Simultaneous assessment of cytotoxic T lymphocyte responses against multiple viral infections by combined usage of optimal epitope matrices, anti- CD3 mAb T-cell expansion and "RecycleSpot"

The assessment of cellular anti-viral immunity is often hampered by the limited availability of adequate samples, especially when attempting simultaneous, high-resolution determination of T cell responses against multiple viral infections. Thus, the development of assay systems, which optimize cell usage, while still allowing for the detailed determination of breadth and magnitude of virus-specific cytotoxic T lymphocyte (CTL) responses, is urgently needed. This study provides an up-to-date listing of currently known, well-defined viral CTL epitopes for HIV, EBV, CMV, HCV and HBV and describes an approach that overcomes some of the above limitations through the use of peptide matrices of optimally defined viral CTL epitopes in combination with anti-CD3 in vitro T cell expansion and re-use of cells from negative ELISpot wells. The data show that, when compared to direct ex vivo cell preparations, antigen-unspecific in vitro T cell expansion maintains the breadth of detectable T cell responses and demonstrates that harvesting cells from negative ELISpot wells for re-use in subsequent ELISpot assays (RecycleSpot), further maximized the use of available cells. Furthermore when combining T cell expansion and RecycleSpot with the use of rationally designed peptide matrices, antiviral immunity against more than 400 different CTL epitopes from five different viruses can be reproducibly assessed from samples of less than 10 milliliters of blood without compromising information on the breadth and magnitude of these responses. Together, these data support an approach that facilitates the assessment of cellular immunity against multiple viral co-infections in settings where sample availability is severely limited

Cell-Cycle Protein Expression in a Population-Based Study of Ovarian and Endometrial Cancers

Aberrant expression of cyclin-dependent kinase (CDK) inhibitors is implicated in the carcinogenesis of many cancers, including ovarian and endometrial cancers. We examined associations between CDK inhibitor expression, cancer risk factors, tumor characteristics, and survival outcomes among ovarian and endometrial cancer patients enrolled in a population-based case control study. Expression (negative vs. positive) of three CDK inhibitors (p16, p21, p27) and ki67 was examined with immunohistochemical staining of tissue microarrays. Logistic regression was used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for associations between biomarkers, risk factors, and tumor characteristics. Survival outcomes were available for ovarian cancer patients and examined using Kaplan-Meier plots and Cox proportional hazards regression. Among ovarian cancer patients (n=175), positive p21 expression was associated with endometrioid tumors (OR=12.22, 95% CI=1.45-102.78) and higher overall survival (log-rank p=0.002). In Cox models adjusted for stage, grade, and histology, the association between p21 expression and overall survival was borderline significant (hazard ratio=0.65, 95% CI=0.42-1.05). Among endometrial cancer patients (n=289), positive p21 expression was inversely associated with age (OR ≥ 65 years of age=0.25, 95% CI=0.07-0.84) and current smoking status (OR: 0.33, 95% CI 0.15, 0.72) compared to negative expression. Our study showed heterogeneity in expression of cell-cycle proteins associated with risk factors and tumor characteristics of gynecologic cancers. Future studies to assess these markers of etiological classification and behavior may be warranted

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesized that regression-based DL outperforms classification-based DL. Therefore, we developed and evaluated a new self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from images in 11,671 patients across nine cancer types. We tested our method for multiple clinically and biologically relevant biomarkers: homologous repair deficiency (HRD) score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the interpretability of the results over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology

Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use.

Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders1. They are heritable2,3 and etiologically related4,5 behaviors that have been resistant to gene discovery efforts6-11. In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures

Sedimentary Environment Influences the Effect of an Infaunal Suspension Feeding Bivalve on Estuarine Ecosystem Function

The suspension feeding bivalve Austrovenus stutchburyi is a key species on intertidal sandflats in New Zealand, affecting the appearance and functioning of these systems, but is susceptible to several environmental stressors including sedimentation. Previous studies into the effect of this species on ecosystem function have been restricted in space and time, limiting our ability to infer the effect of habitat change on functioning. We examined the effect of Austrovenus on benthic primary production and nutrient dynamics at two sites, one sandy, the other composed of muddy-sand to determine whether sedimentary environment alters this key species' role. At each site we established large (16 m2) plots of two types, Austrovenus addition and removal. In winter and summer we deployed light and dark benthic chambers to quantify oxygen and nutrient fluxes and measured sediment denitrification enzyme activity to assess denitrification potential. Rates of gross primary production (GPP) and ammonium uptake were significantly increased when Austrovenus was added, relative to removed, at the sandy site (GPP, 1.5 times greater in winter and summer; ammonium uptake, 8 times greater in summer; 3-factor analysis of variance (ANOVA), p<0.05). Denitrification potential was also elevated in Austrovenus addition plots at the sandy site in summer (by 1.6 times, p<0.1). In contrast, there was no effect of Austrovenus treatment on any of these variables at the muddy-sand site, and overall rates tended to be lower at the muddy-sand site, relative to the sandy site (e.g. GPP was 2.1 to 3.4 times lower in winter and summer, respectively, p<0.001). Our results suggest that the positive effects of Austrovenus on system productivity and denitrification potential is limited at a muddy-sand site compared to a sandy site, and reveal the importance of considering sedimentary environment when examining the effect of key species on ecosystem function

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

Detection of Somatic Mutations by High-Resolution DNA Melting (HRM) Analysis in Multiple Cancers

Identification of somatic mutations in cancer is a major goal for understanding and monitoring the events related to cancer initiation and progression. High resolution melting (HRM) curve analysis represents a fast, post-PCR high-throughput method for scanning somatic sequence alterations in target genes. The aim of this study was to assess the sensitivity and specificity of HRM analysis for tumor mutation screening in a range of tumor samples, which included 216 frozen pediatric small rounded blue-cell tumors as well as 180 paraffin-embedded tumors from breast, endometrial and ovarian cancers (60 of each). HRM analysis was performed in exons of the following candidate genes known to harbor established commonly observed mutations: PIK3CA, ERBB2, KRAS, TP53, EGFR, BRAF, GATA3, and FGFR3. Bi-directional sequencing analysis was used to determine the accuracy of the HRM analysis. For the 39 mutations observed in frozen samples, the sensitivity and specificity of HRM analysis were 97% and 87%, respectively. There were 67 mutation/variants in the paraffin-embedded samples, and the sensitivity and specificity for the HRM analysis were 88% and 80%, respectively. Paraffin-embedded samples require higher quantity of purified DNA for high performance. In summary, HRM analysis is a promising moderate-throughput screening test for mutations among known candidate genomic regions. Although the overall accuracy appears to be better in frozen specimens, somatic alterations were detected in DNA extracted from paraffin-embedded samples

E-cadherin breast tumor expression, risk factors and survival : Pooled analysis of 5,933 cases from 12 studies in the Breast Cancer Association Consortium

E-cadherin (CDH1) is a putative tumor suppressor gene implicated in breast carcinogenesis. Yet, whether risk factors or survival differ by E-cadherin tumor expression is unclear. We evaluated E-cadherin tumor immunohistochemistry expression using tissue microarrays of 5,933 female invasive breast cancers from 12 studies from the Breast Cancer Consortium. H-scores were calculated and case-case odds ratios (OR) and 95% confidence intervals (CIs) were estimated using logistic regression. Survival analyses were performed using Cox regression models. All analyses were stratified by estrogen receptor (ER) status and histologic subtype. E-cadherin low cases (N = 1191, 20%) were more frequently of lobular histology, low grade, > 2 cm, and HER2-negative. Loss of E-cadherin expression (score <100) was associated with menopausal hormone use among ER-positive tumors (ever compared to never users, OR = 1.24, 95% CI = 0.97-1.59), which was stronger when we evaluated complete loss of E-cadherin (i.e. H-score = 0), OR = 1.57, 95% CI = 1.06-2.33. Breast cancer specific mortality was unrelated to E-cadherin expression in multivariable models. E-cadherin low expression is associated with lobular histology, tumor characteristics and menopausal hormone use, with no evidence of an association with breast cancer specific survival. These data support loss of E-cadherin expression as an important marker of tumor subtypes.Peer reviewe

Genome-wide Association Meta-analysis of Childhood and Adolescent Internalizing Symptoms

Objective: To investigate the genetic architecture of internalizing symptoms in childhood and adolescence. Method: In 22 cohorts, multiple univariate genome-wide association studies (GWASs) were performed using repeated assessments of internalizing symptoms, in a total of 64,561 children and adolescents between 3 and 18 years of age. Results were aggregated in meta-analyses that accounted for sample overlap, first using all available data, and then using subsets of measurements grouped by rater, age, and instrument. Results: The meta-analysis of overall internalizing symptoms (INToverall) detected no genome-wide significant hits and showed low single nucleotide polymorphism (SNP) heritability (1.66%, 95% CI = 0.84-2.48%, n(effective) = 132,260). Stratified analyses indicated rater-based heterogeneity in genetic effects, with self-reported internalizing symptoms showing the highest heritability (5.63%, 95% CI = 3.08%-8.18%). The contribution of additive genetic effects on internalizing symptoms appeared to be stable over age, with overlapping estimates of SNP heritability from early childhood to adolescence. Genetic correlations were observed with adult anxiety, depression, and the well-being spectrum (vertical bar r(g)vertical bar > 0.70), as well as with insomnia, loneliness, attention-deficit/hyperactivity disorder, autism, and childhood aggression (range vertical bar r(g)vertical bar = 0.42-0.60), whereas there were no robust associations with schizophrenia, bipolar disorder, obsessive-compulsive disorder, or anorexia nervosa. Conclusion: Genetic correlations indicate that childhood and adolescent internalizing symptoms share substantial genetic vulnerabilities with adult internalizing disorders and other childhood psychiatric traits, which could partially explain both the persistence of internalizing symptoms over time and the high comorbidity among childhood psychiatric traits. Reducing phenotypic heterogeneity in childhood samples will be key in paving the way to future GWAS success.Peer reviewe

Cross-ancestry genome-wide association analysis of corneal thickness strengthens link between complex and Mendelian eye diseases

Central corneal thickness (CCT) is a highly heritable trait associated with complex eye diseases such as keratoconus and glaucoma. We perform a genome-wide association meta-analysis of CCT and identify 19 novel regions. In addition to adding support for known connective tissue-related pathways, pathway analyses uncover previously unreported gene sets. Remarkably, >20% of the CCT-loci are near or within Mendelian disorder genes. These included FBN1, ADAMTS2 and TGFB2 which associate with connective tissue disorders (Marfan, Ehlers-Danlos and Loeys-Dietz syndromes), and the LUM-DCN-KERA gene complex involved in myopia, corneal dystrophies and cornea plana. Using index CCT-increasing variants, we find a significant inverse correlation in effect sizes between CCT and keratoconus (r =-0.62, P = 5.30 × 10-5) but not between CCT and primary open-angle glaucoma (r =-0.17, P = 0.2). Our findings provide evidence for shared genetic influences between CCT and keratoconus, and implicate candidate genes acting in collagen and extracellular matrix regulation