Citations

Zika virus (ZIKV) pandemic and its implication in congenital malformations and severe neurological disorders had created serious threats to global health. ZIKV is a mosquito-borne flavivirus which spread rapidly and infect a large number of people in a shorter time-span. Due to the lack of effective therapeutics, this had become paramount urgency to discover effective drug molecules to encounter the viral infection. Various anti-ZIKV drug discovery efforts during the past several years had been unsuccessful to develop an effective cure. The NS2B-NS3 protein was reported as an attractive therapeutic target for inhibiting viral proliferation, due to its central role in viral replication and maturation of non-structural viral proteins. Therefore, the current in silico drug exploration aimed to identify the novel inhibitors of Zika NS2B-NS3 protease by implementing an e-pharmacophore-based high-throughput virtual screening. A 3D e-pharmacophore model was generated based on the five-featured (ADPRR) pharmacophore hypothesis. Subsequently, the predicted model is further subjected to the high-throughput virtual screening to reveal top hit molecules from the various small molecule databases. Initial hits were examined in terms of binding free energies and ADME properties to identify the candidate hit exhibiting a favourable pharmacokinetic profile. Eventually, molecular dynamic (MD) simulations studies were conducted to evaluate the binding stability of the hit molecule inside the receptor cavity. The findings of the in silico analysis manifested affirmative evidence for three hit molecules with −64.28, −55.15 and −50.16 kcal/mol binding free energies, as potent inhibitors of Zika NS2B-NS3 protease. Hence, these molecules holds the promising potential to serve as a prospective candidates to design effective drugs against ZIKV and related viral infections.

Abstract Aims There are several risk scores designed to predict mortality in patients with heart failure (HF). This study aimed to assess performance of risk scores validated for mortality prediction in patients with acute HF (AHF) and chronic HF. Methods and results MEDLINE and Scopus were searched from January 2015 to January 2021 for studies which internally or externally validated risk models for predicting all-cause mortality in patients with AHF and chronic HF. Discrimination data were analysed using C-statistics, and pooled using generic inverse-variance random-effects model. Nineteen studies (n = 494 156 patients; AHF: 24 762; chronic HF mid-term mortality: 62 000; chronic HF long-term mortality: 452 097) and 11 risk scores were included. Overall, discrimination of risk scores was good across the three subgroups: AHF mortality [C-statistic: 0.76 (0.68–0.83)], chronic HF mid-term mortality [1 year; C-statistic: 0.74 (0.68–0.79)], and chronic HF long-term mortality [≥2 years; C-statistic: 0.71 (0.69–0.73)]. MEESSI-AHF [C-statistic: 0.81 (0.80–0.83)] and MARKER-HF [C-statistic: 0.85 (0.80–0.89)] had an excellent discrimination for AHF and chronic HF mid-term mortality, respectively, whereas MECKI had good discrimination [C-statistic: 0.78 (0.73–0.83)] for chronic HF long-term mortality relative to other models. Overall, risk scores predicting short-term mortality in patients with AHF did not have evidence of poor calibration (Hosmer–Lemeshow P > 0.05). However, risk models predicting mid-term and long-term mortality in patients with chronic HF varied in calibration performance. Conclusions The majority of recently validated risk scores showed good discrimination for mortality in patients with HF. MEESSI-AHF demonstrated excellent discrimination in patients with AHF, and MARKER-HF and MECKI displayed an excellent discrimination in patients with chronic HF. However, modest reporting of calibration and lack of head-to-head comparisons in same populations warrant future studies.

AbstractBackgroundRepresentation trends of women, older adults, and ethnic/racial minorities in randomized controlled trials (RCTs) of atrial fibrillation (AF) are uncertain.MethodsWe systematically reviewed 134 AF related RCTs (phase II and III) encompassing 149,162 participants using Medline and ClinicalTrials.gov through April 2019 to determine representation trends of women, older patients (≥75 years), and ethnic/racial minorities. Weighted data on the prevalence of AF from epidemiological studies were used to compare the representation of the studied groups of interest in AF RCTs to their expected burden of the disease.ResultsOnly 18.7% of the RCTs reported proportion of older patients, and 12.7% RCTs reported ethnic/racial minorities. The proportions of women in RCTs versus general population were 35.2% and 35.1%, of Hispanics were 11.9% and 5.2%, of Blacks were 1.2% and 5.7%, of American Indian/Alaskans were 0.2% and 0.2%, of Asians were 14.2% and 2.4%, of native Hawaiian/Pacific Islanders were 0.05% and 0.1% and of non‐Whites were 19.5% and 22.5%, respectively. The weighted mean age (SD) across the trials was 65.3 (3.2) years which was less than the corresponding weighted mean age of 71.1 (4.5) years in the comparative epidemiological data.ConclusionThe reporting of older patients and ethnic/racial minorities was poor in RCTs of AF. The representation of women and American Indian/Alaskan natives matched their expected population share of disease burden. Hispanics and Asians were over‐represented and Blacks, native Hawaiian/Pacific Islanders and non‐Whites were under‐represented in RCTs of AF.

Abstract Aims Anticoagulants are the mainstay treatment for stroke prevention in patients with non-valvular atrial fibrillation (NVAF), and the CHA2DS2-VASc score is widely used to guide anticoagulation therapy in this cohort. However, utility of CHA2DS2-VASc in NVAF patients is debated, primarily because it is a vascular scoring system, which does not incorporate atrial fibrillation related parameters. Therefore, we conducted a meta-analysis to estimate the discrimination ability of CHA2DS2-VASc in predicting ischaemic stroke overall, and in subgroups of patients with or without NVAF. Methods and results PubMed and Embase databases were searched till June 2020 for published articles that assessed the discrimination ability of CHA2DS2-VASc, as measured by C-statistics, during mid-term (2–5 years) and long-term (>5 years) follow-up. Summary estimates were reported as random effects C-statistics with 95% confidence intervals (CIs). Seventeen articles were included in the analysis. Nine studies (n = 453 747 patients) reported the discrimination ability of CHA2DS2-VASc in NVAF patients, and 10 studies (n = 138 262 patients) in patients without NVAF. During mid-term follow-up, CHA2DS2-VASc predicted stroke with modest discrimination in the overall cohort [0.67 (0.65–0.69)], with similar discrimination ability in patients with NVAF [0.65 (0.63–0.68)] and in those without NVAF [0.69 (0.68–0.71)] (P-interaction = 0.08). Similarly, at long-term follow-up, CHA2DS2-VASc had modest discrimination [0.66 (0.63–0.69)], which was consistent among patients with NVAF [0.63 (0.54–0.71)] and those without NVAF [0.67 (0.64–0.70)] (P-interaction = 0.39). Conclusion This meta-analysis suggests that the discrimination power of the CHA2DS2-VASc score in predicting ischaemic stroke is modest, and is similar in the presence or absence of NVAF. More accurate stroke prediction models are thus needed for the NVAF population.

Abstract Background: It is not known whether modifiable lifestyle factors that predict survival after invasive breast cancer differ by subtype. Methods: We analyzed data for 121,435 women diagnosed with breast cancer from 67 studies in the Breast Cancer Association Consortium with 16,890 deaths (8,554 breast cancer specific) over 10 years. Cox regression was used to estimate associations between risk factors and 10-year all-cause mortality and breast cancer–specific mortality overall, by estrogen receptor (ER) status, and by intrinsic-like subtype. Results: There was no evidence of heterogeneous associations between risk factors and mortality by subtype (Padj > 0.30). The strongest associations were between all-cause mortality and BMI ≥30 versus 18.5–25 kg/m2 [HR (95% confidence interval (CI), 1.19 (1.06–1.34)]; current versus never smoking [1.37 (1.27–1.47)], high versus low physical activity [0.43 (0.21–0.86)], age ≥30 years versus <20 years at first pregnancy [0.79 (0.72–0.86)]; >0–<5 years versus ≥10 years since last full-term birth [1.31 (1.11–1.55)]; ever versus never use of oral contraceptives [0.91 (0.87–0.96)]; ever versus never use of menopausal hormone therapy, including current estrogen–progestin therapy [0.61 (0.54–0.69)]. Similar associations with breast cancer mortality were weaker; for example, 1.11 (1.02–1.21) for current versus never smoking. Conclusions: We confirm associations between modifiable lifestyle factors and 10-year all-cause mortality. There was no strong evidence that associations differed by ER status or intrinsic-like subtype. Impact: Given the large dataset and lack of evidence that associations between modifiable risk factors and 10-year mortality differed by subtype, these associations could be cautiously used in prognostication models to inform patient-centered care.

Abstract Background: Biopsy diagnosis of benign breast disease (BBD) based on the most severe lesion in a sample predicts future breast cancer risk and has implications for screening and management. Lobules are the functional unit of the breast and the structures from which BBD arises. We developed and preliminarily validated an automated computational pathology algorithm to discriminate normal from BBD breast lobules as a step toward automated comprehensive characterization of benign biopsies. Methods: 152 BBD biopsies (27 training, 125 validation) from the Mayo Clinic were examined using scanned digital images of H&E stained sections. For each image, a pathologist annotated up to 10 representative lobules as normal or BBD using standard pathology criteria. A deep learning algorithm to quantify lobular features was developed using 129 lobules from 27 subjects. Nine features were identified that discriminate normal vs BBD lobules, expressing lobule size, acini size and number, acinar lumen size, proportion of lobular stroma, and capillaries. Here, we validate their performance to discriminate normal vs BBD lobules in a set of 1250 lobules from 125 subjects using area under the ROC curve (AUC) analysis. Random forest analysis was used for multivariable modeling; model performance was assessed with a tenfold cross-validation approach. Results: Median subject age was 52 years. Among the 125 validation subjects, BBD findings were nonproliferative in 39%, proliferative in 45%, and atypical hyperplasia in 16%. Sections included 552 (44%) normal lobules and 698 BBD (56%) lobules, with representation of both lobule types on each section. In univariate analyses, four individual features showed good discrimination between normal vs. BBD lobules, yielding the following AUCs: lobule size= 0.74, mean acini size= 0.75, epithelial area= 0.75, and number of acini with large lumens= 0.76. Lobule size and epithelial area were highly correlated (Spearman rank correlation r = 0.95), but both of these features showed lower correlation with mean acini size and number of acini with large lumens (each r < 0.5). With random forest modeling, number of acini with large lumens was the strongest discriminating feature, followed by mean acini size, lobule size, and epithelial area. With ten-fold cross validation of the multivariable random forest model, the overall AUC was 0.82 (95% CI: 0.79-0.85). Conclusion: Our validation showed that automated quantitative computational pathology assessment of breast lobules can discriminate normal versus BBD on a per lobule basis. This finding supports the feasibility of developing automated algorithms to classify every lesion in breast biopsies, expanding beyond visual assessment. Further studies using deep learning may reveal novel pathology features for classifying BBD biopsies with the potential to strengthen estimation of breast cancer risk. Citation Format: Amy C. Degnim, Thomas de Bel, Mark E. Sherman, Derek C. Radisky, Stacey J. Winham, Tanya L. Hoskin, Melody L. Stallings Mann, Marlene Frost, Robert A. Vierkant, Brendan T. Broderick, Ethan P. Heinzen, Rushin Brahmbhatt, Muhammad Arshad, Celine M. Vachon, Jodi M. Carter, Lori A. Denison, Daniel W. Visscher, Jeroen van der Laak. Discrimination of benign breast disease from normal lobules using an automated computational pathology algorithm [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2113.

AbstractPrevious transcriptome‐wide association studies (TWAS) have identified breast cancer risk genes by integrating data from expression quantitative loci and genome‐wide association studies (GWAS), but analyses of breast cancer subtype‐specific associations have been limited. In this study, we conducted a TWAS using gene expression data from GTEx and summary statistics from the hitherto largest GWAS meta‐analysis conducted for breast cancer overall, and by estrogen receptor subtypes (ER+ and ER−). We further compared associations with ER+ and ER− subtypes, using a case‐only TWAS approach. We also conducted multigene conditional analyses in regions with multiple TWAS associations. Two genes, STXBP4 and HIST2H2BA, were specifically associated with ER+ but not with ER– breast cancer. We further identified 30 TWAS‐significant genes associated with overall breast cancer risk, including four that were not identified in previous studies. Conditional analyses identified single independent breast‐cancer gene in three of six regions harboring multiple TWAS‐significant genes. Our study provides new information on breast cancer genetics and biology, particularly about genomic differences between ER+ and ER− breast cancer.