Effects of thymic selection on T cell recognition of foreign and tumor antigenic peptides

The role of promiscuous molecular recognition in the evolution of RNase-based self-incompatibility in plants

How do biological networks evolve and expand? We study these questions in the context of the plant collaborative-non-self recognition self-incompatibility system. Self-incompatibility evolved to avoid self-fertilization among hermaphroditic plants. It relies on specific molecular recognition between highly diverse proteins of two families: female and male determinants, such that the combination of genes an individual possesses determines its mating partners. Though highly polymorphic, previous models struggled to pinpoint the evolutionary trajectories by which new specificities evolved. Here, we construct a novel theoretical framework, that crucially affords interaction promiscuity and multiple distinct partners per protein, as is seen in empirical findings disregarded by previous models. We demonstrate spontaneous self-organization of the population into distinct "classes" with full between-class compatibility and a dynamic long-term balance between class emergence and decay. Our work highlights the importance of molecular recognition promiscuity to network evolvability. Promiscuity was found in additional systems suggesting that our framework could be more broadly applicable.

RACER-m leverages structural features for sparse T cell specificity prediction

Reliable prediction of T cell specificity against antigenic signatures is a formidable task, complicated by the immense diversity of T cell receptor and antigen sequence space and the resulting limited availability of training sets for inferential models. Recent modeling efforts have demonstrated the advantage of incorporating structural information to overcome the need for extensive training sequence data, yet disentangling the heterogeneous TCR-antigen interface to accurately predict MHC-allele-restricted TCR-peptide interactions has remained challenging. Here, we present RACER-m, a coarse-grained structural model leveraging key biophysical information from the diversity of publicly available TCR-antigen crystal structures. Explicit inclusion of structural content substantially reduces the required number of training examples and maintains reliable predictions of TCR-recognition specificity and sensitivity across diverse biological contexts. Our model capably identifies biophysically meaningful point-mutant peptides that affect binding affinity, distinguishing its ability in predicting TCR specificity of point-mutants from alternative sequence-based methods. Its application is broadly applicable to studies involving both closely related and structurally diverse TCR-peptide pairs.

How persistent infection overcomes peripheral tolerance mechanisms to cause T cell-mediated autoimmune disease

T cells help orchestrate immune responses to pathogens, and their aberrant regulation can trigger autoimmunity. Recent studies highlight that a threshold number of T cells (a quorum) must be activated in a tissue to mount a functional immune response. These collective effects allow the T cell repertoire to respond to pathogens while suppressing autoimmunity due to circulating autoreactive T cells. Our computational studies show that increasing numbers of pathogenic peptides targeted by T cells during persistent or severe viral infections increase the probability of activating T cells that are weakly reactive to self-antigens (molecular mimicry). These T cells are easily re-activated by the self-antigens and contribute to exceeding the quorum threshold required to mount autoimmune responses. Rare peptides that activate many T cells are sampled more readily during severe/persistent infections than in acute infections, which amplifies these effects. Experiments in mice to test predictions from these mechanistic insights are suggested.

Aggregation-prone peptides from within a non-self-protein homoaggregate are preferred for MHC association: Historical overview

New technologies assist re-evaluation of hypotheses on generation of immune cell repertoires and distinctions of self from non-self. Findings include positive correlations between peptide propensities to aggregate and their binding to major histocompatibility complex (MHC) proteins. This recalls the hypothesis that foreign proteins may homoaggregate in host cytosols prior to releasing their peptides (p) to form pMHC complexes. Clues to this included aggregation-related phenomena associated with infections (rouleaux formation, pyrexia, certain brain diseases). By virtue of 'promiscuous' gene expression by thymic presenting cells - perhaps adapted from earlier evolving gonadal mechanisms - developing T cells monitor surface pMHC clusterings. This evaluates intracellular concentrations of the corresponding proteins, and hence, following Burnet's two signal principle, degrees of self-reactivity. After positive selection in the thymic cortex for reactivity with 'near-self', high-level pMHC clustering suffices in the medulla for negatively selection. Following Burnet's principle, in the periphery low-level clustering suffices for T cell stimulation and high-level clustering again provokes negative selection (immunological tolerance). For evolving intracellular pathogens, fine-tuned polymorphisms of their host species have limited to 'near-self' some mimicking adaptations. It is proposed that while entire pathogen proteins may have evolved to minimize their aggregability, the greater aggregability of their peptides remains partially hidden within. Two-step proofreading mechanisms in prospective hosts select proteins containing aggregable peptide for the generation of pMHC clusters at the surface of presenting cells. Through mutations, some proteins of pathogens and cancer cells tend to converge towards the host 'near-self' that its T cells have auditioned to address.

Quantitative approaches for decoding the specificity of the human T cell repertoire

T cell receptor (TCR)-peptide-major histocompatibility complex (pMHC) interactions play a vital role in initiating immune responses against pathogens, and the specificity of TCRpMHC interactions is crucial for developing optimized therapeutic strategies. The advent of high-throughput immunological and structural evaluation of TCR and pMHC has provided an abundance of data for computational approaches that aim to predict favorable TCR-pMHC interactions. Current models are constructed using information on protein sequence, structures, or a combination of both, and utilize a variety of statistical learning-based approaches for identifying the rules governing specificity. This review examines the current theoretical, computational, and deep learning approaches for identifying TCR-pMHC recognition pairs, placing emphasis on each method's mathematical approach, predictive performance, and limitations.

Neoantigen-specific CD8 T cells with high structural avidity preferentially reside in and eliminate tumors

The success of cancer immunotherapy depends in part on the strength of antigen recognition by T cells. Here, we characterize the T cell receptor (TCR) functional (antigen sensitivity) and structural (monomeric pMHC-TCR off-rates) avidities of 371 CD8 T cell clones specific for neoantigens, tumor-associated antigens (TAAs) or viral antigens isolated from tumors or blood of patients and healthy donors. T cells from tumors exhibit stronger functional and structural avidity than their blood counterparts. Relative to TAA, neoantigen-specific T cells are of higher structural avidity and, consistently, are preferentially detected in tumors. Effective tumor infiltration in mice models is associated with high structural avidity and CXCR3 expression. Based on TCR biophysicochemical properties, we derive and apply an in silico model predicting TCR structural avidity and validate the enrichment in high avidity T cells in patients' tumors. These observations indicate a direct relationship between neoantigen recognition, T cell functionality and tumor infiltration. These results delineate a rational approach to identify potent T cells for personalized cancer immunotherapy.

Optimal cancer evasion in a dynamic immune microenvironment generates diverse post-escape tumor antigenicity profiles

The failure of cancer treatments, including immunotherapy, continues to be a major obstacle in preventing durable remission. This failure often results from tumor evolution, both genotypic and phenotypic, away from sensitive cell states. Here, we propose a mathematical framework for studying the dynamics of adaptive immune evasion that tracks the number of tumor-associated antigens available for immune targeting. We solve for the unique optimal cancer evasion strategy using stochastic dynamic programming and demonstrate that this policy results in increased cancer evasion rates compared to a passive, fixed strategy. Our foundational model relates the likelihood and temporal dynamics of cancer evasion to features of the immune microenvironment, where tumor immunogenicity reflects a balance between cancer adaptation and host recognition. In contrast with a passive strategy, optimally adaptive evaders navigating varying selective environments result in substantially heterogeneous post-escape tumor antigenicity, giving rise to immunogenically hot and cold tumors.

Immune cells use active tugging forces to distinguish affinity and accelerate evolution

Cells are known to exert forces to sense their physical surroundings for guidance of motion and fate decisions. Here, we propose that cells might do mechanical work to drive their own evolution, taking inspiration from the adaptive immune system. Growing evidence indicates that immune B cells-capable of rapid Darwinian evolution-use cytoskeletal forces to actively extract antigens from other cells' surfaces. To elucidate the evolutionary significance of force usage, we develop a theory of tug-of-war antigen extraction that maps receptor binding characteristics to clonal reproductive fitness, revealing physical determinants of selection strength. This framework unifies mechanosensing and affinity-discrimination capabilities of evolving cells: Pulling against stiff antigen tethers enhances discrimination stringency at the expense of absolute extraction. As a consequence, active force usage can accelerate adaptation but may also cause extinction of cell populations, resulting in an optimal range of pulling strength that matches molecular rupture forces observed in cells. Our work suggests that nonequilibrium, physical extraction of environmental signals can make biological systems more evolvable at a moderate energy cost.

Measures of epitope binding degeneracy from T cell receptor repertoires

Adaptive immunity is driven by specific binding of hypervariable receptors to diverse molecular targets. The sequence diversity of receptors and targets are both individually known but because multiple receptors can recognize the same target, a measure of the effective "functional" diversity of the human immune system has remained elusive. Here, we show that sequence near-coincidences within T cell receptors that bind specific epitopes provide a new window into this problem and allow the quantification of how binding probability covaries with sequence. We find that near-coincidence statistics within epitope-specific repertoires imply a measure of binding degeneracy to amino acid changes in receptor sequence that is consistent across disparate experiments. Paired data on both chains of the heterodimeric receptor are particularly revealing since simultaneous near-coincidences are rare and we show how they can be exploited to estimate the number of epitope responses that created the memory compartment. In addition, we find that paired-chain coincidences are strongly suppressed across donors with different human leukocyte antigens, evidence for a central role of antigen-driven selection in making paired chain receptors public. These results demonstrate the power of coincidence analysis to reveal the sequence determinants of epitope binding in receptor repertoires.

A Proposed Link Between Acute Thymic Involution and Late Adverse Effects of Chemotherapy

Epidemiologic data suggest that cancer survivors tend to develop a protuberant number of adverse late effects, including second primary malignancies (SPM), as a result of cytotoxic chemotherapy. Besides the genotoxic potential of these drugs that directly inflict mutational burden on genomic DNA, the precise mechanisms contributing to SPM development are poorly understood. Cancer is nowadays perceived as a complex process that goes beyond the concept of genetic disease and includes tumor cell interactions with complex stromal and immune cell microenvironments. The cancer immunoediting theory offers an explanation for the development of nascent neoplastic cells. Briefly, the theory suggests that newly emerging tumor cells are mostly eliminated by an effective tissue immunosurveillance, but certain tumor variants may occasionally escape innate and adaptive mechanisms of immunological destruction, entering an equilibrium phase, where immunologic tumor cell death "equals" new tumor cell birth. Subsequent microenvironmental pressures and accumulation of helpful mutations in certain variants may lead to escape from the equilibrium phase, and eventually cause an overt neoplasm. Cancer immunoediting functions as a dedicated sentinel under the auspice of a highly competent immune system. This perspective offers the fresh insight that chemotherapy-induced thymic involution, which is characterized by the extensive obliteration of the sensitive thymic epithelial cell (TEC) compartment, can cause long-term defects in thymopoiesis and in establishment of diverse T cell receptor repertoires and peripheral T cell pools of cancer survivors. Such delayed recovery of T cell adaptive immunity may result in prolonged hijacking of the cancer immunoediting mechanisms, and lead to development of persistent and mortal infections, inflammatory disorders, organ-specific autoimmunity lesions, and SPMs. Acknowledging that chemotherapy-induced thymic involution is a potential risk factor for the emergence of SPM demarcates new avenues for the rationalized development of pharmacologic interventions to promote thymic regeneration in patients receiving cytoreductive chemotherapies.

Contact map dependence of a T-cell receptor binding repertoire

The T-cell arm of the adaptive immune system provides the host protection against unknown pathogens by discriminating between host and foreign material. This discriminatory capability is achieved by the creation of a repertoire of cells each carrying a T-cell receptor (TCR) specific to non-self-antigens displayed as peptides bound to the major histocompatibility complex (pMHC). The understanding of the dynamics of the adaptive immune system at a repertoire level is complex, due to both the nuanced interaction of a TCR-pMHC pair and to the number of different possible TCR-pMHC pairings, making computationally exact solutions currently unfeasible. To gain some insight into this problem, we study an affinity-based model for TCR-pMHC binding in which a crystal structure is used to generate a distance-based contact map that weights the pairwise amino acid interactions. We find that the TCR-pMHC binding energy distribution strongly depends both on the number of contacts and the repeat structure allowed by the topology of the contact map of choice; this in turn influences T-cell recognition probability during negative selection, with higher variances leading to higher survival probabilities. In addition, we quantify the degree to which neoantigens with mutations in sites with higher contacts are recognized at a higher rate.

Antigen processing and presentation in cancer immunotherapy

Background

Knowledge about and identification of T cell tumor antigens may inform the development of T cell receptor-engineered adoptive cell transfer or personalized cancer vaccine immunotherapy. Here, we review antigen processing and presentation and discuss limitations in tumor antigen prediction approaches.

Methods

Original articles covering antigen processing and presentation, epitope discovery, and in silico T cell epitope prediction were reviewed.

Results

Natural processing and presentation of antigens is a complex process that involves proteasomal proteolysis of parental proteins, transportation of digested peptides into the endoplasmic reticulum, loading of peptides onto major histocompatibility complex (MHC) class I molecules, and shuttling of peptide:MHC complexes to the cell surface. A number of T cell tumor antigens have been experimentally validated in patients with cancer. Assessment of predicted MHC class I binding and total score for these validated T cell antigens demonstrated a wide range of values, with nearly one-third of validated antigens carrying an IC₅₀ of greater than 500 nM.

Conclusions

Antigen processing and presentation is a complex, multistep process. In silico epitope prediction techniques can be a useful tool, but comprehensive experimental testing and validation on a patient-by-patient basis may be required to reliably identify T cell tumor antigens.

Unraveling How Tumor-Derived Galectins Contribute to Anti-Cancer Immunity Failure

Simple Summary

This review compiles our current knowledge of one of the main pathways activated by tumors to escape immune attack. Indeed, it integrates the current understanding of how tumor-derived circulating galectins affect the elicitation of effective anti-tumor immunity. It focuses on several relevant topics: which are the main galectins produced by tumors, how soluble galectins circulate throughout biological liquids (taking a body-settled gradient concentration into account), the conditions required for the galectins’ functions to be accomplished at the tumor and tumor-distant sites, and how the physicochemical properties of the microenvironment in each tissue determine their functions. These are no mere semantic definitions as they define which functions can be performed in said tissues instead. Finally, we discuss the promising future of galectins as targets in cancer immunotherapy and some outstanding questions in the field.

Abstract

Current data indicates that anti-tumor T cell-mediated immunity correlates with a better prognosis in cancer patients. However, it has widely been demonstrated that tumor cells negatively manage immune attack by activating several immune-suppressive mechanisms. It is, therefore, essential to fully understand how lymphocytes are activated in a tumor microenvironment and, above all, how to prevent these cells from becoming dysfunctional. Tumors produce galectins-1, -3, -7, -8, and -9 as one of the major molecular mechanisms to evade immune control of tumor development. These galectins impact different steps in the establishment of the anti-tumor immune responses. Here, we carry out a critical dissection on the mechanisms through which tumor-derived galectins can influence the production and the functionality of anti-tumor T lymphocytes. This knowledge may help us design more effective immunotherapies to treat human cancers.

Breast cancer dormancy: need for clinically relevant models to address current gaps in knowledge

Breast cancer is the most commonly diagnosed cancer in the USA. Although advances in treatment over the past several decades have significantly improved the outlook for this disease, most women who are diagnosed with estrogen receptor positive disease remain at risk of metastatic relapse for the remainder of their life. The cellular source of late relapse in these patients is thought to be disseminated tumor cells that reactivate after a long period of dormancy. The biology of these dormant cells and their natural history over a patient's lifetime is largely unclear. We posit that research on tumor dormancy has been significantly limited by the lack of clinically relevant models. This review will discuss existing dormancy models, gaps in biological understanding, and propose criteria for future models to enhance their clinical relevance.

Challenges of Neoantigen Targeting in Lynch Syndrome and Constitutional Mismatch Repair Deficiency Syndrome

Lynch syndrome (LS) and constitutional mismatch repair deficiency (CMMRD) are hereditary disorders characterised by a highly increased risk of cancer development. This is due to germline aberrations in the mismatch repair (MMR) genes, which results in a high mutational load in tumours of these patients, including insertions and deletions in genes bearing microsatellites. This generates microsatellite instability and cause reading frameshifts in coding regions that could lead to the generation of neoantigens and opens up avenues for neoantigen targeting immune therapies prophylactically and therapeutically. However, major obstacles need to be overcome, such as the heterogeneity in tumour formation within and between LS and CMMRD patients, which results in considerable variability in the genes targeted by mutations, hence challenging the choice of suitable neoantigens. The machine-learning methods such as NetMHC and MHCflurry that predict neoantigen- human leukocyte antigen (HLA) binding affinity provide little information on other aspects of neoantigen presentation. Immune escape mechanisms that allow MMR-deficient cells to evade surveillance combined with the resistance to immune checkpoint therapy make the neoantigen targeting regimen challenging. Studies to delineate shared neoantigen profiles across patient cohorts, precise HLA binding algorithms, additional therapies to counter immune evasion and evaluation of biomarkers that predict the response of these patients to immune checkpoint therapy are warranted.

Rapid Assessment of T-Cell Receptor Specificity of the Immune Repertoire

Accurate assessment of TCR-antigen specificity at the whole immune repertoire level lies at the heart of improved cancer immunotherapy, but predictive models capable of high-throughput assessment of TCR-peptide pairs are lacking. Recent advances in deep sequencing and crystallography have enriched the data available for studying TCR-p-MHC systems. Here, we introduce a pairwise energy model, RACER, for rapid assessment of TCR-peptide affinity at the immune repertoire level. RACER applies supervised machine learning to efficiently and accurately resolve strong TCR-peptide binding pairs from weak ones. The trained parameters further enable a physical interpretation of interacting patterns encoded in each specific TCR-p-MHC system. When applied to simulate thymic selection of an MHC-restricted T-cell repertoire, RACER accurately estimates recognition rates for tumor-associated neoantigens and foreign peptides, thus demonstrating its utility in helping address the large computational challenge of reliably identifying the properties of tumor antigen-specific T-cells at the level of an individual patient's immune repertoire.

Fluctuation theory of immune response: A statistical mechanical approach to understand pathogen induced T-cell population dynamics

In this period of intense interest in human immunity, we attempt here to quantify the immune response against pathogen invasion through T-cell population dynamics. Borrowing concepts from equilibrium statistical mechanics, we introduce a new description of the immune response function (IMRF) in terms of fluctuations in the population number of relevant biological cells (effector and regulatory T-cells). We use a coarse-grained chemical reaction network model (CG-CRNM) to calculate the number fluctuations and show that the response function derived as such can, indeed, capture the crossover observed in a T-cell driven immune response. We employ the network model to learn the effect of vitamin-D as an immunomodulator. We solve our CG-CRNM using a stochastic Gillespie algorithm. Depending on the effector T-cell concentration, we can classify immune regulation regimes into three categories: weak, strong, and moderate. The IMRF is found to behave differently in these three regimes. A damped cross-regulatory behavior found in the dynamics of effector and regulatory T-cell concentration in the diseased states correlates well with the same found in a cohort of patients with specific malignancies and autoimmune diseases. Importantly, the crossover from the weakly regulated steady state to the other (the strongly regulated) is accompanied by a divergence-like growth in the fluctuation of both the effector and the regulatory T-cell concentration, characteristic of a dynamic phase transition. We believe such steady-state IMRF analyses could help not only to phase-separate different immune stages but also aid in the valuable connection between autoimmunity, optimal vitamin-D, and consequences of immunosuppressive stress and malignancy.

Implications of Tumor-Immune Coevolution on Cancer Evasion and Optimized Immunotherapy

Cancer represents a diverse collection of diseases characterized by heterogeneous cell populations that dynamically evolve in their environment. As painfully evident in cases of treatment failure and recurrence, this general feature makes identifying long-term successful therapies difficult. It is now well-established that the adaptive immune system recognizes and eliminates cancer cells, and various immunotherapeutic strategies have emerged to augment this effect. These therapies, while promising, often fail as a result of immune-specific cancer evasion. Increasingly available empirical evidence details both cancer and immune system populations pre- and post-treatment, providing rich opportunity for mathematical models of the tumor-immune interaction and subsequent co-evolution. Integrated mathematical and experimental efforts bear immediate relevance for optimized therapies and will undoubtedly accelerate our understanding of this emergent field.

Sustained Coevolution in a Stochastic Model of Cancer-Immune Interaction

The dynamic interactions between an evolving malignancy and the adaptive immune system generate diverse evolutionary trajectories that ultimately result in tumor clearance or immune escape. Here, we create a simple mathematical model coupling T-cell recognition with an evolving cancer population that may randomly produce evasive subclones, imparting transient protection against the effector T cells. T-cell turnover declines and evasion rates together explained differences in early incidence data across almost all cancer types. Fitting the model to TRACERx evolutionary data argued in favor of substantial and sustained immune pressure exerted upon a developing tumor, suggesting that clinically observed incidence is a small proportion of all cancer initiation events. This dynamical model promises to increase our quantitative understanding of many immune escape contexts, including cancer progression and intracellular pathogenic infections. SIGNIFICANCE: The early cancer-immune interaction sculpts intratumor heterogeneity through the selection of immune-evasive clones. This study provides a mathematical framework for investigating the coevolution between an immune-evasive cancer population and the adaptive immune system.

How a well-adapting immune system remembers

The adaptive immune system is able to protect us from a large variety of pathogens, even ones it has not seen yet. Can predicting the future pathogen distribution help in protection? We find that a combination of probabilistic forecasting and occasional sampling of the current environment reduces infection costs—a scheme easily implemented by the memory repertoire. The proposed theoretical framework offers a modular recipe for updating the memory repertoire, which quantitatively predicts the strength of the immune response in flu-vaccination experiments, unlike other update schemes. It also links the observed early life dynamics of the memory pool to the sparseness properties of the pathogen distribution and competitive receptor dynamics for pathogens.

An adaptive agent predicting the future state of an environment must weigh trust in new observations against prior experiences. In this light, we propose a view of the adaptive immune system as a dynamic Bayesian machinery that updates its memory repertoire by balancing evidence from new pathogen encounters against past experience of infection to predict and prepare for future threats. This framework links the observed initial rapid increase of the memory pool early in life followed by a midlife plateau to the ease of learning salient features of sparse environments. We also derive a modulated memory pool update rule in agreement with current vaccine-response experiments. Our results suggest that pathogenic environments are sparse and that memory repertoires significantly decrease infection costs, even with moderate sampling. The predicted optimal update scheme maps onto commonly considered competitive dynamics for antigen receptors.

Epistasis in a Fitness Landscape Defined by Antibody-Antigen Binding Free Energy

Epistasis is the phenomenon by which the effect of a mutation depends on its genetic background. While it is usually defined in terms of organismal fitness, for single proteins, it must reflect physical interactions among residues. Here, we systematically extract the specific contribution pairwise epistasis makes to the physical affinity of antibody-antigen binding relevant to affinity maturation, a process of accelerated Darwinian evolution. We find that, among competing definitions of affinity, the binding free energy is the most appropriate to describe epistasis. We show that epistasis is pervasive, accounting for 25%-35% of variability, of which a large fraction is beneficial. This work suggests that epistasis both constrains, through negative epistasis, and enlarges, through positive epistasis, the set of possible evolutionary paths that can produce high-affinity sequences during repeated rounds of mutation and selection.

Two signal half-century: From negative selection of self-reactivity to positive selection of near-self-reactivity

With the emergence of clonal selection ideas in the 1950s, the development of immune cell repertoires was seen to require the negative selection of self-reacting cells, with surviving cells exhibiting a broad range of specificities. Thus, confronting a universe of not-self-antigens, a potential host organism spread its resources widely. In the 1960s, the two signal hypothesis showed how this might work. However, in the 1970s an affinity/avidity model further proposed that anticipating a pathogen strategy of exploiting "holes" in the repertoire created by negative selection, hosts should also positively select near-self-reacting cells. A microbe mutating an antigen from a form foreign to its host to a form resembling that host should prevail over host defences with respect to that antigen. By mutating a step towards host self, along the path from non-self to self, it should come to dominate the microbe population. By progressive stepwise mutations, such microbes would become better adapted, to the detriment of their hosts. But they would lose this advantage if, as they mutated closer to host self, they encountered progressively stiffer host defences. Thus, as described in the affinity/avidity model, positive selection of lymphocytes for specificities that were very close to, but not quite, anti-self (ie, "anti-near-self") should be an important host adaptation. While positive selection affects both B and T cells, mechanisms are uncertain. Converging evidence from studies of lymphocyte activation, either polyclonally (with lectins as "antigen-analogs") or monoclonally (by specific antigen), supports the original generic affinity/avidity model for countering mutations towards host self.

How nonuniform contact profiles of T cell receptors modulate thymic selection outcomes

T cell receptors (TCRs) bind foreign or self-peptides attached to major histocompatibility complex (MHC) molecules, and the strength of this interaction determines T cell activation. Optimizing the ability of T cells to recognize a diversity of foreign peptides yet be tolerant of self-peptides is crucial for the adaptive immune system to properly function. This is achieved by selection of T cells in the thymus, where immature T cells expressing unique, stochastically generated TCRs interact with a large number of self-peptide-MHC; if a TCR does not bind strongly enough to any self-peptide-MHC, or too strongly with at least one self-peptide-MHC, the T cell dies. Past theoretical work cast thymic selection as an extreme value problem and characterized the statistical enrichment or depletion of amino acids in the postselection TCR repertoire, showing how T cells are selected to be able to specifically recognize peptides derived from diverse pathogens yet have limited self-reactivity. Here, we investigate how the diversity of the postselection TCR repertoire is modified when TCRs make nonuniform contacts with peptide-MHC. Specifically, we were motivated by recent experiments showing that amino acids at certain positions of a TCR sequence have large effects on thymic selection outcomes, and crystal structure data that reveal a nonuniform contact profile between a TCR and its peptide-MHC ligand. Using a representative TCR contact profile as an illustration, we show via simulations that the statistical enrichment or depletion of amino acids now varies by position according to the contact profile, and, importantly, it depends on the implementation of nonuniform contacts during thymic selection. We explain these nontrivial results analytically. Our study has implications for understanding the selection forces that shape the functionality of the postselection TCR repertoire.

Stochastic modeling of tumor progression and immune evasion

It is now well-established that the host's adaptive immune system plays an important role in identifying and eliminating cancer cells in much the same way that intracellular pathogens are cleared during an adaptive immune response to infection. From a therapeutic standpoint, the adaptive immune system is unique in that it can co-evolve alongside a developing tumor. Tumor acquisition of immune evasive phenotypes, such as class-I MHC down-regulation, remains a major limitation of successful T-cell immunotherapy. Here, we consider a population dynamical model coupling tumor and adaptive immune compartments in order to study the dynamics and survival of an evolving threat when faced with adaptive immune pressure. We demonstrate that predicted optimal growth strategies depend on whether or not the threat may acquire an immune-evasive phenotype as well as the mode of immune detection. We parameterize adaptive immune functioning by T-cell turnover and repertoire diversity and predict that decreases in the latter quantity which occur in advanced age may substantially affect the ability to recognize, and therefore control, an immune evasive threat like cancer. This framework recapitulates general features of age-dependent AML incidence, thereby providing a probable association between cancer frequency and adaptive immune functioning. Lastly, we quantify therapeutic efficacy of adjuvant immunotherapeutic strategies, and predict their benefits and limitations with regard to handling immune evasion. Our model generates survival behavior consistent with known growth-dependent characteristics, and serves as a first attempt at modeling stochastic cancer evolution alongside an adaptive immune compartment.