Showing papers by "University of California, San Francisco published in 2019"
TL;DR: The latest version of STRING more than doubles the number of organisms it covers, and offers an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input.
Abstract: Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein-protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein-protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
10,584 citations
Northern Arizona University1, National Institutes of Health2, University of Minnesota3, University of California, Davis4, Woods Hole Oceanographic Institution5, Massachusetts Institute of Technology6, University of Copenhagen7, University of Trento8, Chinese Academy of Sciences9, University of California, San Francisco10, University of Pennsylvania11, Pacific Northwest National Laboratory12, North Carolina State University13, University of California, San Diego14, Institute for Systems Biology15, Dalhousie University16, University of British Columbia17, Statens Serum Institut18, Anschutz Medical Campus19, University of Washington20, Michigan State University21, Stanford University22, Broad Institute23, Harvard University24, Australian National University25, University of Düsseldorf26, University of New South Wales27, Sookmyung Women's University28, San Diego State University29, Howard Hughes Medical Institute30, Cornell University31, Max Planck Society32, Colorado State University33, Google34, Syracuse University35, Webster University36, United States Department of Agriculture37, University of Arkansas for Medical Sciences38, Colorado School of Mines39, University of Southern Mississippi40, National Oceanic and Atmospheric Administration41, University of California, Merced42, Wageningen University and Research Centre43, University of Arizona44, Environment Agency45, University of Florida46, Merck & Co.47
TL;DR: QIIME 2 development was primarily funded by NSF Awards 1565100 to J.G.C. and R.K.P. and partial support was also provided by the following: grants NIH U54CA143925 and U54MD012388.
Abstract: QIIME 2 development was primarily funded by NSF Awards 1565100 to J.G.C. and 1565057 to R.K. Partial support was also provided by the following: grants NIH U54CA143925 (J.G.C. and T.P.) and U54MD012388 (J.G.C. and T.P.); grants from the Alfred P. Sloan Foundation (J.G.C. and R.K.); ERCSTG project MetaPG (N.S.); the Strategic Priority Research Program of the Chinese Academy of Sciences QYZDB-SSW-SMC021 (Y.B.); the Australian National Health and Medical Research Council APP1085372 (G.A.H., J.G.C., Von Bing Yap and R.K.); the Natural Sciences and Engineering Research Council (NSERC) to D.L.G.; and the State of Arizona Technology and Research Initiative Fund (TRIF), administered by the Arizona Board of Regents, through Northern Arizona University. All NCI coauthors were supported by the Intramural Research Program of the National Cancer Institute. S.M.G. and C. Diener were supported by the Washington Research Foundation Distinguished Investigator Award.
8,821 citations
University of Pennsylvania1, University of Chicago2, University of Melbourne3, Emory University4, University of Cologne5, University of Kansas6, Medical University of Vienna7, Ohio State University8, University of California, San Francisco9, University of Texas MD Anderson Cancer Center10, Université de Montréal11, University of Minnesota12, McMaster University13, Royal Prince Alfred Hospital14, University of Würzburg15, Karolinska Institutet16, University of Michigan17, University of Oslo18, Novartis19, University of Lyon20
TL;DR: The chimeric antigen receptor (CAR) T-cell therapy tisagenlecleucel targets and eliminates CD19-expressing B cells and showed efficacy against B-cell lymphomas in a single-center, phase 2a study.
Abstract: Background Patients with diffuse large B-cell lymphoma that is refractory to primary and second-line therapies or that has relapsed after stem-cell transplantation have a poor prognosis. The chimeric antigen receptor (CAR) T-cell therapy tisagenlecleucel targets and eliminates CD19-expressing B cells and showed efficacy against B-cell lymphomas in a single-center, phase 2a study. Methods We conducted an international, phase 2, pivotal study of centrally manufactured tisagenlecleucel involving adult patients with relapsed or refractory diffuse large B-cell lymphoma who were ineligible for or had disease progression after autologous hematopoietic stem-cell transplantation. The primary end point was the best overall response rate (i.e., the percentage of patients who had a complete or partial response), as judged by an independent review committee. Results A total of 93 patients received an infusion and were included in the evaluation of efficacy. The median time from infusion to data cutoff was 14 ...
2,086 citations
TL;DR: The original Cancer Cell Line Encyclopedia is expanded with deeper characterization of over 1,000 cell lines, including genomic, transcriptomic, and proteomic data, and integration with drug-sensitivity and gene-dependency data, which reveals potential targets for cancer drugs and associated biomarkers.
Abstract: Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous framework with which to study genetic variants, candidate targets, and small-molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes, including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from individuals of various lineages and ethnicities. Integration of these data with functional characterizations such as drug-sensitivity, short hairpin RNA knockdown and CRISPR-Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource for the acceleration of cancer research using model cancer cell lines.
1,801 citations
TL;DR: Using scRNA-seq analysis, Bhattacharya and colleagues identify a subset of profibrotic lung macrophages that have a gene expression signature intermediate between those of monocytes and alveolar macrophage.
Abstract: Tissue fibrosis is a major cause of mortality that results from the deposition of matrix proteins by an activated mesenchyme. Macrophages accumulate in fibrosis, but the role of specific subgroups in supporting fibrogenesis has not been investigated in vivo. Here, we used single-cell RNA sequencing (scRNA-seq) to characterize the heterogeneity of macrophages in bleomycin-induced lung fibrosis in mice. A novel computational framework for the annotation of scRNA-seq by reference to bulk transcriptomes (SingleR) enabled the subclustering of macrophages and revealed a disease-associated subgroup with a transitional gene expression profile intermediate between monocyte-derived and alveolar macrophages. These CX3CR1+SiglecF+ transitional macrophages localized to the fibrotic niche and had a profibrotic effect in vivo. Human orthologs of genes expressed by the transitional macrophages were upregulated in samples from patients with idiopathic pulmonary fibrosis. Thus, we have identified a pathological subgroup of transitional macrophages that are required for the fibrotic response to injury.
1,790 citations
Lawrence Berkeley National Laboratory1, Buck Institute for Research on Aging2, Lund University3, University of California, San Francisco4, National Institutes of Health5, University College London6, Harvard University7, Columbia University Medical Center8, Emory University9, Humanitas University10, University of Milan11, Queen Mary University of London12, Stanford University13, Albert Einstein College of Medicine14, Veterans Health Administration15, University of California, Los Angeles16, European University of Madrid17
TL;DR: The multi-level mechanisms underlying SCI and several risk factors that promote this health-damaging phenotype, including infections, physical inactivity, poor diet, environmental and industrial toxicants and psychological stress are described.
Abstract: Although intermittent increases in inflammation are critical for survival during physical injury and infection, recent research has revealed that certain social, environmental and lifestyle factors can promote systemic chronic inflammation (SCI) that can, in turn, lead to several diseases that collectively represent the leading causes of disability and mortality worldwide, such as cardiovascular disease, cancer, diabetes mellitus, chronic kidney disease, non-alcoholic fatty liver disease and autoimmune and neurodegenerative disorders. In the present Perspective we describe the multi-level mechanisms underlying SCI and several risk factors that promote this health-damaging phenotype, including infections, physical inactivity, poor diet, environmental and industrial toxicants and psychological stress. Furthermore, we suggest potential strategies for advancing the early diagnosis, prevention and treatment of SCI.
1,708 citations
TL;DR: It is demonstrated that an end-to-end deep learning approach can classify a broad range of distinct arrhythmias from single-lead ECGs with high diagnostic performance similar to that of cardiologists.
Abstract: Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG workflow1. Widely available digital ECG data and the algorithmic paradigm of deep learning2 present an opportunity to substantially improve the accuracy and scalability of automated ECG analysis. However, a comprehensive evaluation of an end-to-end deep learning approach for ECG analysis across a wide variety of diagnostic classes has not been previously reported. Here, we develop a deep neural network (DNN) to classify 12 rhythm classes using 91,232 single-lead ECGs from 53,549 patients who used a single-lead ambulatory ECG monitoring device. When validated against an independent test dataset annotated by a consensus committee of board-certified practicing cardiologists, the DNN achieved an average area under the receiver operating characteristic curve (ROC) of 0.97. The average F1 score, which is the harmonic mean of the positive predictive value and sensitivity, for the DNN (0.837) exceeded that of average cardiologists (0.780). With specificity fixed at the average specificity achieved by cardiologists, the sensitivity of the DNN exceeded the average cardiologist sensitivity for all rhythm classes. These findings demonstrate that an end-to-end deep learning approach can classify a broad range of distinct arrhythmias from single-lead ECGs with high diagnostic performance similar to that of cardiologists. If confirmed in clinical settings, this approach could reduce the rate of misdiagnosed computerized ECG interpretations and improve the efficiency of expert human ECG interpretation by accurately triaging or prioritizing the most urgent conditions. Analysis of electrocardiograms using an end-to-end deep learning approach can detect and classify cardiac arrhythmia with high accuracy, similar to that of cardiologists.
1,632 citations
VU University Amsterdam1, Karolinska Institutet2, deCODE genetics3, Vanderbilt University4, Jönköping University5, Oslo University Hospital6, University of Oslo7, King's College London8, Stavanger University Hospital9, Akershus University Hospital10, University of Tromsø11, Innlandet Hospital Trust12, Norwegian University of Science and Technology13, University of California, San Francisco14, Vanderbilt University Medical Center15, University of Iceland16, University of Cambridge17, University of Bergen18, University College London19, Namsos Hospital20, University of North Carolina at Chapel Hill21, Charité22, Broad Institute23, Harvard University24, VU University Medical Center25
TL;DR: A large genome-wide association study of clinically diagnosed AD and AD-by-proxy identifies new loci and functional pathways that contribute to AD risk and adds novel insights into the neurobiology of AD.
Abstract: Alzheimer's disease (AD) is highly heritable and recent studies have identified over 20 disease-associated genomic loci. Yet these only explain a small proportion of the genetic variance, indicating that undiscovered loci remain. Here, we performed a large genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 cases, 383,378 controls). AD-by-proxy, based on parental diagnoses, showed strong genetic correlation with AD (rg = 0.81). Meta-analysis identified 29 risk loci, implicating 215 potential causative genes. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver, and microglia). Gene-set analyses indicate biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomization results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD.
1,460 citations
TL;DR: Among patients with a recent myocardial infarction, colchicine at a dose of 0.5 mg daily led to a significantly lower risk of ischemic cardiovascular events than placebo.
Abstract: Background Experimental and clinical evidence supports the role of inflammation in atherosclerosis and its complications. Colchicine is an orally administered, potent antiinflammatory medi...
1,426 citations
TL;DR: Treatment with alpelisib–fulvestrant prolonged progression‐free survival among patients with PIK3CA‐mutated, HR‐positive, HER2‐negative advanced breast cancer who had received endocrine therapy previously.
Abstract: Background PIK3CA mutations occur in approximately 40% of patients with hormone receptor (HR)–positive, human epidermal growth factor receptor 2 (HER2)–negative breast cancer. The PI3Kα-sp...
1,357 citations
Griffith University1, University of Glasgow2, University of Pittsburgh3, Trinity College, Dublin4, University College London5, Public Health Foundation of India6, Heidelberg University7, Radboud University Nijmegen8, Universidade Federal do Rio Grande do Sul9, National University of Colombia10, University of California, San Francisco11, New York University12, McGill University13
TL;DR: The extent and consequences of oral diseases, their social and commercial determinants, and their ongoing neglect in global health policy are described to highlight the urgent need to address oral diseases among other NCDs as a global health priority.
Roswell Park Cancer Institute1, Johns Hopkins University2, Duke University3, Brigham and Women's Hospital4, Mayo Clinic5, City of Hope National Medical Center6, Memorial Sloan Kettering Cancer Center7, Fred Hutchinson Cancer Research Center8, Fox Chase Cancer Center9, Yale Cancer Center10, Washington University in St. Louis11, University of California, San Diego12, University of Wisconsin-Madison13, University Hospitals of Cleveland14, University of Alabama at Birmingham15, Vanderbilt University16, Moffitt Cancer Center17, University of Colorado Boulder18, University of Tennessee Health Science Center19, University of California, San Francisco20, Northwestern University21, Ohio State University22, University of Michigan23, Stanford University24, University of Utah25, National Comprehensive Cancer Network26
TL;DR: The NCCN Guidelines for Prostate Cancer include recommendations regarding diagnosis, risk stratification and workup, treatment options for localized disease, and management of recurrent and advanced disease for clinicians who treat patients with prostate cancer.
Abstract: The NCCN Guidelines for Prostate Cancer include recommendations regarding diagnosis, risk stratification and workup, treatment options for localized disease, and management of recurrent and advanced disease for clinicians who treat patients with prostate cancer. The portions of the guidelines included herein focus on the roles of germline and somatic genetic testing, risk stratification with nomograms and tumor multigene molecular testing, androgen deprivation therapy, secondary hormonal therapy, chemotherapy, and immunotherapy in patients with prostate cancer.
University of British Columbia1, Oregon Health & Science University2, Heidelberg University3, Utrecht University4, Northern Arizona University5, University of Alberta6, George Mason University7, Wingate University8, American Cancer Society9, University of California, San Francisco10, Penn State Cancer Institute11
TL;DR: Enough evidence was available to conclude that specific doses of aerobic, combined aerobic plus resistance training, and/or resistance training could improve common cancer-related health outcomes, including anxiety, depressive symptoms, fatigue, physical functioning, and health-related quality of life.
Abstract: PurposeThe number of cancer survivors worldwide is growing, with over 15.5 million cancer survivors in the United States alone—a figure expected to double in the coming decades. Cancer survivors face unique health challenges as a result of their cancer diagnosis and the impact of treatments
European Bioinformatics Institute1, University of Manchester2, University of California, San Francisco3, Swiss Institute of Bioinformatics4, Laboratory of Molecular Biology5, J. Craig Venter Institute6, University of Delaware7, National Institutes of Health8, University of Southern California9, Georgetown University Medical Center10, University of Padua11, University of Udine12, University College London13
TL;DR: Recent developments with InterPro (version 70.0) and its associated software are reported, including an 18% growth in the size of the database in terms on new InterPro entries, updates to content, the inclusion of an additional entry type, refined modelling of discontinuous domains, and the development of a new programmatic interface and website.
Abstract: The InterPro database (http://www.ebi.ac.uk/interpro/) classifies protein sequences into families and predicts the presence of functionally important domains and sites. Here, we report recent developments with InterPro (version 70.0) and its associated software, including an 18% growth in the size of the database in terms on new InterPro entries, updates to content, the inclusion of an additional entry type, refined modelling of discontinuous domains, and the development of a new programmatic interface and website. These developments extend and enrich the information provided by InterPro, and provide greater flexibility in terms of data access. We also show that InterPro's sequence coverage has kept pace with the growth of UniProtKB, and discuss how our evaluation of residue coverage may help guide future curation activities.
TL;DR: A computational doublet detection tool-DoubletFinder-that identifies doublets using only gene expression data is presented, allowing its application across scRNA-seq datasets with diverse distributions of cell types.
Abstract: Single-cell RNA sequencing (scRNA-seq) data are commonly affected by technical artifacts known as "doublets," which limit cell throughput and lead to spurious biological conclusions. Here, we present a computational doublet detection tool-DoubletFinder-that identifies doublets using only gene expression data. DoubletFinder predicts doublets according to each real cell's proximity in gene expression space to artificial doublets created by averaging the transcriptional profile of randomly chosen cell pairs. We first use scRNA-seq datasets where the identity of doublets is known to show that DoubletFinder identifies doublets formed from transcriptionally distinct cells. When these doublets are removed, the identification of differentially expressed genes is enhanced. Second, we provide a method for estimating DoubletFinder input parameters, allowing its application across scRNA-seq datasets with diverse distributions of cell types. Lastly, we present "best practices" for DoubletFinder applications and illustrate that DoubletFinder is insensitive to an experimentally validated kidney cell type with "hybrid" expression features.
TL;DR: Genome-wide analysis identifies 30 loci associated with bipolar disorder, allowing for comparisons of shared genes and pathways with other psychiatric disorders, including schizophrenia and depression.
Abstract: Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P < 1 × 10-4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P < 5 × 10-8) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder.
TL;DR: A deep neural network is described that accurately predicts splice junctions from an arbitrary pre-mRNA transcript sequence, enabling precise prediction of noncoding genetic variants that cause cryptic splicing.
Dresden University of Technology1, Brigham and Women's Hospital2, University of California, San Francisco3, University of Düsseldorf4, University of Pisa5, Northwestern University6, Medical University of Vienna7, National and Kapodistrian University of Athens8, Medical University of South Carolina9, University of Cambridge10, University of Barcelona11, The Feinstein Institute for Medical Research12, Toronto Western Hospital13, University of California, Los Angeles14, Humboldt University of Berlin15, Copenhagen University Hospital16, University of Michigan17, University of the Basque Country18, University Health Network19, University of Crete20, University of Zagreb21, University of Paris-Sud22, University of Hong Kong23, University of Calgary24, Hospital for Special Surgery25, University of Pécs26, University of Padua27, Medical University of Graz28, National Institutes of Health29, New York University30, Université Paris-Saclay31, University Hospital Complex Of Vigo32, University of Occupational and Environmental Health Japan33, University of Porto34, Leeds Teaching Hospitals NHS Trust35, Cedars-Sinai Medical Center36, Istanbul Bilim University37, McMaster University38
TL;DR: To develop new classification criteria for systemic lupus erythematosus (SLE) jointly supported by the European League Against Rheumatism and the American College of Rheumatology (ACR).
Abstract: Objective To develop new classification criteria for systemic lupus erythematosus (SLE) jointly supported by the European League Against Rheumatism (EULAR) and the American College of Rheumatology (ACR). Methods This international initiative had four phases. 1) Evaluation of antinuclear antibody (ANA) as an entry criterion through systematic review and meta-regression of the literature and criteria generation through an international Delphi exercise, an early patient cohort, and a patient survey. 2) Criteria reduction by Delphi and nominal group technique exercises. 3) Criteria definition and weighting based on criterion performance and on results of a multi-criteria decision analysis. 4) Refinement of weights and threshold scores in a new derivation cohort of 1,001 subjects and validation compared with previous criteria in a new validation cohort of 1,270 subjects. Results The 2019 EULAR/ACR classification criteria for SLE include positive ANA at least once as obligatory entry criterion; followed by additive weighted criteria grouped in 7 clinical (constitutional, hematologic, neuropsychiatric, mucocutaneous, serosal, musculoskeletal, renal) and 3 immunologic (antiphospholipid antibodies, complement proteins, SLE-specific antibodies) domains, and weighted from 2 to 10. Patients accumulating ≥10 points are classified. In the validation cohort, the new criteria had a sensitivity of 96.1% and specificity of 93.4%, compared with 82.8% sensitivity and 93.4% specificity of the ACR 1997 and 96.7% sensitivity and 83.7% specificity of the Systemic Lupus International Collaborating Clinics 2012 criteria. Conclusion These new classification criteria were developed using rigorous methodology with multidisciplinary and international input, and have excellent sensitivity and specificity. Use of ANA entry criterion, hierarchically clustered, and weighted criteria reflects current thinking about SLE and provides an improved foundation for SLE research.
TL;DR: Many of the stringApp features are introduced and shown how they can be used to carry out complex network analysis and visualization tasks on a typical proteomics data set, all through the Cytoscape user interface.
Abstract: Protein networks have become a popular tool for analyzing and visualizing the often long lists of proteins or genes obtained from proteomics and other high-throughput technologies. One of the most popular sources of such networks is the STRING database, which provides protein networks for more than 2000 organisms, including both physical interactions from experimental data and functional associations from curated pathways, automatic text mining, and prediction methods. However, its web interface is mainly intended for inspection of small networks and their underlying evidence. The Cytoscape software, on the other hand, is much better suited for working with large networks and offers greater flexibility in terms of network analysis, import, and visualization of additional data. To include both resources in the same workflow, we created stringApp, a Cytoscape app that makes it easy to import STRING networks into Cytoscape, retains the appearance and many of the features of STRING, and integrates data from associated databases. Here, we introduce many of the stringApp features and show how they can be used to carry out complex network analysis and visualization tasks on a typical proteomics data set, all through the Cytoscape user interface. stringApp is freely available from the Cytoscape app store: http://apps.cytoscape.org/apps/stringapp .
University of California, San Francisco1, University of Birmingham2, University of Liège3, Advocate Lutheran General Hospital4, Kantonsspital St. Gallen5, University of Adelaide6, Baylor College of Medicine7, Mayo Clinic8, University of Southern California9, Asahikawa Medical University10, University of Dundee11, Pontifical Catholic University of Chile12, Uppsala University13, University of Hong Kong14, Royal Adelaide Hospital15, University of Hamburg16, Sunnybrook Health Sciences Centre17, University of Minnesota18, Technische Universität München19, University of Cambridge20, University of Bologna21, Washington University in St. Louis22, Greenville Health System23, University of Bristol24, University of Ottawa25, Nagoya University26, University of Texas Southwestern Medical Center27, Shanghai Jiao Tong University28, Icahn School of Medicine at Mount Sinai29, Brigham and Women's Hospital30, Oregon Health & Science University31, University of Buenos Aires32, Duke University33, St. Elizabeth's Medical Center34, Dartmouth College35, University of Massachusetts Amherst36, University of the Witwatersrand37, Ghent University Hospital38, Sun Yat-sen University39
TL;DR: The GVG proposes a new Global Anatomic Staging System (GLASS), which involves defining a preferred target artery path (TAP) and then estimating limb-based patency (LBP) resulting in three stages of complexity for intervention.
TL;DR: Gingipains from Porphyromonas gingivalis drive Alzheimer’s pathology and can be blocked with small-molecule inhibitors, suggesting that gingipain inhibitors could be valuable for treating P. gedivalis brain colonization and neurodegeneration in Alzheimer's disease.
Abstract: Porphyromonas gingivalis, the keystone pathogen in chronic periodontitis, was identified in the brain of Alzheimer's disease patients. Toxic proteases from the bacterium called gingipains were also identified in the brain of Alzheimer's patients, and levels correlated with tau and ubiquitin pathology. Oral P. gingivalis infection in mice resulted in brain colonization and increased production of Aβ1-42, a component of amyloid plaques. Further, gingipains were neurotoxic in vivo and in vitro, exerting detrimental effects on tau, a protein needed for normal neuronal function. To block this neurotoxicity, we designed and synthesized small-molecule inhibitors targeting gingipains. Gingipain inhibition reduced the bacterial load of an established P. gingivalis brain infection, blocked Aβ1-42 production, reduced neuroinflammation, and rescued neurons in the hippocampus. These data suggest that gingipain inhibitors could be valuable for treating P. gingivalis brain colonization and neurodegeneration in Alzheimer's disease.
TL;DR: The authors define interpretability in the context of machine learning and introduce the predictive, descriptive, relevant (PDR) framework for discussing interpretations, with three overarching desiderata for evaluation: predictive accuracy, descriptive accuracy, and relevancy, with relevance judged relative to a human audience.
Abstract: Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for prediction, the ability to interpret what a model has learned is receiving an increasing amount of attention. However, this increased focus has led to considerable confusion about the notion of interpretability. In particular, it is unclear how the wide array of proposed interpretation methods are related and what common concepts can be used to evaluate them. We aim to address these concerns by defining interpretability in the context of machine learning and introducing the predictive, descriptive, relevant (PDR) framework for discussing interpretations. The PDR framework provides 3 overarching desiderata for evaluation: predictive accuracy, descriptive accuracy, and relevancy, with relevancy judged relative to a human audience. Moreover, to help manage the deluge of interpretation methods, we introduce a categorization of existing techniques into model-based and post hoc categories, with subgroups including sparsity, modularity, and simulatability. To demonstrate how practitioners can use the PDR framework to evaluate and understand interpretations, we provide numerous real-world examples. These examples highlight the often underappreciated role played by human audiences in discussions of interpretability. Finally, based on our framework, we discuss limitations of existing methods and directions for future work. We hope that this work will provide a common vocabulary that will make it easier for both practitioners and researchers to discuss and choose from the full range of interpretation methods.
University of Texas MD Anderson Cancer Center1, American College of Radiology2, University of Michigan3, University of Wisconsin Hospital and Clinics4, Cleveland Clinic5, University of California, San Francisco6, The Ohio State University Wexner Medical Center7, Yale University8, Fox Chase Cancer Center9, Princess Margaret Cancer Centre10, University Hospitals of Cleveland11, Ohio State University12, University of Oklahoma Health Sciences Center13, Stanford University14, Emory University15, University of Louisville16, Kaiser Permanente17, University of Alabama at Birmingham18, University of Chicago19
TL;DR: After median follow-up duration of 4·5 years, radiotherapy plus cetuximab did not meet the non-inferiority criteria for overall survival, and patients were stratified by T category (T1-T2 vs T3-T4), N category (N0-N2a vs N2b-N3), Zubrod performance status (0 vs 1), and tobacco smoking history (≤10 pack-years).
TL;DR: It is suggested that the neoadjuvant administration of PD-1 blockade enhances both the local and systemic antitumor immune response and may represent a more efficacious approach to the treatment of this uniformly lethal brain tumor.
Abstract: Glioblastoma is the most common primary malignant brain tumor in adults and is associated with poor survival. The Ivy Foundation Early Phase Clinical Trials Consortium conducted a randomized, multi-institution clinical trial to evaluate immune responses and survival following neoadjuvant and/or adjuvant therapy with pembrolizumab in 35 patients with recurrent, surgically resectable glioblastoma. Patients who were randomized to receive neoadjuvant pembrolizumab, with continued adjuvant therapy following surgery, had significantly extended overall survival compared to patients that were randomized to receive adjuvant, post-surgical programmed cell death protein 1 (PD-1) blockade alone. Neoadjuvant PD-1 blockade was associated with upregulation of T cell- and interferon-γ-related gene expression, but downregulation of cell-cycle-related gene expression within the tumor, which was not seen in patients that received adjuvant therapy alone. Focal induction of programmed death-ligand 1 in the tumor microenvironment, enhanced clonal expansion of T cells, decreased PD-1 expression on peripheral blood T cells and a decreasing monocytic population was observed more frequently in the neoadjuvant group than in patients treated only in the adjuvant setting. These findings suggest that the neoadjuvant administration of PD-1 blockade enhances both the local and systemic antitumor immune response and may represent a more efficacious approach to the treatment of this uniformly lethal brain tumor.
University of Kentucky1, Mayo Clinic2, University of Pennsylvania3, Rush University Medical Center4, Illinois Institute of Technology5, Uppsala University6, Newcastle University7, University of Cambridge8, University of Southern California9, University of Minnesota10, University of Sydney11, University of California, Irvine12, University of Washington13, Medical University of Vienna14, Emory University15, Stanford University16, University of California, San Diego17, University of California, San Francisco18, Harvard University19, University of Texas Southwestern Medical Center20
TL;DR: A recently recognized brain disorder that mimics the clinical features of Alzheimer’s disease: Limbic-predominant Age-related TDP-43 Encephalopathy (LATE).
Abstract: We describe a recently recognized disease entity, limbic-predominant age-related TDP-43 encephalopathy (LATE). LATE neuropathological change (LATE-NC) is defined by a stereotypical TDP-43 proteinopathy in older adults, with or without coexisting hippocampal sclerosis pathology. LATE-NC is a common TDP-43 proteinopathy, associated with an amnestic dementia syndrome that mimicked Alzheimer's-type dementia in retrospective autopsy studies. LATE is distinguished from frontotemporal lobar degeneration with TDP-43 pathology based on its epidemiology (LATE generally affects older subjects), and relatively restricted neuroanatomical distribution of TDP-43 proteinopathy. In community-based autopsy cohorts, ∼25% of brains had sufficient burden of LATE-NC to be associated with discernible cognitive impairment. Many subjects with LATE-NC have comorbid brain pathologies, often including amyloid-β plaques and tauopathy. Given that the 'oldest-old' are at greatest risk for LATE-NC, and subjects of advanced age constitute a rapidly growing demographic group in many countries, LATE has an expanding but under-recognized impact on public health. For these reasons, a working group was convened to develop diagnostic criteria for LATE, aiming both to stimulate research and to promote awareness of this pathway to dementia. We report consensus-based recommendations including guidelines for diagnosis and staging of LATE-NC. For routine autopsy workup of LATE-NC, an anatomically-based preliminary staging scheme is proposed with TDP-43 immunohistochemistry on tissue from three brain areas, reflecting a hierarchical pattern of brain involvement: amygdala, hippocampus, and middle frontal gyrus. LATE-NC appears to affect the medial temporal lobe structures preferentially, but other areas also are impacted. Neuroimaging studies demonstrated that subjects with LATE-NC also had atrophy in the medial temporal lobes, frontal cortex, and other brain regions. Genetic studies have thus far indicated five genes with risk alleles for LATE-NC: GRN, TMEM106B, ABCC9, KCNMB2, and APOE. The discovery of these genetic risk variants indicate that LATE shares pathogenetic mechanisms with both frontotemporal lobar degeneration and Alzheimer's disease, but also suggests disease-specific underlying mechanisms. Large gaps remain in our understanding of LATE. For advances in prevention, diagnosis, and treatment, there is an urgent need for research focused on LATE, including in vitro and animal models. An obstacle to clinical progress is lack of diagnostic tools, such as biofluid or neuroimaging biomarkers, for ante-mortem detection of LATE. Development of a disease biomarker would augment observational studies seeking to further define the risk factors, natural history, and clinical features of LATE, as well as eventual subject recruitment for targeted therapies in clinical trials.
TL;DR: The findings show that exosomal PD-L1 represents an unexplored therapeutic target, which could overcome resistance to current antibody approaches, and is described as a potential new therapeutic target for cancer patients.
TL;DR: Cytoscape Automation (CA), which marries CyToscape to highly productive workflow systems, for example, Python/R in Jupyter/RStudio, is described, which exposes over 270 Cytoscapes core functions and 34 apps as REST-callable functions with standardized JSON interfaces backed by Swagger documentation.
Abstract: Cytoscape is one of the most successful network biology analysis and visualization tools, but because of its interactive nature, its role in creating reproducible, scalable, and novel workflows has been limited. We describe Cytoscape Automation (CA), which marries Cytoscape to highly productive workflow systems, for example, Python/R in Jupyter/RStudio. We expose over 270 Cytoscape core functions and 34 Cytoscape apps as REST-callable functions with standardized JSON interfaces backed by Swagger documentation. Independent projects to create and publish Python/R native CA interface libraries have reached an advanced stage, and a number of automation workflows are already published.
University of Texas MD Anderson Cancer Center1, Duke University2, Northwestern University3, City of Hope National Medical Center4, University of California, San Francisco5, Fox Chase Cancer Center6, Brigham and Women's Hospital7, University of California, San Diego8, Fred Hutchinson Cancer Research Center9, Memorial Sloan Kettering Cancer Center10, Vanderbilt University11, University of Utah12, University of Michigan13, Roswell Park Cancer Institute14, Mayo Clinic15, Yale Cancer Center16, Johns Hopkins University17, University of Colorado Boulder18, University of Wisconsin-Madison19, University Hospitals of Cleveland20, Washington University in St. Louis21, Ohio State University22, University of South Florida23, Stanford University24, University of Pennsylvania25, Harvard University26, National Comprehensive Cancer Network27
TL;DR: This selection from the NCCN Guidelines for Esophageal and Esophagogastric Junction Cancers focuses on recommendations for the management of locally advanced and metastatic adenocarcinoma of the esophagus and EGJ.
Abstract: Esophageal cancer is the sixth leading cause of cancer-related deaths worldwide. Squamous cell carcinoma is the most common histology in Eastern Europe and Asia, and adenocarcinoma is most common in North America and Western Europe. Surgery is a major component of treatment of locally advanced resectable esophageal and esophagogastric junction (EGJ) cancer, and randomized trials have shown that the addition of preoperative chemoradiation or perioperative chemotherapy to surgery significantly improves survival. Targeted therapies including trastuzumab, ramucirumab, and pembrolizumab have produced encouraging results in the treatment of patients with advanced or metastatic disease. Multidisciplinary team management is essential for all patients with esophageal and EGJ cancers. This selection from the NCCN Guidelines for Esophageal and Esophagogastric Junction Cancers focuses on recommendations for the management of locally advanced and metastatic adenocarcinoma of the esophagus and EGJ.
University of Paris-Sud1, University of California, Los Angeles2, Sheba Medical Center3, University of Barcelona4, Aix-Marseille University5, university of lille6, University of California, San Francisco7, University of Sydney8, Royal Prince Alfred Hospital9, The Royal Marsden NHS Foundation Trust10, University of Manchester11, Netherlands Cancer Institute12, Sunnybrook Health Sciences Centre13, Merck & Co.14, Royal North Shore Hospital15
TL;DR: Pembrolizumab improved progression-free survival and overall survival versus ipilimumab in patients with advanced melanoma and is now a standard of care in the first-line setting, however, the optimal duration of anti-PD-1 administration is unknown.
Abstract: Summary Background Pembrolizumab improved progression-free survival and overall survival versus ipilimumab in patients with advanced melanoma and is now a standard of care in the first-line setting. However, the optimal duration of anti-PD-1 administration is unknown. We present results from 5 years of follow-up of patients in KEYNOTE-006. Methods KEYNOTE-006 was an open-label, multicentre, randomised, controlled, phase 3 study done at 87 academic institutions, hospitals, and cancer centres in 16 countries. Patients aged at least 18 years with Eastern Cooperative Oncology Group performance status of 0 or 1, ipilimumab-naive histologically confirmed advanced melanoma with known BRAFV600 status and up to one previous systemic therapy were randomly assigned (1:1:1) to intravenous pembrolizumab 10 mg/kg every 2 weeks or every 3 weeks or four doses of intravenous ipilimumab 3 mg/kg every 3 weeks. Treatments were assigned using a centralised, computer-generated allocation schedule with blocked randomisation within strata. Exploratory combination of data from the two pembrolizumab dosing regimen groups was not protocol-specified. Pembrolizumab treatment continued for up to 24 months. Eligible patients who discontinued pembrolizumab with stable disease or better after receiving at least 24 months of pembrolizumab or discontinued with complete response after at least 6 months of pembrolizumab and then progressed could receive an additional 17 cycles of pembrolizumab. Co-primary endpoints were overall survival and progression-free survival. Efficacy was analysed in all randomly assigned patients, and safety was analysed in all randomly assigned patients who received at least one dose of study treatment. Exploratory assessment of efficacy and safety at 5 years' follow-up was not specified in the protocol. Data cutoff for this analysis was Dec 3, 2018. Recruitment is closed; the study is ongoing. This study is registered with ClinicalTrials.gov, number NCT01866319. Findings Between Sept 18, 2013, and March 3, 2014, 834 patients were enrolled and randomly assigned to receive pembrolizumab (every 2 weeks, n=279; every 3 weeks, n=277), or ipilimumab (n=278). After a median follow-up of 57·7 months (IQR 56·7–59·2) in surviving patients, median overall survival was 32·7 months (95% CI 24·5–41·6) in the combined pembrolizumab groups and 15·9 months (13·3–22·0) in the ipilimumab group (hazard ratio [HR] 0·73, 95% CI 0·61–0·88, p=0·00049). Median progression-free survival was 8·4 months (95% CI 6·6–11·3) in the combined pembrolizumab groups versus 3·4 months (2·9–4·2) in the ipilimumab group (HR 0·57, 95% CI 0·48–0·67, p Interpretation Pembrolizumab continued to show superiority over ipilimumab after almost 5 years of follow-up. These results provide further support for use of pembrolizumab in patients with advanced melanoma. Funding Merck Sharp & Dohme.
University of Pennsylvania1, University of Texas MD Anderson Cancer Center2, University of Marburg3, Memorial Sloan Kettering Cancer Center4, University of Bologna5, Carlos III Health Institute6, University of Maryland, Baltimore7, University of Chicago8, University of Minnesota9, University of Alabama at Birmingham10, Medical University of South Carolina11, University of California, San Francisco12, Thomas Jefferson University13, National Taiwan University14, Yale University15, Centre Hospitalier Universitaire de Toulouse16, University of Fukui17, Seoul National University18, University of Ulsan19, Wake Forest University20, Harvard University21, Astellas Pharma22, Johns Hopkins University23
TL;DR: Gilteritinib resulted in significantly longer survival and higher percentages of patients with remission than salvage chemotherapy among patients with relapsed or refractory FLT3-mutated AML.
Abstract: Background Patients with relapsed or refractory acute myeloid leukemia (AML) with mutations in the FMS-like tyrosine kinase 3 gene (FLT3) infrequently have a response to salvage chemothera...