Mae Woods

Background Oncolytic adenoviruses (OAds) are the most clinically tested viral vectors for solid tumors. However, most clinically tested “Armed” OAds show limited antitumor effects in patients with various solid tumors even with increased dosages and multiple injections. We developed a binary oncolytic/helper-dependent adenovirus system (CAdVEC), in which tumors are coinfected with an OAd and a non-replicating helper-dependent Ad (HDAd). We recently demonstrated that a single low-dose CAdVEC expressing interleukin-12, programmed death-ligand 1 blocker, and HSV thymidine kinase safety switch (CAdTrio) induces significant antitumor effects in patients, including complete response. Similar to previous OAd studies, all patients primarily amplified Ad-specific T cells after treatment however, CAdVEC was still able to induce clinical responses even given at a 100-fold lower dose. Methods To address the mechanisms of CAdTrio-mediated antitumor effect in patients, we analyzed patients’ samples using Enzyme-linked immunosorbent spot (ELISpot) to measure T-cell specificity and quantitative polymerase chain reaction (qPCR) to measure CAdVEC viral genome copies at tumor sites. We then evaluated potential mechanisms of CAdVEC efficacy in vitro using live-cell imaging. Based on those results, we developed a new CAdVEC additionally expressing a T-cell engager molecule targeting CD44v6 to redirect tumor-infiltrating irrelevant T cells against cancer stem cell populations (CAdTetra) for further improvement of local CAdVEC treatment. We tested its efficacy against different cancer types both in vitro and in vivo including Ad pre-immunized humanized mice. Results We found that HDAd-infected cells escape Ad-specific T-cell recognition with enhanced tumor-specific T-cell activity through immunomodulatory transgenes. Since CAdVEC treatment initially amplified Ad-specific T cells in patients, we re-direct these virus-specific T cells to target tumor cells by additionally expressing CD44v6.BiTE from CAdTetra. CAdTetra significantly controlled tumor growth, repolarizing local and systemic responses against cancer cells in both immunologically “hot” and “cold” tumor models and also induced immunologic memory against rechallenged tumors. Conclusions Our results indicate that CAdTetra effectively induces adaptive T-cell responses against cancer cells by using tumor-infiltrating irrelevant T cells.

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.

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. 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. 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.

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. DNA double-strand breaks occur during metabolism, DNA replication and by exposure to exogenous sources such as ionising radiation. When the genome is inflicted with this type of damage, DNA repair machinery is promoted to restore genome structure. The efficient interplay between DNA damage and repair is crucial to genome stability because the choice of repair mechanism directly affects the probability of mutation. Multiple mechanisms of DNA repair are known to exist, however, the subtleties of how they are activated and their interactions are yet to be fully determined. We hypothesise that a combination of Bayesian statistics and mathematical modeling is essential to elucidate the network dynamics. Studies in the literature have presented time series data of double-strand break repair in wild type and mutant cells. By combining existing time series data, our modeling approach can quantify the differences in activation amongst mutants and in addition identify a number of novel insights into the dynamics of the competing mechanisms. We conclude that alternative mechanisms of repair exhibit variable dynamics dependent on the levels of individual recruitment proteins of the predominant repair pathways.

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. 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. 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. 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.

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 ionizing 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.