Citations

PURPOSE Pretreatment estimates of seminal vesicle invasion (SVI) are challenging and significantly influence the management of prostate cancer. We sought to improve current models to predict SVI through the development of an SVI prediction genomic signature. PATIENTS AND METHODS A total of 15,889 patients who underwent radical prostatectomy (RP) with available baseline clinical, pathology, and transcriptome data were retrieved from the GRID registry (ClinicalTrials.gov identifier: NCT02609269 ) and other retrospective cohorts. These data were divided into a training (n = 6,766), test (n = 3,363), and two validation (n = 5,062 and 698) cohorts. Multivariable logistic regression was performed to assess the predictive effect of the genomic SVI (gSVI) classifier in the presence of established nomograms (Partin Tables and Memorial Sloan Kettering Cancer Center [MSKCC]). RESULTS In the training cohort, univariable filtering identified 2,132 genes that were differentially expressed between RP tumors with and without SVI. Model parameters were tuned to maximize the area under the curve (AUC) in the testing cohort, resulting in a logistic generalized linear model with 581 genes. The gSVI model scores range from 0 to 1. In the first validation set, gSVI showed superior discrimination of patients with and without SVI at RP compared with other prognostic signatures trained to predict distant metastasis or clinical recurrence. Of the 698 patients in the second validation set, gSVI combined with the MSKCC nomogram had a superior AUC (0.86) compared with either nomogram individually (0.81). CONCLUSION The gSVI represents a novel and validated expression signature to predict the presence of SVI before treatment with surgery. This genomic tool adds discriminatory power to existing clinical predictive nomograms and may help with pretreatment counseling and decision making.

Abstract Purpose To compare the dosimetric performances of intensity‐modulated proton therapy (IMPT) plans generated with two different beam angle configurations (the Right–Left oblique posterior beams and the Superior–Inferior oblique posterior beams) for the treatment of distal esophageal carcinoma in the presence of uncertainties and interplay effect. Methods and Materials Twenty patients’ IMPT plans were retrospectively selected, with 10 patients treated with the R‐L oblique posterior beams (Group R‐L) and the other 10 patients treated with the S‐I oblique posterior beams (Group S‐I). Patients in both groups were matched by their clinical target volumes (CTVs—high and low dose levels) and respiratory motion amplitudes. Dose‐volume‐histogram (DVH) indices were used to assess plan quality. DVH bandwidth was calculated to evaluate plan robustness. Interplay effect was quantified using four‐dimensional (4D) dynamic dose calculation with random respiratory starting phase of each fraction. Normal tissue complication probability (NTCP) for heart, liver, and lung was calculated, respectively, to estimate the clinical outcomes. Wilcoxon signed‐rank test was used for statistical comparison between the two groups. Results Compared with plans in Group R‐L, plans in Group S‐I resulted in significantly lower liver D mean and lung V 30Gy[RBE] with slightly higher but clinically acceptable spinal cord D max . Similar plan robustness was observed between the two groups. When interplay effect was considered, plans in Group S‐I performed statistically better for heart D mean and V 30Gy[RBE] , lung Dmean and V 5Gy[RBE] , and liver D mean , with slightly increased but clinically acceptable spinal cord D max . NTCP for liver was significantly better in Group S‐I. Conclusions IMPT plans in Group S‐I have better sparing of liver, heart, and lungs at the slight cost of spinal cord maximum dose protection, and are more interplay‐effect resilient compared to IMPT plans in Group R‐L. Our study supports the routine use of the S‐I oblique posterior beams for the treatments of distal esophageal carcinoma.

AbstractCarotid artery atherosclerotic disease (CAAD) is a risk factor for stroke. We used a genome‐wide association (GWAS) approach to discover genetic variants associated with CAAD in participants in the electronic Medical Records and Genomics (eMERGE) Network. We identified adult CAAD cases with unilateral or bilateral carotid artery stenosis and controls without evidence of stenosis from electronic health records at eight eMERGE sites. We performed GWAS with a model adjusting for age, sex, study site, and genetic principal components of ancestry. In eMERGE we found 1793 CAAD cases and 17,958 controls. Two loci reached genome‐wide significance, on chr6 in LPA (rs10455872, odds ratio [OR] (95% confidence interval [CI]) = 1.50 (1.30–1.73), p = 2.1 × 10−8) and on chr7, an intergenic single nucleotide variant (SNV; rs6952610, OR (95% CI) = 1.25 (1.16–1.36), p = 4.3 × 10−8). The chr7 association remained significant in the presence of the LPA SNV as a covariate. The LPA SNV was also associated with coronary heart disease (CHD; 4199 cases and 11,679 controls) in this study (OR (95% CI) = 1.27 (1.13–1.43), p = 5 × 10−5) but the chr7 SNV was not (OR (95% CI) = 1.03 (0.97–1.09), p = .37). Both variants replicated in UK Biobank. Elevated lipoprotein(a) concentrations ([Lp(a)]) and LPA variants associated with elevated [Lp(a)] have previously been associated with CAAD and CHD, including rs10455872. With electronic health record phenotypes in eMERGE and UKB, we replicated a previously known association and identified a novel locus associated with CAAD.

Integrative single-cell analysis de-constructs the heterogeneity of human reprogramming and uncovers its stage-specific GRNs.

Abstract BackgroundThe effect of angiotensin-converting enzyme inhibitor/angiotensin receptor blockers (ACEI/ARB) on the COVID-19 remains controversial from clinic evidence. MethodsThis is a retrospective, two-center case series of 198 consecutive COVID-19 patients with a history of hypertension. ResultsAmong 198 patients, 58 (29.3%) and 16 (8.1%) were on were on ARB and ACEI, respectively. Patients who were on ARB or ACEI/ARB had a significantly lower rate of severe illness and ARDS when compared with patients treated with ACEI alone or not receiving and RAAS blocker (P＜0.05). The Kaplan-Meier survival curve showed that patients with ARB in their antihypertensive regimen had a trend towards a higher survival rate when compared with individuals without ARB (adjusted hazard ratio, 0.27; 95% CI, 0.07-1.02; P = 0.054). The Cox-regression analysis to compared ACEI vs. ARB groups showed a significantly lower mortality rate in the ARB group (adjusted hazard ratio, 0.03; 95% CI, 0.00-0.58; P = 0.02). ConclusionsUsing of ARB was associated with a reduced rate of severe illness and ARDS, indicating their potential protective impact in COVID-19.

Objective To assess the efficacy of erenumab at the ≥50%, ≥75%, and 100% reduction in monthly migraine days (MMD) response thresholds, using data from the 6‐month double‐blind treatment phase (DBTP) of the Study to Evaluate the Efficacy and Safety of Erenumab in Migraine Prevention (STRIVE) pivotal clinical trial. Methods Enrolled patients with episodic migraine (EM; ≥4 MMD and <15 monthly headache days) were randomized (1:1:1) to erenumab 70 mg (n = 312), erenumab 140 mg (n = 318), or placebo (n = 316) once monthly. We determined the proportions of patients with ≥50%, ≥75% and 100% reduction in MMD over the last 3 months of the STRIVE DBTP (months 4 through 6) and conducted post hoc analyses to contextualize the treatment benefit in patient subgroups achieving, and not achieving, these response thresholds. Outcome measures included changes in MMD, acute migraine‐specific medication days (MSMD), and patient‐reported outcomes. Results The proportions of patients with a reduction in MMD from baseline were greater for erenumab than for placebo at all response thresholds. As previously reported for the ≥50% response threshold, 135/312 (43.3%) of patients on erenumab 70 mg and 159/318 (50.0%) on erenumab 140 mg responded, vs 84/316 (26.6%) for placebo. At months 4 through 6, 65/312 (20.8%) and 70/318 (22.0%) of those on erenumab 70 mg and erenumab 140 mg, respectively, achieved ≥75% reductions vs 25/316 (7.9%) on placebo. A reduction of 100% response, which required no migraine days over 3 consecutive months based on observed data, was achieved by 10/312 (3.2%) of patients treated with erenumab 70 mg and 16/318 (5.0%) for erenumab 140 mg, vs 9/316 (2.8%) for placebo. At all response thresholds, responders achieved numerically greater reductions in mean MMD and MSMD, and greater improvements in disability than did the overall population; importantly, these remarkable responses were noted early. Meanwhile, 60/312 (19.2%) and 53/318 (16.7%) patients on erenumab 70 and 140 mg, respectively, had no reduction in MMD from baseline in months 4 through 6, compared with 104/316 (32.9%) patients on placebo. Conclusions The responses at the ≥50%, ≥75%, and 100% thresholds provide context for establishing realistic patient and physician expectations regarding the magnitude of treatment benefit that may be achieved by patients with EM responding to erenumab (STRIVE, NCT02456740).

Background While there is a long history of measuring death and disability from injuries, modern research methods must account for the wide spectrum of disability that can occur in an injury, and must provide estimates with sufficient demographic, geographical and temporal detail to be useful for policy makers. The Global Burden of Disease (GBD) 2017 study used methods to provide highly detailed estimates of global injury burden that meet these criteria. Methods In this study, we report and discuss the methods used in GBD 2017 for injury morbidity and mortality burden estimation. In summary, these methods included estimating cause-specific mortality for every cause of injury, and then estimating incidence for every cause of injury. Non-fatal disability for each cause is then calculated based on the probabilities of suffering from different types of bodily injury experienced. Results GBD 2017 produced morbidity and mortality estimates for 38 causes of injury. Estimates were produced in terms of incidence, prevalence, years lived with disability, cause-specific mortality, years of life lost and disability-adjusted life-years for a 28-year period for 22 age groups, 195 countries and both sexes. Conclusions GBD 2017 demonstrated a complex and sophisticated series of analytical steps using the largest known database of morbidity and mortality data on injuries. GBD 2017 results should be used to help inform injury prevention policy making and resource allocation. We also identify important avenues for improving injury burden estimation in the future.

AbstractThe mainstream approach to antiviral drugs against COVID‐19 is to focus on key stages of the SARS‐CoV‐2 life cycle. The vast majority of candidates under investigation are repurposed from agents of other indications. Understanding protein–inhibitor interactions at the molecular scale will provide crucial insights for drug discovery to stop this pandemic. In this article, we summarize and analyze the most recent structural data on several viral targets in the presence of promising inhibitors for COVID‐19 in the context of the perspective of modes of action (MOA) to unravel insightful mechanistic features with atomistic resolution. The targets include spike glycoprotein and various host proteases mediating the entry of the virus into the cells, viral chymotrypsin‐ and papain‐like proteases, and RNA‐dependent RNA polymerase. The main purpose of this review is to present detailed MOA analysis to inspire fresh ideas for both de novo drug design and optimization of known scaffolds to combat COVID‐19.

AbstractThe mainstream approach to antiviral drugs against COVID‐19 is to focus on key stages of the SARS‐CoV‐2 life cycle. The vast majority of candidates under investigation are repurposed from agents of other indications. Understanding protein–inhibitor interactions at the molecular scale will provide crucial insights for drug discovery to stop this pandemic. In this article, we summarize and analyze the most recent structural data on several viral targets in the presence of promising inhibitors for COVID‐19 in the context of the perspective of modes of action (MOA) to unravel insightful mechanistic features with atomistic resolution. The targets include spike glycoprotein and various host proteases mediating the entry of the virus into the cells, viral chymotrypsin‐ and papain‐like proteases, and RNA‐dependent RNA polymerase. The main purpose of this review is to present detailed MOA analysis to inspire fresh ideas for both de novo drug design and optimization of known scaffolds to combat COVID‐19.

Abstract Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous entity of B-cell lymphoma. Cell-of-origin (COO) classification of DLBCL is required in routine practice by the World Health Organization classification for biological and therapeutic insights. Genetic subtypes uncovered recently are based on distinct genetic alterations in DLBCL, which are different from the COO subtypes defined by gene expression signatures of normal B cells retained in DLBCL. We hypothesize that classifiers incorporating both genome-wide gene-expression and pathogenetic variables can improve the therapeutic significance of DLBCL classification. To develop such refined classifiers, we performed targeted RNA sequencing (RNA-Seq) with a commercially available next-generation sequencing (NGS) platform in a large cohort of 418 DLBCLs. Genetic and transcriptional data obtained by RNA-Seq in a single run were explored by state-of-the-art artificial intelligence (AI) to develop a NGS-COO classifier for COO assignment and NGS survival models for clinical outcome prediction. The NGS-COO model built through applying AI in the training set was robust, showing high concordance with COO classification by either Affymetrix GeneChip microarray or the NanoString Lymph2Cx assay in 2 validation sets. Although the NGS-COO model was not trained for clinical outcome, the activated B-cell–like compared with the germinal-center B-cell–like subtype had significantly poorer survival. The NGS survival models stratified 30% high-risk patients in the validation set with poor survival as in the training set. These results demonstrate that targeted RNA-Seq coupled with AI deep learning techniques provides reproducible, efficient, and affordable assays for clinical application. The clinical grade assays and NGS models integrating both genetic and transcriptional factors developed in this study may eventually support precision medicine in DLBCL.