SNPs and Other Features as They Predispose to Complex Disease: Genome-Wide Predictive Analysis of a Quantitative Phenotype for Hypertension

Though recently they have fallen into some disrepute, genome-wide association studies (GWAS) have been formulated and applied to understanding essential hypertension. The principal goal here is to use data gathered in a GWAS to gauge the extent to which SNPs and their interactions with other features can be combined to predict mean arterial blood pressure (MAP) in 3138 pre-menopausal and naturally post-menopausal white women. More precisely, we quantify the extent to which data as described permit prediction of MAP beyond what is possible from traditional risk factors such as blood cholesterol levels and glucose levels. Of course, these traditional risk factors are genetic, though typically not explicitly so. In all, there were 44 such risk factors/clinical variables measured and 377,790 single nucleotide polymorphisms (SNPs) genotyped. Data for women we studied are from first visit measurements taken as part of the Atherosclerotic Risk in Communities (ARIC) study. We begin by assessing non-SNP features in their abilities to predict MAP, employing a novel regression technique with two stages, first the discovery of main effects and next discovery of their interactions. The long list of SNPs genotyped is reduced to a manageable list for combining with non-SNP features in prediction. We adapted Efron's local false discovery rate to produce this reduced list. Selected non-SNP and SNP features and their interactions are used to predict MAP using adaptive linear regression. We quantify quality of prediction by an estimated coefficient of determination (R2). We compare the accuracy of prediction with and without information from SNPs

A Randomized Placebo-Controlled Trial of Varenicline for Smoking Cessation Allowing Flexible Quit Dates

Introduction: Current smoking cessation guidelines recommend setting a quit date prior to starting pharmacotherapy. However, providing flexibility in the date of quitting may be more acceptable to some smokers. The objective of this study was to compare varenicline 1 mg twice daily (b.i.d.) with placebo in subjects using a flexible quit date paradigm after starting medication. Methods: In this double-blind, randomized, placebo-controlled international study, smokers of ≥10 cigarettes/day, aged 18-75 years, and who were motivated to quit were randomized (3:1) to receive varenicline 1 mg b.i.d. or placebo for 12 weeks. Subjects were followed up through Week 24. Subjects were instructed to quit between Days 8 and 35 after starting medication. The primary endpoint was carbon monoxide-confirmed continuous abstinence during Weeks 9-12, and a key secondary endpoint was continuous abstinence during Weeks 9-24. Results: Overall, 493 subjects were randomized to varenicline and 166 to placebo. Continuous abstinence was higher for varenicline than for placebo subjects at the end of treatment (Weeks 9-12: 53.1% vs. 19.3%; odds ratio [OR] 5.9; 95% CI, 3.7-9.4; p < .0001) and through 24 weeks follow-up (Weeks 9-24: 34.7% vs. 12.7%; OR 4.4; 95% CI, 2.6-7.5; p < .0001). Serious adverse events occurred in 1.2% varenicline (none were psychiatric) and 0.6% placebo subjects. Fewer varenicline than placebo subjects reported depression-related adverse events (2.3% vs. 6.7%, respectively). Conclusions: Varenicline 1 mg b.i.d. using a flexible quit date paradigm had similar efficacy and safety compared with previous fixed quit date studies. © The Author 2011. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco

ISLES 2016 and 2017-Benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI

Performance of models highly depend not only on the used algorithm but also the data set it was applied to. This makes the comparison of newly developed tools to previously published approaches difficult. Either researchers need to implement others' algorithms first, to establish an adequate benchmark on their data, or a direct comparison of new and old techniques is infeasible. The Ischemic Stroke Lesion Segmentation (ISLES) challenge, which has ran now consecutively for 3 years, aims to address this problem of comparability. ISLES 2016 and 2017 focused on lesion outcome prediction after ischemic stroke: By providing a uniformly pre-processed data set, researchers from all over the world could apply their algorithm directly. A total of nine teams participated in ISLES 2015, and 15 teams participated in ISLES 2016. Their performance was evaluated in a fair and transparent way to identify the state-of-the-art among all submissions. Top ranked teams almost always employed deep learning tools, which were predominately convolutional neural networks (CNNs). Despite the great efforts, lesion outcome prediction persists challenging. The annotated data set remains publicly available and new approaches can be compared directly via the online evaluation system, serving as a continuing benchmark (www.isles-challenge.org).Fundacao para a Ciencia e Tecnologia (FCT), Portugal (scholarship number PD/BD/113968/2015). FCT with the UID/EEA/04436/2013, by FEDER funds through COMPETE 2020, POCI-01-0145-FEDER-006941. NIH Blueprint for Neuroscience Research (T90DA022759/R90DA023427) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health under award number 5T32EB1680. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. PAC-PRECISE-LISBOA-01-0145-FEDER-016394. FEDER-POR Lisboa 2020-Programa Operacional Regional de Lisboa PORTUGAL 2020 and Fundacao para a Ciencia e a Tecnologia. GPU computing resources provided by the MGH and BWH Center for Clinical Data Science Graduate School for Computing in Medicine and Life Sciences funded by Germany's Excellence Initiative [DFG GSC 235/2]. National Research National Research Foundation of Korea (NRF) MSIT, NRF-2016R1C1B1012002, MSIT, No. 2014R1A4A1007895, NRF-2017R1A2B4008956 Swiss National Science Foundation-DACH 320030L_163363

Long-term prognosis of symptomatic isolated middle cerebral artery disease in Korean stroke patients

<p>Abstract</p> <p>Background</p> <p>This study aimed to investigate the long-term mortality and recurrence rate of stroke in first-time stroke patients with symptomatic isolated middle cerebral artery disease (MCAD) under medical management.</p> <p>Methods</p> <p>We identified 141 first ever stroke patients (mean age, 64.4 ± 12.5 years; 53% male) with symptomatic isolated MCAD. MCAD was defined as significant stenosis of more than 50% or occlusion of the MCA as revealed by MR angiography. The median follow-up was 27.7 months. We determined a cumulative rate of stroke recurrence and mortality by Kaplan-Meier survival analyses and sought predictors using the Cox proportional hazard model.</p> <p>Results</p> <p>The cumulative composite outcome rate (stroke recurrence or any-cause death) was 14%, 19%, 22%, and 28% at years 1, 2, 3, and 5, respectively. The annual recurrence rate of stroke was 4.1%. The presence of diabetes mellitus was the only significant independent predictor of stroke recurrence or any cause of death in multivariate analyses of Cox proportional hazard model adjusted for any plausible potential confounding factors.</p> <p>Conclusions</p> <p>We estimated the long-term prognosis of stroke patients with isolated symptomatic MCAD under current medical management in Korea. Diabetes mellitus was found to be a significant predictor for stroke recurrence and mortality.</p

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN