The interplay between neoantigens and immune cells in sarcomas treated with checkpoint inhibition.

Introduction: Sarcomas are comprised of diverse bone and connective tissue tumors with few effective therapeutic options for locally advanced unresectable and/or metastatic disease. Recent advances in immunotherapy, in particular immune checkpoint inhibition (ICI), have shown promising outcomes in several cancer indications. Unfortunately, ICI therapy has provided only modest clinical responses and seems moderately effective in a subset of the diverse subtypes. Methods: To explore the immune parameters governing ICI therapy resistance or immune escape, we performed whole exome sequencing (WES) on tumors and their matched normal blood, in addition to RNA-seq from tumors of 31 sarcoma patients treated with pembrolizumab. We used advanced computational methods to investigate key immune properties, such as neoantigens and immune cell composition in the tumor microenvironment (TME). Results: A multifactorial analysis suggested that expression of high quality neoantigens in the context of specific immune cells in the TME are key prognostic markers of progression-free survival (PFS). The presence of several types of immune cells, including T cells, B cells and macrophages, in the TME were associated with improved PFS. Importantly, we also found the presence of both CD8+ T cells and neoantigens together was associated with improved survival compared to the presence of CD8+ T cells or neoantigens alone. Interestingly, this trend was not identified with the combined presence of CD8+ T cells and TMB; suggesting that a combined CD8+ T cell and neoantigen effect on PFS was important. Discussion: The outcome of this study may inform future trials that may lead to improved outcomes for sarcoma patients treated with ICI.

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs.

The global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The goal of this study was to use artificial intelligence (AI) to predict blueprints for designing universal vaccines against SARS-CoV-2, that contain a sufficiently broad repertoire of T-cell epitopes capable of providing coverage and protection across the global population. To help achieve these aims, we profiled the entire SARS-CoV-2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population, using host-infected cell surface antigen presentation and immunogenicity predictors from the NEC Immune Profiler suite of tools, and generated comprehensive epitope maps. We then used these epitope maps as input for a Monte Carlo simulation designed to identify statistically significant "epitope hotspot" regions in the virus that are most likely to be immunogenic across a broad spectrum of HLA types. We then removed epitope hotspots that shared significant homology with proteins in the human proteome to reduce the chance of inducing off-target autoimmune responses. We also analyzed the antigen presentation and immunogenic landscape of all the nonsynonymous mutations across 3,400 different sequences of the virus, to identify a trend whereby SARS-COV-2 mutations are predicted to have reduced potential to be presented by host-infected cells, and consequently detected by the host immune system. A sequence conservation analysis then removed epitope hotspots that occurred in less-conserved regions of the viral proteome. Finally, we used a database of the HLA haplotypes of approximately 22,000 individuals to develop a "digital twin" type simulation to model how effective different combinations of hotspots would work in a diverse human population; the approach identified an optimal constellation of epitope hotspots that could provide maximum coverage in the global population. By combining the antigen presentation to the infected-host cell surface and immunogenicity predictions of the NEC Immune Profiler with a robust Monte Carlo and digital twin simulation, we have profiled the entire SARS-CoV-2 proteome and identified a subset of epitope hotspots that could be harnessed in a vaccine formulation to provide a broad coverage across the global population.

EEG/MEG source imaging: methods, challenges, and open issues.

We present the four key areas of research-preprocessing, the volume conductor, the forward problem, and the inverse problem-that affect the performance of EEG and MEG source imaging. In each key area we identify prominent approaches and methodologies that have open issues warranting further investigation within the community, challenges associated with certain techniques, and algorithms necessitating clarification of their implications. More than providing definitive answers we aim to identify important open issues in the quest of source localization.

Imaging the cerebral blood flow with enhanced laser speckle contrast analysis (eLASCA) by monotonic point transformation.

Laser speckle contrast analysis (LASCA) has been demonstrated as a full-field method for imaging the cerebral blood flow (CBF). However, conventional LASCA is limited to extremely low dynamic range because of the ambient background field, dark current, and other anomalies in the circuits of a charge-coupled device camera, which makes it difficult to analyze the spatiotemporal variabilities in CBF. In this study, we proposed an enhanced LASCA (eLASCA) method to improve the dynamic range of LASCA based on monotonic point transformation. In investigating the influence of moderate hypothermia (32+/-0.5 degrees C) on capillary CBF change, eLASCA presented much more significant decrease of relative CBF than LASCA (hypothermia: 189% versus 137%, postrewarming: 151% versus 119%). Statistically, eLASCA resulted in a higher confidence degree (p=0.009) of CBF change after the rewarming than LASCA (p=0.013). In addition, eLASCA greatly improves the CBF visualization, which is very helpful in demonstrating the details of CBF change.

A tutorial platform suitable for surgical simulator training (SimMentor).

BACKGROUND: The introduction of simulators in surgical training entails the need to develop pedagogic platforms adapted to the potentials and limitations provided by the information technology. As a solution to the technical challenges in treating all possible interaction events and to obtain a suitable pedagogic approach, we have developed a pedagogic platform for surgical training, SimMentor. METHODS: In SimMentor the procedure to be practiced is divided into a number of natural phases. The trainee will practice on one phase at a time, however he can select the sequence of phases arbitrarily. A phase is taught by letting the trainee alternate freely between 2 modes: 1: A 3-dimensional animated guidance designed for learning the objectives and challenges in a procedure. 2: An interactive training session through the instrument manipulator device designed for training motoric responses based on visual and tactile responses produced by the simulator. The two modes are interfaced with the same virtual reality platform, thus SimMentor allows a seamless transition between the modes. RESULTS: We have developed a prototype simulator for robotic assisted endoscopic CABG (Coronary Artery Bypass Grafting) procedure by first focusing on the anastomosis part of the operation. Tissue, suture and instrument models have been developed and integrated with a simulated model of a beating heart comprises the elements in the simulator engine that is used in construction a training platform for learning different methods for performing a coronary anastomosis procedure. CONCLUSION: The platform is designed for integrating the following features: 1) practical approach to handle interactivity events with flexible-objects 3D simulators, 2) methods for quantitative evaluations of performance, 3) didactic presentations, 4) effective ways of producing diversity of clinical and pathological training scenarios.