Epidemiology of COVID-19: A systematic review and meta-analysis of clinical characteristics, risk factors, and outcomes.

Coronavirus disease 2019 (COVID-19) has become a pandemic, but its reported characteristics and outcomes vary greatly amongst studies. We determined pooled estimates for clinical characteristics and outcomes in COVID-19 patients including subgroups by disease severity (based on World Health Organization Interim Guidance Report or Infectious Disease Society of America/American Thoracic Society criteria) and by country/region. We searched Pubmed, Embase, Scopus, Cochrane, Chinese Medical Journal, and preprint databases from 1 January 2020 to 6 April 2020. Studies of laboratory-confirmed COVID-19 patients with relevant data were included. Two reviewers independently performed study selection and data extraction. From 6007 articles, 212 studies from 11 countries/regions involving 281 461 individuals were analyzed. Overall, mean age was 46.7 years, 51.8% were male, 22.9% had severe disease, and mortality was 5.6%. Underlying immunosuppression, diabetes, and malignancy were most strongly associated with severe COVID-19 (coefficient = 53.9, 23.4, 23.4, respectively, all P < .0007), while older age, male gender, diabetes, and hypertension were also associated with higher mortality (coefficient = 0.05 per year, 5.1, 8.2, 6.99, respectively; P = .006-.0002). Gastrointestinal (nausea, vomiting, abdominal pain) and respiratory symptoms (shortness of breath, chest pain) were associated with severe COVID-19, while pneumonia and end-organ failure were associated with mortality. COVID-19 is associated with a severe disease course in about 23% and mortality in about 6% of infected persons. Individuals with comorbidities and clinical features associated with severity should be monitored closely, and preventive efforts should especially target those with diabetes, malignancy, and immunosuppression.

Intratumoral CD39+CD8+ T Cells Predict Response to Programmed Cell Death Protein-1 or Programmed Death Ligand-1 Blockade in Patients With NSCLC.

INTRODUCTION: Programmed cell death protein-1 (PD-1) and programmed death-ligand 1 (PD-L1) blockade is currently widely used in the treatment of metastatic NSCLC. Despite available biomarker stratification, clinical responses vary. Thus, the search for novel biomarkers with improved response prediction is ongoing. Previously, using mass cytometry or cytometry by time-of-flight (CyTOF), our group demonstrated that CD39+CD8+ immune cells represent tumor antigen-specific, cytotoxic T cells in treatment-naive NSCLC. We hypothesized that accurate quantitation of this T cell subset would predict immunotherapy outcome. METHODS: To translate this to a clinical setting, the present study compared CyTOF data with a range of clinically relevant methods, including conventional immunohistochemistry (IHC), multiplex IHC or immunofluorescence (mIHC), and gene expression assay by NanoString. RESULTS: Quantification using mIHC but not conventional IHC or NanoString correlated with the CyTOF results. The specificity and sensitivity of mIHC were then evaluated in a separate retrospective NSCLC cohort. CD39+CD8+ T cell proportion, as determined by mIHC, successfully stratified responders and nonresponders to PD-1 or PD-L1 inhibitors (objective response rate of 63.6%, compared with 0% for the negative group). This predictive capability was independent from other confounding factors, such as total CD8+ T cell proportion, CD39+ lymphocyte proportion, PD-L1 positivity, EGFR mutation status, and other clinicopathologic parameters. CONCLUSIONS: Our results suggest that the mIHC platform is a clinically relevant method to evaluate CD39+CD8+ T cell proportion and that this marker can serve as a potential biomarker that predicts response to PD-1 or PD-L1 blockade in patients with NSCLC. Further validation in additional NSCLC cohorts is warranted.

The Virological, Immunological, and Imaging Approaches for COVID-19 Diagnosis and Research.

In 2019, a novel coronavirus (SARS-CoV-2) was found to cause a highly contagious disease characterized by pneumonia. The disease (COVID-19) quickly spread around the globe, escalating to a global pandemic. In this review, we discuss the virological, immunological, and imaging approaches harnessed for COVID-19 diagnosis and research. COVID-19 shares many clinical characteristics with other respiratory illnesses.Accurate and early detection of the infection is pivotal to controlling the outbreak, as this enables case identification, isolation, and contact tracing. We summarize the available literature on current laboratory and point-of-care diagnostics, highlight their strengths and limitations, and describe the emerging diagnostic approaches on the horizon.We also discuss the various research techniques that are being used to evaluate host immunity in laboratory-confirmed patients. Additionally, pathological imaging of tissue samples from affected patients has a critical role in guiding investigations on this disease. Conventional techniques, such as immunohistochemistry and immunofluorescence, have been frequently used to characterize the immune microenvironment in COVID-19. We also outline the emerging imaging techniques, such as the RNAscope, which might also aid in our understanding of the significance of COVID-19-specific biomarkers, such as the angiotensin-converting enzyme 2 (ACE2) cellular receptor.Overall, great progress has been made in COVID-19 research in a short period. Extensive, global collation of our current knowledge of SARS-CoV-2 will provide insights into novel treatment modalities, such as monoclonal antibodies, and support the development of a SARS-CoV-2 vaccine.

Transcriptional Spatial Profiling of Cancer Tissues in the Era of Immunotherapy: The Potential and Promise.

Intratumoral heterogeneity poses a major challenge to making an accurate diagnosis and establishing personalized treatment strategies for cancer patients. Moreover, this heterogeneity might underlie treatment resistance, disease progression, and cancer relapse. For example, while immunotherapies can confer a high success rate, selective pressures coupled with dynamic evolution within a tumour can drive the emergence of drug-resistant clones that allow tumours to persist in certain patients. To improve immunotherapy efficacy, researchers have used transcriptional spatial profiling techniques to identify and subsequently block the source of tumour heterogeneity. In this review, we describe and assess the different technologies available for such profiling within a cancer tissue. We first outline two well-known approaches, in situ hybridization and digital spatial profiling. Then, we highlight the features of an emerging technology known as Visium Spatial Gene Expression Solution. Visium generates quantitative gene expression data and maps them to the tissue architecture. By retaining spatial information, we are well positioned to identify novel biomarkers and perform computational analyses that might inform on novel combinatorial immunotherapies.

Overview of multiplex immunohistochemistry/immunofluorescence techniques in the era of cancer immunotherapy.

Conventional immunohistochemistry (IHC) is a widely used diagnostic technique in tissue pathology. However, this technique is associated with a number of limitations, including high inter-observer variability and the capacity to label only one marker per tissue section. This review details various highly multiplexed techniques that have emerged to circumvent these constraints, allowing simultaneous detection of multiple markers on a single tissue section and the comprehensive study of cell composition, cellular functional and cell-cell interactions. Among these techniques, multiplex Immunohistochemistry/Immunofluorescence (mIHC/IF) has emerged to be particularly promising. mIHC/IF provides high-throughput multiplex staining and standardized quantitative analysis for highly reproducible, efficient and cost-effective tissue studies. This technique has immediate potential for translational research and clinical practice, particularly in the era of cancer immunotherapy.