Deep Immune Profiling of an Arginine-Enriched Nutritional Intervention in Patients Undergoing Surgery

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.

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.

Signature for Pain Recovery IN Teens (SPRINT): protocol for a multisite prospective signature study in chronic musculoskeletal pain

INTRODUCTION

Current treatments for chronic musculoskeletal (MSK) pain are suboptimal. Discovery of robust prognostic markers separating patients who recover from patients with persistent pain and disability is critical for developing patient-specific treatment strategies and conceiving novel approaches that benefit all patients. Given that chronic pain is a biopsychosocial process, this study aims to discover and validate a robust prognostic signature that measures across multiple dimensions in the same adolescent patient cohort with a computational analysis pipeline. This will facilitate risk stratification in adolescent patients with chronic MSK pain and more resourceful allocation of patients to costly and potentially burdensome multidisciplinary pain treatment approaches.

METHODS AND ANALYSIS

Here we describe a multi-institutional effort to collect, curate and analyse a high dimensional data set including epidemiological, psychometric, quantitative sensory, brain imaging and biological information collected over the course of 12 months. The aim of this effort is to derive a multivariate model with strong prognostic power regarding the clinical course of adolescent MSK pain and function.

ETHICS AND DISSEMINATION

The study complies with the National Institutes of Health policy on the use of a single internal review board (sIRB) for multisite research, with Cincinnati Children's Hospital Medical Center Review Board as the reviewing IRB. Stanford's IRB is a relying IRB within the sIRB. As foreign institutions, the University of Toronto and The Hospital for Sick Children (SickKids) are overseen by their respective ethics boards. All participants provide signed informed consent. We are committed to open-access publication, so that patients, clinicians and scientists have access to the study data and the signature(s) derived. After findings are published, we will upload a limited data set for sharing with other investigators on applicable repositories.

TRIAL REGISTRATION NUMBER

NCT04285112.

NAMPT-targeting PROTAC promotes antitumor immunity via suppressing myeloid-derived suppressor cell expansion

Nicotinamide phosphoribosyl transferase (NAMPT) is considered as a promising target for cancer therapy given its critical engagement in cancer metabolism and inflammation. However, therapeutic benefit of NAMPT enzymatic inhibitors appears very limited, likely due to the failure to intervene non-enzymatic functions of NAMPT. Herein, we show that NAMPT dampens antitumor immunity by promoting the expansion of tumor infiltrating myeloid derived suppressive cells (MDSCs) via a mechanism independent of its enzymatic activity. Using proteolysis-targeting chimera (PROTAC) technology, PROTAC A7 is identified as a potent and selective degrader of NAMPT, which degrades intracellular NAMPT (iNAMPT) via the ubiquitin–proteasome system, and in turn decreases the secretion of extracellular NAMPT (eNAMPT), the major player of the non-enzymatic activity of NAMPT. In vivo, PROTAC A7 efficiently degrades NAMPT, inhibits tumor infiltrating MDSCs, and boosts antitumor efficacy. Of note, the anticancer activity of PROTAC A7 is superior to NAMPT enzymatic inhibitors that fail to achieve the same impact on MDSCs. Together, our findings uncover the new role of enzymatically-independent function of NAMPT in remodeling the immunosuppressive tumor microenvironment, and reports the first NAMPT PROTAC A7 that is able to block the pro-tumor function of both iNAMPT and eNAMPT, pointing out a new direction for the development of NAMPT-targeted therapies.

Measuring the human immune response to surgery: multiomics for the prediction of postoperative outcomes

PURPOSE OF REVIEW

Postoperative complications including infections, cognitive impairment, and protracted recovery occur in one-third of the 300 million surgeries performed annually worldwide. Complications cause personal suffering along with a significant economic burden on our healthcare system. However, the accurate prediction of postoperative complications and patient-targeted interventions for their prevention remain as major clinical challenges.

RECENT FINDINGS

Although multifactorial in origin, the dysregulation of immunological mechanisms that occur in response to surgical trauma is a key determinant of postoperative complications. Prior research, primarily focusing on inflammatory plasma markers, has provided important clues regarding their pathogenesis. However, the recent advent of high-content, single-cell transcriptomic, and proteomic technologies has considerably improved our ability to characterize the immune response to surgery, thereby providing new means to understand the immunological basis of postoperative complications and to identify prognostic biological signatures.

SUMMARY

The comprehensive and single-cell characterization of the human immune response to surgery has significantly advanced our ability to predict the risk of postoperative complications. Multiomic modeling of patients' immune states holds promise for the discovery of preoperative predictive biomarkers, ultimately providing patients and surgeons with actionable information to improve surgical outcomes. Although recent studies have generated a wealth of knowledge, laying the foundation for a single-cell atlas of the human immune response to surgery, larger-scale multiomic studies are required to derive robust, scalable, and sufficiently powerful models to accurately predict the risk of postoperative complications in individual patients.

Harnessing the Potential of Multiomics Studies for Precision Medicine in Infectious Disease

The field of infectious diseases currently takes a reactive approach and treats infections as they present in patients. Although certain populations are known to be at greater risk of developing infection (eg, immunocompromised), we lack a systems approach to define the true risk of future infection for a patient. Guided by impressive gains in "omics" technologies, future strategies to infectious diseases should take a precision approach to infection through identification of patients at intermediate and high-risk of infection and deploy targeted preventative measures (ie, prophylaxis). The advances of high-throughput immune profiling by multiomics approaches (ie, transcriptomics, epigenomics, metabolomics, proteomics) hold the promise to identify patients at increased risk of infection and enable risk-stratifying approaches to be applied in the clinic. Integration of patient-specific data using machine learning improves the effectiveness of prediction, providing the necessary technologies needed to propel the field of infectious diseases medicine into the era of personalized medicine.

Deep immune profiling of whole blood to identify early immune signatures that correlate to patient outcome after major trauma

BACKGROUND

Major injury results in an early cascade of immunologic responses that increase susceptibility to infection and multiorgan dysfunction. Detailed immune profiling by mass cytometry has the potential to identify immune signatures that correspond to patient outcomes. Our objective was to determine the prognostic value of immune signatures early after major trauma injury.

METHODS

Trauma patients (n = 17) were prospectively enrolled between September 2018 and December 2019. Serial whole blood samples were obtained from trauma patients (mean Injury Severity Score, 26.2; standard error of the mean, 3.7) at Days 1 and 3 after injury, and from age- and sex-matched uninjured controls using a standardized protocol for fixation, storage, and labeling. Computational analyses including K-nearest neighbor automated clustering of immune cells and Spearman's correlation analysis were used to identify correlations between cell populations, clinical measures, and patient outcomes.

RESULTS

Analysis revealed nine immune cell clusters that correlated with one or more clinical outcomes. On Days 1 and 3 postinjury, the abundance of immature neutrophil and classical monocytes exhibited a strong positive correlation with increased intensive care unit and hospital length of stay. Conversely, the abundance of CD4 T-cell subsets, namely Th17 cells, is associated with improved patient outcomes including decreased ventilator days (r = -0.76), hospital-acquired pneumonia (r = -0.69), and acute kidney injury (r = -0.73).

CONCLUSION

Here, we provide a comprehensive multitime point immunophenotyping analysis of whole blood from patients soon after traumatic injury to determine immune correlates of adverse outcomes. Our findings indicate that alterations in myeloid-origin cell types may contribute to immune dysfunction after injury. Conversely, the presence of effector T cell populations corresponds with decreased hospital length of stay and organ dysfunction. Overall, these data identify novel immune signatures following traumatic injury that support the view that monitoring of immune (sub)-populations may provide clinical decision-making support for at-risk patients early in their hospital course.

LEVEL OF EVIDENCE

Prognostic/Epidemiologic, Level IV.

Multi-Biomarker Prediction Models for Multiple Infection Episodes Following Blunt Trauma

Severe trauma predisposes patients to multiple independent infection episodes (MIIEs), leading to augmented morbidity and mortality. We developed a method to identify increased MIIE risk before clinical signs appear, which is fundamentally different from existing approaches entailing infections' detection after their establishment. Applying machine learning algorithms to genome-wide transcriptome data from 128 adult blunt trauma patients' (42 MIIE cases and 85 non-cases) leukocytes collected ≤48 hr of injury and ≥3 days before any infection, we constructed a 15-transcript and a 26-transcript multi-biomarker panel model with the least absolute shrinkage and selection operator (LASSO) and Elastic Net, respectively, which accurately predicted MIIE (Area Under Receiver Operating Characteristics Curve [AUROC] [95% confidence intervals, CI]: 0.90 [0.84–0.96] and 0.92 [0.86–0.96]) and significantly outperformed clinical models. Gene Ontology and network analyses found various pathways to be relevant. External validation found our model to be generalizable. Our unique precision medicine approach can be applied to a wide range of patient populations and outcomes.

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We describe a method for predicting multiple independent infection episodes (MIIEs).

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We applied machine learning algorithms to transcriptome data to develop models

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The biomarker prediction models significantly outperformed clinical models

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External validation in another trauma cohort found evidence of generalizability

Preferential inhibition of adaptive immune system dynamics by glucocorticoids in patients after acute surgical trauma

Glucocorticoids (GC) are a controversial yet commonly used intervention in the clinical management of acute inflammatory conditions, including sepsis or traumatic injury. In the context of major trauma such as surgery, concerns have been raised regarding adverse effects from GC, thereby necessitating a better understanding of how GCs modulate the immune response. Here we report the results of a randomized controlled trial (NCT02542592) in which we employ a high-dimensional mass cytometry approach to characterize innate and adaptive cell signaling dynamics after a major surgery (primary outcome) in patients treated with placebo or methylprednisolone (MP). A robust, unsupervised bootstrap clustering of immune cell subsets coupled with random forest analysis shows profound (AUC = 0.92, p-value = 3.16E-8) MP-induced alterations of immune cell signaling trajectories, particularly in the adaptive compartments. By contrast, key innate signaling responses previously associated with pain and functional recovery after surgery, including STAT3 and CREB phosphorylation, are not affected by MP. These results imply cell-specific and pathway-specific effects of GCs, and also prompt future studies to examine GCs' effects on clinical outcomes likely dependent on functional adaptive immune responses.

Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities

Pain medication plays an important role in the treatment of acute and chronic pain conditions, but some drugs, opioids in particular, have been overprescribed or prescribed without adequate safeguards, leading to an alarming rise in medication-related overdose deaths. The NIH Helping to End Addiction Long-term (HEAL) Initiative is a trans-agency effort to provide scientific solutions to stem the opioid crisis. One component of the initiative is to support biomarker discovery and rigorous validation in collaboration with industry leaders to accelerate high-quality clinical research into neurotherapeutics and pain. The use of objective biomarkers and clinical trial end points throughout the drug discovery and development process is crucial to help define pathophysiological subsets of pain, evaluate target engagement of new drugs and predict the analgesic efficacy of new drugs. In 2018, the NIH-led Discovery and Validation of Biomarkers to Develop Non-Addictive Therapeutics for Pain workshop convened scientific leaders from academia, industry, government and patient advocacy groups to discuss progress, challenges, gaps and ideas to facilitate the development of biomarkers and end points for pain. The outcomes of this workshop are outlined in this Consensus Statement.

Stabilization of blood for long-term storage can affect antibody-based recognition of cell surface markers

Whole-blood fixation provides a rapid and simplified method for cell preservation compared to isolation of peripheral blood mononuclear cells (PBMCs). This can be especially important for sample acquisition and storage in resource-limited settings. However, some caveats have been reported, such as reduced cell marker recognition. Here, we evaluated the whole-blood proteomic stabilizer PROT1 and compared recognition of 53 common cell markers in fixed buffy coats and cryopreserved PBMCs isolated from the same donor. Several antibodies completely lost their binding to the cells, while others presented with partial loss of marker recognition or no effect at all. Based on the screened antibodies, we designed two antibody panels allowing phenotyping of B cells, monocytes, and dendritic cells and also T cells and NK cells in both fixed and non-fixed material. Taken together, our observations suggest that antibodies intended to be used with fixed blood first need to be evaluated for marker recognition and staining intensity, in comparison with fresh samples or cryopreserved PBMCs.

Denver and Marshall scores successfully predict susceptibility to multiple independent infections in trauma patients

Trauma patients are at risk of repeated hospital-acquired infections, however predictive scores aiming to identify susceptibility to such infections are lacking. The objective of this study was to investigate whether commonly employed disease-severity scores can successfully predict susceptibility to multiple independent infectious episodes (MIIEs) among trauma patients. A secondary analysis of data derived from the prospective, longitudinal study "Inflammation and the Host Response to Injury" ("Glue Grant") was performed. 1,665 trauma patients, older than 16, were included. Patients who died within seven days from the time of injury were excluded. Five commonly used disease-severity scores [Denver, Marshall, Acute Physiology and Chronic Health Evaluation II (APACHE II), Injury Severity Score (ISS), and New Injury Severity Score (NISS)] were examined as independent predictors of susceptibility to MIIEs. The latter was defined as two or more independent infectious episodes during the index hospital stay. Multivariable logistic regression was used for the statistical analysis. 22.58% of the population was found to be susceptible to MIIEs. Denver and Marshall scores were highly predictive of the MIIE status. For every 1-unit increase in the Denver or the Marshall score, there was a respective 15% (Odds Ratio:1.15; 95% CI: 1.07-1.24; p < 0.001) or 16% (Odds Ratio:1.16; 95% CI: 1.09-1.24; p < 0.001) increase in the odds of MIIE occurrence. APACHE II, ISS, and NISS were not independent predictors of susceptibility to MIIEs. In conclusion, the Denver and Marshall scores can reliably predict which trauma patients are prone to MIIEs, prior to any clinical sign of infection. Early identification of these individuals would potentially allow the implementation of rapid, personalized, preventative measures, thus improving patient outcomes and reducing healthcare costs.

Lactoferrin deficiency induces a pro-metastatic tumor microenvironment through recruiting myeloid-derived suppressor cells in mice

Lactoferrin, an innate immunity molecule, is involved in anti-inflammatory, anti-microbial, and anti-tumor activities. We previously reported that lactoferrin is downregulated in specimens of nasopharyngeal carcinoma and negatively associated with tumor progression and metastasis of patients with nasopharyngeal carcinoma. However, the relationship between lactoferrin and the pro-metastatic microenvironment has not been reported yet. Here, by using the lactoferrin knockout mouse, we found that lactoferrin deficiency facilitated melanoma cells metastasizing to lungs, through recruiting myeloid-derived suppressor cells (MDSCs) in the lungs. Mechanistic studies showed that in the lung microenvironment of the lactoferrin knockout mice, the TLR9 signaling was the most repressed signaling. Lactoferrin can induce MDSCs differentiation and apoptosis, as well as upregulate TLR9 expression. TLR9 agonist or lactoferrin treatment can rescue this phenotype in the tumor metastasis mouse model. Our results suggest a protective role of lactoferrin in cancer metastasis, along with a deficiency in certain components of the innate immune system, may lead to a pro-metastatic tumor microenvironment.

Systemic Immunologic Consequences of Chronic Periodontitis

Chronic periodontitis (ChP) is a prevalent inflammatory disease affecting 46% of the US population. ChP produces a profound local inflammatory response to dysbiotic oral microbiota that leads to destruction of alveolar bone and tooth loss. ChP is also associated with systemic illnesses, including cardiovascular diseases, malignancies, and adverse pregnancy outcomes. However, the mechanisms underlying these adverse health outcomes are poorly understood. In this prospective cohort study, we used a highly multiplex mass cytometry immunoassay to perform an in-depth analysis of the systemic consequences of ChP in patients before (n = 28) and after (n = 16) periodontal treatment. A high-dimensional analysis of intracellular signaling networks revealed immune system-wide dysfunctions differentiating patients with ChP from healthy controls. Notably, we observed exaggerated proinflammatory responses to Porphyromonas gingivalis-derived lipopolysaccharide in circulating neutrophils and monocytes from patients with ChP. Simultaneously, natural killer cell responses to inflammatory cytokines were attenuated. Importantly, the immune alterations associated with ChP were no longer detectable 3 wk after periodontal treatment. Our findings demarcate systemic and cell-specific immune dysfunctions in patients with ChP, which can be temporarily reversed by the local treatment of ChP. Future studies in larger cohorts are needed to test the boundaries of generalizability of our results.

Beyond the message: advantages of snapshot proteomics with single-cell mass cytometry in solid tumors

Single-cell technologies that can quantify features of individual cells within a tumor are critical for treatment strategies aiming to target cancer cells while sparing or activating beneficial cells. Given that key players in protein networks are often the primary targets of precision oncology strategies, it is imperative to transcend the nucleic acid message and read cellular actions in human solid tumors. Here, we review the advantages of multiplex, single-cell mass cytometry in tissue and solid tumor investigations. Mass cytometry can quantitatively probe nearly any cellular feature or target. In discussing the ability of mass cytometry to reveal and characterize a broad spectrum of cell types, identify rare cells, and study functional behavior through protein signaling networks in millions of individual cells from a tumor, this review surveys publications of scientific advances in solid tumor biology made with the aid of mass cytometry. Advances discussed include functional identification of rare tumor and tumor-infiltrating immune cells and dissection of cellular mechanisms of immunotherapy in solid tumors and the periphery. The review concludes by highlighting ways to incorporate single-cell mass cytometry in solid tumor precision oncology efforts and rapidly developing cytometry techniques for quantifying cell location and sequenced nucleic acids.

Proof of concept study of mass cytometry in septic shock patients reveals novel immune alterations

Innovative single cell technologies such as mass cytometry (CyTOF) widen possibilities to deeply improve characterisation of immune alterations mechanisms in human diseases. So far, CyTOF has not been used in sepsis – a condition characterized by complex immune disorders. Here, we evaluated feasibility of CyTOF analysis in patients with septic shock. We designed a mass cytometry panel of 25 extracellular markers to study mononuclear cells from 5 septic shock patients and 5 healthy donors. We explored single-cell data with global and specific unsupervised approaches such as heatmaps, SPADE and viSNE. We first validated relevance of our CyTOF results by highlighting established immune hallmarks of sepsis, such as decreased monocyte HLA-DR expression and increased expressions of PD1 and PD-L1 on CD4 T cells and monocytes. We then showed that CyTOF analysis reveals novel aspects of sepsis-induced immune alterations, e.g. B cell shift towards plasma cell differentiation and uniform response of several monocyte markers defining an immune signature in septic patients. This proof of concept study demonstrates CyTOF suitability to analyse immune features of septic patients. Mass cytometry could thus represent a powerful tool to identify novel pathophysiological mechanisms and therapeutic targets for immunotherapy in septic shock patients.

What is the evidence for the use of parenteral nutrition (PN) in critically ill surgical patients: a systematic review and meta-analysis

BACKGROUND

Malnutrition is associated with poor outcomes in surgical patients and corrective enteral feeding may not be possible. This is a particular problem in the acute setting where malnutrition is prevalent. The aim of this systematic review was to evaluate the use of parenteral nutrition (PN) in critically ill surgical patients.

METHODS

This review was registered with PROSPERO (CRD42017079567). Searches of the CENTRAL, EMBASE, and MEDLINE databases were performed using a predefined strategy. Randomised trials published in English since 1995, reporting a comparison of PN vs any comparator in a critically ill surgical population were included. The primary outcome was mortality. Risk of bias was assessed using the Cochrane risk of bias tool and the Grading of Recommendations Assessment, Development and Evaluation assessment. Meta-analysis was performed using a random effects model to assess variation in mortality and length of stay.

RESULTS

Fourteen RCTs were identified; standard PN was compared vs other forms of PN in ten studies, to PN with variable dose amino acids in one, and to enteral nutrition (EN) in three. In trials comparing glutamine-supplemented PN (PN-GLN) to PN, a non-significant reduction in mortality was noted (risk difference - 0.08. 95% CI - 0.17, 0.01, p = 0.08). A trend for a reduction in length of stay was seen in PN-GLN to PN comparator (mean reduction - 2.4, 95% CI - 7.19 to 2.32 days, I² = 92%). Impact on other outcome measures varied in direction of effect.

CONCLUSIONS

PN may offer benefit in critically ill surgical patients. The size and quality of studies lead to uncertainty around the estimates of clinical effect, meaning a robust trial is required.