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Reinforcement Learning (RL) approaches are lately deployed for orchestrating wireless communications empowered by Reconfigurable Intelligent Surfaces (RISs), leveraging their online optimization capabilities. Most commonly, in RL-based formulations for realistic RISs with low resolution phase-tunable elements, each configuration is modeled as a distinct reflection action, resulting to inefficient exploration due to the exponential nature of the search space. In this paper, we consider RISs with 1-bit phase resolution elements, and model the action of each of them as a binary vector including the feasible reflection coefficients. We then introduce two variations of the well-established Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) agents, aiming for effective exploration of the binary action spaces. For the case of DQN, we make use of an efficient approximation of the Q-function, whereas a discretization post-processing step is applied to the output of DDPG. Our simulation results showcase that the proposed techniques greatly outperform the baseline in terms of the rate maximization objective, when large-scale RISs are considered. In addition, when dealing with moderate scale RIS sizes, where the conventional DQN based on configuration-based action spaces is feasible, the performance of the latter technique is similar to the proposed learning approach., 5 pages, 3 figures, submitted to an IEEE conference

Sarcomas are uncommon malignancies of mesenchymal origin that can arise throughout the human lifespan, at any part of the body. Surgery remains the optimal treatment modality whilst response to conventional treatments, such as chemotherapy and radiation, is minimal. Immunotherapy has emerged as a novel approach to treat different cancer types but efficacy in soft tissue sarcoma and bone sarcoma is limited to distinct subtypes. Growing evidence shows that cancer-stroma cell interactions and their microenvironment play a key role in the effectiveness of immunotherapy. However, the pathophysiological and immunological properties of the sarcoma tumor microenvironment in relation to immunotherapy advances, has not been broadly reviewed. Here, we provide an up-to-date overview of the different immunotherapy modalities as potential treatments for sarcoma, identify barriers posed by the sarcoma microenvironment to immunotherapy, highlight their relevance for impeding effectiveness, and suggest mechanisms to overcome these barriers.

Brain cancer therapy remains a formidable challenge in oncology. Convection-enhanced delivery (CED) is an innovative and promising local drug delivery method for the treatment of brain cancer, overcoming the challenges of the systemic delivery of drugs to the brain. To improve our understanding about CED efficacy and drug transport, we present an

The purpose of this study is to develop a methodology that incorporates a more accurate assessment of tissue mechanical properties compared to current mathematical modeling by use of biomechanical data from magnetic resonance elastography. The elastography data were derived from five glioblastoma patients and a healthy subject and used in a model that simulates tumor growth, vascular changes due to mechanical stresses and delivery of therapeutic agents. The model investigates the effect of tumor-specific biomechanical properties on tumor anisotropic growth, vascular density heterogeneity and chemotherapy delivery. The results showed that including elastography data provides a more realistic distribution of the mechanical stresses in the tumor and induces anisotropic tumor growth. Solid stress distribution differs among patients, which, in turn, induces a distinct functional vascular density distribution—owing to the compression of tumor vessels—and intratumoral drug distribution for each patient. In conclusion, incorporating elastography data results in a more accurate calculation of intratumoral mechanical stresses and enables a better mathematical description of subsequent events, such as the heterogeneous development of the tumor vasculature and intrapatient variations in tumor perfusion and delivery of drugs.

Summary: Background: Mathematical modelling may aid in understanding the complex interactions between injury and immune response in critical illness. Methods: We utilize a system biology model of COVID-19 to analyze the effect of altering baseline patient characteristics on the outcome of immunomodulatory therapies. We create example parameter sets meant to mimic diverse patient types. For each patient type, we define the optimal treatment, identify biologic programs responsible for clinical responses, and predict biomarkers of those programs. Findings: Model states representing older and hyperinflamed patients respond better to immunomodulation than those representing obese and diabetic patients. The disparate clinical responses are driven by distinct biologic programs. Optimal treatment initiation time is determined by neutrophil recruitment, systemic cytokine expression, systemic microthrombosis and the renin-angiotensin system (RAS) in older patients, and by RAS, systemic microthrombosis and trans IL6 signalling for hyperinflamed patients. For older and hyperinflamed patients, IL6 modulating therapy is predicted to be optimal when initiated very early (

IntroductionNanomedicines have been developed to preferentially delivery drugs to cancer cells or improve the drug pharmacokinetic properties for the treatment of solid tumors. Recent preclinical studies have shown that nanomedicines cannot only simply deliver drugs but also to normalize the tumor microenvironment, improving blood vessel functionality, tumor oxygenation and immunostimulation. Metronomic therapy is a different approach to substitute conventional chemotherapy, which is based on the low and more frequent administration of the therapeutic agent compared to the maximum tolerated dose (MTD) schedule of conventional treatment. When beneficial metronomic strategy can also induce normalization. We hypothesize that these two approaches can be viewed using the same unified framework as strategies that normalize the tumor microenvironment to potentiate immune checkpoint inhibition(ICI) in resistant to ICI murine models of breast cancer and sarcoma. MethodologyTo test our hypothesis, we performed in vivo experiments in murine breast and fibrosarcoma tumor models, and employed the clinically approved nanoparticle formulation Doxil(PEGylated liposomal doxorubicin). Doxil has an elimination half-life 20-30 hours and thus, we hypothesized that to produce similar to metronomic therapy effects, effective drug levels should be maintained. To this end, we employed three different dose schedules providing the same amount of Doxil on a weekly basis: every day(1mg/kg), every other day(2mg/kg) and every 6 days(6mg/kg). At the second week of Doxil treatment, we added to the treatment regimen an immune checkpoint inhibitor aPDL1(10 mg/kg) every 3 days (totally 3 doses) alone or in combination with Doxil. Given the increasing interest in nano-immunotherapy, our purpose was to investigate whether pretreatment with a normalizing dose of nanomedicine can optimize nano-immunotherapy. ResultsWe found that low and more frequent doses of Doxil (either 1mg/kg or 2mg/kg) -compared to high and less frequent doses- decrease tumor stiffness, improve perfusion and exhibit enhanced anti-tumor effects either alone or in combination with immunotherapy. Use of appropriately-sized nanoparticles to normalize the tumor microenvironment could be advantageous over MTD chemotherapy because preferential targeting of tumor tissue could ameliorate adverse effects of MTD chemotherapy, whereas improved vascular function could increase the effectiveness of the immune system and importantly the intratumoral delivery of nanoparticles, which is the main barrier for the effective use of nanomedicines today. Importantly, to account for heterogeneity among different tumors types, we employed two tumor models, the 4T1 breast cancer and the MCA205 fibrosarcoma and found similar normalization and anti-tumor effects.ConclusionPretreatment with a normalizing dose of Doxil can improve the efficacy of immune checkpoint inhibition in murine models of breast cancer and sarcoma. Citation Format: Fotios Mpekris, Chrysovalantis Voutouri, Myrofora Panagi, James W Baish, Rakesh K Jain, Triantafyllos Stylianopoulos. Normalizing the microenvironment with metronomic nanomedicine potentiates immune checkpoint inhibition in preclincial tumor models [abstract]. In: Proceedings of the AACR Special Conference: Tumor Immunology and Immunotherapy; 2022 Oct 21-24; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2022;10(12 Suppl):Abstract nr A63.

IntroductionImmunotherapy is changing the standard of care in cancer treatment. Immune checkpoint blockers (ICBs) that block the checkpoint programs used by tumor cells to inhibit the immune response have been approved by the US Food and Drug Administration with varying efficacy across cancer types. Given the clear benefits of ICB in some patients, there is an urgent research need to understand the determinants of ICB efficacy so more patients can benefit. To this end, there is growing interest in the interactions between immune cells and tumor cells as well as other extrinsic factors that can impact ICB therapy. Recent studies have shown that the microbiome can modulate the efficacy of immunotherapy. Specifically, clinical and therapeutic outcomes improved in a subset of ICB-refractory patients treated with fecal microbiota transplants (FMTs) from melanoma patients who responded to ICB therapy in combination with additional cycles of ICB therapy suggesting a synergistic effect of the gut microbiome. However, there is limited information and often inconsistencies among studies about the effect of bacteria on the immune system and checkpoint immunotherapy.MethodologyTo study the mechanisms by which the gut microbiome affects components of the immune system and response to ICB, we developed a mechanistic mathematical model for tumor growth in terms of immune components as features. The model simulates the temporal changes in the immune system during tumor progression and checkpoint immunotherapy. More specifically, the model simulates the tumor cells, relevant immune cells, and cytokines. The model was used for the reproduction of tumor growth curves from several preclinical FMT studies with ICB treatment. To determine the possible effect of the microbiome on the immune components, existing statistical approaches were used between experimental microbiome data and the immune components of the model.ResultsThe mathematical model was able to reproduce the experimental tumor growth curves by varying only two critical parameters. These parameters identify two possible mechanisms by which the gut microbiome can influence the immune response: i) the killing potential of tumor cells by immune cells and ii) the activation of the adaptive immune system. Furthermore, the model predictions agree with the observation that ICB treatment is only effective with FMT from responder patients. Additionally, the association analysis was able to identify specific microbes that induce positive and negative effects for ICB, and how these microbes affect various components of the immune system.ConclusionOur analysis identifies possible mechanisms by which the microbiome affects checkpoint immunotherapy and predicts not only which constituents of the microbiome have positive and negative effects but also identifies how these microbes are related to immune mechanisms. This information could be valuable for designing future targeted studies to validate these possible mechanisms at the cell and molecular levels. Citation Format: Constantinos Harkos, Andreas Hadjigeorgiou, Aditya K. Mishra, Golnaz Morad, Sarah Johnson, Nadim J. Ajami, Triantafyllos Stylianopoulos, Lance L. Munn, Jeniffer A. Wargo, Rakesh K. Jain. Modulation of cancer immunotherapy by the microbiome: Insights from computational analyses of preclinical studies [abstract]. In: Proceedings of the AACR Special Conference: Tumor Immunology and Immunotherapy; 2022 Oct 21-24; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2022;10(12 Suppl):Abstract nr B39.

Introduction: Current SARS-CoV-2 vaccines are effective at preventing COVID-19 or limiting disease severity in healthy individuals, but effectiveness is lower among patients with cancer or immunosuppression. Vaccine effectiveness wanes with time and varies by vaccine type. Moreover, current vaccines are based on the ancestral SARS-CoV-2 spike protein sequence and emerging viral variants evade vaccine induced immunity. Booster doses partially overcome these issues, but there are limited clinical data on the durability of protection afforded by boosters – especially against SARS-CoV-2 variants.Methods: Here we describe a mechanistic mathematical model for vaccination-induced immunity and use it to predict vaccine effectiveness taking into account current and possible future viral, host and vaccine characteristics. Crucially, this allows predictions over time frames currently not reported in the clinical literature. The model incorporates the infection of lung epithelium by SARS-CoV-2, the response of innate and adaptive immune cells to infection, the production of pro-and anti-inflammatory cytokines, the activation of the coagulation cascade, as well as the effects of cancer cells on the lung and on the immune response. The model further accounts for the interactions between the virus, the immune cells and the tumor cells as well as for vaccination-induced immunity.Results: Model predictions were validated with clinical data. The model predicts that for healthy individuals vaccinated and boosted with mRNA-1273, BNT-162b2a, and Ad26.COV2.S, robust immunogenicity against the ancestral and delta variant extends beyond a year. Immunogenicity is also enhanced following booster vaccination in patients with cancer on various anti-cancer therapies, including immunotherapy, and for patients without cancer on immunosuppressive agents.Conclusion: Our model predicts that ³1 booster doses will be required for these individuals to maintain protective immunity. Furthermore, for immunosuppressed individuals, simulated new SARS-CoV2 variants with enhanced ability to bind to target cells, faster replication or reduced immunogenicity could lead to breakthrough infections even after a single booster dose. Modelling data such as these may be used to anticipate and plan for future vaccination needs. Citation Format: Chrysovalantis Voutouri, Corey C Hardin, Vivek Naranbhai, Mohammad R. Nikmaneshi, Melin J. Khandekar, Justin F Gainor, Triantafyllos Stylianopoulos, Lance L. Munn, Rakesh K. Jain. Mechanistic model for booster doses effectiveness in healthy, cancer and immunosuppressed patients infected with SARS-CoV-2 [abstract]. In: Proceedings of the AACR Special Conference: Tumor Immunology and Immunotherapy; 2022 Oct 21-24; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2022;10(12 Suppl):Abstract nr A64.

Double-network hydrogels were prepared using well-defined first networks comprising interconnected amphiphilic “in-out” star copolymers synthesized via sequential reversible addition-fragmentation chain transfer (RAFT) polymerization, and second networks based on a photopolymerized mixture of acrylamide and N,N′-methlyenebisacrylamide. All first and double-network hydrogels were characterized in terms of their aqueous degrees of swelling and mechanical properties in compression. The most hydrophobic first and double-network hydrogels exhibited the best mechanical properties, which may be attributed to their low aqueous swelling degrees and good mesoscale organization in water as revealed using small-angle neutron scattering (SANS) which showed that the size of the formed hydrophobic domains could be controlled by the polymer conetwork structure. © 2018 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2018, 56, 2161–2174 56 19 2161 2174

Brain cancer therapy remains a formidable challenge in oncology. Convection-enhanced delivery (CED) is an innovative and promising local drug delivery method for the treatment of brain cancer, overcoming the challenges of the systemic delivery of drugs to the brain. To improve our understanding about CED efficacy and drug transport, we present an in silico methodology for brain cancer CED treatment simulation. To achieve this, a three-dimensional finite element biomechanics formulation is utilized which employs patient-specific brain model representation and is used to predict the drug deposition in CED regimes. The model encompasses nonlinear biomechanics and the transport of drugs in the brain parenchyma. Drug distribution was studied under various patho-physiological conditions of the tumor, in terms of tumor vessel wall pore size and tumor tissue hydraulic conductivity as well as for drugs of various sizes, spanning from small molecules to nanoparticles. Our contribution reports for the first time the impact of the size of the vascular wall pores and that of the therapeutic agent on drug distribution during and after CED. The in silico findings provide useful insights of the spatio-temporal distribution and average drug concentration in the tumor towards an effective treatment of brain cancer.

Focus of Presentation ‘Cluster Tracker’ is an automated tool for spatial cluster detection of notifiable disease data collected by the Department of Health (DH), Victoria. The tool combines R statistical software and a SaTScan cluster detection algorithm (prospective space-time permutation scan statistic) to detect notifiable disease case clusters in Victoria and is presently implemented for salmonellosis (categorised by type and/or MLVA). The objective of the tool is to conduct an initial screening of case data to improve the prioritisation of salmonellosis cases for epidemiological investigation. Findings The Cluster Tracker tool parameters have been validated using historical data from 2017-2018, comparing DH outbreak and cluster investigations identified by usual surveillance activities with clusters detected by the Cluster Tracker tool. Parameter selection considered cluster detection agreement and disagreement, disease-specific epidemiological characteristics, and operational requirements. The Cluster Tracker tool was able to provide closely-aligned agreement with existing DH outbreak and cluster investigations using the validated parameters. Implications This automated spatial cluster detection tool complements existing desktop surveillance of salmonellosis notifications to enhance public health decision making, and serves as an example of how spatial methods can improve real-time surveillance. Key messages Advanced spatial statistical tools have a role alongside traditional methods to make better use of limited epidemiological capacity and improve the timeliness and prioritisation of surveillance activities for notifiable diseases.

Significance Immunotherapy has changed the standard of care in cancer treatment, but an estimated 87% of patients currently do not derive long-term benefit from immune checkpoint blocker monotherapy. Therefore, new therapeutic strategies are needed to improve the response rates in patients who are resistant to immune checkpoint inhibition. We have developed a mathematical framework to determine how tumor microenvironment normalization strategies—specifically, vascular and stroma normalization—might improve immunotherapy efficacy. By incorporating complex interactions among various types of cancer cells, immune cells, stromal cells, and the vasculature, as well as physical mechanisms, we provide guidelines for designing effective combinatorial therapeutic strategies and point out areas for future investigation., Advances in immunotherapy have revolutionized the treatment of multiple cancers. Unfortunately, tumors usually have impaired blood perfusion, which limits the delivery of therapeutics and cytotoxic immune cells to tumors and also results in hypoxia—a hallmark of the abnormal tumor microenvironment (TME)—that causes immunosuppression. We proposed that normalization of TME using antiangiogenic drugs and/or mechanotherapeutics can overcome these challenges. Recently, immunotherapy with checkpoint blockers was shown to effectively induce vascular normalization in some types of cancer. Although these therapeutic approaches have been used in combination in preclinical and clinical studies, their combined effects on TME are not fully understood. To identify strategies for improved immunotherapy, we have developed a mathematical framework that incorporates complex interactions among various types of cancer cells, immune cells, stroma, angiogenic molecules, and the vasculature. Model predictions were compared with the data from five previously reported experimental studies. We found that low doses of antiangiogenic treatment improve immunotherapy when the two treatments are administered sequentially, but that high doses are less efficacious because of excessive vessel pruning and hypoxia. Stroma normalization can further increase the efficacy of immunotherapy, and the benefit is additive when combined with vascular normalization. We conclude that vessel functionality dictates the efficacy of immunotherapy, and thus increased tumor perfusion should be investigated as a predictive biomarker of response to immunotherapy.

Tumor normalization strategies aim to improve tumor blood vessel functionality (i.e., perfusion) by reducing the hyper-permeability of tumor vessels or restoring compressed vessels. Despiteprogress in strategies to normalize the tumor microenvironment (TME), their combinatorialantitumor effects with nanomedicine and immunotherapy remain unexplored.Methods: Here, we re-purposed the TGF-β inhibitor tranilast, an approved anti-fibrotic andantihistamine drug, and combined it with Doxil nanomedicine to normalize the TME, increaseperfusion and oxygenation, and enhance anti-tumor immunity. Specifically, we employed twotriple-negative breast cancer (TNBC) mouse models to primarily evaluate the therapeutic andnormalization effects of tranilast combined with doxorubicin and Doxil. We demonstrated theoptimized normalization effects of tranilast combined with Doxil and extended our analysis to investigate the effect of TME normalization to the efficacy of immune checkpoint inhibitors.Results: Combination of tranilast with Doxil caused a pronounced reduction in extracellular matrixcomponents and an increase in the intratumoral vessel diameter and pericyte coverage, indicators ofTME normalization. These modifications resulted in a significant increase in tumor perfusion and oxygenation and enhanced treatment efficacy as indicated by the notable reduction in tumor size.Tranilast further normalized the immune TME by restoring the infiltration of T cells and increasingthe fraction of T cells that migrate away from immunosuppressive cancer-associated fibroblasts.Furthermore, we found that combining tranilast with Doxil nanomedicine, significantly improved immunostimulatory M1 macrophage content in the tumorigenic tissue and improved the efficacy ofthe immune checkpoint blocking antibodies anti-PD-1/anti-CTLA-4.Conclusion: Combinatorial treatment of tranilast with Doxil optimizes TME normalization, improves immunostimulation and enhances the efficacy of immunotherapy.&nbsp

The aim of this study was to investigate the prevalence and distribution of Campylobacter species in a variety of fresh and frozen meat and offal products collected from retail outlets in New South Wales (NSW), Queensland (Qld), and Victoria (Vic). A total of 1,490 chicken, beef, lamb, and pork samples were collected from Australian supermarkets and butcher shops over a 2-year sampling period (October 2016 to October 2018). Campylobacter spp. were detected in 90% of chicken meat and 73% of chicken offal products (giblet and liver), with significantly lower prevalence in lamb (38%), pork (31%), and beef (14%) offal (kidney and liver). Although retail chicken meat was frequently contaminated with Campylobacter, the level of contamination was generally low. Where quantitative analysis was conducted, 98% of chicken meat samples, on average, had