Cabozantinib with immune checkpoint inhibitor versus cabozantinib monotherapy in patients with metastatic clear cell renal cell carcinoma progressing after prior immune checkpoint inhibitor.

INTRODUCTION: Rechallenge with antibodies targeting programmed cell death protein-1 or its ligand (PD-1/L1) after discontinuation or disease progression in solid tumors following a prior PD-1/L1 treatment is often practiced in clinic. This study aimed to investigate if adding PD-1/L1 inhibitors to cabozantinib, the most used second-line treatment in real-world patients with metastatic clear cell renal cell carcinoma (mccRCC), offers additional benefits. METHODS: Using de-identified patient-level data from a large real-world US-based database, patients diagnosed with mccRCC, who received any PD-1/L1-based combination in first-line (1L) setting, followed by second-line (2L) therapy with either cabozantinib alone or in combination with PD-1/L1 inhibitors were included. Patients given a cabozantinib-containing regimen in 1L were excluded. The study end points were real-world time to next therapy (rwTTNT) and real-world overall survival (rwOS) by 2L. RESULTS: Of 12,285 patients with metastatic renal cell carcinoma in the data set, 348 patients met eligibility and were included in the analysis. After propensity score matching weighting, cabozantinib with PD-1/L1 inhibitors versus cabozantinib (ref.) had similar rwTTNT and rwOS in the 2L setting. Hazard ratios and 95% confidence interval (CI) for rwTTNT and rwOS are 0.74 (95% CI, 0.49-1.12) and 1.15 (95% CI, 0.73-1.79), respectively. CONCLUSION: In this study, the results align with the phase 3 CONTACT-03 trial results, which showed no additional benefit of adding PD-L1 inhibitor to cabozantinib compared to cabozantinib alone in 2L following PD-1/L1-based therapies in 1L. These results from real-world patients strengthen the evidence regarding the futility of rechallenge with PD-1/L1 inhibitors.

Performance of Off-the-Shelf Machine Learning Architectures and Biases in Detection of Low Left Ventricular Ejection Fraction.

Artificial intelligence - machine learning (AI-ML) is a computational technique that has been demonstrated to be able to extract meaningful clinical information from diagnostic data that are not available using either human interpretation or more simple analysis methods. Recent developments have shown that AI-ML approaches applied to ECGs can accurately predict different patient characteristics and pathologies not detectable by expert physician readers. There is an extensive body of literature surrounding the use of AI-ML in other fields, which has given rise to an array of predefined open-source AI-ML architectures which can be translated to new problems in an "off-the-shelf" manner. Applying "off-the-shelf" AI-ML architectures to ECG-based datasets opens the door for rapid development and identification of previously unknown disease biomarkers. Despite the excellent opportunity, the ideal open-source AI-ML architecture for ECG related problems is not known. Furthermore, there has been limited investigation on how and when these AI-ML approaches fail and possible bias or disparities associated with particular network architectures. In this study, we aimed to: (1) determine if open-source, "off-the-shelf" AI-ML architectures could be trained to classify low LVEF from ECGs, (2) assess the accuracy of different AI-ML architectures compared to each other, and (3) to identify which, if any, patient characteristics are associated with poor AI-ML performance.

Phase I Trial of Combination Therapy With Avelumab and Cabozantinib in Patients With Newly Diagnosed Metastatic Clear Cell Renal Cell Carcinoma.

BACKGROUND: Combination immunotherapy is now considered the standard first-line therapy for patients with metastatic clear cell renal cell carcinoma (mccRCC) after multiple clinical trials demonstrated improved overall survival compared with single-agent tyrosine kinase inhibitors. Cabozantinib modulates critical components of the immune system, such as decreasing regulatory T cells and increasing T-effector cell populations, and is approved for the treatment of mRCC. Avelumab is a human IgG1 monoclonal antibody that binds to programmed death-ligand 1 protein and inhibits the interaction with PD-1. This phase I trial assessed the safety and clinical activity of avelumab and cabozantinib combination therapy in mccRCC. METHODS: This study was a phase I, 3+3 dose escalation clinical trial. The primary endpoint was the safety and identification of the recommended phase II dose (RP2D). Secondary endpoints included objective response rate (ORR) and radiographic progression-free survival (rPFS). There were 3 dose cohorts: cabozantinib 20, 40, and 60 mg/day, each combined with avelumab (10 mg/kg intravenously every 2 weeks). An additional 3 patients were included in the final dose cohort as a confirmation of the RP2D. No dose modifications were allowed for avelumab, but dose delays were permitted. Both dose reductions and holds were allowed for cabozantinib. Response Evaluation Criteria in Solid Tumors (RECIST), version 1.1, was used to determine ORR, and treatment beyond progression was allowed. RESULTS: Twelve patients with newly diagnosed mccRCC were enrolled from July 2018 until March 2020. Three patients were enrolled in the 20 and 40 mg cohorts each, and 6 were enrolled in the 60 mg cohort. The International Metastatic RCC Database Consortium (IMDC) risk categories for these patients were: 4 patients (favorable risk), 6 patients (intermediate risk), and 2 patients (poor risk). No dose-limiting toxicities (DLTs) were observed in any cohort. Six patients developed serious adverse events related to study treatment after the DLT window period. Immune-related adverse events (iRAEs) were reported in 11 patients; fatigue and diarrhea were the most common (each with n = 4, 33.3%), followed by maculopapular rash and hand-foot syndrome (each with n = 3, 25%). Dose reductions were required in 5 of 6 patients in the cabozantinib 60 mg cohort after the DLT period. One patient discontinued avelumab due to irAE (nephritis), while none discontinued cabozantinib due to toxicity. The ORR was 50%, with one complete response (CR) and 5 partial responses (PR). The disease control rate (CR + PR + stable disease) was noted in 92% of the patients. Radiological PFS survival rate at 6 and 12 months was reported in 67.7% and 33.5% of patients, respectively. CONCLUSION: Combination therapy with avelumab and cabozantinib is safe and showed preliminary clinical activity in mccRCC. Even though the DLT was not met in any of the 3 cohorts, the recommended RP2D dose for the combination is cabozantinib 40 mg/day due to a high incidence of grade 2 toxicity for cabozantinib 60 mg/day after the DLT period. (ClinicalTrials.gov Identifier: NCT03200587).

Opioid-Induced Reductions in Amygdala Lateral Paracapsular GABA Neuron Circuit Activity.

Opioid use and withdrawal evokes behavioral adaptations such as drug seeking and anxiety, though the underlying neurocircuitry changes are unknown. The basolateral amygdala (BLA) regulates these behaviors through principal neuron activation. Excitatory BLA pyramidal neuron activity is controlled by feedforward inhibition provided, in part, by lateral paracapsular (LPC) GABAergic inhibitory neurons, residing along the BLA/external capsule border. LPC neurons express µ-opioid receptors (MORs) and are potential targets of opioids in the etiology of opioid-use disorders and anxiety-like behaviors. Here, we investigated the effects of opioid exposure on LPC neuron activity using immunohistochemical and electrophysiological approaches. We show that LPC neurons, and other nearby BLA GABA and non-GABA neurons, express MORs and δ-opioid receptors. Additionally, DAMGO, a selective MOR agonist, reduced GABA but not glutamate-mediated spontaneous postsynaptic currents in LPC neurons. Furthermore, in LPC neurons, abstinence from repeated morphine-exposure in vivo (10 mg/kg/day, 5 days, 2 days off) decrease the intrinsic membrane excitability, with a ~75% increase in afterhyperpolarization and ~40-50% enhanced adenylyl cyclase-dependent activity in LPC neurons. These data show that MORs in the BLA are a highly sensitive targets for opioid-induced inhibition and that repeated opioid exposure results in impaired LPC neuron excitability.

Mechanical Stimulation Alters Chronic Ethanol-Induced Changes to VTA GABA Neurons, NAc DA Release and Measures of Withdrawal.

Therapeutic activation of mechanoreceptors (MStim) in osteopathy, chiropractic and acupuncture has been in use for hundreds of years with a myriad of positive outcomes. It has been previously shown to modulate the firing rate of neurons in the ventral tegmental area (VTA) and dopamine (DA) release in the nucleus accumbens (NAc), an area of interest in alcohol-use disorder (AUD). In this study, we examined the effects of MStim on VTA GABA neuron firing rate, DA release in the NAc, and behavior during withdrawal from chronic EtOH exposure in a rat model. We demonstrate that concurrent administration of MStim and EtOH significantly reduced adaptations in VTA GABA neurons and DA release in response to a reinstatement dose of EtOH (2.5 g/kg). Behavioral indices of EtOH withdrawal (rearing, open-field crosses, tail stiffness, gait, and anxiety) were substantively ameliorated with concurrent application of MStim. Additionally, MStim significantly increased the overall frequency of ultrasonic vocalizations, suggesting an increased positive affective state.

A Stress Test of Artificial Intelligence: Can Deep Learning Models Trained From Formal Echocardiography Accurately Interpret Point-of-Care Ultrasound?

OBJECTIVES: To test if a deep learning (DL) model trained on echocardiography images could accurately segment the left ventricle (LV) and predict ejection fraction on apical 4-chamber images acquired by point-of-care ultrasound (POCUS). METHODS: We created a dataset of 333 videos from cardiac POCUS exams acquired in the emergency department. For each video we derived two ground-truth labels. First, we segmented the LV from one image frame and second, we classified the EF as normal, reduced, or severely reduced. We then classified the media's quality as optimal, adequate, or inadequate. With this dataset we tested the accuracy of automated LV segmentation and EF classification by the best-in-class echocardiography trained DL model EchoNet-Dynamic. RESULTS: The mean Dice similarity coefficient for LV segmentation was 0.72 (N = 333; 95% CI 0.70-0.74). Cohen's kappa coefficient for agreement between predicted and ground-truth EF classification was 0.16 (N = 333). The area under the receiver-operating curve for the diagnosis of heart failure was 0.74 (N = 333). Model performance improved with video quality for the tasks of LV segmentation and diagnosis of heart failure, but was unchanged with EF classification. For all tasks the model was less accurate than the published benchmarks for EchoNet-Dynamic. CONCLUSIONS: Performance of a DL model trained on formal echocardiography worsened when challenged with images captured during resuscitations. DL models intended for assessing bedside ultrasound should be trained on datasets composed of POCUS images. Such datasets have yet to be made publicly available.

Modulation of dopamine release by ethanol is mediated by atypical GABAA receptors on cholinergic interneurons in the nucleus accumbens.

Previous studies indicate that moderate-to-high ethanol (EtOH) concentrations enhance dopamine (DA) neurotransmission in the mesolimbic DA system from the ventral tegmental area (VTA) and projecting to the nucleus accumbens core (NAc). However, voltammetry studies demonstrate that moderate-to-high EtOH concentrations decrease evoked DA release at NAc terminals. The involvement of γ-aminobutyric acid (GABA) receptors (GABAA Rs), glycine (GLY) receptors (GLYRs) and cholinergic interneurons (CINs) in mediating EtOH inhibition of evoked NAc DA release were examined. Fast scan cyclic voltammetry, electrophysiology, optogenetics and immunohistochemistry techniques were used to evaluate the effects of acute and chronic EtOH exposure on DA release and CIN activity in C57/BL6, CD-1, transgenic mice and δ-subunit knockout (KO) mice (δ-/-). Ethanol decreased DA release in mice with an IC50 of 80 mM ex vivo and 2.0 g/kg in vivo. GABA and GLY decreased evoked DA release at 1-10 mM. Typical GABAA R agonists inhibited DA release at high concentrations. Typical GABAA R antagonists had minimal effects on EtOH inhibition of evoked DA release. However, EtOH inhibition of DA release was blocked by the α4 ß3 δ GABAA R antagonist Ro15-4513, the GLYR antagonist strychnine and by the GABA ρ1 (Rho-1) antagonist TPMPA (10 µM) and reduced significantly in GABAA R δ-/- mice. Rho-1 expression was observed in CINs. Ethanol inhibited GABAergic synaptic input to CINs from the VTA and enhanced firing rate, both of which were blocked by TPMPA. Results herein suggest that EtOH inhibition of DA release in the NAc is modulated by GLYRs and atypical GABAA Rs on CINs containing δ- and Rho-subunits.

Regional and sex differences in spontaneous striatal dopamine transmission.

Striatal dopamine release is key for learning and motivation and is composed of subregions including the dorsal striatum (DS), nucleus accumbens core, and the nucleus accumbens shell. Spontaneously occurring dopamine release was compared across these subregions. Dopamine release/uptake dynamics differ across striatal subregions, with dopamine transient release amplitude and release frequency greatest in male mice, and the largest signals observed in the DS. Surprisingly, female mice exhibited little regional differences in dopamine release for DS and nucleus accumbens core regions, but lower release in the nucleus accumbens shell. Blocking voltage-gated K+ channel (Kv channels) with 4-aminopyridine enhanced dopamine detection without affecting reuptake. The 4-aminopyridine effects were greatest in ventral regions of female mice, suggesting regional differences in Kv channel expression. The dopamine transporter blocker cocaine also enhanced detection across subregions in both sexes, with greater overall increased release in females than males. Thus, sex differences in dopamine transmission are apparent and likely include differences in the Kv channel and dopamine transporter function. The lack of regional differences in dopamine release observed in females indicates differential regulation of spontaneous and evoked dopamine release.

Myocardial Ischemia Detection Using Body Surface Potential Mappings and Machine Learning.

Recent improvements in detecting acute myocardial ischemia via noninvasive body surface recordings have been driven by modern machine learning. While extensive research has been done using single and 12 lead ECGs, almost no models have incorporated body surface potential mappings. We created two contrasting machine learning models, logistic regression and XGBoost Classifier, and trained them on experimentally acquired body surface mappings with ground truth ischemia measurements recorded from within the heart. These models achieved a mean accuracy of 96.46% and 97.63%, as well as a mean AUC of 0.9927 and 0.9972 for the Logistic Regression and XGBoost classifiers, respectively. The anatomical location and relative contribution of each electrode were visualized and ranked. Then, new models were trained using data from only the top 12, 8, and 3 electrodes. These models trained on only a subset of the electrodes still exhibited relatively high accuracy and AUC, although at much faster training times.

Body Surface Potential Mapping: Contemporary Applications and Future Perspectives.

Body surface potential mapping (BSPM) is a noninvasive modality to assess cardiac bioelectric activity with a rich history of practical applications for both research and clinical investigation. BSPM provides comprehensive acquisition of bioelectric signals across the entire thorax, allowing for more complex and extensive analysis than the standard electrocardiogram (ECG). Despite its advantages, BSPM is not a common clinical tool. BSPM does, however, serve as a valuable research tool and as an input for other modes of analysis such as electrocardiographic imaging and, more recently, machine learning and artificial intelligence. In this report, we examine contemporary uses of BSPM, and provide an assessment of its future prospects in both clinical and research environments. We assess the state of the art of BSPM implementations and explore modern applications of advanced modeling and statistical analysis of BSPM data. We predict that BSPM will continue to be a valuable research tool, and will find clinical utility at the intersection of computational modeling approaches and artificial intelligence.

Corticotropin releasing factor, but not alcohol, modulates norepinephrine release in the rat central nucleus of the amygdala.

Alcohol misuse and dependence is a widespread health problem. The central nucleus of the amygdala (CeA) plays important roles in both the anxiety associated with alcohol (ethanol) dependence and the increased alcohol intake that is observed during withdrawal in dependent animals. We and others have shown the essential involvement of the corticotropin releasing factor (CRF) system in alcohol's synaptic effects on the CeA and in the development of ethanol dependence. Another system that has been shown to be critically involved in the molecular underpinnings of alcohol dependence is the norepinephrine (NE) system originating in the locus coeruleus. Both the CRF and NE systems act in concert to facilitate a stress response: central amygdalar afferents release CRF in the locus coeruleus promoting widespread release of NE. In this study, we are the first to use fast-scan cyclic voltammetry to classify local electrically-evoked NE release in the CeA and to determine if acute alcohol and CRF modulate it. Evoked NE release is action potential dependent, is abolished after depletion of monoaminergic vesicles, differs pharmacologically from dopamine release, is insensitive to acute alcohol, and decreases in response to locally applied CRF. Taken together, these results indicate that NE release in the CeA is released canonically in a vesicular-dependent manner, and that while acute alcohol does not directly alter NE release, CRF decreases it. Our results suggest that CRF acts locally on NE terminals as negative feedback and potentially prevents hyperactivation of the CRF-norepinephrine stress pathway.

Mechanical stimulation of cervical vertebrae modulates the discharge activity of ventral tegmental area neurons and dopamine release in the nucleus accumbens.

BACKGROUND: Growing evidence suggests that mechanical stimulation modulates substrates in the supraspinal central nervous system (CNS) outside the canonical somatosensory circuits. OBJECTIVE/METHODS: We evaluate mechanical stimulation applied to the cervical spine at the C7-T1 level (termed "MStim") on neurons and neurotransmitter release in the mesolimbic dopamine (DA) system, an area implicated in reward and motivation, utilizing electrophysiological, pharmacological, neurochemical and immunohistochemical techniques in Wistar rats. RESULTS: Low frequency (45-80 Hz), but not higher frequency (115 Hz), MStim inhibited the firing rate of ventral tegmental area (VTA) GABA neurons (52.8% baseline; 450 s) while increasing the firing rate of VTA DA neurons (248% baseline; 500 s). Inactivation of the nucleus accumbens (NAc), or systemic or in situ antagonism of delta opioid receptors (DORs), blocked MStim inhibition of VTA GABA neuron firing rate. MStim enhanced both basal (178.4% peak increase at 60 min) and evoked DA release in NAc (135.0% peak increase at 40 min), which was blocked by antagonism of DORs or acetylcholine release in the NAc. MStim enhanced c-FOS expression in the NAc, but inhibited total expression in the VTA, and induced translocation of DORs to neuronal membranes in the NAc. CONCLUSION: These findings demonstrate that MStim modulates neuron firing and DA release in the mesolimbic DA system through endogenous opioids and acetylcholine in the NAc. These findings demonstrate the need to explore more broadly the extra-somatosensory effects of peripheral mechanoreceptor activation and the specific role for mechanoreceptor-based therapies in the treatment of substance abuse.