Incongruity between T cell receptor recognition of breast cancer hotspot mutations ESR1 Y537S and D538G following exogenous peptide loading versus endogenous antigen processing.

T cell receptor engineered T cell (TCR T) therapies have shown recent efficacy against certain types of solid metastatic cancers. However, to extend TCR T therapies to treat more patients across additional cancer types, new TCRs recognizing cancer-specific antigen targets are needed. Driver mutations in AKT1, ESR1, PIK3CA, and TP53 are common in patients with metastatic breast cancer (MBC) and if immunogenic could serve as ideal tumor-specific targets for TCR T therapy to treat this disease. Through IFN-γ ELISpot screening of in vitro expanded neopeptide-stimulated T cell lines from healthy donors and MBC patients, we identified reactivity towards 11 of 13 of the mutations. To identify neopeptide-specific TCRs, we then performed single-cell RNA sequencing of one of the T cell lines following neopeptide stimulation. Here, we identified an ESR1 Y537S specific T cell clone, clonotype 16, and an ESR1 Y537S/D538G dual-specific T cell clone, clonotype 21, which were HLA-B*40:02 and HLA-C*01:02 restricted, respectively. TCR Ts expressing these TCRs recognized and killed target cells pulsed with ESR1 neopeptides with minimal activity against ESR1 WT peptide. However, these TCRs failed to recognize target cells expressing endogenous mutant ESR1. To investigate the basis of this lack of recognition we performed immunopeptidomics analysis of a mutant-overexpressing lymphoblastoid cell line and found that the ESR1 Y537S neopeptide was not endogenously processed, despite binding to HLA-B*40:02 when exogenously pulsed onto the target cell. These results indicate that stimulation of T cells that likely derive from the naïve repertoire with pulsed minimal peptides may lead to the expansion of clones that recognize non-processed peptides, and highlights the importance of using methods that selectively expand T cells with specificity for antigens that are efficiently processed and presented.

Naive T cells inhibit the outgrowth of intractable antigen-activated memory T cells: implications for T-cell immunotherapy.

BACKGROUND: The wider application of T cells targeting viral tumor-antigens via their native receptors is hampered by the failure to expand potent tumor-specific T cells from patients. Here, we examine reasons for and solutions to this failure, taking as our model the preparation of Epstein-Barr virus (EBV)-specific T cells (EBVSTs) for the treatment of EBV-positive lymphoma. EBVSTs could not be manufactured from almost one-third of patients, either because they failed to expand, or they expanded, but lacked EBV specificity. We identified an underlying cause of this problem and established a clinically feasible approach to overcome it. METHODS: CD45RO+CD45RA- memory compartment residing antigen-specific T cells were enriched by depleting CD45RA positive (+) peripheral blood mononuclear cells (PBMCs) that include naïve T cells, among other subsets, prior to EBV antigen stimulation. We then compared the phenotype, specificity, function and T-cell receptor (TCR) Vß repertoire of EBVSTs expanded from unfractionated whole (W)-PBMCs and CD45RA-depleted (RAD)-PBMCs on day 16. To identify the CD45RA component that inhibited EBVST outgrowth, isolated CD45RA+ subsets were added back to RAD-PBMCs followed by expansion and characterization. The in vivo potency of W-EBVSTs and RAD-EBVSTs was compared in a murine xenograft model of autologous EBV+ lymphoma. RESULTS: Depletion of CD45RA+ PBMCs before antigen stimulation increased EBVST expansion, antigen-specificity and potency in vitro and in vivo. TCR sequencing revealed a selective outgrowth in RAD-EBVSTs of clonotypes that expanded poorly in W-EBVSTs. Inhibition of antigen-stimulated T cells by CD45RA+ PBMCs could be reproduced only by the naïve T-cell fraction, while CD45RA+ regulatory T cells, natural killer cells, stem cell memory and effector memory subsets lacked inhibitory activity. Crucially, CD45RA depletion of PBMCs from patients with lymphoma enabled the outgrowth of EBVSTs that failed to expand from W-PBMCs. This enhanced specificity extended to T cells specific for other viruses. CONCLUSION: Our findings suggest that naïve T cells inhibit the outgrowth of antigen-stimulated memory T cells, highlighting the profound effects of intra-T-cell subset interactions. Having overcome our inability to generate EBVSTs from many patients with lymphoma, we have introduced CD45RA depletion into three clinical trials: NCT01555892 and NCT04288726 using autologous and allogeneic EBVSTs to treat lymphoma and NCT04013802 using multivirus-specific T cells to treat viral infections after hematopoietic stem cell transplantation.

Ultralow-dose binary oncolytic/helper-dependent adenovirus promotes antitumor activity in preclinical and clinical studies.

We show that a binary oncolytic/helper-dependent adenovirus (CAdVEC) that both lyses tumor cells and locally expresses the proinflammatory cytokine IL-12 and PD-L1 blocking antibody has potent antitumor activity in humanized mouse models. On the basis of these preclinical studies, we treated four patients with a single intratumoral injection of an ultralow dose of CAdVEC (NCT03740256), representing a dose of oncolytic adenovirus more than 100-fold lower than used in previous trials. While CAdVEC caused no significant toxicities, it repolarized the tumor microenvironment with increased infiltration of CD8 T cells. A single administration of CAdVEC was associated with both locoregional and abscopal effects on metastases and, in combination with systemic administration of immune checkpoint antibodies, induced sustained antitumor responses, including one complete and two partial responses. Hence, in both preclinical and clinical studies, CAdVEC is safe and even at extremely low doses is sufficiently potent to induce significant tumor control through oncolysis and immune repolarization.

Mirrored stainless steel substrate provides improved signal for Raman spectroscopy of tissue and cells.

Raman spectroscopy (RS) is a powerful technique that permits the non-destructive chemical analysis of cells and tissues without the need for expensive and complex sample preparation. To date, samples have been routinely mounted onto calcium fluoride (CaF2) as this material possesses the desired mechanical and optical properties for analysis, but CaF2 is both expensive and brittle and this prevents the technique from being routinely adopted. Furthermore, Raman scattering is a weak phenomenon and CaF2 provides no means of increasing signal. For RS to be widely adopted, particularly in the clinical field, it is crucial that spectroscopists identify an alternative, low-cost substrate capable of providing high spectral signal to noise ratios with good spatial resolution. Results show that these desired properties are attainable when using mirrored stainless steel as a Raman substrate. When compared with CaF2, data show that stainless steel has a low background signal and provides an average signal increase of 1.43 times during tissue analysis and 1.64 times when analyzing cells. This result is attributed to a double-pass of the laser beam through the sample where the photons from the source laser and the forward scattered Raman signal are backreflected and retroreflected from the mirrored steel surface and focused towards collection optics. The spatial resolution on stainless steel is at least comparable to that on CaF2 and it is not compromised by the reflection of the laser. Steel is a fraction of the cost of CaF2 and the reflection and focusing of photons improve signal to noise ratios permitting more rapid mapping. The low cost of steel coupled with its Raman signal increasing properties and robust durability indicates that steel is an ideal substrate for biological and clinical RS as it possesses key advantages over routinely used CaF2. © 2016 The Authors. Journal of Raman Spectroscopy Published by John Wiley & Sons Ltd.

A computational method for the investigation of multistable systems and its application to genetic switches.

BACKGROUND: Genetic switches exhibit multistability, form the basis of epigenetic memory, and are found in natural decision making systems, such as cell fate determination in developmental pathways. Synthetic genetic switches can be used for recording the presence of different environmental signals, for changing phenotype using synthetic inputs and as building blocks for higher-level sequential logic circuits. Understanding how multistable switches can be constructed and how they function within larger biological systems is therefore key to synthetic biology. RESULTS: Here we present a new computational tool, called StabilityFinder, that takes advantage of sequential Monte Carlo methods to identify regions of parameter space capable of producing multistable behaviour, while handling uncertainty in biochemical rate constants and initial conditions. The algorithm works by clustering trajectories in phase space, and iteratively minimizing a distance metric. Here we examine a collection of models of genetic switches, ranging from the deterministic Gardner toggle switch to stochastic models containing different positive feedback connections. We uncover the design principles behind making bistable, tristable and quadristable switches, and find that rate of gene expression is a key parameter. We demonstrate the ability of the framework to examine more complex systems and examine the design principles of a three gene switch. Our framework allows us to relax the assumptions that are often used in genetic switch models and we show that more complex abstractions are still capable of multistable behaviour. CONCLUSIONS: Our results suggest many ways in which genetic switches can be enhanced and offer designs for the construction of novel switches. Our analysis also highlights subtle changes in correlation of experimentally tunable parameters that can lead to bifurcations in deterministic and stochastic systems. Overall we demonstrate that StabilityFinder will be a valuable tool in the future design and construction of novel gene networks.

Mechanistic Modelling and Bayesian Inference Elucidates the Variable Dynamics of Double-Strand Break Repair.

DNA double-strand breaks are lesions that form during metabolism, DNA replication and exposure to mutagens. When a double-strand break occurs one of a number of repair mechanisms is recruited, all of which have differing propensities for mutational events. Despite DNA repair being of crucial importance, the relative contribution of these mechanisms and their regulatory interactions remain to be fully elucidated. Understanding these mutational processes will have a profound impact on our knowledge of genomic instability, with implications across health, disease and evolution. Here we present a new method to model the combined activation of non-homologous end joining, single strand annealing and alternative end joining, following exposure to ionising radiation. We use Bayesian statistics to integrate eight biological data sets of double-strand break repair curves under varying genetic knockouts and confirm that our model is predictive by re-simulating and comparing to additional data. Analysis of the model suggests that there are at least three disjoint modes of repair, which we assign as fast, slow and intermediate. Our results show that when multiple data sets are combined, the rate for intermediate repair is variable amongst genetic knockouts. Further analysis suggests that the ratio between slow and intermediate repair depends on the presence or absence of DNA-PKcs and Ku70, which implies that non-homologous end joining and alternative end joining are not independent. Finally, we consider the proportion of double-strand breaks within each mechanism as a time series and predict activity as a function of repair rate. We outline how our insights can be directly tested using imaging and sequencing techniques and conclude that there is evidence of variable dynamics in alternative repair pathways. Our approach is an important step towards providing a unifying theoretical framework for the dynamics of DNA repair processes.

In vivo confinement promotes collective migration of neural crest cells.

Collective cell migration is fundamental throughout development and in many diseases. Spatial confinement using micropatterns has been shown to promote collective cell migration in vitro, but its effect in vivo remains unclear. Combining computational and experimental approaches, we show that the in vivo collective migration of neural crest cells (NCCs) depends on such confinement. We demonstrate that confinement may be imposed by the spatiotemporal distribution of a nonpermissive substrate provided by versican, an extracellular matrix molecule previously proposed to have contrasting roles: barrier or promoter of NCC migration. We resolve the controversy by demonstrating that versican works as an inhibitor of NCC migration and also acts as a guiding cue by forming exclusionary boundaries. Our model predicts an optimal number of cells in a given confinement width to allow for directional migration. This optimum coincides with the width of neural crest migratory streams analyzed across different species, proposing an explanation for the highly conserved nature of NCC streams during development.

Decision support for risk prioritisation of environmental health hazards in a UK city.

BACKGROUND: There is increasing appreciation of the proportion of the health burden that is attributed to modifiable population exposure to environmental health hazards. To manage this avoidable burden in the United Kingdom (UK), government policies and interventions are implemented. In practice, this procedure is interdisciplinary in action and multi-dimensional in context. Here, we demonstrate how Multi Criteria Decision Analysis (MCDA) can be used as a decision support tool to facilitate priority setting for environmental public health interventions within local authorities. We combine modelling and expert elicitation to gather evidence on the impacts and ranking of interventions. METHODS: To present the methodology, we consider a hypothetical scenario in a UK city. We use MCDA to evaluate and compare the impact of interventions to reduce the health burden associated with four environmental health hazards and rank them in terms of their overall performance across several criteria. For illustrative purposes, we focus on heavy goods vehicle controls to reduce outdoor air pollution, remediation to control levels of indoor radon, carbon monoxide and fitting alarms, and encouraging cycling to target the obesogenic environment. Regional data was included as model evidence to construct a ratings matrix for the city. RESULTS: When MCDA is performed with uniform weights, the intervention of heavy goods vehicle controls to reduce outdoor air pollution is ranked the highest. Cycling and the obesogenic environment is ranked second. CONCLUSIONS: We argue that a MCDA based approach provides a framework to guide environmental public health decision makers. This is demonstrated through an online interactive MCDA tool. We conclude that MCDA is a transparent tool that can be used to compare the impact of alternative interventions on a set of pre-defined criteria. In our illustrative example, we ranked the best intervention across the equally weighted selected criteria out of the four alternatives. Further work is needed to test the tool with decision makers and stakeholders.

A Statistical Approach Reveals Designs for the Most Robust Stochastic Gene Oscillators.

The engineering of transcriptional networks presents many challenges due to the inherent uncertainty in the system structure, changing cellular context, and stochasticity in the governing dynamics. One approach to address these problems is to design and build systems that can function across a range of conditions; that is they are robust to uncertainty in their constituent components. Here we examine the parametric robustness landscape of transcriptional oscillators, which underlie many important processes such as circadian rhythms and the cell cycle, plus also serve as a model for the engineering of complex and emergent phenomena. The central questions that we address are: Can we build genetic oscillators that are more robust than those already constructed? Can we make genetic oscillators arbitrarily robust? These questions are technically challenging due to the large model and parameter spaces that must be efficiently explored. Here we use a measure of robustness that coincides with the Bayesian model evidence, combined with an efficient Monte Carlo method to traverse model space and concentrate on regions of high robustness, which enables the accurate evaluation of the relative robustness of gene network models governed by stochastic dynamics. We report the most robust two and three gene oscillator systems, plus examine how the number of interactions, the presence of autoregulation, and degradation of mRNA and protein affects the frequency, amplitude, and robustness of transcriptional oscillators. We also find that there is a limit to parametric robustness, beyond which there is nothing to be gained by adding additional feedback. Importantly, we provide predictions on new oscillator systems that can be constructed to verify the theory and advance design and modeling approaches to systems and synthetic biology.

Directional collective cell migration emerges as a property of cell interactions.

Collective cell migration is a fundamental process, occurring during embryogenesis and cancer metastasis. Neural crest cells exhibit such coordinated migration, where aberrant motion can lead to fatality or dysfunction of the embryo. Migration involves at least two complementary mechanisms: contact inhibition of locomotion (a repulsive interaction corresponding to a directional change of migration upon contact with a reciprocating cell), and co-attraction (a mutual chemoattraction mechanism). Here, we develop and employ a parameterized discrete element model of neural crest cells, to investigate how these mechanisms contribute to long-range directional migration during development. Motion is characterized using a coherence parameter and the time taken to reach, collectively, a target location. The simulated cell group is shown to switch from a diffusive to a persistent state as the response-rate to co-attraction is increased. Furthermore, the model predicts that when co-attraction is inhibited, neural crest cells can migrate into restrictive regions. Indeed, inhibition of co-attraction in vivo and in vitro leads to cell invasion into restrictive areas, confirming the prediction of the model. This suggests that the interplay between the complementary mechanisms may contribute to guidance of the neural crest. We conclude that directional migration is a system property and does not require action of external chemoattractants.

Complement fragment C3a controls mutual cell attraction during collective cell migration.

Collective cell migration is a mode of movement crucial for morphogenesis and cancer metastasis. However, little is known about how migratory cells coordinate collectively. Here we show that mutual cell-cell attraction (named here coattraction) is required to maintain cohesive clusters of migrating mesenchymal cells. Coattraction can counterbalance the natural tendency of cells to disperse via mechanisms such as contact inhibition and epithelial-to-mesenchymal transition. Neural crest cells are coattracted via the complement fragment C3a and its receptor C3aR, revealing an unexpected role of complement proteins in early vertebrate development. Loss of coattraction disrupts collective and coordinated movements of these cells. We propose that coattraction and contact inhibition act in concert to allow cell collectives to self-organize and respond efficiently to external signals, such as chemoattractants and repellents.