this:

Now we know that there is a threshold effect for activating and suppressing. Namely there has to be more activators than suppressors. Just what that balance is of course is uncertain. Again, the statement has no physical meaning.

They continue:

This can be further influenced by other elements of immunity, including tumour-derived immunomodulatory components, as well as by exogenous factors such as infection and exposure to pharmacological agents. A given patient with cancer may have a low set point, making it easier to generate an anticancer immune response, or a high set point, which makes it more difficult.

The aim of immunotherapy is to increase Fstim, decrease Finhib or increase TCR signalling to drive progression of the cancer-immunity cycle. These values are difficult to quantify with current techniques but represent a useful theoretical construct. It is probable that the cancer–immune set point of a particular person is already determined by the time of clinical presentation, driven by the inherent immunogenicity of the tumour and by the responsiveness of the individual’s immune system. Although it is reasonable to assume that various lines of cancer therapy or changes in environmental factors might alter Fstim and Finhib, such changes might only be transient. Often, the set point that is identified using pretreatment biopsies is similar to the set point determined by biomarker profiling from biopsies taken on progression after therapy. Likewise, despite the continued accumulation of mutations in a tumour as a function of time, primary and metastatic lesions can exhibit similar immune profiles. The features that determine the set point may therefore reflect genetic factors that are specific to a given tumour, the genetics of the person with cancer, or the extent to which antitumor immunity had developed initially. Conceivably, immunotherapy may work as a consequence of either its direct effect on Fstim and Finhib (that is, by assisting the completion of a single revolution of the cancer-immunity cycle) or its ability to alter the set point (for example, by propagating the cancer-immunity cycle, which enhances the cancer-specific T-cell response). Although largely conceptual, the idea of a set point provides a framework to help organize the torrent of clinical and biomarker data that will emerge over the coming months and years. The number of targets that could prove effective for cancer immunotherapy is great; the number of potential combinations of therapeutic agents that are directed against these targets (or combinations of such agents with conventional standard-of-care agents) is even greater.

Thus, let us try and construct meaning which may be measurable and verifiable as well as actionable. Consider the following model:
1. Let us assume we have a tumor cell. Let us assume there are N possible activator ligands and M inhibitor ligands.

2. Let us assume that for each of the above ligands we have on a T cell some receptor. If there is a ligand without a receptor we shall ignore it.

3. Assume we can count and differentiate the differing ligand-receptor possibilities on a cell.

4. Now calculate the following:

Namely, we count the number of different activators and the number of different inhibitors and then weight them by some metric, yet to be determined, and then weigh them by the total present.

This approach may have merit. The weights may be unity, but that is a mere guess. The weights may be reflective of the enzymatic consistency of the contact. Frankly we just do not know but it is worth exploring.

We now return to following Chen and Mellman and their observations. They note:

The role of the immune system in cancer remained unappreciated for many decades because tumors effectively suppress immune responses by activating negative regulatory pathways (also called checkpoints) that are associated with immune homeostasis or by adopting features that enable them to actively escape detection. Two such checkpoints, cytotoxic T-lymphocyte protein 4 (CTLA4) and programmed cell death protein 1 (PD-1), have garnered the most attention so far. CTLA4 is a negative regulator of T cells that acts to control T-cell activation by competing with the co-stimulatory molecule CD28 for binding to shared ligands CD80 (also known as B7.1) and CD86 (also known as B7.2). The cell-surface receptor PD-1 is expressed by T cells on activation during priming or expansion and binds to one of two ligands, PD-L1 and PD-L2. Many types of cells can express PD-L1, including tumour cells and immune cells after exposure to cytokines such as interferon (IFN)-γ; however, PD-L2 is expressed mainly on dendritic cells in normal tissues. Binding of PD-L1 or PD-L2 to PD-1 generates an inhibitory signal that attenuates the activity of T cells. The ‘exhaustion’ of effector T cells was identified through studies of chronic viral infection in mice in which the PD-L1/PD-1 axis was found to be an important negative feedback loop that ensures immune homeostasis; it is also an important axis for restricting tumour immunity.

They then proceed to characterize three differing states of tumors with respect to their T cell response. They are:

1. Inflamed Tumor: This a tumor with lots of cells and penetrating the tumor space.

2. Immune Desert Tumor: This is a tumor with lots of cells but no significant penetration of the tumor space.

3. Immune Excluded Tumor: This is a tumor with a paucity of any T cells present.

Now we consider the descriptions as presented by Chen and Mellman:

The first profile, the immune-inflamed phenotype, is characterized by the presence in the tumour parenchyma of both CD4- and CD8-expressing T cells, often accompanied by myeloid cells and monocytic cells; the immune cells are positioned in proximity to the tumour cells. Samples from inflamed tumors may exhibit staining for PD-L1 on infiltrating immune cells and, in some cases, tumour cells. Many proinflammatory and effector cytokines can also be detected by mRNA analysis in these sections of tumors. This profile suggests the presence of a pre-existing antitumor immune response that was arrested probably by immunosuppression in the tumour bed. Indeed, clinical responses to anti-PD-L1/PD-1 therapy occur most often in patients with inflamed tumors…The second profile is the immune-excluded phenotype, which is also characterized by the presence of abundant immune cells. However, the immune cells do not penetrate the parenchyma of these tumors but instead are retained in the stroma that surrounds nests of tumour cells. The stroma may be limited to the tumour capsule or might penetrate the tumour itself, making it seem that the immune cells are actually inside the tumour. After treatment with anti-PD-L1/PD-1 agents, stroma-associated T cells can show evidence of activation and proliferation but not infiltration, and clinical responses are uncommon. These features suggest that a pre-existing antitumor response might have been present but was rendered ineffective by a block in tumour penetration through the stroma or by the retention of immune cells in the stroma. T-cell migration through the tumour stroma is therefore the rate-limiting step in the cancer–immunity cycle for this phenotype.

Finally, the third type is characterized as follows:

The third profile, the immune-desert phenotype, is characterized by a paucity of T cells in either the parenchyma or the stroma of the tumour. Although myeloid cells may be present, the general feature of this profile is the presence of a non-inflamed tumour microenvironment with few or no CD8-carrying T cells. Unsurprisingly, such tumors rarely respond to anti-PD-L1/PD-1 therapy. This phenotype probably reflects the absence of pre-existing antitumor immunity, which suggests that the generation of tumour-specific T cells is the rate-limiting step. The immune-desert phenotype and the immune-excluded phenotype can both be considered as non-inflamed tumors.

Thus, this does pose the question; how does one identify these cells and how could one move one category to the other for better response? Frankly one asks just what is happening from one class to another?

Set Points, Check Points, and other elements of the control of the immune system as a mechanism to understand and deal with cancer has been evolving at a rapid pace. Where the Check Point field seeks new and effective ligand-receptor pairs, the Set Point field seems to examine the process in a more holistic manner. Perhaps that is an approach which would enable a more systematic approach.

How does this process change as a cell matures? What of cell differentiation. T cells like many of the lymphoid line go through varying degrees of maturation. Thus, we ask: what is the difference?

We have discussed the stem cell constructs at length. In McGarty (Stem Cells) we have tried to bring some of these ideas up to data. The problem is that stem cells may very well have different markers than the cells we can attack with the tools at hand. Thus, attacking PD-1 and CTLA4 markers may work for the mass of the tumor and result in shrinkage but it may totally miss the stem cell. How best to address this is uncertain?

What are the therapeutic dimensions of this principle? We have discussed a few here but there are many which present themselves.

CAR-T cells are "engineered" T cells which are designed by use of such tools as a lentivirus to attack a specific malignant cell. They have been shown to be useful for hematological cancers and have been examined for solid tumors. As Ramachandran notes in his Thesis:

As the name suggests, a CAR is a chimera of domains from different proteins assembled together to create a functional receptor. These novel receptors initiate a functional downstream effector T-cell signaling pathway when they encounter target antigen, usually the TAA on a cancer cell. This gives the opportunity to engineer a large variety of TAA-specific receptors targeting a broad range of cancer types.

CARs typically contain four domains

(a) extracellular antigen binding domain: It confers the antigen-specificity to the engineered T-cell. A majority of the engineered CARs for cancer therapy have antibody-derived antigen binding domains called single-chain variable fragment (scFv). CARs containing a scFv extracellular domain retain the specificity of an antibody. A major advantage of having scFv extracellular domain is that it bypasses the need for antigen presentation by MHC-I on tumor cells, as antibodies directly bind to cell surface antigens. (b) Spacer or hinge region: It gives flexibility and length to allow proper dimerization of scFv, thus improving its stability. The most commonly used spacer regions are derived from IgG Fc CH2-CH3 domains, CD28 hinge domain and CD8α spacer domain (c) Transmembrane domain: It determines the stability of CAR expression on cell surface. The most commonly used transmembrane regions are derived from CD3ζ CD4, CD8 and CD28 molecules138 and (d) Cytoplasmic signaling domain(s): This region has the domains that provide the necessary downstream signaling for T-cell effector functions. CARs are classified into different generations based on the number of cytoplasmic signaling domains namely first, second and third generation CARs. First generation CARs have only one cytoplasmic domain, usually T-cell activation signaling domain (CD3ζ chain). In addition to the T-cell activation domain second generation CARs have one extra co-stimulatory signaling domain, e.g., CD28, 4-1BB, ICOS or OX40 and third generation CARs have two extra co-stimulatory domains…

In a recent Technical Note McGarty has further developed the CAR-T cell concepts for both hematological and solid tumors. CAR-T are engineered to specific targets. The question then is; can a better understanding of set points allow for improved targeting for CAR-T cells or are CAR-T cells perforce of their design not really useful for attacking solid tumors?

The enzyme kinetics of the reactions on the surfaces of T cells and APC or tumor cells are critical. We have almost always assumed that once a protein is bound it stays. Yet we know it is not the case. Furthermore, when understanding the set point model, if we have a paucity of activators on a T cell it will not function. If the paucity is due to enzymatic action, then perhaps we can indirectly address the low level by increasing the retention via enzyme kinetic improvement.

The pathway factors are both integral to immunotherapeutic approaches, they facilitate the process inside a T cell for example, but they may also be poorly understood. Let us briefly review that issue. We must look at pathways from the perspective of the T cell and the tumor Ag presenting cell.

1. From the T cell perspective we have internal genetic pathways which facilitate the process of cytokine release. If there are faults on the pathway, then we would not expect the T cell to function. Thus, we may ask if these are somatic defects or a result of some change in the T cell.

2. From the perspective of the tumor cell, we know its pathways have usually been altered. Then does this altering result in the excess expression of inhibitors or the suppression of activators. Do the pathways alter the MHC I presentation efforts?

Both dimensions are worth examining.

A recent National Academies Report by Balogh et al present several policy issues regarding immunotherapy. The report was meant to present a simplified overview of immunotherapeutics as well as present some key policy issues. Concerns regarding costs, patient value, physician-patient expectations were discussed.  Also, was a discussion on evidence based approaches. The problem is that the experience is limited and the costs high. Furthermore, what seemed not discussed was the fact that the complexity of this field is great and the depth of understanding by physicians quite limited. One could say that most Oncologists are trained to administer chemotherapy, and have a limited if not aged understanding of the immune system.

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