Precision oncology in neurofibromatosis type 1: quantification of differential sensitivity to selumetinib in plexiform neurofibromas using single-cell RNA sequencing.

PURPOSE: Selumetinib is an FDA-approved targeted therapy for plexiform neurofibromas in neurofibromatosis type 1(NF1) with durable response rates seen in most, but not all patients. In this proof-of-concept study, we demonstrate single-cell RNA sequencing(scRNAseq) as a technique for quantifying drug response to selumetinib at the single cell level. METHODS: scRNAseq data from neurofibroma biopsies was obtained from a public genomics repository. Schwann cell populations were identified through standard clustering techniques and single-cell selumetinib sensitivity was quantified on a scale of 0(resistant) to 1(sensitive) based on the expression pattern of a 500 gene selumetinib sensitivity signature from the BeyondCell sensitivity library. RESULTS: A total of seven plexiform neurofibromas were included in our final analysis. The median absolute number of Schwann cells across samples was 658 cells (IQR: 1,029 cells, Q1-Q3: 135 cells to 1,163 cells). There was a statistically significant difference in selumetinib sensitivity profiles across samples (p < 0.001). The tumor with the highest median selumetinib sensitivity score had a median selumetinib sensitivity score of 0.64(IQR: 0.14, Q1-Q3: 0.59-0.70, n = 112 cells) and the tumor with the lowest median selumetinib sensitivity score had a median score of 0.37 (IQR: 0.21, Q1-Q3: 0.27-0.48, n = 1,034 cells). CONCLUSIONS: scRNAseq of plexiform neurofibroma biopsies reveals differential susceptibilities to selumetinib on a single cell level. These findings may explain the partial responses seen in clinical trials of selumetinib for NF1 and demonstrate the value of collecting scRNAseq data for future NF1 trials.

Emergent Applications of Machine Learning for Diagnosing and Managing Appendicitis: A State-of-the-Art Review.

Background: Appendicitis is an inflammatory condition that requires timely and effective intervention. Despite being one of the most common surgically treated diseases, the condition is difficult to diagnose because of atypical presentations. Ultrasound and computed tomography (CT) imaging improve the sensitivity and specificity of diagnoses, yet these tools bear the drawbacks of high operator dependency and radiation exposure, respectively. However, new artificial intelligence tools (such as machine learning) may be able to address these shortcomings. Methods: We conducted a state-of-the-art review to delineate the various use cases of emerging machine learning algorithms for diagnosing and managing appendicitis in recent literature. The query ("Appendectomy" OR "Appendicitis") AND ("Machine Learning" OR "Artificial Intelligence") was searched across three databases for publications ranging from 2012 to 2022. Upon filtering for duplicates and based on our predefined inclusion criteria, 39 relevant studies were identified. Results: The algorithms used in these studies performed with an average accuracy of 86% (18/39), a sensitivity of 81% (16/39), a specificity of 75% (16/39), and area under the receiver operating characteristic curves (AUROCs) of 0.82 (15/39) where reported. Based on accuracy alone, the optimal model was logistic regression in 18% of studies, an artificial neural network in 15%, a random forest in 13%, and a support vector machine in 10%. Conclusions: The identified studies suggest that machine learning may provide a novel solution for diagnosing appendicitis and preparing for patient-specific post-operative complications. However, further studies are warranted to assess the feasibility and advisability of implementing machine learning-based tools in clinical practice.

Racial disparities in complications following elective colon cancer resection: Impact of laparoscopic versus robotic approaches.

BACKGROUND: We sought to examine differences in outcomes for Black and White patients undergoing robotic or laparoscopic colectomy to assess the potential impact of technological advancement. METHODS: We queried the ACS-NSQIP database for elective robotic (RC) and laparoscopic (LC) colectomy for cancer from 2012 to 2020. Outcomes included 30-day mortality and complications. We analyzed the association between outcomes, operative approach, and race using multivariable logistic regression. RESULTS: We identified 64,460 patients, 80.9% laparoscopic and 19.1% robotic. RC patients were most frequently younger, male, and White, with fewer comorbidities (P â€‹< â€‹0.001). After adjustment, there was no difference in mortality by approach or race. Black patients who underwent LC had higher complications (OR 1.10, 95% CI 1.03-1.08, P â€‹= â€‹0.005) than their White LC counterparts and RC patients. CONCLUSIONS: Robotic colectomy was associated with lower rates of complications in minority patients. Further investigation is required to identify the causal pathway that leads to our finding.

Characterizing T-cell dysfunction and exclusion signatures in malignant peripheral nerve sheath tumors reveals susceptibilities to immunotherapy.

PURPOSE: Malignant peripheral nerve sheath tumors (MPNSTs) are malignant tumors that arise from peripheral nerves and are the leading cause of mortality in Neurofibromatosis Type 1 (NF1). In this study, we characterized whether transcriptomic signatures of T-cell dysfunction (TCD) and exclusion (TCE) that inversely correlate with response to immune checkpoint blockade (ICB) immunotherapy exist in MPNSTs. METHODS: MPNST transcriptomes were pooled from Gene Expression Omnibus (GEO). For each sample, a tumor immune dysfunction and exclusion (TIDE) score, TCD and TCE subscores, and cytotoxic T-cell(CTL) level were calculated. In the TIDE predictive algorithm, tumors are predicted to have an ICB response if they are either immunologically hot (CTL-high) without TCD or immunologically cold (CTL-low) without TCE. TIDE scores greater than zero correspond with ICB nonresponse. RESULTS: 73 MPNST samples met inclusion criteria, including 50 NF1-associated MPNSTs (68.5%). The average TIDE score was + 0.41 (SD = 1.16) with 22 (30.1%) predicted ICB responders. 11 samples were CTL-high (15.1%) with an average TCD score of + 0.99 (SD = 0.63). Among 62 CTL-low tumors, 21 were predicted to have ICB response with an average TCE score of + 0.31(SD = 1.20). Age(p = 0.18), sex(p = 0.41), NF1 diagnosis (p = 0.17), and PRC2 loss(p = 0.29) were not associated with ICB responder status. CONCLUSIONS: Transcriptomic analysis of TCD and TCE signatures in MPNST samples reveals that a select subset of patients with MPNSTs may benefit from ICB immunotherapy.

Microfluidic 'brain-on chip' systems to supplement neurological practice: development, applications and considerations.

Among the greatest general challenges in bioengineering is to mimic human physiology. Advanced efforts in tissue engineering have led to sophisticated 'brain-on-chip' (BoC) microfluidic devices that can mimic structural and functional aspects of brain tissue. BoC may be used to understand the biochemical pathways of neurolgical pathologies and assess promising therapeutic agents for facilitating regenerative medicine. We evaluated the potential of microfluidic BoC devices in various neurological pathologies, such as Alzheimer's, glioblastoma, traumatic brain injury, stroke and epilepsy. We also discuss the principles, limitations and future considerations of BoC technology. Results suggest that BoC models can help understand complex neurological pathologies and augment drug testing efforts for regenerative applications. However, implementing organ-on-chip technology to clinical practice has some practical limitations that warrant greater attention to improve large-scale applicability. Nevertheless, they remain to be versatile and powerful tools that can broaden our understanding of pathophysiological and therapeutic uncertainties to neurological diseases.

In this paper, the authors describe the role of microfluidic 'brain-on-chip' systems as a tool to model and study the human brain. While animal studies have provided significant insights, they lack the complexity of human brain tissue in order to verify the effects of drugs on patients, study complex physiological pathways or personalize regenerative therapies. This makes studying diseases of complex human organs challenging. Microfluidics is a field of study that can address these challenges by developing sophisticated and miniaturized devices that can chamber human tissue. These devices could allow scientists to better study diseases on a model that is accurate and controllable, allowing researchers to better understand complex diseases, assess drug efficacy to specific areas of the brain and potentially accelerate the development of new therapies. Herein, we characterize the principles, development and challenges of microfluidics and the role they have served in different neurological diseases.

Smartphone-Based Light Detection and Ranging for Remote Patient Evaluation and Monitoring.

LIDAR (from "light detection and ranging" or "laser imaging, detection, and ranging") is an evolving three-dimensional scanning technology with historical applications in various fields. However, the applicability of LIDAR in the field of medicine has mostly not been examined thus far. Here, we review the basic principles governing LIDAR and its potential to be used in three notable use cases in the context of remote patient monitoring: geriatric fall prevention, postoperative recovery monitoring, and home safety assessment. For assisting geriatric populations, LIDAR can create 3D renderings of their home environments and classify which objects may be associated with risk for falls. These risk factors can then be forwarded to both patients and providers in order for them to discuss how to make the patient's environment safer. LIDAR is also capable of mapping the range of extremity motion in patients undergoing postoperative recovery. Such LIDAR data is simple to acquire and record for these patients and could enable unique metrics to be developed to assess patient outcomes in postoperative recovery. Finally, LIDAR can also reproduce 3D home models to identify attributes of their environments that could be harmful to infants. Given the recent momentum in telehealth following the events of the coronavirus 2019 disease (COVID-19) pandemic, LIDAR may also be a powerful tool in driving new insights from quality improvement initiatives through remote patient monitoring.

Astrocyte Heterogeneity in Multiple Sclerosis: Current Understanding and Technical Challenges.

The emergence of single cell technologies provides the opportunity to characterize complex immune/central nervous system cell assemblies in multiple sclerosis (MS) and to study their cell population structures, network activation and dynamics at unprecedented depths. In this review, we summarize the current knowledge of astrocyte subpopulations in MS tissue and discuss the challenges associated with resolving astrocyte heterogeneity with single-nucleus RNA-sequencing (snRNA-seq). We further discuss multiplexed imaging techniques as tools for defining population clusters within a spatial context. Finally, we will provide an outlook on how these technologies may aid in answering unresolved questions in MS, such as the glial phenotypes that drive MS progression and/or neuropathological differences between different clinical MS subtypes.