Comprehensive overview of the anesthesiology research landscape: A machine Learning Analysis of 737 NIH-funded anesthesiology primary Investigator's publication trends

Background

Anesthesiology plays a crucial role in perioperative care, critical care, and pain management, impacting patient experiences and clinical outcomes. However, our understanding of the anesthesiology research landscape is limited. Accordingly, we initiated a data-driven analysis through topic modeling to uncover research trends, enabling informed decision-making and fostering progress within the field.

Methods

The easyPubMed R package was used to collect 32,300 PubMed abstracts spanning from 2000 to 2022. These abstracts were authored by 737 Anesthesiology Principal Investigators (PIs) who were recipients of National Institute of Health (NIH) funding from 2010 to 2022. Abstracts were preprocessed, vectorized, and analyzed with the state-of-the-art BERTopic algorithm to identify pillar topics and trending subtopics within anesthesiology research. Temporal trends were assessed using the Mann-Kendall test.

Results

The publishing journals with most abstracts in this dataset were Anesthesia & Analgesia 1133, Anesthesiology 992, and Pain 671. Eight pillar topics were identified and categorized as basic or clinical sciences based on a hierarchical clustering analysis. Amongst the pillar topics, "Cells & Proteomics" had both the highest annual and total number of abstracts. Interestingly, there was an overall upward trend for all topics spanning the years 2000-2022. However, when focusing on the period from 2015 to 2022, topics "Cells & Proteomics" and "Pulmonology" exhibit a downward trajectory. Additionally, various subtopics were identified, with notable increasing trends in "Aneurysms", "Covid 19 Pandemic", and "Artificial intelligence & Machine Learning".

Conclusion

Our work offers a comprehensive analysis of the anesthesiology research landscape by providing insights into pillar topics, and trending subtopics. These findings contribute to a better understanding of anesthesiology research and can guide future directions.

Reassessing acquired neonatal intestinal diseases using unsupervised machine learning

BACKGROUND

Acquired neonatal intestinal diseases have an array of overlapping presentations and are often labeled under the dichotomous classification of necrotizing enterocolitis (which is poorly defined) or spontaneous intestinal perforation, hindering more precise diagnosis and research. The objective of this study was to take a fresh look at neonatal intestinal disease classification using unsupervised machine learning.

METHODS

Patients admitted to the University of Florida Shands Neonatal Intensive Care Unit January 2013-September 2019 diagnosed with an intestinal injury, or had imaging findings of portal venous gas, pneumatosis, abdominal free air, or had an abdominal drain placed or exploratory laparotomy during admission were included. Congenital gastroschisis, omphalocele, intestinal atresia, malrotation were excluded. Data was collected via retrospective chart review with subsequent hierarchal, unsupervised clustering analysis.

RESULTS

Five clusters of intestinal injury were identified: Cluster 1 deemed the "Low Mortality" cluster, Cluster 2 deemed the "Mature with Inflammation" cluster, Cluster 3 deemed the "Immature with High Mortality" cluster, Cluster 4 deemed the "Late Injury at Full Feeds" cluster, and Cluster 5 deemed the "Late Injury with High Rate of Intestinal Necrosis" cluster.

CONCLUSION

Unsupervised machine learning can be used to cluster acquired neonatal intestinal injuries. Future study with larger multicenter datasets is needed to further refine and classify types of intestinal diseases.

IMPACT

Unsupervised machine learning can be used to cluster types of acquired neonatal intestinal injury. Five major clusters of acquired neonatal intestinal injury are described, each with unique features. The clusters herein described deserve future, multicenter study to determine more specific early biomarkers and tailored therapeutic interventions to improve outcomes of often devastating neonatal acquired intestinal injuries.

Quantitative estimate of cognitive resilience and its medical and genetic associations

BACKGROUND

We have proposed that cognitive resilience (CR) counteracts brain damage from Alzheimer's disease (AD) or AD-related dementias such that older individuals who harbor neurodegenerative disease burden sufficient to cause dementia remain cognitively normal. However, CR traditionally is considered a binary trait, capturing only the most extreme examples, and is often inconsistently defined.

METHODS

This study addressed existing discrepancies and shortcomings of the current CR definition by proposing a framework for defining CR as a continuous variable for each neuropsychological test. The linear equations clarified CR's relationship to closely related terms, including cognitive function, reserve, compensation, and damage. Primarily, resilience is defined as a function of cognitive performance and damage from neuropathologic damage. As such, the study utilized data from 844 individuals (age = 79 ± 12, 44% female) in the National Alzheimer's Coordinating Center cohort that met our inclusion criteria of comprehensive lesion rankings for 17 neuropathologic features and complete neuropsychological test results. Machine learning models and GWAS then were used to identify medical and genetic factors that are associated with CR.

RESULTS

CR varied across five cognitive assessments and was greater in female participants, associated with longer survival, and weakly associated with educational attainment or APOE ε4 allele. In contrast, damage was strongly associated with APOE ε4 allele (P value < 0.0001). Major predictors of CR were cardiovascular health and social interactions, as well as the absence of behavioral symptoms.

CONCLUSIONS

Our framework explicitly decoupled the effects of CR from neuropathologic damage. Characterizations and genetic association study of these two components suggest that the underlying CR mechanism has minimal overlap with the disease mechanism. Moreover, the identified medical features associated with CR suggest modifiable features to counteract clinical expression of damage and maintain cognitive function in older individuals.

Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity

Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs). However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy. In this study, we use physical activity data collected using a wearable device comprising over 181,944 h of data across N = 1083 patients. Using a new state-of-the art deep learning time-series classification architecture, we develop a 'clock' of healthy dynamics during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also develop novel interpretability algorithms that integrate unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy. We found that deviations from a normal 'clock' of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e - 67, permutation test) and when our model overestimates GA, there are 1.44 times (P = 2.82e - 39, permutation test) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043, Spearman's), indicating that our model assigns a more advanced GA when an individual's daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e - 21, Mann-Whitney U). Combining prediction and interpretability allows us to signal when activity behaviors alter the likelihood of preterm birth and advocates for the development of clinical decision support through passive monitoring and exercise habit and sleep recommendations, which can be easily implemented in LMICs.

Cross-species comparative analysis of single presynapses

Comparing brain structure across species and regions enables key functional insights. Leveraging publicly available data from a novel mass cytometry-based method, synaptometry by time of flight (SynTOF), we applied an unsupervised machine learning approach to conduct a comparative study of presynapse molecular abundance across three species and three brain regions. We used neural networks and their attractive properties to model complex relationships among high dimensional data to develop a unified, unsupervised framework for comparing the profile of more than 4.5 million single presynapses among normal human, macaque, and mouse samples. An extensive validation showed the feasibility of performing cross-species comparison using SynTOF profiling. Integrative analysis of the abundance of 20 presynaptic proteins revealed near-complete separation between primates and mice involving synaptic pruning, cellular energy, lipid metabolism, and neurotransmission. In addition, our analysis revealed a strong overlap between the presynaptic composition of human and macaque in the cerebral cortex and neostriatum. Our unique approach illuminates species- and region-specific variation in presynapse molecular composition.

Whole genome deconvolution unveils Alzheimer's resilient epigenetic signature

Assay for Transposase Accessible Chromatin by sequencing (ATAC-seq) accurately depicts the chromatin regulatory state and altered mechanisms guiding gene expression in disease. However, bulk sequencing entangles information from different cell types and obscures cellular heterogeneity. To address this, we developed Cellformer, a deep learning method that deconvolutes bulk ATAC-seq into cell type-specific expression across the whole genome. Cellformer enables cost-effective cell type-specific open chromatin profiling in large cohorts. Applied to 191 bulk samples from 3 brain regions, Cellformer identifies cell type-specific gene regulatory mechanisms involved in resilience to Alzheimer's disease, an uncommon group of cognitively healthy individuals that harbor a high pathological load of Alzheimer's disease. Cell type-resolved chromatin profiling unveils cell type-specific pathways and nominates potential epigenetic mediators underlying resilience that may illuminate therapeutic opportunities to limit the cognitive impact of the disease. Cellformer is freely available to facilitate future investigations using high-throughput bulk ATAC-seq data.

Prediction of neuropathologic lesions from clinical data

INTRODUCTION

Post-mortem analysis provides definitive diagnoses of neurodegenerative diseases; however, only a few can be diagnosed during life.

METHODS

This study employed statistical tools and machine learning to predict 17 neuropathologic lesions from a cohort of 6518 individuals using 381 clinical features (Table S1). The multisite data allowed validation of the model's robustness by splitting train/test sets by clinical sites. A similar study was performed for predicting Alzheimer's disease (AD) neuropathologic change without specific comorbidities.

RESULTS

Prediction results show high performance for certain lesions that match or exceed that of research annotation. Neurodegenerative comorbidities in addition to AD neuropathologic change resulted in compounded, but disproportionate, effects across cognitive domains as the comorbidity number increased.

DISCUSSION

Certain clinical features could be strongly associated with multiple neurodegenerative diseases, others were lesion-specific, and some were divergent between lesions. Our approach could benefit clinical research, and genetic and biomarker research by enriching cohorts for desired lesions.

Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries

Preterm birth (PTB) is the leading cause of death in children under five, yet comprehensive studies are hindered by its multiple complex etiologies. Epidemiological associations between PTB and maternal characteristics have been previously described. This work used multiomic profiling and multivariate modeling to investigate the biological signatures of these characteristics. Maternal covariates were collected during pregnancy from 13,841 pregnant women across five sites. Plasma samples from 231 participants were analyzed to generate proteomic, metabolomic, and lipidomic datasets. Machine learning models showed robust performance for the prediction of PTB (AUROC = 0.70), time-to-delivery (r = 0.65), maternal age (r = 0.59), gravidity (r = 0.56), and BMI (r = 0.81). Time-to-delivery biological correlates included fetal-associated proteins (e.g., ALPP, AFP, and PGF) and immune proteins (e.g., PD-L1, CCL28, and LIFR). Maternal age negatively correlated with collagen COL9A1, gravidity with endothelial NOS and inflammatory chemokine CXCL13, and BMI with leptin and structural protein FABP4. These results provide an integrated view of epidemiological factors associated with PTB and identify biological signatures of clinical covariates affecting this disease.

In-silico generation of high-dimensional immune response data in patients using a deep neural network

Technologies for single-cell profiling of the immune system have enabled researchers to extract rich interconnected networks of cellular abundance, phenotypical and functional cellular parameters. These studies can power machine learning approaches to understand the role of the immune system in various diseases. However, the performance of these approaches and the generalizability of the findings have been hindered by limited cohort sizes in translational studies, partially due to logistical demands and costs associated with longitudinal data collection in sufficiently large patient cohorts. An evolving challenge is the requirement for ever-increasing cohort sizes as the dimensionality of datasets grows. We propose a deep learning model derived from a novel pipeline of optimal temporal cell matching and overcomplete autoencoders that uses data from a small subset of patients to learn to forecast an entire patient's immune response in a high dimensional space from one timepoint to another. In our analysis of 1.08 million cells from patients pre- and post-surgical intervention, we demonstrate that the generated patient-specific data are qualitatively and quantitatively similar to real patient data by demonstrating fidelity, diversity, and usefulness.

Large-scale correlation network construction for unraveling the coordination of complex biological systems

Advanced measurement and data storage technologies have enabled high-dimensional profiling of complex biological systems. For this, modern multiomics studies regularly produce datasets with hundreds of thousands of measurements per sample, enabling a new era of precision medicine. Correlation analysis is an important first step to gain deeper insights into the coordination and underlying processes of such complex systems. However, the construction of large correlation networks in modern high-dimensional datasets remains a major computational challenge owing to rapidly growing runtime and memory requirements. Here we address this challenge by introducing CorALS (Correlation Analysis of Large-scale (biological) Systems), an open-source framework for the construction and analysis of large-scale parametric as well as non-parametric correlation networks for high-dimensional biological data. It features off-the-shelf algorithms suitable for both personal and high-performance computers, enabling workflows and downstream analysis approaches. We illustrate the broad scope and potential of CorALS by exploring perspectives on complex biological processes in large-scale multiomics and single-cell studies.

Objective Activity Parameters Track Patient-specific Physical Recovery Trajectories After Surgery and Link With Individual Preoperative Immune States

OBJECTIVE

The longitudinal assessment of physical function with high temporal resolution at a scalable and objective level in patients recovering from surgery is highly desirable to understand the biological and clinical factors that drive the clinical outcome. However, physical recovery from surgery itself remains poorly defined and the utility of wearable technologies to study recovery after surgery has not been established.

BACKGROUND

Prolonged postoperative recovery is often associated with long-lasting impairment of physical, mental, and social functions. Although phenotypical and clinical patient characteristics account for some variation of individual recovery trajectories, biological differences likely play a major role. Specifically, patient-specific immune states have been linked to prolonged physical impairment after surgery. However, current methods of quantifying physical recovery lack patient specificity and objectivity.

METHODS

Here, a combined high-fidelity accelerometry and state-of-the-art deep immune profiling approach was studied in patients undergoing major joint replacement surgery. The aim was to determine whether objective physical parameters derived from accelerometry data can accurately track patient-specific physical recovery profiles (suggestive of a 'clock of postoperative recovery'), compare the performance of derived parameters with benchmark metrics including step count, and link individual recovery profiles with patients' preoperative immune state.

RESULTS

The results of our models indicate that patient-specific temporal patterns of physical function can be derived with a precision superior to benchmark metrics. Notably, 6 distinct domains of physical function and sleep are identified to represent the objective temporal patterns: ''activity capacity'' and ''moderate and overall activity (declined immediately after surgery); ''sleep disruption and sedentary activity (increased after surgery); ''overall sleep'', ''sleep onset'', and ''light activity'' (no clear changes were observed after surgery). These patterns can be linked to individual patients preopera-tive immune state using cross-validated canonical-correlation analysis. Importantly, the pSTAT3 signal activity in monocytic myeloid-derived suppressor cells predicted a slower recovery.

CONCLUSIONS

Accelerometry-based recovery trajectories are scalable and objective outcomes to study patient-specific factors that drive physical recovery.

Data-driven longitudinal characterization of neonatal health and morbidity

Although prematurity is the single largest cause of death in children under 5 years of age, the current definition of prematurity, based on gestational age, lacks the precision needed for guiding care decisions. Here, we propose a longitudinal risk assessment for adverse neonatal outcomes in newborns based on a deep learning model that uses electronic health records (EHRs) to predict a wide range of outcomes over a period starting shortly before conception and ending months after birth. By linking the EHRs of the Lucile Packard Children's Hospital and the Stanford Healthcare Adult Hospital, we developed a cohort of 22,104 mother-newborn dyads delivered between 2014 and 2018. Maternal and newborn EHRs were extracted and used to train a multi-input multitask deep learning model, featuring a long short-term memory neural network, to predict 24 different neonatal outcomes. An additional cohort of 10,250 mother-newborn dyads delivered at the same Stanford Hospitals from 2019 to September 2020 was used to validate the model. Areas under the receiver operating characteristic curve at delivery exceeded 0.9 for 10 of the 24 neonatal outcomes considered and were between 0.8 and 0.9 for 7 additional outcomes. Moreover, comprehensive association analysis identified multiple known associations between various maternal and neonatal features and specific neonatal outcomes. This study used linked EHRs from more than 30,000 mother-newborn dyads and would serve as a resource for the investigation and prediction of neonatal outcomes. An interactive website is available for independent investigators to leverage this unique dataset: https://maternal-child-health-associations.shinyapps.io/shiny_app/.

Early prediction and longitudinal modeling of preeclampsia from multiomics

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16 weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC] = 0.88, 95% confidence interval [CI] [0.76, 0.99] cross-validated; AUC = 0.83, 95% CI [0.62,1] validated). Univariate analysis demonstrated statistical significance of identified metabolites. An integrated multiomics model further improved accuracy (AUC = 0.94). Several biological pathways were identified including tryptophan, caffeine, and arachidonic acid metabolisms. Integration with immune cytometry data suggested novel associations between immune and proteomic dynamics. While further validation in a larger population is necessary, these encouraging results can serve as a basis for a simple, early diagnostic test for preeclampsia.

A data-driven health index for neonatal morbidities

Whereas prematurity is a major cause of neonatal mortality, morbidity, and lifelong impairment, the degree of prematurity is usually defined by the gestational age (GA) at delivery rather than by neonatal morbidity. Here we propose a multi-task deep neural network model that simultaneously predicts twelve neonatal morbidities, as the basis for a new data-driven approach to define prematurity. Maternal demographics, medical history, obstetrical complications, and prenatal fetal findings were obtained from linked birth certificates and maternal/infant hospitalization records for 11,594,786 livebirths in California from 1991 to 2012. Overall, our model outperformed traditional models to assess prematurity which are based on GA and/or birthweight (area under the precision-recall curve was 0.326 for our model, 0.229 for GA, and 0.156 for small for GA). These findings highlight the potential of using machine learning techniques to predict multiple prematurity phenotypes and inform clinical decisions to prevent, diagnose and treat neonatal morbidities.

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Traditional definitions of prematurity based on gestational age need to be updated

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Deep learning of maternal clinical data improves predictions of neonatal morbidity

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Data-driven model leverages birthweight, type of delivery and maternal race

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Accurate risk prediction can inform clinical decisions

Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning

Psychosocial and stress-related factors (PSFs), defined as internal or external stimuli that induce biological changes, are potentially modifiable factors and accessible targets for interventions that are associated with adverse pregnancy outcomes (APOs). Although individual APOs have been shown to be connected to PSFs, they are biologically interconnected, relatively infrequent, and therefore challenging to model. In this context, multi-task machine learning (MML) is an ideal tool for exploring the interconnectedness of APOs on the one hand and building on joint combinatorial outcomes to increase predictive power on the other hand. Additionally, by integrating single cell immunological profiling of underlying biological processes, the effects of stress-based therapeutics may be measurable, facilitating the development of precision medicine approaches.

OBJECTIVES

The primary objectives were to jointly model multiple APOs and their connection to stress early in pregnancy, and to explore the underlying biology to guide development of accessible and measurable interventions.

MATERIALS AND METHODS

In a prospective cohort study, PSFs were assessed during the first trimester with an extensive self-filled questionnaire for 200 women. We used MML to simultaneously model, and predict APOs (severe preeclampsia, superimposed preeclampsia, gestational diabetes and early gestational age) as well as several risk factors (BMI, diabetes, hypertension) for these patients based on PSFs. Strongly interrelated stressors were categorized to identify potential therapeutic targets. Furthermore, for a subset of 14 women, we modeled the connection of PSFs to the maternal immune system to APOs by building corresponding ML models based on an extensive single cell immune dataset generated by mass cytometry time of flight (CyTOF).

RESULTS

Jointly modeling APOs in a MML setting significantly increased modeling capabilities and yielded a highly predictive integrated model of APOs underscoring their interconnectedness. Most APOs were associated with mental health, life stress, and perceived health risks. Biologically, stressors were associated with specific immune characteristics revolving around CD4/CD8 T cells. Immune characteristics predicted based on stress were in turn found to be associated with APOs.

CONCLUSIONS

Elucidating connections among stress, multiple APOs simultaneously, and immune characteristics has the potential to facilitate the implementation of ML-based, individualized, integrative models of pregnancy in clinical decision making. The modifiable nature of stressors may enable the development of accessible interventions, with success tracked through immune characteristics.

Single-synapse analyses of Alzheimer's disease implicate pathologic tau, DJ1, CD47, and ApoE

Synaptic molecular characterization is limited for Alzheimer’s disease (AD). Our newly invented mass cytometry–based method, synaptometry by time of flight (SynTOF), was used to measure 38 antibody probes in approximately 17 million single-synapse events from human brains without pathologic change or with pure AD or Lewy body disease (LBD), nonhuman primates (NHPs), and PS/APP mice. Synaptic molecular integrity in humans and NHP was similar. Although not detected in human synapses, Aβ was in PS/APP mice single-synapse events. Clustering and pattern identification of human synapses showed expected disease-specific differences, like increased hippocampal pathologic tau in AD and reduced caudate dopamine transporter in LBD, and revealed previously unidentified findings including increased hippocampal CD47 and lowered DJ1 in AD and higher ApoE in AD with dementia. Our results were independently supported by multiplex ion beam imaging of intact tissue. This highlights the higher depth and breadth of insight on neurodegenerative diseases obtainable through SynTOF.

Multiomics Characterization of Preterm Birth in Low- and Middle-Income Countries

IMPORTANCE

Worldwide, preterm birth (PTB) is the single largest cause of deaths in the perinatal and neonatal period and is associated with increased morbidity in young children. The cause of PTB is multifactorial, and the development of generalizable biological models may enable early detection and guide therapeutic studies.

OBJECTIVE

To investigate the ability of transcriptomics and proteomics profiling of plasma and metabolomics analysis of urine to identify early biological measurements associated with PTB.

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic/prognostic study analyzed plasma and urine samples collected from May 2014 to June 2017 from pregnant women in 5 biorepository cohorts in low- and middle-income countries (LMICs; ie, Matlab, Bangladesh; Lusaka, Zambia; Sylhet, Bangladesh; Karachi, Pakistan; and Pemba, Tanzania). These cohorts were established to study maternal and fetal outcomes and were supported by the Alliance for Maternal and Newborn Health Improvement and the Global Alliance to Prevent Prematurity and Stillbirth biorepositories. Data were analyzed from December 2018 to July 2019.

EXPOSURES

Blood and urine specimens that were collected early during pregnancy (median sampling time of 13.6 weeks of gestation, according to ultrasonography) were processed, stored, and shipped to the laboratories under uniform protocols. Plasma samples were assayed for targeted measurement of proteins and untargeted cell-free ribonucleic acid profiling; urine samples were assayed for metabolites.

MAIN OUTCOMES AND MEASURES

The PTB phenotype was defined as the delivery of a live infant before completing 37 weeks of gestation.

RESULTS

Of the 81 pregnant women included in this study, 39 had PTBs (48.1%) and 42 had term pregnancies (51.9%) (mean [SD] age of 24.8 [5.3] years). Univariate analysis demonstrated functional biological differences across the 5 cohorts. A cohort-adjusted machine learning algorithm was applied to each biological data set, and then a higher-level machine learning modeling combined the results into a final integrative model. The integrated model was more accurate, with an area under the receiver operating characteristic curve (AUROC) of 0.83 (95% CI, 0.72-0.91) compared with the models derived for each independent biological modality (transcriptomics AUROC, 0.73 [95% CI, 0.61-0.83]; metabolomics AUROC, 0.59 [95% CI, 0.47-0.72]; and proteomics AUROC, 0.75 [95% CI, 0.64-0.85]). Primary features associated with PTB included an inflammatory module as well as a metabolomic module measured in urine associated with the glutamine and glutamate metabolism and valine, leucine, and isoleucine biosynthesis pathways.

CONCLUSIONS AND RELEVANCE

This study found that, in LMICs and high PTB settings, major biological adaptations during term pregnancy follow a generalizable model and the predictive accuracy for PTB was augmented by combining various omics data sets, suggesting that PTB is a condition that manifests within multiple biological systems. These data sets, with machine learning partnerships, may be a key step in developing valuable predictive tests and intervention candidates for preventing PTB.

Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions

The dense network of interconnected cellular signalling responses that are quantifiable in peripheral immune cells provides a wealth of actionable immunological insights. Although high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, the limited cohort size and high dimensionality of data increase the possibility of false-positive discoveries and model overfitting. We introduce a generalizable machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive capabilities even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictions for clinically relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset. The iEN is available under an open-source licence.

VoPo leverages cellular heterogeneity for predictive modeling of single-cell data

High-throughput single-cell analysis technologies produce an abundance of data that is critical for profiling the heterogeneity of cellular systems. We introduce VoPo (https://github.com/stanleyn/VoPo), a machine learning algorithm for predictive modeling and comprehensive visualization of the heterogeneity captured in large single-cell datasets. In three mass cytometry datasets, with the largest measuring hundreds of millions of cells over hundreds of samples, VoPo defines phenotypically and functionally homogeneous cell populations. VoPo further outperforms state-of-the-art machine learning algorithms in classification tasks, and identified immune-correlates of clinically-relevant parameters.

HIF-1α Is a Metabolic Switch between Glycolytic-Driven Migration and Oxidative Phosphorylation-Driven Immunosuppression of Tregs in Glioblastoma

The mechanisms by which regulatory T cells (Tregs) migrate to and function within the hypoxic tumor microenvironment are unclear. Our studies indicate that specific ablation of hypoxia-inducible factor 1α (HIF-1α) in Tregs results in enhanced CD8⁺ T cell suppression versus wild-type Tregs under hypoxia, due to increased pyruvate import into the mitochondria. Importantly, HIF-1α-deficient Tregs are minimally affected by the inhibition of lipid oxidation, a fuel that is critical for Treg metabolism in tumors. Under hypoxia, HIF-1α directs glucose away from mitochondria, leaving Tregs dependent on fatty acids for mitochondrial metabolism within the hypoxic tumor. Indeed, inhibition of lipid oxidation enhances the survival of mice with glioma. Interestingly, HIF-1α-deficient-Treg mice exhibit significantly enhanced animal survival in a murine model of glioma, due to their stymied migratory capacity, explaining their reduced abundance in tumor-bearing mice. Thus HIF-1α acts as a metabolic switch for Tregs between glycolytic-driven migration and oxidative phosphorylation-driven immunosuppression.

Miska et al. demonstrate that regulatory T cell (Treg)-specific depletion of HIF-1α promotes enhanced immune suppression at the cost of migration under hypoxic conditions. Within the hypoxic brain-tumor environment, Tregs are uniquely able to metabolize extracellular free fatty acids to promote their immunosuppressive functionality, which can be targeted in vivo.

Gene Therapy and Virotherapy of Gliomas

Despite many recent advances in the management of gliomas, such as aggressive surgical resection, chemoradiotherapy, antiangiogenic therapy, and molecular targeted therapy, the survival of patients with high-grade neoplasms remains dismal. Gene therapy and oncolytic virotherapy have emerged as highly promising strategies for treatment of malignant brain tumors due to recent progress in understanding of the underlying cancer biology as well as improved techniques for genetic modification of potential therapeutics.

Abstract P2-11-09: Excellent acute toxicity outcomes with proton therapy for partial breast irradiation in early stage breast cancer: Initial results of a multi-institutional phase II trial

Background and Purpose: Partial breast irradiation (PBI) with proton therapy after lumpectomy for early stage invasive breast cancer is an area of active investigation. Advantages of this technique include a shorter treatment course and the potential for decreased morbidity versus external beam photon radiation therapy given superior sparing of the surrounding normal breast tissue. To date, multiple single-institutional studies have reported conflicting results on the acute toxicity of PBI. This prospective phase II trial investigates the feasibility, safety, and efficacy of delivering PBI with proton therapy in a multi-institutional setting.

Methods: Patients over the age of 50 years with ER-positive nonlobular invasive breast cancer or ductal carcinoma in situ ≤3 cm in size who had undergone lumpectomy with at least 2 mm negative surgical margins were treated with proton therapy to a dose of 40 Gy delivered over 10 daily fractions. In this initial analysis, we assess early toxicity and treatment efficacy of proton PBI. Patients were followed up at 4 weeks post-treatment and annually thereafter, along with annual mammograms. Patient-reported quality of life and physician-reported cosmesis assessments including photographs were obtained at 1 and 3 years post-treatment.

Results: Forty patients were enrolled, of which 38 were evaluable. At a median follow-up of 17.8 months (range 2-36 months), all patients had overall breast cosmesis that was scored “good” or “excellent”. Of 6 grade 2 acute adverse events that occurred, only 1 was radiation dermatitis, with others including lymphedema, hot flashes, and fatigue. One grade 3 acute toxicity occurred 3 weeks after radiation completion in the form of vascular disease requiring stent placement, highly unlikely to be attributable to radiation effects. Patient-reported quality of life outcomes were recorded using the standardized Breast Cancer Treatment Outcome Scale (BCTOS) scored from 1-4 (1: none; 2: mild; 3: moderate; 4: large), with endpoints receiving a score of 3 or 4 most frequently involving change in breast size, breast texture, nipple appearance, or scar tissue. Patients assigned a score of 4 for change in nipple appearance (n=2), breast shape (n=2), and scar tissue formation (n=2). To date, local, locoregional, and distant disease control are 100%, although one patient has developed a new hormone receptor-negative invasive ductal carcinoma of the contralateral breast.

Conclusion: Proton PBI provides excellent early cancer control with acceptable cosmetic outcomes and minimal adverse effects as per patient- and physician-reported assessments. On continued follow-up, late toxicity and cosmesis, as well as long-term disease control outcomes, will be assessed.

Citation Format: Choi JI, Chang AL. Excellent acute toxicity outcomes with proton therapy for partial breast irradiation in early stage breast cancer: Initial results of a multi-institutional phase II trial [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P2-11-09.

Fatty Acid Uptake in T Cell Subsets Using a Quantum Dot Fatty Acid Conjugate

Fatty acid (FA) metabolism directly influences the functional capabilities of T cells in tumor microenvironments. Thus, developing tools to interrogate FA-uptake by T cell subsets is important for understanding tumor immunosuppression. Herein, we have generated a novel FA-Qdot 605 dye conjugate with superior sensitivity and flexibility to any of the previously commercially available alternatives. For the first time, we demonstrate that this nanoparticle can be used as a specific measure of fatty acid uptake by T cells both in-vitro and in-vivo. Flow cytometric analysis shows that both the location and activation status of T cells determines their FA uptake. Additionally, CD4+ Foxp3+ regulatory T cells (Tregs) uptake FA at a higher rate than effector T cell subsets, supporting the role of FA metabolism for Treg function. Furthermore, we are able to simultaneously detect glucose and fatty acid uptake directly within the tumor microenvironment. Cumulatively, our results suggest that this novel fluorescent probe is a powerful tool to understand FA utilization within the tumor, thereby providing an unprecedented opportunity to study T cell FA metabolism in-vivo.

Anti-GITR therapy promotes immunity against malignant glioma in a murine model

Regulatory T cells (Tregs) are potently immunosuppressive cells that accumulate within the glioma microenvironment. The reduction in their function and/or trafficking has been previously shown to enhance survival in preclinical models of glioma. Glucocorticoid-induced TNFR-related protein (GITR) is a tumor necrosis factor superfamily receptor enriched on Tregs that has shown promise as a target for immunotherapy. An agonistic antibody against GITR has been demonstrated to inhibit Tregs in a number of models and has only been recently addressed in glioma. In this study, we examined the modality of the antibody function at the tumor site as opposed to the periphery as the blood-brain barrier prevents efficient antibody delivery to brain tumors. Mice harboring established GL261 tumors were treated with anti-GITR monotherapy and were shown to have a significant increase in overall survival (p < 0.01) when antibodies were injected directly into the glioma core, whereas peripheral antibody treatment only had a modest effect. Peripheral treatment resulted in a significant decrease in granzyme B (GrB) expression by Tregs, whereas intratumoral treatment resulted in both a decrease in GrB expression by Tregs and their selective depletion, which was largely mediated by FcγR-mediated destruction. We also discovered that anti-GITR treatment results in the enhanced survival and functionality of dendritic cells (DCs)-a previously unreported effect of this immunotherapy. In effect, this study demonstrates that the targeting of GITR is a feasible and noteworthy treatment option for glioma, but is largely dependent on the anatomical location in which the antibodies are delivered.

CCL2 Produced by the Glioma Microenvironment Is Essential for the Recruitment of Regulatory T Cells and Myeloid-Derived Suppressor Cells

In many aggressive cancers, such as glioblastoma multiforme, progression is enabled by local immunosuppression driven by the accumulation of regulatory T cells (Treg) and myeloid-derived suppressor cells (MDSC). However, the mechanistic details of how Tregs and MDSCs are recruited in various tumors are not yet well understood. Here we report that macrophages and microglia within the glioma microenvironment produce CCL2, a chemokine that is critical for recruiting both CCR4⁺ Treg and CCR2⁺Ly-6C⁺ monocytic MDSCs in this disease setting. In murine gliomas, we established novel roles for tumor-derived CCL20 and osteoprotegerin in inducing CCL2 production from macrophages and microglia. Tumors grown in CCL2-deficient mice failed to maximally accrue Tregs and monocytic MDSCs. In mixed-bone marrow chimera assays, we found that CCR4-deficient Treg and CCR2-deficient monocytic MDSCs were defective in glioma accumulation. Furthermore, administration of a small-molecule antagonist of CCR4 improved median survival in the model. In clinical specimens of glioblastoma multiforme, elevated levels of CCL2 expression correlated with reduced overall survival of patients. Finally, we found that CD163-positive infiltrating macrophages were a major source of CCL2 in glioblastoma multiforme patients. Collectively, our findings show how glioma cells influence the tumor microenvironment to recruit potent effectors of immunosuppression that drive progression. Cancer Res; 76(19); 5671-82. ©2016 AACR.

Single dose GLP toxicity and biodistribution study of a conditionally replicative adenovirus vector, CRAd-S-pk7, administered by intracerebral injection to Syrian hamsters

Background

CRAd-S-pk7 is a conditionally replicative oncolytic adenoviral vector that contains a survivin promoter and a pk7 fiber modification that confer tumor-specific transcriptional targeting and preferential replication in glioma while sparing the surrounding normal brain parenchyma.

Methods

This IND-enabling study performed under GLP conditions evaluated the toxicity and biodistribution of CRAd-S-pk7 administered as a single intracerebral dose to Syrian hamsters, a permissive model of adenoviral replication. Two hundred and forty animals were stereotactically administered either vehicle (n = 60) or CRAd-S-pk7 at 2.5 × 10⁷, 2.5 × 10⁸, or 2.5 × 10⁹ viral particles (vp)/animal (each n = 60) on day 1. The animals were closely monitored for toxicology evaluation, assessment of viral distribution, and immunogenicity of CRAd-S-pk7.

Results

Changes in hematology, clinical chemistry, and coagulation parameters were minor and transient, and consistent with the inflammatory changes observed microscopically. These changes were considered to be of little toxicological significance. The vector remained localized primarily in the brain and to some degree in the tissues at the incision site. Low levels of vector DNA were detected in other tissues in a few animals suggesting systemic circulation of the virus. Viral DNA was detected in brains of hamsters for up to 62 days. However, microscopic changes and virus-related toxicity to the central nervous system were considered minor and decreased in incidence and severity over time. Such changes are not uncommon in studies using adenoviral vectors.

Conclusion

This study provides safety and toxicology data justifying a clinical trial of CRAd-S-pk7 loaded in FDA-approved HB1.F3.CD neural stem cell carriers administered at the tumor resection bed in humans with recurrent malignant glioma.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-016-0895-8) contains supplementary material, which is available to authorized users.

A Genetically Modified Adenoviral Vector with a Phage Display-Derived Peptide Incorporated into Fiber Fibritin Chimera Prolongs Survival in Experimental Glioma

The dismal clinical context of advanced-grade glioma demands the development of novel therapeutic strategies with direct patient impact. Adenovirus-mediated virotherapy represents a potentially effective approach for glioma therapy. In this research, we generated a novel glioma-specific adenovirus by instituting more advanced genetic modifications that can maximize the efficiency and safety of therapeutic adenoviral vectors. In this regard, a glioma-specific targeted fiber was developed through the incorporation of previously published glioma-specific, phage-panned peptide (VWT peptide) on a fiber fibritin-based chimeric fiber, designated as "GliomaFF." We showed that the entry of this virus was highly restricted to glioma cells, supporting the specificity imparted by the phage-panned peptide. In addition, the stability of the targeting moiety presented by fiber fibritin structure permitted greatly enhanced infectivity. Furthermore, the replication of this virus was restricted in glioma cells by controlling expression of the E1 gene under the activity of the tumor-specific survivin promoter. Using this approach, we were able to explore the combinatorial efficacy of various adenoviral modifications that could amplify the specificity, infectivity, and exclusive replication of this therapeutic adenovirus in glioma. Finally, virotherapy with this modified virus resulted in up to 70% extended survival in an in vivo murine glioma model. These data demonstrate that this novel adenoviral vector is a safe and efficient treatment for this difficult malignancy.

Dendritic Cell-Based Vaccines that Utilize Myeloid Rather than Plasmacytoid Cells Offer a Superior Survival Advantage in Malignant Glioma

Dendritic cells (DCs) are professional APCs that are traditionally divided into two distinct subsets, myeloid DC (mDCs) and plasmacytoid DC (pDCs). pDCs are known for their ability to secrete large amounts of IFN-α. Apart from IFN-α production, pDCs can also process Ag and induce T cell immunity or tolerance. In several solid tumors, pDCs have been shown to play a critical role in promoting tumor immunosuppression. We investigated the role of pDCs in the process of glioma progression in the syngeneic murine model of glioma. We show that glioma-infiltrating pDCs are the major APC in glioma and are deficient in IFN-α secretion (p < 0.05). pDC depletion leads to increased survival of the mice bearing intracranial tumor by decreasing the number of regulatory T cells (Tregs) and by decreasing the suppressive capabilities of Tregs. We subsequently compared the ability of mDCs and pDCs to generate effective antiglioma immunity in a GL261-OVA mouse model of glioma. Our data suggest that mature pDCs and mDCs isolated from naive mice can be effectively activated and loaded with SIINFEKL Ag in vitro. Upon intradermal injection in the hindleg, a fraction of both types of DCs migrate to the brain and lymph nodes. Compared to mice vaccinated with pDC or control mice, mice vaccinated with mDCs generate a robust Th1 type immune response, characterized by high frequency of CD4(+)T-bet(+) T cells and CD8(+)SIINFEKEL(+) T cells. This robust antitumor T cell response results in tumor eradication and long-term survival in 60% of the animals (p < 0.001).

Intratumoral oncolytic adenoviral treatment modulates the glioma microenvironment and facilitates systemic tumor-antigen-specific T cell therapy

Glioblastoma multiforme (GBM) is the most aggressive form of primary brain tumor and is associated with poor survival. Virotherapy is a promising candidate for the development of effective, novel treatments for GBM. Recent studies have underscored the potential of virotherapy in enhancing antitumor immunity despite the fact that its mechanisms remain largely unknown. Here, using a syngeneic GBM mouse model, we report that intratumoral virotherapy significantly modulates the tumor microenvironment. We found that intratumoral administration of an oncolytic adenovirus, AdCMVdelta24, decreased tumor-infiltrating CD4⁺ Foxp3⁺ regulatory T cells (Tregs) and increased IFNγ-producing CD8⁺ T cells in treated tumors, even in late stage disease in which a highly immunosuppressive tumor microenvironment is considered to be a significant barrier to immunotherapy. Importantly, intratumoral AdCMVdelta24 treatment augmented systemically transferred tumor-antigen-specific T cell therapy. Furthermore, mechanistic studies showed (1) downregulation of Foxp3 in Tregs that were incubated with media conditioned by virus-infected tumor cells, (2) downregulation of indoleamine 2,3 dioxygenase 1 (IDO) in glioma cells upon infection by AdCMVdelta24, and (3) reprograming of Tregs from an immunosuppressive to a stimulatory state. Taken together, our findings demonstrate the potency of intratumoral oncolytic adenoviral treatment in enhancing antitumor immunity through the regulation of multiple aspects of immune suppression in the context of glioma, supporting further clinical development of oncolytic adenovirus-based immune therapies for malignant brain cancer.

Neural stem cell-mediated delivery of oncolytic adenovirus

The use of stem cells (SCs) as carriers for therapeutic agents has now progressed to early clinical trials. These clinical trials exploring SC-mediated delivery of oncolytic adenoviruses will commence in the near future, hopefully yielding meritorious results that can provoke further scientific inquiry. Preclinical animal studies have demonstrated that SCs can be successfully loaded with conditionally-replicative adenoviruses and delivered to the tumor, whereupon they may evoke pronounced therapeutic efficacy. In this protocol, we describe the maintenance of SCs, provide an analysis of optimal adenoviral titers for SC loading, and evaluate the optimized viral loading on SCs.

Redox regulation of mitophagy in the lung during murine Staphylococcus aureus sepsis

Oxidative mitochondrial damage is closely linked to inflammation and cell death, but low levels of reactive oxygen and nitrogen species serve as signals that involve mitochondrial repair and resolution of inflammation. More specifically, cytoprotection relies on the elimination of damaged mitochondria by selective autophagy (mitophagy) during mitochondrial quality control. This aim of this study was to identify and localize mitophagy in the mouse lung as a potentially upregulatable redox response to Staphylococcus aureus sepsis. Fibrin clots loaded with S. aureus (1×10(7) CFU) were implanted abdominally into anesthetized C57BL/6 and B6.129X1-Nfe2l2tm1Ywk/J (Nrf2(-/-)) mice. At the time of implantation, mice were given vancomycin (6mg/kg) and fluid resuscitation. Mouse lungs were harvested at 0, 6, 24, and 48h for bronchoalveolar lavage (BAL), Western blot analysis, and qRT-PCR. To localize mitochondria with autophagy protein LC3, we used lung immunofluorescence staining in LC3-GFP transgenic mice. In C57BL/6 mice, sepsis-induced pulmonary inflammation was detected by significant increases in mRNA for the inflammatory markers IL-1β and TNF-α at 6 and 24h, respectively. BAL cell count and protein also increased. Sepsis suppressed lung Beclin-1 protein, but not mRNA, suggesting activation of canonical autophagy. Notably sepsis also increased the LC3-II autophagosome marker, as well as the lung׳s noncanonical autophagy pathway as evidenced by loss of p62, a redox-regulated scaffolding protein of the autophagosome. In LC3-GFP mouse lungs, immunofluorescence staining showed colocalization of LC3-II to mitochondria, mainly in type 2 epithelium and alveolar macrophages. In contrast, marked accumulation of p62, as well as attenuation of LC3-II in Nrf2-knockout mice supported an overall decrease in autophagic turnover. The downregulation of canonical autophagy during sepsis may contribute to lung inflammation, whereas the switch to noncanonical autophagy selectively removes damaged mitochondria and accompanies tissue repair and cell survival. Furthermore, mitophagy in the alveolar region appears to depend on activation of Nrf2. Thus, efforts to promote mitophagy may be a useful therapeutic adjunct for acute lung injury in sepsis.

The role of IDO in brain tumor immunotherapy

Malignant glioma comprises the majority of primary brain tumors. Coincidently, most of those malignancies express an inducible tryptophan catabolic enzyme, indoleamine 2,3 dioxygenase 1 (IDO1). While IDO1 is not normally expressed at appreciable levels in the adult central nervous system, it's rapidly induced and/or upregulated upon inflammatory stimulus. The primary function of IDO1 is associated with conversion of the essential amino acid, tryptophan, into downstream catabolites known as kynurenines. The depletion of tryptophan and/or accumulation of kynurenine has been shown to induce T cell deactivation, apoptosis and/or the induction of immunosuppressive programming via the expression of FoxP3. This understanding has informed immunotherapeutic design for the strategic development of targeted molecular therapeutics that inhibit IDO1 activity. Here, we review the current knowledge of IDO1 in brain tumors, pre-clinical studies targeting this enzymatic pathway, alternative tryptophan catabolic mediators that compensate for IDO1 loss and/or inhibition, as well as proposed clinical strategies and questions that are critical to address for increasing future immunotherapeutic effectiveness in patients with incurable brain cancer.

Abstract 1091: CCR4+ regulatory T cells progressively accumulate in the presence of leukocyte-derived CCL22/CCL17 in an experimental model of glioblastoma multiforme

Glioblastoma multiforme (GBM) is the most common malignant primary brain tumor in adults. One hallmark of GBM is the accumulation of infiltrating regulatory T cells (Tregs), a highly immunosuppressive T cell subset that suppresses T cell-mediated GBM rejection. Previous work has demonstrated that the Treg-recruiting chemokine, CCL22, is expressed by patient-resected GBM. Importantly, the number of Tregs expressing CCR4, the cognate chemokine receptor for CCL22 and CCL17, is increased in the peripheral blood of GBM patients. To investigate the therapeutic potential of targeting the CCR4 chemokine-receptor axis in brain tumors, we performed FACS analysis to determine the level of CCR4-expressing Tregs and other T cell populations. In addition, we analyzed the mRNA expression levels of the Treg-recruiting chemokines CCL22 and CCL17 in different cellular populations by Percoll gradient isolation followed by qRT-PCR. Using the orthotopic GL261 model of GBM, we found a progressive accumulation of CCR4+ Tregs in the brain tumor. Coincidently, the expression of CCL17 and CCL22 was predominantly localized to the tumor-infiltrating leukocytes, rather than the tumor tissue itself. Finally, we tested the potential impact of a CCR4 antagonist on Treg recruitment and CD8+/Foxp3+ T cell ratio. We found that CCR4 antagonist treatment was able to increase the CD8+/Foxp3+ T cell ratio during the early time points post-GL261 injection. These results implicate the chemokine receptor CCR4 as a therapeutic target for further investigation as a potential strategy to target Treg trafficking to GBM.

Citation Format: Alan L. Chang, Derek A. Wainwright, Mahua Dey, Yu Han, Maciej S. Lesniak. CCR4+ regulatory T cells progressively accumulate in the presence of leukocyte-derived CCL22/CCL17 in an experimental model of glioblastoma multiforme. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1091. doi:10.1158/1538-7445.AM2014-1091

Development of a novel preclinical model of pneumococcal pneumonia in nonhuman primates

Pneumococcal pneumonia is a leading cause of bacterial infection and death worldwide. Current diagnostic tests for detecting Streptococcus pneumoniae can be unreliable and can mislead clinical decision-making and treatment. To address this concern, we developed a preclinical model of pneumococcal pneumonia in nonhuman primates useful for identifying novel biomarkers, diagnostic tests, and therapies for human S. pneumoniae infection. Adult colony-bred baboons (n = 15) were infected with escalating doses of S. pneumoniae (Serotype 19A-7). We characterized the pathophysiological and serological profiles of healthy and infected animals over 7 days. Pneumonia was prospectively defined by the presence of three criteria: (1) change in white blood cell count, (2) isolation of S. pneumoniae from bronchoalveolar lavage fluid (BALF) or blood, and (3) concurrent signs/symptoms of infection. Animals given 10(9) CFU consistently met our definition and developed a phenotype of tachypnea, tachycardia, fever, hypoxemia, and radiographic lobar infiltrates at 48 hours. BALF and plasma cytokines, including granulocyte colony-stimulating factor, IL-6, IL-10, and IL-1ra, peaked at 24 to 48 hours. At necropsy, there was lobar consolidation with frequent pleural involvement. Lung histopathology showed alveolar edema and macrophage influx in areas of organizing pneumonia. Hierarchical clustering of peripheral blood RNA data at 48 hours correctly identified animals with and without pneumonia. Dose-dependent inoculation of baboons with S. pneumoniae produces a host response ranging from spontaneous clearance (10(6) CFU) to severe pneumonia (10(9) CFU). Selected BALF and plasma cytokine levels and RNA profiles were associated with severe pneumonia and may provide clinically useful parameters after validation.

Durable therapeutic efficacy utilizing combinatorial blockade against IDO, CTLA-4, and PD-L1 in mice with brain tumors

PURPOSE

Glioblastoma (GBM) is the most common form of malignant glioma in adults. Although protected by both the blood-brain and blood-tumor barriers, GBMs are actively infiltrated by T cells. Previous work has shown that IDO, CTLA-4, and PD-L1 are dominant molecular participants in the suppression of GBM immunity. This includes IDO-mediated regulatory T-cell (Treg; CD4(+)CD25(+)FoxP3(+)) accumulation, the interaction of T-cell-expressed, CTLA-4, with dendritic cell-expressed, CD80, as well as the interaction of tumor- and/or macrophage-expressed, PD-L1, with T-cell-expressed, PD-1. The individual inhibition of each pathway has been shown to increase survival in the context of experimental GBM. However, the impact of simultaneously targeting all three pathways in brain tumors has been left unanswered.

EXPERIMENTAL DESIGN AND RESULTS

In this report, we demonstrate that, when dually challenged, IDO-deficient tumors provide a selectively competitive survival advantage against IDO-competent tumors. Next, we provide novel observations regarding tryptophan catabolic enzyme expression, before showing that the therapeutic inhibition of IDO, CTLA-4, and PD-L1 in a mouse model of well-established glioma maximally decreases tumor-infiltrating Tregs, coincident with a significant increase in T-cell-mediated long-term survival. In fact, 100% of mice bearing intracranial tumors were long-term survivors following triple combination therapy. The expression and/or frequency of T cell expressed CD44, CTLA-4, PD-1, and IFN-γ depended on timing after immunotherapeutic administration.

CONCLUSIONS

Collectively, these data provide strong preclinical evidence that combinatorially targeting immunosuppression in malignant glioma is a strategy that has high potential value for future clinical trials in patients with GBM.

Radiation dose for 345 CT-guided interlaminar lumbar epidural steroid injections

BACKGROUND AND PURPOSE

CT guidance is increasingly being used to localize the epidural space during epidural steroid injections. A common concern is that CT may be associated with significantly higher radiation doses compared with conventional fluoroscopy. The goal of this retrospective study was to determine the average dose-length product and effective dose delivered while interlaminar epidural steroid injections are performed and allow comparison with other modalities.

MATERIALS AND METHODS

A total of 281 patients who had undergone 345 consecutive CT-guided epidural steroid injections of the lumbar spine were evaluated for radiation exposure. The dose-length product for each scan was derived from the CT dose index volume and scan length. Effective dose was then calculated from the dose-length product and a κ coefficient of 0.015. Procedure time was calculated from the PACS time stamp on the scout image to the last CT image of the last image series.

RESULTS

The average dose-length product across all procedures was 89.6 ± 3.33 mGy·cm, which represents an effective dose of 1.34 ± 0.05 mSv. No complications from the procedure were observed, and average procedure time was 8 minutes.

CONCLUSIONS

The use of a stationary table and an intermittent scanning technique allow for short procedures and doses that are significantly lower than those of conventional diagnostic CT scans. Furthermore, because CT dose index overestimates radiation dose in stationary table procedures, the actual radiation dose may be even lower than stated here.

IDO expression in brain tumors increases the recruitment of regulatory T cells and negatively impacts survival

PURPOSE

Glioblastoma multiforme (GBM) is an aggressive adult brain tumor with a poor prognosis. One hallmark of GBM is the accumulation of immunosuppressive and tumor-promoting CD4(+)FoxP3(+)GITR(+) regulatory T cells (Tregs). Here, we investigated the role of indoleamine 2,3 dioxygenase (IDO) in brain tumors and the impact on Treg recruitment.

EXPERIMENTAL DESIGN

To determine the clinical relevance of IDO expression in brain tumors, we first correlated patient survival to the level of IDO expression from resected glioma specimens. We also used novel orthotopic and transgenic models of glioma to study how IDO affects Tregs. The impact of tumor-derived and peripheral IDO expression on Treg recruitment, GITR expression, and long-term survival was determined.

RESULTS

Downregulated IDO expression in glioma predicted a significantly better prognosis in patients. Coincidently, both IDO-competent and deficient mice showed a survival advantage bearing IDO-deficient brain tumors, when compared with IDO-competent brain tumors. Moreover, IDO deficiency was associated with a significant decrease in brain-resident Tregs, both in orthotopic and transgenic mouse glioma models. IDO deficiency was also associated with lower GITR expression levels on Tregs. Interestingly, the long-term survival advantage conferred by IDO deficiency was lost in T-cell-deficient mice.

CONCLUSIONS

These clinical and preclinical data confirm that IDO expression increases the recruitment of immunosuppressive Tregs that lead to tumor outgrowth. In contrast, IDO deficiency decreases Treg recruitment and enhances T-cell-mediated tumor rejection. Thus, the data suggest a critical role for IDO-mediated immunosuppression in glioma and support the continued investigation of IDO-Treg interactions in the context of brain tumors.

Quality of life of adults with intellectual disabilities who live with families in Taiwan

BACKGROUND

Little research has been conducted about the quality of life (QOL) of people with intellectual disabilities (ID) in Taiwan, particularly their subjective QOL. This study examined the personal perceptions of these individuals as measured on internationally recognized core QOL domains and indicators.

METHODS

A census interview survey was conducted in Hsin-Chu City in Taiwan; 233 adults aged over 16 years with mild ID and living with their families participated in the study. Data were collected using the Cross-Cultural QOL Indicators (CCQOLI) together with socio-demographic data that included 'activities of daily living' and 'instrumental activities of daily living' (IADL). The CCQOLI were based on the three most commonly reported indicators of each of the eight QOL domains: emotional well-being, interpersonal relations, material well-being, personal development, physical well-being, self-determination, social inclusion and rights. Each indicator has two sets of questions related to the indicator's 'importance' and 'use'. These are answered by the respondent using a 4-point Likert scale.

RESULTS

The importance and use of the QOL indicators were evaluated positively by the respondents. The adults' individual characteristics, namely IADL and educational level, were significant predictors for the 'importance' while the adults' perceptions of 'use' for overall QOL were significantly affected by his/her socio-economic data, that is, residence location and father's educational level.

CONCLUSIONS

The present study addressed the issue of self-reported QOL in people with ID in Taiwanese society, becoming a possible benchmark for similar measurements carried out by disability movements there. These results contribute to current advocacy efforts towards creating a supportive environment for people with ID.

A comparative resonance Raman analysis of heme-binding PAS domains: heme iron coordination structures of the BjFixL, AxPDEA1, EcDos, and MtDos proteins

The heme-PAS is a specialized domain with which a broad class of signal-transducing heme proteins detect physiological heme ligands. Such domains exhibit a wide range of ligand binding parameters, yet they are all expected to feature an alpha-beta heme binding fold and a predominantly hydrophobic heme distal pocket without a distal histidine. We have compared, for the first time, the resonance Raman spectra of several heme-PASs: the heme-binding domains of Bradyrhizobium japonicum FixL, Escherichia coli Dos, Acetobacter xylinum PDEA1, and Methanobacterium thermoautotrophicum Dos. In all cases, the nu(Fe)-(CO) and nu(C-O) values of the carbonmonoxy forms were consistent with coordination of the heme iron to histidine on the proximal side and binding of the CO without electrostatic interaction with the heme distal pocket. EcDos was unusual in having predominantly hexacoordinate heme iron in the deoxy and met forms. Despite an evident lack of CO interaction with the EcDos heme pocket, relatively low Fe-O(2) (562 cm(-1)) and N-O (1576 cm(-1)) stretching frequencies indicated that strong polar interactions with that heme distal pocket are possible for highly bent ligands such as O(2) or NO. None of the newly studied NO adducts exhibited evidence of the Fe-His rupture and pentacoordination previously noted for Sinorhizobium meliloti FixL. A low Fe-His stretching frequency, formerly interpreted as a strained Fe-His bond, and the slow association of O(2) with S. meliloti FixL failed to correlate with the newly studied proteins having low association rate or low equilibrium association constants for binding of O(2). We conclude that although heme-PASs share some features, they represent distinct signal transduction mechanisms.

Phosphodiesterase A1, a regulator of cellulose synthesis in Acetobacter xylinum, is a heme-based sensor

The phosphodiesterase A1 protein of Acetobacter xylinum, AxPDEA1, is a key regulator of bacterial cellulose synthesis. This phosphodiesterase linearizes cyclic bis(3'-->5')diguanylic acid, an allosteric activator of the bacterial cellulose synthase, to the ineffectual pGpG. Here we show that AxPDEA1 contains heme and is regulated by reversible binding of O(2) to the heme. Apo-AxPDEA1 has less than 2% of the phosphodiesterase activity of holo-AxPDEA1, and reconstitution with hemin restores full activity. O(2) regulation is due to deoxyheme being a better activator than oxyheme. AxPDEA1 is homologous to the Escherichia coli direct oxygen sensor protein, EcDos, over its entire length and is homologous to the FixL histidine kinases over only a heme-binding PAS domain. The properties of the heme-binding domain of AxPDEA1 are significantly different from those of other O(2)-responsive heme-based sensors. The rate of AxPDEA1 autoxidation (half-life > 12 h) is the slowest observed so far for this type of heme protein fold. The O(2) affinity of AxPDEA1 (K(d) approximately 10 microM) is comparable to that of EcDos, but the rate constants for O(2) association (k(on) = 6.6 microM(-)(1) s(-)(1)) and dissociation (k(off) = 77 s(-)(1)) are 2000 times higher. Our results illustrate the versatility of signal transduction mechanisms for the heme-PAS class of O(2) sensors and provide the first example of O(2) regulation of a second messenger.