Human Inborn Errors of Immunity: 2019 Update of the IUIS Phenotypical Classification.

Since 2013, the International Union of Immunological Societies (IUIS) expert committee (EC) on Inborn Errors of Immunity (IEI) has published an updated phenotypic classification of IEI, which accompanies and complements their genotypic classification into ten tables. This phenotypic classification is user-friendly and serves as a resource for clinicians at the bedside. There are now 430 single-gene IEI underlying phenotypes as diverse as infection, malignancy, allergy, autoimmunity, and autoinflammation. We herein report the 2019 phenotypic classification, including the 65 new conditions. The diagnostic algorithms are based on clinical and laboratory phenotypes for each of the ten broad categories of IEI

Discovering Affordances Through Perception and Manipulation

International audienceConsidering perception as an observation process only is the very reason for which robotic perception methods are to date unable to provide a general capacity of scene understanding. Related work in neuroscience has shown that there is a strong relationship between perception and action. We believe that considering perception in relation to action requires to interpret the scene in terms of the agent's own potential capabilities. In this paper, we propose a Bayesian approach for learning sensorimotor representations through the interaction between action and observation capabilities. We represent the notion of affordance as a probabilistic relation between three elements: objects, actions and effects. Experiments for affordances discovery were performed on a real robotic platform in an unsupervised way assuming a limited set of innate capabilities. Results show dependency relations that connect the three elements in a common frame: affordances. The increasing number of interactions and observations results in a Bayesian network that captures the relationships between them. The learned representation can be used for prediction tasks

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Ras-p53 genomic cooperativity as a model to investigate mechanisms of innate immune regulation in gastrointestinal cancers

Despite increasingly thorough mechanistic understanding of the dominant genetic drivers of gastrointestinal (GI) tumorigenesis (e.g., Ras/Raf, TP53, etc.), only a small proportion of these molecular alterations are therapeutically actionable. In an attempt to address this therapeutic impasse, our group has proposed an innovative extreme outlier model to identify novel cooperative molecular vulnerabilities in high-risk GI cancers which dictate prognosis, correlate with distinct patterns of metastasis, and define therapeutic sensitivity or resistance. Our model also proposes comprehensive investigation of their downstream transcriptomic, immunomic, metabolic, or upstream epigenomic cellular consequences to reveal novel therapeutic targets in previously “undruggable” tumors with high-risk genomic features. Leveraging this methodology, our and others’ data reveal that the genomic cooperativity between Ras and p53 alterations is not only prognostically relevant in GI malignancy, but may also represent the incipient molecular events that initiate and sustain innate immunoregulatory signaling networks within the GI tumor microenvironment, driving T-cell exclusion and therapeutic resistance in these cancers. As such, deciphering the unique transcriptional programs encoded by Ras-p53 cooperativity that promote innate immune trafficking and chronic inflammatory tumor-stromal-immune crosstalk may uncover immunologic vulnerabilities that could be exploited to develop novel therapeutic strategies for these difficult-to-treat malignancies

Identifying and characterizing COPD patients in US managed care. A retrospective, cross-sectional analysis of administrative claims data

<p>Abstract</p> <p>Background</p> <p>Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death among US adults and is projected to be the third by 2020. In anticipation of the increasing burden imposed on healthcare systems and payers by patients with COPD, a means of identifying COPD patients who incur higher healthcare utilization and costs is needed.</p> <p>Methods</p> <p>This retrospective, cross-sectional analysis of US managed care administrative claims data describes a practical way to identify COPD patients. We analyze 7.79 million members for potential inclusion in the COPD cohort, who were continuously eligible during a 1-year study period. A younger commercial population (7.7 million) is compared with an older Medicare population (0.115 million). We outline a novel approach to stratifying COPD patients using "complexity" of illness, based on occurrence of claims for given comorbid conditions. Additionally, a unique algorithm was developed to identify and stratify COPD exacerbations using claims data.</p> <p>Results</p> <p>A total of 42,565 commercial (median age 56 years; 51.4% female) and 8507 Medicare patients (median 75 years; 53.1% female) were identified as having COPD. Important differences were observed in comorbidities between the younger commercial versus the older Medicare population. Stratifying by complexity, 45.0%, 33.6%, and 21.4% of commercial patients and 36.6%, 35.8%, and 27.6% of older patients were low, moderate, and high, respectively. A higher proportion of patients with high complexity disease experienced multiple (≥2) exacerbations (61.7% commercial; 49.0% Medicare) than patients with moderate- (56.9%; 41.6%), or low-complexity disease (33.4%; 20.5%). Utilization of healthcare services also increased with an increase in complexity.</p> <p>Conclusion</p> <p>In patients with COPD identified from Medicare or commercial claims data, there is a relationship between complexity as determined by pulmonary and non-pulmonary comorbid conditions and the prevalence of exacerbations and utilization of healthcare services. Identification of COPD patients at highest risk of exacerbations using complexity stratification may facilitate improved disease management by targeting those most in need of treatment.</p

A method to study the effect of bronchodilators on smoke retention in COPD patients: study protocol for a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Chronic obstructive pulmonary disease (COPD) is a common disease, associated with cardiovascular disease. Many patients use (long-acting) bronchodilators, whilst they continue smoking alongside. We hypothesised an interaction between bronchodilators and smoking that enhances smoke exposure, and hence cardiovascular disease. In this paper, we report our study protocol that explores the fundamental interaction, i.e. smoke retention.</p> <p>Method</p> <p>The design consists of a double-blinded, placebo-controlled, randomised crossover trial, in which 40 COPD patients smoke cigarettes during both undilated and maximal bronchodilated conditions. Our primary outcome is the retention of cigarette smoke, expressed as tar and nicotine weight. The inhaled tar weights are calculated from the correlated extracted nicotine weights in cigarette filters, whereas the exhaled weights are collected on Cambridge filters. We established the inhaled weight calculations by a pilot study, that included paired measurements from several smoking regimes. Our study protocol is approved by the local accredited medical review ethics committee.</p> <p>Discussion</p> <p>Our study is currently in progress. The pilot study revealed valid equations for inhaled tar and nicotine, with an R²of 0.82 and 0.74 (p < 0.01), respectively. We developed a method to study pulmonary smoke retentions in COPD patients under the influence of bronchodilation which may affect smoking-related disease. This trial will provide fundamental knowledge about the (cardiovascular) safety of bronchodilators in patients with COPD who persist in their habit of cigarette smoking.</p> <p>Trial registration</p> <p>ClinicalTrials.gov: <a href="http://www.clinicaltrials.gov/ct2/show/NCT00981851">NCT00981851</a></p