Minimising prescription errors-a quality improvement project in the ophthalmology department in a tertiary referral hospital.

PURPOSE: The aim of this quality improvement project was to look into the hospital prescriptions and to identify and record the type and number of errors, to implement measures to reduce the risk of these errors and then to reaudit to assess the impact of changes implemented. METHODS: The initial audit was conducted prospectively over a eleven-week period. Prescriptions written by doctors of all grades and members of the staff, such as optometrists and nurses, were analysed. A glaucoma prescription guide along with more training at prescribing for doctors was introduced with a view to reducing these errors. A reaudit later demonstrated a significant reduction in these errors. RESULTS: After the introduction of a glaucoma prescription guide and more training for all grades of staff members, prescription errors reduced to 73/2342 (3.1%). Reaudit showed a reduction in both prescription writing errors 50/73(68.4%) and drug-related errors 23/73(31.6%). CONCLUSION: Prescription errors are avoidable. This audit demonstrated that providing an accessible, easy to read and understand glaucoma prescription guide in the outpatient department along with targeted training for medical staff in prescribing can help in minimising these errors and can lead to safer practice.

Predicting clinically promising therapeutic hypotheses using tensor factorization.

BACKGROUND: Determining which target to pursue is a challenging and error-prone first step in developing a therapeutic treatment for a disease, where missteps are potentially very costly given the long-time frames and high expenses of drug development. With current informatics technology and machine learning algorithms, it is now possible to computationally discover therapeutic hypotheses by predicting clinically promising drug targets based on the evidence associating drug targets with disease indications. We have collected this evidence from Open Targets and additional databases that covers 17 sources of evidence for target-indication association and represented the data as a tensor of 21,437 × 2211 × 17. RESULTS: As a proof-of-concept, we identified examples of successes and failures of target-indication pairs in clinical trials across 875 targets and 574 disease indications to build a gold-standard data set of 6140 known clinical outcomes. We designed and executed three benchmarking strategies to examine the performance of multiple machine learning models: Logistic Regression, LASSO, Random Forest, Tensor Factorization and Gradient Boosting Machine. With 10-fold cross-validation, tensor factorization achieved AUROC = 0.82 ± 0.02 and AUPRC = 0.71 ± 0.03. Across multiple validation schemes, this was comparable or better than other methods. CONCLUSION: In this work, we benchmarked a machine learning technique called tensor factorization for the problem of predicting clinical outcomes of therapeutic hypotheses. Results have shown that this method can achieve equal or better prediction performance compared with a variety of baseline models. We demonstrate one application of the method to predict outcomes of trials on novel indications of approved drug targets. This work can be expanded to targets and indications that have never been clinically tested and proposing novel target-indication hypotheses. Our proposed biologically-motivated cross-validation schemes provide insight into the robustness of the prediction performance. This has significant implications for all future methods that try to address this seminal problem in drug discovery.

Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets.

Target selection is the first and pivotal step in drug discovery. An incorrect choice may not manifest itself for many years after hundreds of millions of research dollars have been spent. We collected a set of 332 targets that succeeded or failed in phase III clinical trials, and explored whether Omic features describing the target genes could predict clinical success. We obtained features from the recently published comprehensive resource: Harmonizome. Nineteen features appeared to be significantly correlated with phase III clinical trial outcomes, but only 4 passed validation schemes that used bootstrapping or modified permutation tests to assess feature robustness and generalizability while accounting for target class selection bias. We also used classifiers to perform multivariate feature selection and found that classifiers with a single feature performed as well in cross-validation as classifiers with more features (AUROC = 0.57 and AUPR = 0.81). The two predominantly selected features were mean mRNA expression across tissues and standard deviation of expression across tissues, where successful targets tended to have lower mean expression and higher expression variance than failed targets. This finding supports the conventional wisdom that it is favorable for a target to be present in the tissue(s) affected by a disease and absent from other tissues. Overall, our results suggest that it is feasible to construct a model integrating interpretable target features to inform target selection. We anticipate deeper insights and better models in the future, as researchers can reuse the data we have provided to improve methods for handling sample biases and learn more informative features. Code, documentation, and data for this study have been deposited on GitHub at https://github.com/arouillard/omic-features-successful-targets.

Mortality and Health Outcomes in North Carolina Communities Located in Close Proximity to Hog Concentrated Animal Feeding Operations.

BACKGROUND Life expectancy in southeastern North Carolina communities located in an area with multiple concentrated animal feeding operations (CAFOs) after adjusting for socioeconomic factors remains low. We hypothesized that poor health outcomes in this region may be due to converging demographic, socioeconomic, behavioral, and access-to-care factors and are influenced by the presence of hog CAFOs.METHODS We studied mortality, hospital admissions, and emergency department (ED) usage for health conditions potentially associated with hog CAFOs-anemia, kidney disease, infectious diseases, and low birth weight (LBW)-in North Carolina communities located in zip codes with hog CAFOs (Study group 1), in zip codes with > 215hogs/km2 (Study group 2), and without hog CAFOs (Control group). We compared cause-specific age-adjusted rates, the odds ratios (ORs) of events in multivariable analyses (adjusted for 6 co-factors), and the changes of ORs relative to the distance to hog CAFOs.RESULTS Residents from Study groups 1 and 2 had higher rates of all-cause mortality, infant mortality, mortality of patients with multimorbidity, mortality from anemia, kidney disease, tuberculosis, and septicemia, and higher rates of ED visits and hospital admissions for LBW infants than the residents in the Control group. In zip codes with > 215hogs/km2, mortality ORs were 1.50 for anemia (P < 0.0001), 1.31 for kidney disease (P < 0.0001), 2.30 for septicemia (P < 0.0001), and 2.22 for tuberculosis (P = 0.0061).LIMITATIONS This study included a lack of individual measurements on environmental contaminants, biomarkers of exposures and co-factors, and differences in residential and occupational locations.CONCLUSION North Carolina communities located near hog CAFOs had higher all-cause and infant mortality, mortality due to anemia, kidney disease, tuberculosis, septicemia, and higher hospital admissions/ED visits of LBW infants. Although not establishing causality with exposures from hog CAFOs, our findings support the need for future studies to determine factors that influence these outcomes, as well as the need to improve screening and diagnostic strategies for these diseases in North Carolina communities adjacent to hog CAFOs.

The Effect of Socio-Economic Status on Severity of Periocular Basal Cell Carcinoma at Presentation.

PURPOSE: To evaluate the influence of socio-economic factors on size of periocular basal cell carcinoma at presentation. METHODS: All periocular basal cell carcinoma cases receiving treatment from the oculoplastics team in South Glasgow Hospitals NHS Trust, Glasgow, between 1999 and 2009, were identified retrospectively. Information collected included demographic details of patients, side and site of lesions, type of lesions, and size of lesions. The size of lesion was defined as small for any dimension not exceeding 5 mm, medium for dimensions between 6 mm and 10 mm, and large for dimensions exceeding 11 mm. Home address was used to determine the Scottish Index of Multiple Deprivation rank. The demographics, size of lesion, and Scottish Index of Multiple Deprivation rank were investigated using the general linear regression modelling. RESULTS: Of the 67 cases, 24 were men and 43 were women. The mean age was 71.5 years. There were a total of 67 identified cases, of which 38 presented with small-size lesions, 24 with medium-size lesions, and 5 with large-size lesions. Scottish Index of Multiple Deprivation is related to the presenting incidence of basal cell carcinoma, with the lower ranks presenting more frequently. CONCLUSIONS: Socio-economic deprivation is associated with larger and more frequent presentation of periocular basal cell carcinoma. This highlights the importance of raising awareness among populations of the more deprived areas of the significance of lumps and bumps within the periocular regions.

In vivo dopaminergic and serotonergic dysfunction in DCTN1 gene mutation carriers.

INTRODUCTION: We used positron emission tomography (PET) to assess dopaminergic and serotonergic terminal density in three subjects carrying a mutation in the DCT1 gene, two clinically affected with Perry syndrome. METHODS: All subjects had brain imaging using 18F-6-fluoro-l-dopa (FDOPA, dopamine synthesis and storage), (+)-11C-dihydrotetrabenazine (DTBZ, vesicular monoamine transporter type 2), and 11C-raclopride (RAC, dopamine D2/D3 receptors). One subject also underwent PET with 11C-3-amino-4-(2-dimethylaminomethyl-phenylsulfanyl)-benzonitrile (DASB, serotonin transporter). RESULTS: FDOPA-PET and DTBZ-PET in the affected individuals showed a reduction of striatal tracer uptake. Also, RAC-PET showed higher uptake in these area. DASB-PET showed significant uptake changes in left orbitofrontal cortex, bilateral anterior insula, left dorsolateral prefrontal cortex, left orbitofrontal cortex, left posterior cingulate cortex, left caudate, and left ventral striatum. CONCLUSIONS: Our data showed evidence of both striatal dopaminergic and widespread cortical/subcortical serotonergic dysfunctions in individuals carrying a mutation in the DCTN1 gene.

On Channel-Discontinuity-Constraint Routing in Wireless Networks.

Multi-channel wireless networks are increasingly deployed as infrastructure networks, e.g. in metro areas. Network nodes frequently employ directional antennas to improve spatial throughput. In such networks, between two nodes, it is of interest to compute a path with a channel assignment for the links such that the path and link bandwidths are the same. This is achieved when any two consecutive links are assigned different channels, termed as "Channel-Discontinuity-Constraint" (CDC). CDC-paths are also useful in TDMA systems, where, preferably, consecutive links are assigned different time-slots. In the first part of this paper, we develop a t-spanner for CDC-paths using spatial properties; a sub-network containing O(n/θ) links, for any θ > 0, such that CDC-paths increase in cost by at most a factor t = (1-2 sin (θ/2))-2. We propose a novel distributed algorithm to compute the spanner using an expected number of O(n log n) fixed-size messages. In the second part, we present a distributed algorithm to find minimum-cost CDC-paths between two nodes using O(n2) fixed-size messages, by developing an extension of Edmonds' algorithm for minimum-cost perfect matching. In a centralized implementation, our algorithm runs in O(n2) time improving the previous best algorithm which requires O(n3) running time. Moreover, this running time improves to O(n/θ) when used in conjunction with the spanner developed.

Biomarkers for trials of neuroprotection in Parkinson's disease.

With increased understanding of disease pathogenesis and the foreseeable reality of disease-modifying therapies, there is a growing need to find biomarkers that will allow early (preferably preclinical) detection of disease and that will provide an independent readout of disease progression. In this article, we review a variety of markers, with a focus on functional imaging techniques, which while imperfect, currently provide the best approach to this problem. We consider the limitations of functional imaging of the dopamine system in assessing the progression of Parkinson's Disease (PD) as well as the potential use of structural imaging and emerging progress in other biochemical and molecular markers. While there is no single biomarker that will satisfy all requirements, some combination is likely to be of great use in identifying those subjects most likely to benefit from neuroprotective therapies, as well as in monitoring the effects of these interventions.

PACS plus criteria: a retrospective cohort review of 612 consecutive patients treated with bilateral YAG peripheral iridotomies.

INTRODUCTION: With an ageing population and better life expectancy, the prevalence of angle closure disease is expected to increase by 20% per decade. In 2022, the Royal College of Ophthalmologists (RCOphth) issued a guideline on managing angle closure disease. Hospital eye service (HES) referral and prophylactic treatment are recommended only for primary angle closure suspect (PACS) with "Plus" features only. We aimed to examine patients previously treated with YAG peripheral iridotomies (YAG PI) for the presence of "PACS Plus" features. METHODS: A retrospective cohort study of consecutive patients treated with YAG PI between 2015 and 2019 at a tertiary referral NHS eye centre was reviewed. Cases were examined to identify and classify patients into Primary Angle Closure (PAC), PACS, and Primary Angle Closure Glaucoma (PACG). Patients with PACS were studied for "Plus" features. RESULTS: Six hundred twelve patients with gonioscopy-confirmed angle closure (defined as a minimum 180 degrees iridotrabecular contact) treated with YAG PI from years 2015 to 2019 were included in the analysis. The mean age of patients presenting with angle closure disease was 68.5 years (SD 11.3). There were 390 (63.7%) patients with PACS, 102 (16.6%) with PAC and 120 (19.7%) with PACG. Of the PACS patients, 159(40.8%) patients had no "Plus" features. 181 (40.2%) patients had 1 "Plus" feature, 37 (9.5%) had 2 "Plus" features and 13 (3.3%) patients had 3 "Plus" features. CONCLUSION: In our cohort, a considerable proportion (40.8%) of PACS patients treated with YAG PI did not have Plus features and therefore that would not meet the proposed criteria for HES referral and YAG PI. With the proposed guidance, we expect a considerable reduction in HES referrals. Nonetheless, community optometry services should be supported and trained to provide monitoring for patients with PACS not referred to the HES.

Mutational profiles of head and neck squamous cell carcinomas based upon human papillomavirus status in the Veterans Affairs National Precision Oncology Program.

BACKGROUND: Patients with advanced head and neck squamous cell carcinoma (HNSCC) associated with human papillomavirus (HPV) demonstrate favorable clinical outcomes compared to patients bearing HPV-negative HNSCC. We sought to characterize the association between HPV status and mutational profiles among patients served by the Veterans Health Administration (VHA). METHODS: We performed a retrospective analysis of all Veterans with primary HNSCC tumors who underwent next-generation sequencing (NGS) through the VHA's National Precision Oncology Program between July 2016 and February 2019. HPV status was determined by clinical pathology reports of p16 immunohistochemical staining; gene variant pathogenicity was classified using OncoKB, an online precision oncology knowledge database, and mutation frequencies were compared using Fisher's exact test. RESULTS: A total of 79 patients met inclusion criteria, of which 48 (60.8%) had p16-positive tumors. Patients with p16-negative HNSCC were more likely to have mutations in TP53 (p < 0.0001), and a trend towards increased mutation frequency was observed within NOTCH1 (p = 0.032) and within the composite CDK/Rb pathway (p = 0.065). Mutations in KRAS, NRAS, HRAS, and FBXW7 were exclusively identified within p16-positive tumors, and a trend towards increased frequency was observed within the PI3K pathway (p = 0.051). No difference in overall mutational burden was observed between the two groups. CONCLUSIONS: In accordance with the previous studies, no clear molecular basis for improved prognosis among patients harboring HPV-positive disease has been elucidated. Though no targeted therapies are approved based upon HPV-status, current efforts to trial PI3K inhibitors and mTOR inhibitors across patients with HPV-positive disease bear genomic rationale based upon the current findings.

Transitional cell states sculpt tissue topology during lung regeneration.

Organ regeneration requires dynamic cell interactions to reestablish cell numbers and tissue architecture. While we know the identity of progenitor cells that replace lost tissue, the transient states they give rise to and their role in repair remain elusive. Here, using multiple injury models, we find that alveolar fibroblasts acquire distinct states marked by Sfrp1 and Runx1 that influence tissue remodeling and reorganization. Unexpectedly, ablation of alveolar epithelial type-1 (AT1) cells alone is sufficient to induce tissue remodeling and transitional states. Integrated scRNA-seq followed by genetic interrogation reveals RUNX1 is a key driver of fibroblast states. Importantly, the ectopic induction or accumulation of epithelial transitional states induce rapid formation of transient alveolar fibroblasts, leading to organ-wide fibrosis. Conversely, the elimination of epithelial or fibroblast transitional states or RUNX1 loss, leads to tissue simplification resembling emphysema. This work uncovered a key role for transitional states in orchestrating tissue topologies during regeneration.

Is there an association of socioeconomic deprivation with acute primary angle closure?

BACKGROUND: Socioeconomic deprivation is known to increase the risk of late presentation of many diseases. This is the largest study in United Kingdom investigating the relationship between socioeconomic deprivation and acute primary angle closure (APAC). METHODS: A retrospective review of case notes was conducted of 718 consecutive patients who underwent laser peripheral iridotomy (LPI) in Edinburgh (Princess Alexandra Eye Pavilion) and Fife (Queen Margaret Hospital) between 2015 and 2019. Baseline demographics including sex, age, ethnicity, pre-existing diabetes, use of anti-depressants, and family history of glaucoma were collected. Deprivation was scored using the Scottish Index of Multiple Deprivation (SIMD) Index 2020v2. A lower rank and decile indicate higher degrees of deprivation. We investigated differences in characteristics between patients who were referred routinely versus patients who referred as APAC. RESULTS: The SIMD rank and deciles were consistently lower in patients who were referred urgently with APAC in both centres (P = <0.05) when compared to those referred routinely for LPI. On univariate and multivariate logistic regression, the presentation of APAC is negatively associated with SIMD Decile (OR = -0.101, 95% CI -0.178 to -0.026, P = 0.008) and family history of glaucoma (OR = -1.010, 95% CI -1.670 to -0.426, P = 0.001), and positively associated with age (OR = 0.029, 95% CI 0.009-0.049, P = 0.004). CONCLUSIONS: Socioeconomic deprivation is an important risk factors for patients presenting with APAC. Socioeconomic deprivation should be incorporated into the design of glaucoma services and considered when triaging patients for prophylactic and therapeutic LPI and cataract surgery.

Cyber-Internet Security Framework to Conquer Energy-Related Attacks on the Internet of Things with Machine Learning Techniques.

The Internet of Things (IoT) ushers in a new era of communication that depends on a broad range of things and many types of communication technologies to share information. This new age of communication will be characterised by the following characteristics: Because all of the IoT's objects are connected to one another and because they function in environments that are not protected, it poses a significantly greater number of issues, constraints, and challenges than do traditional computing systems. This is due to the fact that traditional computing systems do not have as many interconnected components. Because of this, it is imperative that security be prioritised in a new approach, which is not something that is currently present in conventional computer systems. The Wireless Sensor Network, often known as WSN, and the Mobile Ad hoc Network are two technologies that play significant roles in the process of building an Internet of Things system. These technologies are used in a wide variety of activities, including sensing, environmental monitoring, data collecting, heterogeneous communication techniques, and data processing, amongst others. Because it incorporates characteristics of both MANET and WSN, IoT is susceptible to the same kinds of security issues that affect those other networks. An assault known as a Delegate Entity Attack (DEA) is a subclass of an attack known as a Denial of Service (DoS). The attacker sends an unacceptable number of control packets that have the appearance of being authentic. DoS assaults may take many different forms, and one of those kinds is an SD attack. Because of this, it is far more difficult to recognise this form of attack than a simple one that depletes the battery's capacity. One of the other key challenges that arise in a network during an SD attack is that there is the need to enhance energy management and prolong the lifespan of IoT nodes. This is one of the other significant issues that arise in a network when an SD attack is occurs. It is recommended that you make use of a Random Number Generator with Hierarchical Intrusion Detection System, abbreviated as RNGHID for short. The ecosystem of the Internet of Things is likely to be segmented into a great number of separate sectors and clusters. The HIPS system has been partitioned into two entities, which are referred to as the Delegate Entity (DE) and the Pivotal Entity, in order to identify any nodes in the network that are behaving in an abnormal manner. These entities are known, respectively, as the Delegate Entity and the Pivotal Entity (PE). Once the anomalies have been identified, it will be possible to pinpoint the area of the SD attack torture and the damaging activities that have been taken place. A warning message, generated by the Malicious Node Alert System (MNAS), is broadcast across the network in order to inform the other nodes that the network is under attack. This message classifies the various sorts of attacks based on the results of an algorithm that employs machine learning. The proposed protocol displays various desired properties, such as the capacity to conduct indivisible authentication, rapid authentication, and minimum overhead in both transmission and storage. These are only a few of the desirable attributes.

Biomedical Application of Identified Biomarkers Gene Expression Based Early Diagnosis and Detection in Cervical Cancer with Modified Probabilistic Neural Network.

Cervical squamous cell carcinoma (CSC) is expected to rise to become the fourth most prevalent cancer in women globally and to replace breast cancer as the top cause of death in women in the future years, according to the World Health Organization. According to the World Health Organization, developing countries are responsible for 86 percent of all cervical cancer cases globally in women aged 15 to 44 (WHO). Cancer mortality is associated with the largest amount of monotonous antecedent in low- and middle-income nations, while cancer mortality is associated with the least amount of monotonous antecedent in high-income countries. Cervical cancer is thought to be caused by aberrant proliferation of cells in the cervix that is capable of stealing or invading other human organs, according to current thinking. Cancer of the cerebral cell is the most prevalent kind of cancer in women. It is expected that cervical squamous cell carcinoma (CSC) will be the fourth most frequent cancer in the world and the main cause of death in women by the year 2050. Despite the fact that technology has improved tremendously since then, this is still the case. When compared to high-income countries, low- and middle-income countries have the highest consistent antecedent for cancer mortality, according to the World Cancer Research Fund. Cancerous growths of cells in the cervix, such as cervical cancer, are caused by cells that have the ability to steal from or invade auxiliary organs of the body, as is the case with cervical cancer. Although technological advances have been made in recent years, gene expression profiling continues to be a prominent approach in the investigation of cervical cancer. Since then, researchers have had the opportunity to examine a gene coexpression network, which has evolved into an exceptionally comprehensive technique for microarray research. This has helped them to get a better understanding of the human genome. When a specific biological issue is addressed, gene coexpression networks retain a considerable percentage of their once vast component of physiognomy, which was previously immense. When comparing the properties of genes in a population, it is well known that feature selection may be used to choose genes that outperform the rest of the genes in the population. There are several benefits to feature selection, and this is only one of them. Typically used gene selection approaches have been shown to be insufficient in acquiring the best potential sequence of genes for training purposes, and as a result, the accuracy of the classifier has likely suffered as a result of this. Recently, a considerable number of scientists have advocated for the use of optimization approaches in the process of gene selection, and this trend is expected to continue. A metaheuristic algorithm may be used to choose a suitable subset of genes, according to the preceding assertion, which is also consistent with the metaheuristic approach. A Modified Probabilistic Neural Network differs from other networks in that the underlying gene expression associated with DEGs and standard data in a Modified Probabilistic Neural Network is not uniformly distributed as it is in other networks (MPN). As previously said, selecting the most relevant genes or repeating genes is a vital step in the prediction process. It was this technique that was used in the research of cervical cancer. Since then, researchers have had the opportunity to examine a gene coexpression network, which has evolved into an exceptionally comprehensive technique for microarray research. This has helped them to get a better understanding of the human genome. When a specific biological issue is addressed, gene coexpression networks are able to preserve a previously major section of the face that had been lost. When comparing the properties of genes in a population, it is well known that feature selection may be used to choose genes that outperform the rest of the genes in the population. There are several benefits to feature selection, and this is only one of them. Typically used gene selection approaches have been shown to be insufficient in acquiring the best potential sequence of genes for training purposes, and as a result, the accuracy of the classifier has likely suffered as a result of this. In the field of gene selection, several scholars have argued in favor of the employment of optimization approaches. A metaheuristic algorithm may be used to choose a suitable subset of genes, according to the preceding assertion, which is also consistent with the metaheuristic approach. It was discovered that Modified Probabilistic Neural Networks (MPNs) had a different distribution of gene expression linked with DEGs and normal data than other networks, which had not been previously seen. This was previously unknown. Following what has been said before, selecting the most appropriate or repeated genes is a critical task throughout the prediction process.

Sensitizing immune unresponsive colorectal cancers to immune checkpoint inhibitors through MAVS overexpression.

BACKGROUND: The majority of colorectal carcinomas (CRCs) are insensitive to programmed death protein-1/programmed death-ligand 1 (anti-PD-1/PD-L1) immune checkpoint inhibitor (ICI) antibodies. While there are many causes for ICI insensitivity, recent studies suggest that suppression of innate immune gene expression in tumor cells could be a root cause of this insensitivity and an important factor in the evolution of tumor immunosuppression. METHODS: We first assessed the reduction of mitochondrial antiviral signaling gene (MAVS) and related RIG-I pathway gene expression in several patient RNA expression datasets. We then engineered MAVS expressing tumor cells and tested their ability to elicit innate and adaptive anti-tumor immunity using both in vitro and in vivo approaches, which we then confirmed using MAVS expressing viral vectors. Finally, we observed that MAVS stimulated PD-L1 expression in multiple cell types and then assessed the combination of PD-L1 ICI antibodies with MAVS tumor expression in vivo. RESULTS: MAVS was significantly downregulated in CRCs, but its re-expression could stimulate broad cellular interferon-related responses, in both murine and patient-derived CRCs. In vivo, local MAVS expression elicited significant anti-tumor responses in both immune-sensitive and insensitive CRC models, through the stimulation of an interferon responsive axis that provoked tumor antigen-specific adaptive immunity. Critically, we found that tumor-intrinsic MAVS expression triggered systemic adaptive immune responses that enabled abscopal CD8 +T cell cytotoxicity against distant CRCs. As MAVS also induced PD-L1 expression, we further found synergistic anti-tumor responses in combination with anti-PD-L1 ICIs. CONCLUSION: These data demonstrate that intratumoral MAVS expression results in local and systemic tumor antigen-specific T cell responses, which could be combined with PD-L1 ICI to permit effective anti-tumor immunotherapy in ICI resistant cancers.

Combination of ultrasound-based mechanical disruption of tumor with immune checkpoint blockade modifies tumor microenvironment and augments systemic antitumor immunity.

BACKGROUND: Despite multimodal adjuvant management with radiotherapy, chemotherapy and hormonal therapies, most surgically resected primary breast cancers relapse or metastasize. A potential solution to late and distant recurrence is to augment systemic antitumor immunity, in part by appropriately presenting tumor antigens, but also by modulating the immunosuppressive tumor microenvironment (TME). We previously validated this concept in models of murine carcinoma treated with a novel predominately microcavitating version of high-intensity focused ultrasound (HIFU), mechanical high-intensity focused ultrasound (M-HIFU). Here we elucidated the mechanisms of enhanced antitumor immunity by M-HIFU over conventional thermal high-intensity focused ultrasound (T-HIFU) and investigated the potential of the combinatorial strategy with an immune checkpoint inhibitor, anti-PD-L1 antibody. METHODS: The antitumor efficacy of treatments was investigated in syngeneic murine breast cancer models using triple-negative (E0771) or human ErbB-2 (HER2) expressing (MM3MG-HER2) tumors in C57BL/6 or BALB/c mice, respectively. Induction of systemic antitumor immunity by the treatments was tested using bilateral tumor implantation models. Flow cytometry, immunohistochemistry, and single-cell RNA sequencing were performed to elucidate detailed effects of HIFU treatments or combination treatment on TME, including the activation status of CD8 T cells and polarization of tumor-associated macrophages (TAMs). RESULTS: More potent systemic antitumor immunity and tumor growth suppression were induced by M-HIFU compared with T-HIFU. Molecular characterization of the TME after M-HIFU by single-cell RNA sequencing demonstrated repolarization of TAM to the immunostimulatory M1 subtype compared with TME post-T-HIFU. Concurrent anti-PD-L1 antibody administration or depletion of CD4+ T cells containing a population of regulatory T cells markedly increased T cell-mediated antitumor immunity and tumor growth suppression at distant, untreated tumor sites in M-HIFU treated mice compared with M-HIFU monotherapy. CD8 T and natural killer cells played major roles as effector cells in the combination treatment. CONCLUSIONS: Physical disruption of the TME by M-HIFU repolarizes TAM, enhances T-cell infiltration, and, when combined with anti-PD-L1 antibody, mediates superior systemic antitumor immune responses and distant tumor growth suppression. These findings suggest M-HIFU combined with anti-PD-L1 may be useful in reducing late recurrence or metastasis when applied to primary tumors.