A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer.

BACKGROUND: Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. METHODS: Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. RESULTS: A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1. CONCLUSIONS: This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy. TRIAL REGISTRATION: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.

Obesity-associated microglial inflammatory activation paradoxically improves glucose tolerance.

Hypothalamic gliosis associated with high-fat diet (HFD) feeding increases susceptibility to hyperphagia and weight gain. However, the body-weight-independent contribution of microglia to glucose regulation has not been determined. Here, we show that reducing microglial nuclear factor κB (NF-κB) signaling via cell-specific IKKß deletion exacerbates HFD-induced glucose intolerance despite reducing body weight and adiposity. Conversely, two genetic approaches to increase microglial pro-inflammatory signaling (deletion of an NF-κB pathway inhibitor and chemogenetic activation through a modified Gq-coupled muscarinic receptor) improved glucose tolerance independently of diet in both lean and obese rodents. Microglial regulation of glucose homeostasis involves a tumor necrosis factor alpha (TNF-α)-dependent mechanism that increases activation of pro-opiomelanocortin (POMC) and other hypothalamic glucose-sensing neurons, ultimately leading to a marked amplification of first-phase insulin secretion via a parasympathetic pathway. Overall, these data indicate that microglia regulate glucose homeostasis in a body-weight-independent manner, an unexpected mechanism that limits the deterioration of glucose tolerance associated with obesity.

Machine learning in rare disease.

High-throughput profiling methods (such as genomics or imaging) have accelerated basic research and made deep molecular characterization of patient samples routine. These approaches provide a rich portrait of genes, molecular pathways and cell types involved in disease phenotypes. Machine learning (ML) can be a useful tool for extracting disease-relevant patterns from high-dimensional datasets. However, depending upon the complexity of the biological question, machine learning often requires many samples to identify recurrent and biologically meaningful patterns. Rare diseases are inherently limited in clinical cases, leading to few samples to study. In this Perspective, we outline the challenges and emerging solutions for using ML for small sample sets, specifically in rare diseases. Advances in ML methods for rare diseases are likely to be informative for applications beyond rare diseases for which few samples exist with high-dimensional data. We propose that the method community prioritize the development of ML techniques for rare disease research.

COVID-19 in people with neurofibromatosis 1, neurofibromatosis 2, or schwannomatosis.

PURPOSE: People with pre-existing conditions may be more susceptible to severe COVID-19 when infected by SARS-CoV-2. The relative risk and severity of SARS-CoV-2 infection in people with rare diseases such as neurofibromatosis type 1 (NF1), neurofibromatosis type 2 (NF2), or schwannomatosis (SWN) is unknown. METHODS: We investigated the proportions of people with NF1, NF2, or SWN in the National COVID Cohort Collaborative (N3C) electronic health record data set who had a positive test result for SARS-CoV-2 or COVID-19. RESULTS: The cohort sizes in N3C were 2501 (NF1), 665 (NF2), and 762 (SWN). We compared these with N3C cohorts of patients with other rare diseases (98-9844 individuals) and the general non-NF population of 5.6 million. The site- and age-adjusted proportion of people with NF1, NF2, or SWN who had a positive test result for SARS-CoV-2 or COVID-19 (collectively termed positive cases) was not significantly higher than in individuals without NF or other selected rare diseases. There were no severe outcomes reported in the NF2 or SWN cohorts. The proportion of patients experiencing severe outcomes was no greater for people with NF1 than in cohorts with other rare diseases or the general population. CONCLUSION: Having NF1, NF2, or SWN does not appear to increase the risk of being SARS-CoV-2 positive or of being a patient with COVID-19 or of developing severe complications from SARS-CoV-2.

Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes.

BACKGROUND: Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. METHODS: We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning. FINDINGS: We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems. INTERPRETATION: Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC. FUNDING: NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.

Centralizing neurofibromatosis experimental tool knowledge with the NF Research Tools Database.

Experimental tools and resources, such as animal models, cell lines, antibodies, genetic reagents and biobanks, are key ingredients in biomedical research. Investigators face multiple challenges when trying to understand the availability, applicability and accessibility of these tools. A major challenge is keeping up with current information about the numerous tools available for a particular research problem. A variety of disease-agnostic projects such as the Mouse Genome Informatics database and the Resource Identification Initiative curate a number of types of research tools. Here, we describe our efforts to build upon these resources to develop a disease-specific research tool resource for the neurofibromatosis (NF) research community. This resource, the NF Research Tools Database, is an open-access database that enables the exploration and discovery of information about NF type 1-relevant animal models, cell lines, antibodies, genetic reagents and biobanks. Users can search and explore tools, obtain detailed information about each tool as well as read and contribute their observations about the performance, reliability and characteristics of tools in the database. NF researchers will be able to use the NF Research Tools Database to promote, discover, share, reuse and characterize research tools, with the goal of advancing NF research. Database URL: https://tools.nf.synapse.org/.

Generalizable Long COVID Subtypes: Findings from the NIH N3C and RECOVER Programs.

Accurate stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, the natural history of long COVID is incompletely understood and characterized by an extremely wide range of manifestations that are difficult to analyze computationally. In addition, the generalizability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. We present a method for computationally modeling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning procedures. Using k-means clustering of this similarity matrix, we found six distinct clusters of PASC patients, each with distinct profiles of phenotypic abnormalities. There was a significant association of cluster membership with a range of pre-existing conditions and with measures of severity during acute COVID-19. Two of the clusters were associated with severe manifestations and displayed increased mortality. We assigned new patients from other healthcare centers to one of the six clusters on the basis of maximum semantic similarity to the original patients. We show that the identified clusters were generalizable across different hospital systems and that the increased mortality rate was consistently observed in two of the clusters. Semantic phenotypic clustering can provide a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.

CX3CL1 Action on Microglia Protects from Diet-Induced Obesity by Restoring POMC Neuronal Excitability and Melanocortin System Activity Impaired by High-Fat Diet Feeding.

Both hypothalamic microglial inflammation and melanocortin pathway dysfunction contribute to diet-induced obesity (DIO) pathogenesis. Previous studies involving models of altered microglial signaling demonstrate altered DIO susceptibility with corresponding POMC neuron cytological changes, suggesting a link between microglia and the melanocortin system. We addressed this hypothesis using the specific microglial silencing molecule, CX3CL1 (fractalkine), to determine whether reducing hypothalamic microglial activation can restore POMC/melanocortin signaling to protect against DIO. We performed metabolic analyses in high fat diet (HFD)-fed mice with targeted viral overexpression of CX3CL1 in the hypothalamus. Electrophysiologic recording in hypothalamic slices from POMC-MAPT-GFP mice was used to determine the effects of HFD feeding and microglial silencing via minocycline or CX3CL1 on GFP-labeled POMC neurons. Finally, mice with hypothalamic overexpression of CX3CL1 received central treatment with the melanocortin receptor antagonist SHU9119 to determine whether melanocortin signaling is required for the metabolic benefits of CX3CL1. Hypothalamic overexpression of CX3CL1 increased leptin sensitivity and POMC gene expression, while reducing weight gain in animals fed an HFD. In electrophysiological recordings from hypothalamic slice preparations, HFD feeding was associated with reduced POMC neuron excitability and increased amplitude of inhibitory postsynaptic currents. Microglial silencing using minocycline or CX3CL1 treatment reversed these HFD-induced changes in POMC neuron electrophysiologic properties. Correspondingly, blockade of melanocortin receptor signaling in vivo prevented both the acute and chronic reduction in food intake and body weight mediated by CX3CL1. Our results show that suppressing microglial activation during HFD feeding reduces DIO susceptibility via a mechanism involving increased POMC neuron excitability and melanocortin signaling.

Integrated molecular and clinical analysis of low-grade gliomas in children with neurofibromatosis type 1 (NF1).

Low-grade gliomas (LGGs) are the most common childhood brain tumor in the general population and in individuals with the Neurofibromatosis type 1 (NF1) cancer predisposition syndrome. Surgical biopsy is rarely performed prior to treatment in the setting of NF1, resulting in a paucity of tumor genomic information. To define the molecular landscape of NF1-associated LGGs (NF1-LGG), we integrated clinical data, histological diagnoses, and multi-level genetic/genomic analyses on 70 individuals from 25 centers worldwide. Whereas, most tumors harbored bi-allelic NF1 inactivation as the only genetic abnormality, 11% had additional mutations. Moreover, tumors classified as non-pilocytic astrocytoma based on DNA methylation analysis were significantly more likely to harbor these additional mutations. The most common secondary alteration was FGFR1 mutation, which conferred an additional growth advantage in multiple complementary experimental murine Nf1 models. Taken together, this comprehensive characterization has important implications for the management of children with NF1-LGG, distinct from their sporadic counterparts.

A clinically and genomically annotated nerve sheath tumor biospecimen repository.

Nerve sheath tumors occur as a heterogeneous group of neoplasms in patients with neurofibromatosis type 1 (NF1). The malignant form represents the most common cause of death in people with NF1, and even when benign, these tumors can result in significant disfigurement, neurologic dysfunction, and a range of profound symptoms. Lack of human tissue across the peripheral nerve tumors common in NF1 has been a major limitation in the development of new therapies. To address this unmet need, we have created an annotated collection of patient tumor samples, patient-derived cell lines, and patient-derived xenografts, and carried out high-throughput genomic and transcriptomic characterization to serve as a resource for further biologic and preclinical therapeutic studies. In this work, we release genomic and transcriptomic datasets comprised of 55 tumor samples derived from 23 individuals, complete with clinical annotation. All data are publicly available through the NF Data Portal and at http://synapse.org/jhubiobank.

Integrative Analysis Identifies Candidate Tumor Microenvironment and Intracellular Signaling Pathways that Define Tumor Heterogeneity in NF1.

Neurofibromatosis type 1 (NF1) is a monogenic syndrome that gives rise to numerous symptoms including cognitive impairment, skeletal abnormalities, and growth of benign nerve sheath tumors. Nearly all NF1 patients develop cutaneous neurofibromas (cNFs), which occur on the skin surface, whereas 40-60% of patients develop plexiform neurofibromas (pNFs), which are deeply embedded in the peripheral nerves. Patients with pNFs have a ~10% lifetime chance of these tumors becoming malignant peripheral nerve sheath tumors (MPNSTs). These tumors have a severe prognosis and few treatment options other than surgery. Given the lack of therapeutic options available to patients with these tumors, identification of druggable pathways or other key molecular features could aid ongoing therapeutic discovery studies. In this work, we used statistical and machine learning methods to analyze 77 NF1 tumors with genomic data to characterize key signaling pathways that distinguish these tumors and identify candidates for drug development. We identified subsets of latent gene expression variables that may be important in the identification and etiology of cNFs, pNFs, other neurofibromas, and MPNSTs. Furthermore, we characterized the association between these latent variables and genetic variants, immune deconvolution predictions, and protein activity predictions.

Wireless control of cellular function by activation of a novel protein responsive to electromagnetic fields.

The Kryptopterus bicirrhis (glass catfish) is known to respond to electromagnetic fields (EMF). Here we tested its avoidance behavior in response to static and alternating magnetic fields stimulation. Using expression cloning we identified an electromagnetic perceptive gene (EPG) from the K. bicirrhis encoding a protein that responds to EMF. This EPG gene was cloned and expressed in mammalian cells, neuronal cultures and in rat's brain. Immunohistochemistry showed that the expression of EPG is confined to the mammalian cell membrane. Calcium imaging in mammalian cells and cultured neurons expressing EPG demonstrated that remote activation by EMF significantly increases intracellular calcium concentrations, indicative of cellular excitability. Moreover, wireless magnetic activation of EPG in rat motor cortex induced motor evoked responses of the contralateral forelimb in vivo. Here we report on the development of a new technology for remote, non-invasive modulation of cell function.

Immediate Effects of Repetitive Magnetic Stimulation on Single Cortical Pyramidal Neurons.

Repetitive Transcranial Magnetic Stimulation (rTMS) has been successfully used as a non-invasive therapeutic intervention for several neurological disorders in the clinic as well as an investigative tool for basic neuroscience. rTMS has been shown to induce long-term changes in neuronal circuits in vivo. Such long-term effects of rTMS have been investigated using behavioral, imaging, electrophysiological, and molecular approaches, but there is limited understanding of the immediate effects of TMS on neurons. We investigated the immediate effects of high frequency (20 Hz) rTMS on the activity of cortical neurons in an effort to understand the underlying cellular mechanisms activated by rTMS. We used whole-cell patch-clamp recordings in acute rat brain slices and calcium imaging of cultured primary neurons to examine changes in neuronal activity and intracellular calcium respectively. Our results indicate that each TMS pulse caused an immediate and transient activation of voltage gated sodium channels (9.6 ± 1.8 nA at -45 mV, p value < 0.01) in neurons. Short 500 ms 20 Hz rTMS stimulation induced action potentials in a subpopulation of neurons, and significantly increased the steady state current of the neurons at near threshold voltages (at -45 mV: before TMS: I = 130 ± 17 pA, during TMS: I = 215 ± 23 pA, p value = 0.001). rTMS stimulation also led to a delayed increase in intracellular calcium (153.88 ± 61.94% increase from baseline). These results show that rTMS has an immediate and cumulative effect on neuronal activity and intracellular calcium levels, and suggest that rTMS may enhance neuronal responses when combined with an additional motor, sensory or cognitive stimulus. Thus, these results could be translated to optimize rTMS protocols for clinical as well as basic science applications.