γ-aminobutyric acid receptor B signaling drives glioblastoma in females in an immune-dependent manner.

Sex differences in immune responses impact cancer outcomes and treatment response, including in glioblastoma (GBM). However, host factors underlying sex specific immune-cancer interactions are poorly understood. Here, we identify the neurotransmitter γ-aminobutyric acid (GABA) as a driver of GBM-promoting immune response in females. We demonstrated that GABA receptor B (GABBR) signaling enhances L-Arginine metabolism and nitric oxide synthase 2 (NOS2) expression in female granulocytic myeloid-derived suppressor cells (gMDSCs). GABBR agonist and GABA analog promoted GBM growth in females in an immune-dependent manner, while GABBR inhibition reduces gMDSC NOS2 production and extends survival only in females. Furthermore, female GBM patients have enriched GABA transcriptional signatures compared to males, and the use of GABA analogs in GBM patients is associated with worse short-term outcomes only in females. Collectively, these results highlight that GABA modulates anti-tumor immune response in a sex-specific manner, supporting future assessment of GABA pathway inhibitors as part of immunotherapy approaches.

PPIA dictates NRF2 stability to promote lung cancer progression.

Nuclear factor erythroid 2-related factor 2 (NRF2) hyperactivation has been established as an oncogenic driver in a variety of human cancers, including non-small cell lung cancer (NSCLC). However, despite massive efforts, no specific therapy is currently available to target NRF2 hyperactivation. Here, we identify peptidylprolyl isomerase A (PPIA) is required for NRF2 protein stability. Ablation of PPIA promotes NRF2 protein degradation and blocks NRF2-driven growth in NSCLC cells. Mechanistically, PPIA physically binds to NRF2 and blocks the access of ubiquitin/Kelch Like ECH Associated Protein 1 (KEAP1) to NRF2, thus preventing ubiquitin-mediated degradation. Our X-ray co-crystal structure reveals that PPIA directly interacts with a NRF2 interdomain linker via a trans-proline 174-harboring hydrophobic sequence. We further demonstrate that an FDA-approved drug, cyclosporin A (CsA), impairs the interaction of NRF2 with PPIA, inducing NRF2 ubiquitination and degradation. Interestingly, CsA interrupts glutamine metabolism mediated by the NRF2/KLF5/SLC1A5 pathway, consequently suppressing the growth of NRF2-hyperactivated NSCLC cells. CsA and a glutaminase inhibitor combination therapy significantly retard tumor progression in NSCLC patient-derived xenograft (PDX) models with NRF2 hyperactivation. Our study demonstrates that targeting NRF2 protein stability is an actionable therapeutic approach to treat NRF2-hyperactivated NSCLC.

Structurally-informed human interactome reveals proteome-wide perturbations by disease mutations.

Human genome sequencing studies have identified numerous loci associated with complex diseases. However, translating human genetic and genomic findings to disease pathobiology and therapeutic discovery remains a major challenge at multiscale interactome network levels. Here, we present a deep-learning-based ensemble framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that accurately predicts protein binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms, generating comprehensive structurally-informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods. We further systematically validated PIONEER predictions experimentally through generating 2,395 mutations and testing their impact on 6,754 mutation-interaction pairs, confirming the high quality and validity of PIONEER predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces after mapping mutations from ~60,000 germline exomes and ~36,000 somatic genomes. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from pan-cancer analysis of ~11,000 tumor whole-exomes across 33 cancer types. We show that PIONEER-predicted oncoPPIs are significantly associated with patient survival and drug responses from both cancer cell lines and patient-derived xenograft mouse models. We identify a landscape of PPI-perturbing tumor alleles upon ubiquitination by E3 ligases, and we experimentally validate the tumorigenic KEAP1-NRF2 interface mutation p.Thr80Lys in non-small cell lung cancer. We show that PIONEER-predicted PPI-perturbing alleles alter protein abundance and correlates with drug responses and patient survival in colon and uterine cancers as demonstrated by proteogenomic data from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.

GAP43-dependent mitochondria transfer from astrocytes enhances glioblastoma tumorigenicity.

The transfer of intact mitochondria between heterogeneous cell types has been confirmed in various settings, including cancer. However, the functional implications of mitochondria transfer on tumor biology are poorly understood. Here we show that mitochondria transfer is a prevalent phenomenon in glioblastoma (GBM), the most frequent and malignant primary brain tumor. We identified horizontal mitochondria transfer from astrocytes as a mechanism that enhances tumorigenesis in GBM. This transfer is dependent on network-forming intercellular connections between GBM cells and astrocytes, which are facilitated by growth-associated protein 43 (GAP43), a protein involved in neuron axon regeneration and astrocyte reactivity. The acquisition of astrocyte mitochondria drives an increase in mitochondrial respiration and upregulation of metabolic pathways linked to proliferation and tumorigenicity. Functionally, uptake of astrocyte mitochondria promotes cell cycle progression to proliferative G2/M phases and enhances self-renewal and tumorigenicity of GBM. Collectively, our findings reveal a host-tumor interaction that drives proliferation and self-renewal of cancer cells, providing opportunities for therapeutic development.

Team principles for successful interdisciplinary research teams.

Interdisciplinary research teams can be extremely beneficial when addressing difficult clinical problems. The incorporation of conceptual and methodological strategies from a variety of research disciplines and health professions yields transformative results. In this setting, the long-term goal of team science is to improve patient care, with emphasis on population health outcomes. However, team principles necessary for effective research teams are rarely taught in health professional schools. To form successful interdisciplinary research teams in cardio-oncology and beyond, guiding principles and organizational recommendations are necessary. Cardiovascular disease results in annual direct costs of $220 billion (about $680 per person in the US) and is the leading cause of death for cancer survivors, including adult survivors of childhood cancers. Optimizing cardio-oncology research in interdisciplinary research teams has the potential to aid in the investigation of strategies for saving hundreds of thousands of lives each year in the United States and mitigating the annual cost of cardiovascular disease. Despite published reports on experiences developing research teams across organizations, specialties and settings, there is no single journal article that compiles principles for cardiology or cardio-oncology research teams. In this review, recurring threads linked to working as a team, as well as optimal methods, advantages, and problems that arise when managing teams are described in the context of career development and research. The worth and hurdles of a team approach, based on practical lessons learned from establishing our multidisciplinary research team and information gleaned from relevant specialties in the development of a successful team are presented.

Multimodal machine learning in precision health: A scoping review.

Machine learning is frequently being leveraged to tackle problems in the health sector including utilization for clinical decision-support. Its use has historically been focused on single modal data. Attempts to improve prediction and mimic the multimodal nature of clinical expert decision-making has been met in the biomedical field of machine learning by fusing disparate data. This review was conducted to summarize the current studies in this field and identify topics ripe for future research. We conducted this review in accordance with the PRISMA extension for Scoping Reviews to characterize multi-modal data fusion in health. Search strings were established and used in databases: PubMed, Google Scholar, and IEEEXplore from 2011 to 2021. A final set of 128 articles were included in the analysis. The most common health areas utilizing multi-modal methods were neurology and oncology. Early fusion was the most common data merging strategy. Notably, there was an improvement in predictive performance when using data fusion. Lacking from the papers were clear clinical deployment strategies, FDA-approval, and analysis of how using multimodal approaches from diverse sub-populations may improve biases and healthcare disparities. These findings provide a summary on multimodal data fusion as applied to health diagnosis/prognosis problems. Few papers compared the outputs of a multimodal approach with a unimodal prediction. However, those that did achieved an average increase of 6.4% in predictive accuracy. Multi-modal machine learning, while more robust in its estimations over unimodal methods, has drawbacks in its scalability and the time-consuming nature of information concatenation.

Alcohol use disorder is associated with higher risks of Alzheimer's and Parkinson's diseases: A study of US insurance claims data.

Introduction: Alcohol use disorder (AUD) is on the ascendancy in the US older adult population. The association between AUD and adverse brain outcomes remains inconclusive. Method: In a retrospective cohort design using US insurance claim data (2007-2020), 129,182 individuals with AUD were matched with 129,182 controls by age, sex, race, and clinical characteristics. We investigated the association between AUD and adverse brain outcomes using Cox analysis, Kaplan-Meier analysis, and log-rank test. Results: After adjusting for covariates, AUD was associated with a higher risk of Alzheimer's disease (female adjusted hazard ratio [HR] = 1.78, 95% confidence interval [CI]: 1.68-1.90, p < 0.001; male adjusted HR = 1.80, 95% CI: 1.71-1.91, p < 0.001) and a higher risk of Parkinson's disease (female adjusted HR = 1.49, 95% CI: 1.32-1.68, p < 0.001; male adjusted HR = 1.42, 95% CI: 1.32-1.52, p < 0.001) in the overall sample. In separate analyses of Black, White, and Hispanic individuals, those with AUD had higher risk of Alzheimer's disease (adjusted HRs ≥1.58; Ps ≤ 0.001). A significantly elevated risk for Parkinson's disease was found only in the White subpopulation (female adjusted HR = 1.55, 95% CI: 1.36-1.77, p < 0.001; male adjusted HR = 1.45, 95% CI: 1.33-1.57, p < 0.001). Conclusions: AUD is associated with Alzheimer's disease. AUD is associated with Parkinson's disease in White people. Cognitive screening and neurological examination among older adults with AUD hold the promise for early detection of Alzheimer's disease and Parkinson's disease. HIGHLIGHTS: Alcohol use disorder is associated with Alzheimer's disease and dementia.Alcohol use disorder is associated with Parkinson's disease in White people.

Artificial intelligence and imaging: Opportunities in cardio-oncology.

Cardiovascular disease is a leading cause of death in cancer survivors. It is critical to apply new predictive and early diagnostic methods in this population, as this can potentially inform cardiovascular treatment and surveillance decision-making. We discuss the application of artificial intelligence (AI) technologies to cardiovascular imaging in cardio-oncology, with a particular emphasis on prevention and targeted treatment of a variety of cardiovascular conditions in cancer patients. Recently, the use of AI-augmented cardiac imaging in cardio-oncology is gaining traction. A large proportion of cardio-oncology patients are screened and followed using left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS), currently obtained using echocardiography. This use will continue to increase with new cardiotoxic cancer treatments. AI is being tested to increase precision, throughput, and accuracy of LVEF and GLS, guide point-of-care image acquisition, and integrate imaging and clinical data to optimize the prediction and detection of cardiac dysfunction. The application of AI to cardiovascular magnetic resonance imaging (CMR), computed tomography (CT; especially coronary artery calcium or CAC scans), single proton emission computed tomography (SPECT) and positron emission tomography (PET) imaging acquisition is also in early stages of analysis for prediction and assessment of cardiac tumors and cardiovascular adverse events in patients treated for childhood or adult cancer. The opportunities for application of AI in cardio-oncology imaging are promising, and if availed, will improve clinical practice and benefit patient care.

Association Between Atrial Uptake on Cardiac Scintigraphy With Technetium-99m-Pyrophosphate Labeled Bone-Seeking Tracers and Atrial Fibrillation.

BACKGROUND: Transthyretin cardiac amyloidosis (ATTR-CA) is an increasingly recognized disease, in which atrial fibrillation (AF) has been shown to be prevalent. Cardiac scintigraphy with technetium-99m-pyrophosphate (99mTc-PyP) labeled bone-seeking tracers is used to noninvasively make the diagnosis of ATTR-CA, based on ventricular myocardial uptake. Assessment of atrial wall uptake (AU) on 99mTc-PyP is currently not used in the clinical setting Methods: We analyzed a cohort of patients referred for 99mTc-PyP scan at a tertiary center to explore AU and associations between any and incident AF, ATTR-CA, and all-cause mortality. RESULTS: Among 580 patients included, 296 patients (51%) had a diagnosis of AF; 164 patients (28%) had scans consistent with ATTR-CA while 117 patients (20%) had AU. Of 117 patients with AU, 107 (91%) had any AF. In contrast, of 463 patients without AU 191(41%) had any AF. Of those with AU, 59/117(50%) patients had a 99mTc-PyP diagnosis of ATTR-CA while 58/117(50%) patients did not have such a diagnosis (P=1.00). Patients with AU had significantly more any AF (hazard ratio [HR], 1.03 [95% CI, 1.02-1.04]; P<0.001), independent of ATTR-CA diagnosis and sex. On multivariable Cox proportional hazards analyses adjusting for age, AU, ATTR-CA diagnosis, sex, smoking, hypertension, diabetes, left ventricular ejection fraction, and coronary artery disease, both age (HR, 1.03 [95% CI, 1.02-1.04]; P<0.0001) and AU (HR, 2.68 [95% CI, 2.11-3.41]; P<0.0001) were independently associated with the development of any AF. Freedom from incident AF at 1-year was significantly lower in patients with AU, both in patients with and without ATTR-CA respectively (HR, 2.27 [95% CI, 1.37-3.78]; P<0.0001 versus HR, 2.21 [95% CI, 1.46-3.34]; P<0.0001). CONCLUSIONS: In a consecutive cohort of patients undergoing 99mTc-PyP scans, 20% had AU, which was statistically associated with any AF, independently of ATTR-CA diagnosis and sex. AU was associated with significantly lower freedom from incident AF at 1-year. Overlooking AU on 99mTc-PyP scans could potentially miss an earlier disease manifestation, or an additional risk factor for any/incident AF.

Shape shifting: The multiple conformational substates of the PTEN N-terminal PIP2 -binding domain.

The Phosphatase and TENsin homolog deleted on chromosome 10 (PTEN) is a chief regulator of a variety of cellular processes including cell proliferation, migration, growth, and death. It is also a major tumor suppressor gene that is frequently mutated or lost under cancerous conditions. PTEN encodes a dual-specificity (lipid and protein) phosphatase that negatively regulates the PI3K/AKT/mTOR signaling pathway where the PIP2 -binding domain (PBD) regulates the lipid phosphatase function. Unfortunately, despite two decades of research, a full-length structure of PTEN remains elusive, leaving open questions regarding PTEN's disordered regions that mediate protein stability, post-translational modifications, protein-protein interactions, while also hindering the design of small molecules that can regulate PTEN's function. Here, we utilized a combination of crosslinking mass spectrometry, in silico predicted structural modeling (including AlphaFold2), molecular docking, molecular dynamics simulations, and residue interaction network modeling to obtain structural details and molecular insight into the behavior of the PBD of PTEN. Our study shows that the PBD exists in multiple conformations which suggests its ability to regulate PTEN's variety of functions. Studying how these specific conformational substates contribute to PTEN function is imperative to defining its function in disease pathogenesis, and to delineate ways to modulate its tumor suppressor activity.

Open Structural Data in Precision Medicine.

Three-dimensional protein structural data at the molecular level are pivotal for successful precision medicine. Such data are crucial not only for discovering drugs that act to block the active site of the target mutant protein but also for clarifying to the patient and the clinician how the mutations harbored by the patient work. The relative paucity of structural data reflects their cost, challenges in their interpretation, and lack of clinical guidelines for their utilization. Rapid technological advancements in experimental high-resolution structural determination increasingly generate structures. Computationally, modeling algorithms, including molecular dynamics simulations, are becoming more powerful, as are compute-intensive hardware, particularly graphics processing units, overlapping with the inception of the exascale era. Accessible, freely available, and detailed structural and dynamical data can be merged with big data to powerfully transform personalizedpharmacology. Here we review protein and emerging genome high-resolution data, along with means, applications, and examples underscoring their usefulness in precision medicine.

Establishing an interdisciplinary research team for cardio-oncology artificial intelligence informatics precision and health equity.

Study objective: A multi-institutional interdisciplinary team was created to develop a research group focused on leveraging artificial intelligence and informatics for cardio-oncology patients. Cardio-oncology is an emerging medical field dedicated to prevention, screening, and management of adverse cardiovascular effects of cancer/ cancer therapies. Cardiovascular disease is a leading cause of death in cancer survivors. Cardiovascular risk in these patients is higher than in the general population. However, prediction and prevention of adverse cardiovascular events in individuals with a history of cancer/cancer treatment is challenging. Thus, establishing an interdisciplinary team to create cardiovascular risk stratification clinical decision aids for integration into electronic health records for oncology patients was considered crucial. Design/setting/participants: Core team members from the Medical College of Wisconsin (MCW), University of Wisconsin-Milwaukee (UWM), and Milwaukee School of Engineering (MSOE), and additional members from Cleveland Clinic, Mayo Clinic, and other institutions have joined forces to apply high-performance computing in cardio-oncology. Results: The team is comprised of clinicians and researchers from relevant complementary and synergistic fields relevant to this work. The team has built an epidemiological cohort of ~5000 cancer survivors that will serve as a database for interdisciplinary multi-institutional artificial intelligence projects. Conclusion: Lessons learned from establishing this team, as well as initial findings from the epidemiology cohort, are presented. Barriers have been broken down to form a multi-institutional interdisciplinary team for health informatics research in cardio-oncology. A database of cancer survivors has been created collaboratively by the team and provides initial insight into cardiovascular outcomes and comorbidities in this population.

Aging-related cell type-specific pathophysiologic immune responses that exacerbate disease severity in aged COVID-19 patients.

Coronavirus disease 2019 (COVID-19) is especially severe in aged patients, defined as 65 years or older, for reasons that are currently unknown. To investigate the underlying basis for this vulnerability, we performed multimodal data analyses on immunity, inflammation, and COVID-19 incidence and severity as a function of age. Our analysis leveraged age-specific COVID-19 mortality and laboratory testing from a large COVID-19 registry, along with epidemiological data of ~3.4 million individuals, large-scale deep immune cell profiling data, and single-cell RNA-sequencing data from aged COVID-19 patients across diverse populations. We found that decreased lymphocyte count and elevated inflammatory markers (C-reactive protein, D-dimer, and neutrophil-lymphocyte ratio) are significantly associated with age-specific COVID-19 severities. We identified the reduced abundance of naïve CD8 T cells with decreased expression of antiviral defense genes (i.e., IFITM3 and TRIM22) in aged severe COVID-19 patients. Older individuals with severe COVID-19 displayed type I and II interferon deficiencies, which is correlated with SARS-CoV-2 viral load. Elevated expression of SARS-CoV-2 entry factors and reduced expression of antiviral defense genes (LY6E and IFNAR1) in the secretory cells are associated with critical COVID-19 in aged individuals. Mechanistically, we identified strong TGF-beta-mediated immune-epithelial cell interactions (i.e., secretory-non-resident macrophages) in aged individuals with critical COVID-19. Taken together, our findings point to immuno-inflammatory factors that could be targeted therapeutically to reduce morbidity and mortality in aged COVID-19 patients.

Tempol Reverses the Negative Effects of Morphine on Arterial Blood-Gas Chemistry and Tissue Oxygen Saturation in Freely-Moving Rats.

We have reported that pretreatment with the clinically approved superoxide dismutase mimetic, Tempol (4-hydroxy-2,2,6,6-tetramethylpiperidine-N-oxyl), blunts the cardiorespiratory depressant responses elicited by a subsequent injection of fentanyl, in halothane-anesthetized rats. The objective of the present study was to determine whether Tempol is able to reverse the effects of morphine on arterial blood-gas (ABG) chemistry in freely-moving Sprague Dawley rats. The intravenous injection of morphine (10 mg/kg) elicited substantial decreases in pH, pO2 and sO2 that were accompanied by substantial increases in pCO2 and Alveolar-arterial gradient, which results in diminished gas-exchange within the lungs. Intravenous injection of a 60 mg/kg dose of Tempol 15 min after the injection of morphine caused minor improvements in pO2 and pCO2 but not in other ABG parameters. In contrast, the 100 mg/kg dose of Tempol caused an immediate and sustained reversal of the negative effects of morphine on arterial blood pH, pCO2, pO2, sO2 and Alveolar-arterial gradient. In other rats, we used pulse oximetry to determine that the 100 mg/kg dose of Tempol, but not the 60 mg/kg dose elicited a rapid and sustained reversal of the negative effects of morphine (10 mg/kg, IV) on tissue O2 saturation (SpO2). The injection of morphine caused a relatively minor fall in mean arterial blood pressure that was somewhat exacerbated by Tempol. These findings demonstrate that Tempol can reverse the negative effects of morphine on ABG chemistry in freely-moving rats paving the way of structure-activity and mechanisms of action studies with the host of Tempol analogues that are commercially available.

Cardiac risk stratification in cancer patients: A longitudinal patient-patient network analysis.

BACKGROUND: Cardiovascular disease is a leading cause of death in general population and the second leading cause of mortality and morbidity in cancer survivors after recurrent malignancy in the United States. The growing awareness of cancer therapy-related cardiac dysfunction (CTRCD) has led to an emerging field of cardio-oncology; yet, there is limited knowledge on how to predict which patients will experience adverse cardiac outcomes. We aimed to perform unbiased cardiac risk stratification for cancer patients using our large-scale, institutional electronic medical records. METHODS AND FINDINGS: We built a large longitudinal (up to 22 years' follow-up from March 1997 to January 2019) cardio-oncology cohort having 4,632 cancer patients in Cleveland Clinic with 5 diagnosed cardiac outcomes: atrial fibrillation, coronary artery disease, heart failure, myocardial infarction, and stroke. The entire population includes 84% white Americans and 11% black Americans, and 59% females versus 41% males, with median age of 63 (interquartile range [IQR]: 54 to 71) years old. We utilized a topology-based K-means clustering approach for unbiased patient-patient network analyses of data from general demographics, echocardiogram (over 25,000), lab testing, and cardiac factors (cardiac). We performed hazard ratio (HR) and Kaplan-Meier analyses to identify clinically actionable variables. All confounding factors were adjusted by Cox regression models. We performed random-split and time-split training-test validation for our model. We identified 4 clinically relevant subgroups that are significantly correlated with incidence of cardiac outcomes and mortality. Among the 4 subgroups, subgroup I (n = 625) has the highest risk of de novo CTRCD (28%) with an HR of 3.05 (95% confidence interval (CI) 2.51 to 3.72). Patients in subgroup IV (n = 1,250) had the worst survival probability (HR 4.32, 95% CI 3.82 to 4.88). From longitudinal patient-patient network analyses, the patients in subgroup I had a higher percentage of de novo CTRCD and a worse mortality within 5 years after the initiation of cancer therapies compared to long-time exposure (6 to 20 years). Using clinical variable network analyses, we identified that serum levels of NT-proB-type Natriuretic Peptide (NT-proBNP) and Troponin T are significantly correlated with patient's mortality (NT-proBNP > 900 pg/mL versus NT-proBNP = 0 to 125 pg/mL, HR = 2.95, 95% CI 2.28 to 3.82, p < 0.001; Troponin T > 0.05 µg/L versus Troponin T ≤ 0.01 µg/L, HR = 2.08, 95% CI 1.83 to 2.34, p < 0.001). Study limitations include lack of independent cardio-oncology cohorts from different healthcare systems to evaluate the generalizability of the models. Meanwhile, the confounding factors, such as multiple medication usages, may influence the findings. CONCLUSIONS: In this study, we demonstrated that the patient-patient network clustering methodology is clinically intuitive, and it allows more rapid identification of cancer survivors that are at greater risk of cardiac dysfunction. We believed that this study holds great promise for identifying novel cardiac risk subgroups and clinically actionable variables for the development of precision cardio-oncology.

Systemic Administration of Tempol Attenuates the Cardiorespiratory Depressant Effects of Fentanyl.

Fentanyl is a high-potency opioid receptor agonist that elicits profound analgesia and suppression of breathing in humans and animals. To date, there is limited evidence as to whether changes in oxidant stress are important factors in any of the actions of acutely administered fentanyl. This study determined whether the clinically approved superoxide dismutase mimetic, Tempol (4-hydroxy-2,2,6,6-tetramethylpiperidine-N-oxyl), or a potent antioxidant, N-acetyl-L-cysteine methyl ester (L-NACme), modify the cardiorespiratory and analgesic actions of fentanyl. We examined whether the prior systemic injection of Tempol or L-NACme affects the cardiorespiratory and/or analgesic responses elicited by the subsequent injection of fentanyl in isoflurane-anesthetized and/or freely moving male Sprague-Dawley rats. Bolus injections of Tempol (25, 50 or 100 mg/kg, IV) elicited minor increases in frequency of breathing, tidal volume and minute ventilation. The ventilatory-depressant effects of fentanyl (5 µg/kg, IV) given 15 min later were dose-dependently inhibited by prior injections of Tempol. Tempol elicited dose-dependent and transient hypotension that had (except for the highest dose) resolved when fentanyl was injected. The hypotensive responses elicited by fentanyl were markedly blunted after Tempol pretreatment. The analgesic actions of fentanyl (25 µg/kg, IV) were not affected by Tempol (100 mg/kg, IV). L-NACme did not modify any of the effects of fentanyl. We conclude that prior administration of Tempol attenuates the cardiorespiratory actions of fentanyl without affecting the analgesic effects of this potent opioid. As such, Tempol may not directly affect opioid-receptors that elicit the effects of fentanyl. Whether, the effects of Tempol are solely due to alterations in oxidative stress is in doubt since the powerful antioxidant, L-NACme, did not affect fentanyl-induced suppression of breathing.

A retrospective analysis of cardiovascular adverse events associated with immune checkpoint inhibitors.

BACKGROUND: Modern therapies in oncology have increased cancer survivorship, as well as the incidence of cardiovascular adverse events. While immune checkpoint inhibitors have shown significant clinical impact in several cancer types, the incidence of immune-related cardiovascular (CV) adverse events poses an additional health concern and has been reported. METHODS: We performed a retrospective analysis of the FDA Adverse Event Reporting System data of suspect product reports for immunotherapy and classical chemotherapy from January 2010-March 2020. We identified 90,740 total adverse event reports related to immune checkpoint inhibitors and classical chemotherapy. RESULTS: We found that myocarditis was significantly associated with patients receiving anti-program cell death protein 1 (PD-1) or anti-program death ligand 1 (PD-L1), odds ratio (OR) = 23.86 (95% confidence interval [CI] 11.76-48.42, (adjusted p-value) q <  0.001), and combination immunotherapy, OR = 7.29 (95% CI 1.03-51.89, q = 0.047). Heart failure was significantly associated in chemotherapy compared to PD-(L)1, OR = 0.50 (95% CI 0.37-0.69, q <  0.001), CTLA4, OR = 0.08 (95% CI 0.03-0.20, q <  0.001), and combination immunotherapy, OR = 0.25 (95% CI 0.13-0.48, q <  0.001). Additionally, we observe a sex-specificity towards males in cardiac adverse reports for arrhythmias, OR = 0.81 (95% CI 0.75-0.87, q <  0.001), coronary artery disease, 0.63 (95% CI 0.53-0.76, q <  0.001), myocardial infarction, OR = 0.60 (95% CI 0.53-0.67, q <  0.001), myocarditis, OR = 0.59 (95% CI 0.47-0.75, q <  0.001) and pericarditis, OR = 0.5 (95% CI 0.35-0.73, q <  0.001). CONCLUSION: Our study provides the current risk estimates of cardiac adverse events in patients treated with immunotherapy compared to conventional chemotherapy. Understanding the clinical risk factors that predispose immunotherapy-treated cancer patients to often fatal CV adverse events will be crucial in Cardio-Oncology management.

Glutathione ethyl ester reverses the deleterious effects of fentanyl on ventilation and arterial blood-gas chemistry while prolonging fentanyl-induced analgesia.

There is an urgent need to develop novel compounds that prevent the deleterious effects of opioids such as fentanyl on minute ventilation while, if possible, preserving the analgesic actions of the opioids. We report that L-glutathione ethyl ester (GSHee) may be such a novel compound. In this study, we measured tail flick latency (TFL), arterial blood gas (ABG) chemistry, Alveolar-arterial gradient, and ventilatory parameters by whole body plethysmography to determine the responses elicited by bolus injections of fentanyl (75 µg/kg, IV) in male adult Sprague-Dawley rats that had received a bolus injection of GSHee (100 µmol/kg, IV) 15 min previously. GSHee given alone had minimal effects on TFL, ABG chemistry and A-a gradient whereas it elicited changes in some ventilatory parameters such as an increase in breathing frequency. In vehicle-treated rats, fentanyl elicited (1) an increase in TFL, (2) decreases in pH, pO2 and sO2 and increases in pCO2 (all indicative of ventilatory depression), (3) an increase in Alveolar-arterial gradient (indicative of a mismatch in ventilation-perfusion in the lungs), and (4) changes in ventilatory parameters such as a reduction in tidal volume, that were indicative of pronounced ventilatory depression. In GSHee-pretreated rats, fentanyl elicited a more prolonged analgesia, relatively minor changes in ABG chemistry and Alveolar-arterial gradient, and a substantially milder depression of ventilation. GSHee may represent an effective member of a novel class of thiolester drugs that are able to prevent the ventilatory depressant effects elicited by powerful opioids such as fentanyl and their deleterious effects on gas-exchange in the lungs without compromising opioid analgesia.

Comprehensive characterization of protein-protein interactions perturbed by disease mutations.

Technological and computational advances in genomics and interactomics have made it possible to identify how disease mutations perturb protein-protein interaction (PPI) networks within human cells. Here, we show that disease-associated germline variants are significantly enriched in sequences encoding PPI interfaces compared to variants identified in healthy participants from the projects 1000 Genomes and ExAC. Somatic missense mutations are also significantly enriched in PPI interfaces compared to noninterfaces in 10,861 tumor exomes. We computationally identified 470 putative oncoPPIs in a pan-cancer analysis and demonstrate that oncoPPIs are highly correlated with patient survival and drug resistance/sensitivity. We experimentally validate the network effects of 13 oncoPPIs using a systematic binary interaction assay, and also demonstrate the functional consequences of two of these on tumor cell growth. In summary, this human interactome network framework provides a powerful tool for prioritization of alleles with PPI-perturbing mutations to inform pathobiological mechanism- and genotype-based therapeutic discovery.

A network-based deep learning methodology for stratification of tumor mutations.

MOTIVATION: Tumor stratification has a wide range of biomedical and clinical applications, including diagnosis, prognosis and personalized treatment. However, cancer is always driven by the combination of mutated genes, which are highly heterogeneous across patients. Accurately subdividing the tumors into subtypes is challenging. RESULTS: We developed a network-embedding based stratification (NES) methodology to identify clinically relevant patient subtypes from large-scale patients' somatic mutation profiles. The central hypothesis of NES is that two tumors would be classified into the same subtypes if their somatic mutated genes located in the similar network regions of the human interactome. We encoded the genes on the human protein-protein interactome with a network embedding approach and constructed the patients' vectors by integrating the somatic mutation profiles of 7344 tumor exomes across 15 cancer types. We firstly adopted the lightGBM classification algorithm to train the patients' vectors. The AUC value is around 0.89 in the prediction of the patient's cancer type and around 0.78 in the prediction of the tumor stage within a specific cancer type. The high classification accuracy suggests that network embedding-based patients' features are reliable for dividing the patients. We conclude that we can cluster patients with a specific cancer type into several subtypes by using an unsupervised clustering algorithm to learn the patients' vectors. Among the 15 cancer types, the new patient clusters (subtypes) identified by the NES are significantly correlated with patient survival across 12 cancer types. In summary, this study offers a powerful network-based deep learning methodology for personalized cancer medicine. AVAILABILITY AND IMPLEMENTATION: Source code and data can be downloaded from https://github.com/ChengF-Lab/NES. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.