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The SARS-CoV-2 lineage B.1.1.7, designated variant of concern (VOC) 202012/01 by Public Health England1, was first identified in the UK in late summer to early autumn 20202. Whole-genome SARS-CoV-2 sequence data collected from community-based diagnostic testing for COVID-19 show an extremely rapid expansion of the B.1.1.7 lineage during autumn 2020, suggesting that it has a selective advantage. Here we show that changes in VOC frequency inferred from genetic data correspond closely to changes inferred by S gene target failures (SGTF) in community-based diagnostic PCR testing. Analysis of trends in SGTF and non-SGTF case numbers in local areas across England shows that B.1.1.7 has higher transmissibility than non-VOC lineages, even if it has a different latent period or generation time. The SGTF data indicate a transient shift in the age composition of reported cases, with cases of B.1.1.7 including a larger share of under 20-year-olds than non-VOC cases. We estimated time-varying reproduction numbers for B.1.1.7 and co-circulating lineages using SGTF and genomic data. The best-supported models did not indicate a substantial difference in VOC transmissibility among different age groups, but all analyses agreed that B.1.1.7 has a substantial transmission advantage over other lineages, with a 50% to 100% higher reproduction number.

Objectives To evaluate the long-term effects on airway in patients with mini-screw–assisted rapid palatal expansion (MARPE), rapid palatal expansion (RPE), and controls with three-dimensional cone-beam computed tomography (CBCT) analysis. Materials and Methods A total of 180 CBCTs of 60 patients were analyzed at different time points, such as pretreatment, postexpansion, and posttreatment. Patients were divided into three groups: mini-screw assisted rapid palatal expansion (MARPE), rapid palatal expansion (RPE), and controls. The nasal cavity, nasopharyngeal, oropharyngeal, and laryngopharyngeal airway volume and area were measured. Changes in total airway volume, total airway area, minimal cross-sectional area, maxillary intermolar width, external maxillary width, and palatal width were also evaluated. Results Both MARPE and RPE caused a statistically significant increase in the airway after expansion as compared with the control group, but there was no statistically significant difference in the change in airway between MARPE, RPE, and the control group at posttreatment, except for nasopharyngeal volume, which was significantly increased in the MARPE group. There was no correlation between the amount of expansion and increase in total airway volume. Conclusions There was a significant increase in total airway volume, total airway area, and minimal cross-sectional area with MARPE and RPE immediately after expansion, but at posttreatment, the changes in the MARPE and RPE groups were similar to the change in the control group. However, MARPE led to a significant long-term increase in nasopharyngeal volume. The amount of expansion did not correlate with the increase in pharyngeal airway volume.

We identify amino acid variants within dominant SARS-CoV-2 T-cell epitopes by interrogating global sequence data. Several variants within nucleocapsid and ORF3a epitopes have arisen independently in multiple lineages and result in loss of recognition by epitope-specific T-cells assessed by IFN-γ and cytotoxic killing assays. Complete loss of T-cell responsiveness was seen due to Q213K in the A*01:01-restricted CD8+ ORF3a epitope FTSDYYQLY207-215, due to P13L, P13S and P13T in the B*27:05-restricted CD8+ nucleocapsid epitope QRNAPRITF9-17, and due to T362I and P365S in the A*03:01/A*11:01-restricted CD8+ nucleocapsid epitope KTFPPTEPK361-369. CD8+ T-cell lines unable to recognise variant epitopes have diverse T-cell receptor repertoires. These data demonstrate the potential for T-cell evasion and highlight the need for ongoing surveillance for variants capable of escaping T-cell as well as humoral immunity., Graphical Abstract

BACKGROUND Aneurysm recurrence after coiling has been associated with aneurysm growth, (re)hemorrhage, and a greater need for follow-up. The second-generation HydroCoil Embolic System (HES; MicroVention, Inc) consists of a platinum core with integrated hydrogel and was developed to reduce recurrence through enhancing packing density and healing within the aneurysm. OBJECTIVE To compare recurrence between the second-generation HES and bare platinum coil (BPC) in the new-generation Hydrogel Endovascular Aneurysm Treatment Trial (HEAT). METHODS HEAT is a randomized, controlled trial that enrolled subjects with ruptured or unruptured 3- to 14-mm intracranial aneurysms amenable to coiling. The primary endpoint was aneurysm recurrence using the Raymond-Roy scale. Secondary endpoints included minor and major recurrence, packing density, adverse events related to the procedure and/or device, mortality, initial complete occlusion, aneurysm retreatment, hemorrhage from target aneurysm during follow-up, aneurysm occlusion stability, and clinical outcome at final follow-up. RESULTS A total of 600 patients were randomized (HES, n = 297 and BPC, n = 303), including 28% with ruptured aneurysms. Recurrence occurred in 11 (4.4%) subjects in the HES arm and 44 (15.4%) subjects in the BPC arm (P = .002). While the initial occlusion rate was higher with BPC, the packing density and both major and minor recurrence rates were in favor of HES. Secondary endpoints including adverse events, retreatment, hemorrhage, mortality, and clinical outcome did not differ between arms. CONCLUSION Coiling of small-to-medium aneurysms with second-generation HES resulted in less recurrence when compared to BPC, without increased harm. These data further support the use of the second-generation HES for the embolization of intracranial aneurysms. VIDEO ABSTRACT

Background Pulmonary arterial hypertension (PAH) is a rare but life shortening disease, the diagnosis of which is often delayed, and requires an invasive right heart catheterisation. Identifying diagnostic biomarkers may improve screening to identify patients at risk of PAH earlier and provide new insights into disease pathogenesis. MicroRNAs are small, non-coding molecules of RNA, previously shown to be dysregulated in PAH, and contribute to the disease process in animal models. Methods Plasma from 64 treatment naive patients with PAH and 43 disease and healthy controls were profiled for microRNA expression by Agilent Microarray. Following quality control and normalisation, the cohort was split into training and validation sets. Four separate machine learning feature selection methods were applied to the training set, along with a univariate analysis. Findings 20 microRNAs were identified as putative biomarkers by consensus feature selection from all four methods. Two microRNAs (miR-636 and miR-187-5p) were selected by all methods and used to predict PAH diagnosis with high accuracy. Integrating microRNA expression profiles with their associated target mRNA revealed 61 differentially expressed genes verified in two independent, publicly available PAH lung tissue data sets. Two of seven potentially novel gene targets were validated as differentially expressed in vitro in human pulmonary artery smooth muscle cells. Interpretation This consensus of multiple machine learning approaches identified two miRNAs that were able to distinguish PAH from both disease and healthy controls. These circulating miRNA, and their target genes may provide insight into PAH pathogenesis and reveal novel regulators of disease and putative drug targets. Funding This work was supported by a National Institute for Health Research Rare Disease Translational Research Collaboration (R29065/CN500) and British Heart Foundation Project Grant (PG/11/116/29288).

We report severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike ΔH69/V70 in multiple independent lineages, often occurring after acquisition of receptor binding motif replacements such as N439K and Y453F, known to increase binding affinity to the ACE2 receptor and confer antibody escape. In vitro, we show that, although ΔH69/V70 itself is not an antibody evasion mechanism, it increases infectivity associated with enhanced incorporation of cleaved spike into virions. ΔH69/V70 is able to partially rescue infectivity of spike proteins that have acquired N439K and Y453F escape mutations by increased spike incorporation. In addition, replacement of the H69 and V70 residues in the Alpha variant B.1.1.7 spike (where ΔH69/V70 occurs naturally) impairs spike incorporation and entry efficiency of the B.1.1.7 spike pseudotyped virus. Alpha variant B.1.1.7 spike mediates faster kinetics of cell-cell fusion than wild-type Wuhan-1 D614G, dependent on ΔH69/V70. Therefore, as ΔH69/V70 compensates for immune escape mutations that impair infectivity, continued surveillance for deletions with functional effects is warranted., Graphical abstract, Meng et al. report that the SARS-CoV-2 spike ΔH69/V70 has arisen multiple times. The deletion increases entry efficiency associated with increased cleaved spike in virions and can compensate for loss of infectivity. The B.1.1.7 spike requires ΔH69/V70 for efficient cell entry and cell-cell fusion activity.

Background: The SARS-CoV-2 variant B.1.1.7 was first identified in December, 2020, in England. We aimed to investigate whether increases in the proportion of infections with this variant are associated with differences in symptoms or disease course, reinfection rates, or transmissibility. Methods: We did an ecological study to examine the association between the regional proportion of infections with the SARS-CoV-2 B.1.1.7 variant and reported symptoms, disease course, rates of reinfection, and transmissibility. Data on types and duration of symptoms were obtained from longitudinal reports from users of the COVID Symptom Study app who reported a positive test for COVID-19 between Sept 28 and Dec 27, 2020 (during which the prevalence of B.1.1.7 increased most notably in parts of the UK). From this dataset, we also estimated the frequency of possible reinfection, defined as the presence of two reported positive tests separated by more than 90 days with a period of reporting no symptoms for more than 7 days before the second positive test. The proportion of SARS-CoV-2 infections with the B.1.1.7 variant across the UK was estimated with use of genomic data from the COVID-19 Genomics UK Consortium and data from Public Health England on spike-gene target failure (a non-specific indicator of the B.1.1.7 variant) in community cases in England. We used linear regression to examine the association between reported symptoms and proportion of B.1.1.7. We assessed the Spearman correlation between the proportion of B.1.1.7 cases and number of reinfections over time, and between the number of positive tests and reinfections. We estimated incidence for B.1.1.7 and previous variants, and compared the effective reproduction number, R t, for the two incidence estimates. Findings: From Sept 28 to Dec 27, 2020, positive COVID-19 tests were reported by 36 920 COVID Symptom Study app users whose region was known and who reported as healthy on app sign-up. We found no changes in reported symptoms or disease duration associated with B.1.1.7. For the same period, possible reinfections were identified in 249 (0·7% [95% CI 0·6–0·8]) of 36 509 app users who reported a positive swab test before Oct 1, 2020, but there was no evidence that the frequency of reinfections was higher for the B.1.1.7 variant than for pre-existing variants. Reinfection occurrences were more positively correlated with the overall regional rise in cases (Spearman correlation 0·56–0·69 for South East, London, and East of England) than with the regional increase in the proportion of infections with the B.1.1.7 variant (Spearman correlation 0·38–0·56 in the same regions), suggesting B.1.1.7 does not substantially alter the risk of reinfection. We found a multiplicative increase in the R t of B.1.1.7 by a factor of 1·35 (95% CI 1·02–1·69) relative to pre-existing variants. However, R t fell below 1 during regional and national lockdowns, even in regions with high proportions of infections with the B.1.1.7 variant. Interpretation: The lack of change in symptoms identified in this study indicates that existing testing and surveillance infrastructure do not need to change specifically for the B.1.1.7 variant. In addition, given that there was no apparent increase in the reinfection rate, vaccines are likely to remain effective against the B.1.1.7 variant. Funding: Zoe Global, Department of Health (UK), Wellcome Trust, Engineering and Physical Sciences Research Council (UK), National Institute for Health Research (UK), Medical Research Council (UK), Alzheimer's Society.

Cancer treatments can be highly toxic and frequently only a subset of the patient population will benefit from a given treatment. Tumour genetic makeup plays an important role in cancer drug sensitivity. We suspect that gene expression markers could be used as a decision aid for treatment selection or dosage tuning. Using in vitro cancer cell line dose-response and gene expression data from the Genomics of Drug Sensitivity in Cancer (GDSC) project, we build a dose-varying regression model. Unlike existing approaches, this allows us to estimate dosage-dependent associations with gene expression. We include the transcriptomic profiles as dose-invariant covariates into the regression model and assume that their effect varies smoothly over the dosage levels. A two-stage variable selection algorithm (variable screening followed by penalized regression) is used to identify genetic factors that are associated with drug response over the varying dosages. We evaluate the effectiveness of our method using simulation studies focusing on the choice of tuning parameters and cross-validation for predictive accuracy assessment. We further apply the model to data from five BRAF targeted compounds applied to different cancer cell lines under different dosage levels. We highlight the dosage-dependent dynamics of the associations between the selected genes and drug response, and we perform pathway enrichment analysis to show that the selected genes play an important role in pathways related to tumorigenesis and DNA damage response., Author summary Tumour cell lines allow scientists to test anticancer drugs in a laboratory environment. Cells are exposed to the drug in increasing concentrations, and the drug response, or amount of surviving cells, is measured. Generally, drug response is summarized via a single number such as the concentration at which 50% of the cells have died (IC50). To avoid relying on such summary measures, we adopted a functional regression approach that takes the dose-response curves as inputs, and uses them to find biomarkers of drug response. One major advantage of our approach is that it describes how the effect of a biomarker on the drug response changes with the drug dosage. This is useful for determining optimal treatment dosages and predicting drug response curves for unseen drug-cell line combinations. Our method scales to large numbers of biomarkers by using regularization and, in contrast with existing literature, selects the most informative genes by accounting for drug response at untested dosages. We demonstrate its value using data from the Genomics of Drug Sensitivity in Cancer project to identify genes whose expression is associated with drug response. We show that the selected genes recapitulate prior biological knowledge, and belong to known cancer pathways.

Global dispersal and increasing frequency of the SARS-CoV-2 spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large dataset, well represented by both spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant., Graphical Abstract, Highlights • Increasing frequency of SARS-CoV-2 D614G is consistent with a selective advantage • Phylodynamic analyses do not show significantly different growth of D614G clusters • There is no association of D614G replacement with greater severity of infection • The D614G replacement is associated with higher viral loads and younger patient age, Analysis of the spread and frequency of SARS-CoV-2 D614G in the United Kingdom suggests a selective advantage for this strain that is associated with higher viral loads in younger patients but not higher COVID-19 clinical severity or mortality.

Amyotrophic lateral sclerosis is a fatal neurodegenerative disease causing upper and lower motor neuron loss and currently no effective disease-modifying treatment is available. A pathological feature of this disease is neuroinflammation, a mechanism which involves both CNS-resident and peripheral immune system cells. Regulatory T-cells are immune-suppressive agents known to be dramatically and progressively decreased in patients with amyotrophic lateral sclerosis. Low-dose interleukin-2 promotes regulatory T-cell expansion and was proposed as an immune-modulatory strategy for this disease. A randomized placebo-controlled pilot phase-II clinical trial called Immuno-Modulation in Amyotrophic Lateral Sclerosis was carried out to test safety and activity of low-dose interleukin-2 in 36 amyotrophic lateral sclerosis patients (NCT02059759). Participants were randomized to 1MIU, 2MIU-low-dose interleukin-2 or placebo and underwent one injection daily for 5 days every 28 days for three cycles. In this report, we describe the results of microarray gene expression profiling of trial participants' leukocyte population. We identified a dose-dependent increase in regulatory T-cell markers at the end of the treatment period. Longitudinal analysis revealed an alteration and inhibition of inflammatory pathways occurring promptly at the end of the first treatment cycle. These responses are less pronounced following the end of the third treatment cycle, although an activation of immune-regulatory pathways, involving regulatory T-cells and T helper 2 cells, was evident only after the last cycle. This indicates a cumulative effect of repeated low-dose interleukin-2 administration on regulatory T-cells. Our analysis suggested the existence of inter-individual variation amongst trial participants and we therefore classified patients into low, moderate and high-regulatory T-cell-responders. NanoString profiling revealed substantial baseline differences between participant immunological transcript expression profiles with the least responsive patients showing a more inflammatory-prone phenotype at the beginning of the trial. Finally, we identified two genes in which pre-treatment expression levels correlated with the magnitude of drug responsiveness. Therefore, we proposed a two-biomarker based regression model able to predict patient regulatory T-cell-response to low-dose interleukin-2. These findings and the application of this methodology could be particularly relevant for future precision medicine approaches to treat amyotrophic lateral sclerosis., Amyotrophic lateral sclerosis patients included in the IMODALS clinical trial (NCT02059759) were transcriptionally profiled to assess longitudinal blood expression changes. Low-dose-interleukin-2 was shown to promote dose and time-dependent Treg-marker upregulation. However, inter-individual variations were reported in the magnitude of Treg expansion and a two-biomarker model to predict target-engagement was proposed., Graphical Abstract Graphical Abstract

Background Previously, we introduced our Patient Health Information Dialogue Ontology (PHIDO) that manages the dialogue and contextual information of the session between an agent and a health consumer. In this study, we take the next step and introduce the Conversational Ontology Operator (COO), the software engine harnessing PHIDO. We also developed a question-answering subsystem called Frankenstein Ontology Question-Answering for User-centric Systems (FOQUS) to support the dialogue interaction. Methods We tested both the dialogue engine and the question-answering system using application-based competency questions and questions furnished from our previous Wizard of OZ simulation trials. Results Our results revealed that the dialogue engine is able to perform the core tasks of communicating health information and conversational flow. Inter-rater agreement and accuracy scores among four reviewers indicated perceived, acceptable responses to the questions asked by participants from the simulation studies, yet the composition of the responses was deemed mediocre by our evaluators. Conclusions Overall, we present some preliminary evidence of a functioning ontology-based system to manage dialogue and consumer questions. Future plans for this work will involve deploying this system in a speech-enabled agent to assess its usage with potential health consumer users.

Expression quantitative trait loci (eQTL) can provide a link between disease susceptibility variants discovered by genetic association studies and biology. To date, eQTL mapping studies have been primarily conducted in healthy individuals from population-based cohorts. Genetic effects have been known to be context-specific and vary with changing environmental stimuli. We conducted a transcriptome- and genome-wide eQTL mapping study in a cohort of patients with idiopathic or heritable pulmonary arterial hypertension (PAH) using RNA sequencing (RNAseq) data from whole blood. We sought confirmation from three published population-based eQTL studies, including the GTEx Project, and followed up potentially novel eQTL not observed in the general population. In total, we identified 2314 eQTL of which 90% were cis-acting and 75% were confirmed by at least one of the published studies. While we observed a higher GWAS trait colocalization rate among confirmed eQTL, colocalisation rate of novel eQTL reported for lung-related phenotypes was twice as high as that of confirmed eQTL. Functional enrichment analysis of genes with novel eQTL in PAH highlighted immune-related processes, a suspected contributor to PAH. These potentially novel eQTL specific to or active in PAH could be useful in understanding genetic risk factors for other diseases that share common mechanisms with PAH.

Summary A SARS-CoV-2 variant carrying the Spike protein amino acid change D614G has become the most prevalent form in the global pandemic. Dynamic tracking of variant frequencies revealed a recurrent pattern of G614 increase at multiple geographic levels: national, regional and municipal. The shift occurred even in local epidemics where the original D614 form was well established prior to the introduction of the G614 variant. The consistency of this pattern was highly statistically significant, suggesting that the G614 variant may have a fitness advantage. We found that the G614 variant grows to higher titer as pseudotyped virions. In infected individuals G614 is associated with lower RT-PCR cycle thresholds, suggestive of higher upper respiratory tract viral loads, although not with increased disease severity. These findings illuminate changes important for a mechanistic understanding of the virus, and support continuing surveillance of Spike mutations to aid in the development of immunological interventions., Highlights: - A SARS-CoV-2 variant with Spike G614 has replaced D614 as the dominant pandemic form - The consistent increase of G614 at regional levels may indicate a fitness advantage - G614 is associated with lower RT PCR Ct’s, suggestive of higher viral loads in patients - The G614 variant grows to higher titers as pseudotyped virions

Ascertaining the state of coronavirus outbreaks is crucial for public health decision-making. Absent repeated representative viral test samples in the population, public health officials and researchers alike have relied on lagging indicators of infection to make inferences about the direction of the outbreak and attendant policy decisions. Recently researchers have shown that SARS-CoV-2 RNA can be detected in municipal sewage sludge with measured RNA concentrations rising and falling suggestively in the shape of an epidemic curve while providing an earlier signal of infection than hospital admissions data. The present paper presents a SARS-CoV-2 epidemic model to serve as a basis for estimating the incidence of infection, and shows mathematically how modeled transmission dynamics translate into infection indicators by incorporating probability distributions for indicator-specific time lags from infection. Hospital admissions and SARS-CoV-2 RNA in municipal sewage sludge are simultaneously modeled via maximum likelihood scaling to the underlying transmission model. The results demonstrate that both data series plausibly follow from the transmission model specified and provide a 95% confidence interval estimate of the reproductive number R0 ≈ 2.4 ±0.2. Sensitivity analysis accounting for alternative lag distributions from infection until hospitalization and sludge RNA concentration respectively suggests that the detection of viral RNA in sewage sludge leads hospital admissions by 3 to 5 days on average. The analysis suggests that stay-at-home restrictions plausibly removed 89% of the population from the risk of infection with the remaining 11% exposed to an unmitigated outbreak that infected 9.3% of the total population.HighlightsA maximum likelihood method for aligning observed lagged epidemic indicators via an underlying transmission model is derived and illustrated using observed COVID-19 hospital admissions and SARS-CoV-2 RNA concentrations measured in sewage sludge to model a local SARS-CoV-2 outbreakThe method enables direct estimation of the reproductive number R0 from the observed indicators along with the initial prevalence of SARS-CoV-2 infection in the population at riskThe analysis suggests tracking SARS-CoV-2 RNA concentration in sewage sludge provides a 3 to 5 day lead time over tracking hospital admissions, consistent with purely statistical time series analysis previously reportedThe model enables estimation of the fraction of the population compliant with government-mandated stay-at-home restrictions, the size of the exposed population, and the fraction of the population infected with SARS-CoV-2 over the outbreak

We measured severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in primary sewage sludge in the New Haven, Connecticut, USA, metropolitan area during the Coronavirus Disease 2019 (COVID-19) outbreak in Spring 2020. SARS-CoV-2 RNA was detected throughout the more than 10-week study and, when adjusted for time lags, tracked the rise and fall of cases seen in SARS-CoV-2 clinical test results and local COVID-19 hospital admissions. Relative to these indicators, SARS-CoV-2 RNA concentrations in sludge were 0–2 d ahead of SARS-CoV-2 positive test results by date of specimen collection, 0–2 d ahead of the percentage of positive tests by date of specimen collection, 1–4 d ahead of local hospital admissions and 6–8 d ahead of SARS-CoV-2 positive test results by reporting date. Our data show the utility of viral RNA monitoring in municipal wastewater for SARS-CoV-2 infection surveillance at a population-wide level. In communities facing a delay between specimen collection and the reporting of test results, immediate wastewater results can provide considerable advance notice of infection dynamics. Testing sewage for the novel coronavirus reveals epidemiological trends.

Drug high-throughput screenings across large molecular-characterised cancer cell line panels enable the discovery of biomarkers, and thereby, cancer precision medicine. The ability to experimentally generate drug response data has accelerated. However, this data is typically quantified by a summary statistic from a best-fit dose response curve, whilst neglecting the uncertainty of the curve fit and the potential variability in the raw readouts. Here, we model the experimental variance using Gaussian Processes, and subsequently, leverage this uncertainty for identifying associated biomarkers with a new statistical framework based on Bayesian testing. Applied to the Genomics of Drug Sensitivity in Cancer, in vitro screening data on 265 compounds across 1,074 cell lines, our uncertainty models identified 24 clinically established drug response biomarkers, and in addition provided evidence for 6 novel biomarkers. We validated our uncertainty estimates with an additional drug screen of 26 drugs, 10 cell lines with 8 to 9 replicates. Our method is applicable to drug high-throughput screens without replicates, and enables robust biomarker discovery for new cancer therapies.

To develop a consensus statement for left atrial appendage occlusion (LAAO) in Asian-Pacific patients with non-valvular atrial fibrillation (NVAF) at risk of ischemic stroke. The need for such a region-specific consensus was indicated by the relative paucity of clinical evidence for LAAO and oral anticoagulation therapy obtained in Asian-Pacific populations and the specific stroke and bleeding characteristics of this population. Consensus was developed by discussion and evaluation of available evidence and expert opinions during a 2-day meeting attended by clinical experts from the Asian-Pacific regions. The consensus statement arrived at provides recommendations based on available evidence and expert opinions regarding LAAO in Asian-Pacific patients. Gaps in the evidence and other areas requiring further research were identified. LAAO is an alternative device-based therapy in carefully selected patients with NVAF at risk of ischemic stroke. However, evidence for LAAO is primarily obtained from Caucasian populations, and data on LAAO in Asian-Pacific patients are scarce. While the present consensus statement addresses several therapy-related aspects based on careful interpretation of available evidence and expert opinions, other areas require additional evidence derived from Asian-Pacific populations.

Rationale: Idiopathic and heritable pulmonary arterial hypertension (PAH) are rare but comprise a genetically heterogeneous patient group. RNA sequencing linked to the underlying genetic architecture can be used to better understand the underlying pathology by identifying key signaling pathways and stratify patients more robustly according to clinical risk. Objectives: To use a three-stage design of RNA discovery, RNA validation and model construction, and model validation to define a set of PAH-associated RNAs and a single summarizing RNA model score. To define genes most likely to be involved in disease development, we performed Mendelian randomization (MR) analysis. Methods: RNA sequencing was performed on whole-blood samples from 359 patients with idiopathic, heritable, and drug-induced PAH and 72 age- and sex-matched healthy volunteers. The score was evaluated against disease severity markers including survival analysis using all-cause mortality from diagnosis. MR used known expression quantitative trait loci and summary statistics from a PAH genome-wide association study. Measurements and Main Results: We identified 507 genes with differential RNA expression in patients with PAH compared with control subjects. A model of 25 RNAs distinguished PAH with 87% accuracy (area under the curve 95% confidence interval: 0.791–0.945) in model validation. The RNA model score was associated with disease severity and long-term survival (P = 4.66 × 10−6) in PAH. MR detected an association between SMAD5 levels and PAH disease susceptibility (odds ratio, 0.317; 95% confidence interval, 0.129–0.776; P = 0.012). Conclusions: A whole-blood RNA signature of PAH, which includes RNAs relevant to disease pathogenesis, associates with disease severity and identifies patients with poor clinical outcomes. Genetic variants associated with lower SMAD5 expression may increase susceptibility to PAH.

This study aims to evaluate the in vivo distribution of Gold Nanoparticles (GNPs) at different time points after intratumoral (IT) injection, exploiting their properties as contrast agents for Computed Tomography (CT). GNPs approximately 40 nm in diameter were synthesized with a surface plasmon peak at ~530 nm, capped with Bovine Serum Albumin (BSA) to improve colloidal stability, and characterized with standard methods. CT phantom imaging was performed to quantify X-ray attenuation as a function of GNP concentration and surface functionalization and to determine the appropriate particle dose for in vivo studies. Concentrated GNPs were intratumorally (IT) injected into Lewis Lung Carcinoma (LLC) solid tumors grown on the right flank of 6-week old female C57BL/6 mice. Ten days post-injection, follow up CT imaging was performed to assess the distribution and retention of the particles in the tumor. Using the CT attenuation quantification, images for each timepoint were segmented, and 3D volumes rendered, to conduct biodistribution analyses. The successful retention and permanence of the GNPs into the solid tumor after ten days suggests the significance of GNPs as a potential theranostic agent.

Personalised medicine has predominantly focused on genetically altered cancer genes that stratify drug responses, but there is a need to objectively evaluate differential pharmacology patterns at a subpopulation level. Here, we introduce an approach based on unsupervised machine learning to compare the pharmacological response relationships between 327 pairs of cancer therapies. This approach integrated multiple measures of response to identify subpopulations that react differently to inhibitors of the same or different targets to understand mechanisms of resistance and pathway cross-talk. MEK, BRAF, and PI3K inhibitors were shown to be effective as combination therapies for particular BRAF mutant subpopulations. A systematic analysis of preclinical data for a failed phase III trial of selumetinib combined with docetaxel in lung cancer suggests potential indications in pancreatic and colorectal cancers with KRAS mutation. This data-informed study exemplifies a method for stratified medicine to identify novel cancer subpopulations, their genetic biomarkers, and effective drug combinations., Cancer: Subpopulations based on differential drug response Two drugs, even with the same target, rarely have the same potency across all cancer patients - so how do we objectively select the right patients to treat with each drug? An international effort led by Michael P. Menden and Dennis Wang developed a machine learning approach called SEABED to identify groups of individuals from a population who respond distinctly to specific targeted therapies. They used SEABED to systematically compare 327 pairs of anti-cancer drugs across a panel of >800 cancer cells and 30 cancer types. SEABED identified groups of cells where one drug was more effective than the other. Interestingly, groups responding differently to the pairs of drugs could be treated more effectively when both drugs are given together as a combination, which can be in the presence or absence of drug synergy. This approach enables systematic explorations of personalised medicine to reveal biomarkers and drug combination opportunities.

The burden of somatic mutations and neoantigens has been associated with improved survival in cancer treated with immunotherapies, especially non-small cell lung cancer (NSCLC). However, there is uncertainty about their effect on outcome in early-stage untreated cases. We posited that the burden of mutations in a specific set of genes may also contribute to the prognosis of early NSCLC patients. From a small cohort of 36 NSCLC cases, we were able to identify somatic mutations and copy number alterations in 865 genes that contributed to patient overall survival. Simply, the number of altered genes (NAG) among these 865 genes was associated with longer disease-free survival (hazard ratio (HR) = 0.153, p = 1.48 &times, 10&minus, 4). The gene expression signature distinguishing patients with high/low NAG was also prognostic in three independent datasets. Patients with a high NAG could be further stratified based on the presence of immunogenic mutations, revealing a further subgroup of stage I NSCLC with even better prognosis (85% with &gt, 5 years survival), and associated with cytotoxic T-cell expression. Importantly, 95% of the highly-altered genes lacked direct relation to cancer, but were implicated in pathways regulating cell proliferation, motility and immune response.

Background: Highly accurate next-generation sequencing (NGS) of genetic variants is key to many areas of science and medicine, such as cataloguing population genetic variation and diagnosing patients with genetic diseases. Certain genomic loci and regions can be prone to higher rates of systematic sequencing and alignment bias that pose a challenge to achieving high accuracy, resulting in false positive variant calls. Current standard practices to differentiate between loci that can and cannot be sequenced with high confidence utilise consensus between different sequencing methods as a proxy for sequencing confidence. This assumption is not accurate in cases where all sequencing pipelines have consensus on the same errors due to similar systematic biases in sequencing. Alternative methods are therefore required to identify systematic biases. Methods: We have developed a novel statistical method based on summarising sequenced reads from whole genome clinical samples and cataloguing them in "Incremental Databases" (IncDBs) that maintain individual confidentiality. Variant statistics were analysed and catalogued for each genomic position that consistently showed systematic biases with the corresponding sequencing pipeline. Results: We have demonstrated that systematic errors in NGS data are widespread, with persistent low-fraction alleles present at 1.26-2.43% of the human autosomal genome across three different Illumina-based pipelines, each consisting of at least 150 patient samples. We have identified a variety of genomic regions that are more or less prone to systematic biases, such as GC-rich regions (OR = 6.47-8.19) and the NIST high-confidence genomic regions (OR = 0.154-0.191). We have verified our predictions on a gold-standard reference genome and have shown that these systematic biases can lead to suspect variant calls at clinically important loci, including within introns and exons. Conclusions: Our results recommend increased caution to minimise the effect of systematic biases in whole genome sequencing and alignment. This study supports the utility of a statistical approach to enhance quality control of clinically sequenced samples in order to flag up variant calls made at known suspect loci for further analysis or exclusion, using anonymised summary databases from which individual patients cannot be re-identified, so that results can be shared more widely.

Carcinoma-associated fibroblasts (CAFs) are abundant stromal cells in tumor microenvironment that are critically involved in cancer progression. Contrasting reports have shown that CAFs can have either pro- or antitumorigenic roles, indicating that CAFs are functionally heterogeneous. Therefore, to precisely target the cancer-promoting CAF subsets, it is necessary to identify specific markers to define these subpopulations and understand their functions. We characterized two CAFs subsets from 28 non–small cell lung cancer (NSCLC) patient tumors that were scored and classified based on desmoplasia [mainly characterized by proliferating CAFs; high desmoplastic CAFs (HD-CAF; n = 15) and low desmoplastic CAFs (LD-CAF; n = 13)], which is an independent prognostic factor. Here, for the first time, we demonstrate that HD-CAFs and LD-CAFs show different tumor-promoting abilities. HD-CAFs showed higher rate of collagen matrix remodeling, invasion, and tumor growth compared to LD-CAFs. Transcriptomic analysis identified 13 genes that were differentially significant (fold ≥1.5; adjusted P value < .1) between HD-CAFs and LD-CAFs. The top upregulated differentially expressed gene, ST8SIA2 (11.3 fold; adjusted P value = .02), enhanced NSCLC tumor cell invasion in 3D culture compared to control when it was overexpressed in CAFs, suggesting an important role of ST8SIA2 in cancer cell invasion. We confirmed the protumorigenic role of ST8SIA2, showing that ST8SIA2 was significantly associated with the risk of relapse in three independent NSCLC clinical datasets. In summary, our studies show that functional heterogeneity in CAF plays key role in promoting cancer cell invasion in NSCLC.

Availability of lung cancer models that closely mimic human tumors remains a significant gap in cancer research, as tumor cell lines and mouse models may not recapitulate the spectrum of lung cancer heterogeneity seen in patients. We aimed to establish a patient-derived tumor xenograft (PDX) resource from surgically resected non-small cell lung cancer (NSCLC). Fresh tumor tissue from surgical resection was implanted and grown in the subcutaneous pocket of non-obese severe combined immune deficient (NOD SCID) gamma mice. Subsequent passages were in NOD SCID mice. A subset of matched patient and PDX tumors and non-neoplastic lung tissues were profiled by whole exome sequencing, single nucleotide polymorphism (SNP) and methylation arrays, and phosphotyrosine (pY)-proteome by mass spectrometry. The data were compared to published NSCLC datasets of NSCLC primary and cell lines. 127 stable PDXs were established from 441 lung carcinomas representing all major histological subtypes: 52 adenocarcinomas, 62 squamous cell carcinomas, one adeno-squamous carcinoma, five sarcomatoid carcinomas, five large cell neuroendocrine carcinomas, and two small cell lung cancers. Somatic mutations, gene copy number and expression profiles, and pY-proteome landscape of 36 PDXs showed greater similarity with patient tumors than with established cell lines. Novel somatic mutations on cancer associated genes were identified but only in PDXs, likely due to selective clonal growth in the PDXs that allows detection of these low allelic frequency mutations. The results provide the strongest evidence yet that PDXs established from lung cancers closely mimic the characteristics of patient primary tumors.

Altered immune response may be a part of the pathogenesis of preeclampsia. The few epidemiologic studies that have investigated the associations between genetic variations in the complement system genes and preeclampsia risk have reached inconsistent results. The aim of this study is to determine if polymorphisms in the complement system genes could influence the risk of preeclampsia. We examined 51 SNPs in the C3, C5, C6, MASP1, MBL2 and CD55 genes and the risk of preeclampsia and its clinical subtypes in a nested case–control study of 203 preeclampsia cases and 233 controls. Both C6 and MASP1 were associated with the risk of preeclampsia. C6 (rs7444800, rs4957381) and MASP1 (rs1108450, rs3774282, rs698106) polymorphisms were associated with the risk of early-onset preeclampsia and severe preeclampsia, while MASP1 (rs1357134, rs698090) polymorphisms were associated with the risk of late-onset preeclampsia and severe preeclampsia. Our study provided novel evidence that genetic variations in complement genes C6 and MASP1were associated with preeclampsia risk, and that the risk varied by preeclampsia subtypes.

BackgroundGenomic profiling of patient tumors has linked somatic driver mutations to survival outcomes of non-small cell lung cancer (NSCLC) patients, especially for those receiving targeted therapies. However, it remains unclear whether specific non-driver mutations have any prognostic utility.MethodsWhole exomes and transcriptomes were measured from NSCLC xenograft models of patients with diverse clinical outcomes. Penalised regression analysis was performed to identify a set of 865 genes associated with patient survival. The number of somatic copy number aberrations, point mutations and associated expression changes within the 865 genes were used to stratify independent NSCLC patient populations, filtered for chemotherapy naive and early-stage. In-depth genomic analysis and functional testing was conducted on the genomic alterations to understand their effect on improving survival.ResultsHigh burden of somatic alterations are associated with longer disease-free survival (HR=0.153, P=1.48×10-4) in NSCLC patients. When somatic alterations burden was integrated with gene expression, we were able to predict prognosis in three independent patient datasets. Patients with high alteration burden could be further stratified based on the presence of immunogenic mutations, revealing another subgroup of patients with even better prognosis (85% with >5 years survival), and associated with cytotoxic T-cell expression. In addition, 95% of these 865 genes lack documented activity relevant to cancer, but are in pathways regulating cell proliferation, motility and immune response were implicated.ConclusionOur results demonstrate that non-driver somatic alterations may influence the outcome of cancer patients by increasing beneficial immune response and inhibiting processes associated to tumorigenesis.

Integrin α11β1 is a stromal cell-specific receptor for fibrillar collagens and is overexpressed in carcinoma-associated fibroblasts (CAFs). We have investigated its direct role in cancer progression by generating severe combined immune deficient (SCID) mice deficient in integrin α11 (α11) expression. The growth of A549 lung adenocarcinoma cells and two patient-derived non-small cell lung carcinoma (NSCLC) xenografts in these α11 knockout (α11(-/-)) mice was significantly impeded, as compared with wild-type (α11(+/+)) SCID mice. Orthotopic implantation of a spontaneously metastatic NCI-H460SM cell line into the lungs of α11(-/-) and α11(+/+) mice showed significant reduction in the metastatic potential of these cells in the α11(-/-) mice. We identified that collagen cross-linking is associated with stromal α11 expression, and the loss of tumor stromal α11 expression was correlated with decreased collagen reorganization and stiffness. This study shows the role of integrin α11β1, a receptor for fibrillar collagen in differentiation of fibroblasts into CAFs. Furthermore, our data support an important role for α11 signaling pathway in CAFs, promoting tumor growth and metastatic potential of NSCLC cells and being closely associated with collagen cross-linking and the organization and stiffness of fibrillar collagen matrices.

Background To evaluate the independent and joint effects of maternal pre-pregnancy BMI and gestational weight gain (GWG) on the risk of preeclampsia and its subtypes. Methods A birth cohort study was conducted from 2010 to 2012 in Lanzhou, China. Three hundred fourty seven pregnant women with preeclampsia and 9516 normotensive women at Gansu Provincial Maternity and Child Care Hospital were included in the present study. Unconditional logistic regression models were used to evaluate the associations between pre-pregnancy BMI, GWG, and risk of preeclampsia and its subtypes. Results Compared to women with normal pre-pregnancy BMI, those who were overweight/obese had an increased risk of preeclampsia (OR = 1.81; 95%CI: 1.37–2.39). Women with excessive GWG had an increased risk of preeclampsia (OR = 2.28; 95%CI: 1.70–3.05) compared to women with adequate GWG. The observed increased risk was similar for mild-, severe- and late-onset preeclampsia. No association was found for early-onset preeclampsia. Overweight/obese women with excessive GWG had the highest risk of developing preeclampsia compared to normal weight women with no excessive weight gain (OR = 3.78; 95%CI: 2.65–5.41). Conclusions Our results suggested that pre-pregnancy BMI and GWG are independent risk factors for preeclampsia and that the risk might vary by preeclampsia subtypes. Our study also proposed a potential synergistic effect of pre-pregnancy BMI and GWG that warrants further investigation.

Murine syngeneic tumor models are critical to novel immuno-based therapy development, but the molecular and immunologic features of these models are still not clearly defined. The translational relevance of differences between the models is not fully understood, impeding appropriate preclinical model selection for target validation, and ultimately hindering drug development. Across a panel of commonly used murine syngeneic tumor models, we showed variable responsiveness to immunotherapies. We used array comparative genomic hybridization, whole-exome sequencing, exon microarray analysis, and flow cytometry to extensively characterize these models, which revealed striking differences that may underlie these contrasting response profiles. We identified strong differential gene expression in immune-related pathways and changes in immune cell–specific genes that suggested differences in tumor immune infiltrates between models. Further investigation using flow cytometry showed differences in both the composition and magnitude of the tumor immune infiltrates, identifying models that harbor “inflamed” and “non-inflamed” tumor immune infiltrate phenotypes. We also found that immunosuppressive cell types predominated in syngeneic mouse tumor models that did not respond to immune-checkpoint blockade, whereas cytotoxic effector immune cells were enriched in responsive models. A cytotoxic cell–rich tumor immune infiltrate has been correlated with increased efficacy of immunotherapies in the clinic, and these differences could underlie the varying response profiles to immunotherapy between the syngeneic models. This characterization highlighted the importance of extensive profiling and will enable investigators to select appropriate models to interrogate the activity of immunotherapies as well as combinations with targeted therapies in vivo. Cancer Immunol Res; 5(1); 29–41. ©2016 AACR.

The aim of the study was to explore the association between serum uric acid (SUA) and metabolic syndrome (MetS) in premenopausal and postmenopausal women in the Jinchang Cohort. We studied 3808 female Jinchuan Nonferrous Metals Corporation workers aged 40-60 years. Cohort data from epidemiological surveys and medical exams were used. MetS was defined using the 2009 Joint Interim Society criteria. The relationship between SUA and MetS was evaluated using multiple logistic regression after adjusting for potential confounders. MetS and hyperuricemia were more prevalent in postmenopausal women than premenopausal ones (35.3% versus 15.2% and 9.2% versus 4.2%, respectively). Premenopausal and postmenopausal women with hyperuricemia had 2.81 (95% CI: 1.72-4.61) and 2.10 (95% CI: 1.44-3.08), respectively, times the odds of having MetS than their counterparts without hyperuricemia. Even within normal SUA quartiles, only premenopausal women in the highest and second-highest quartile had 3.57 (95% CI: 2.24-5.68) and 2.78 (95% CI: 1.71-4.50), respectively, times the odds of having MetS than those in the lowest quartile. Even in the normal range, the odds ratios for MetS increased gradually according to SUA levels in all women (Ptrend

Personalized therapy is a major goal of modern oncology, as patient responses vary greatly even within a histologically defined cancer subtype. This is especially true in acute myeloid leukemia (AML), which exhibits striking heterogeneity in molecular segmentation. When calibrated to cell-specific data, executable network models can reveal subtle differences in signaling that help explain differences in drug response. Furthermore, they can suggest drug combinations to increase efficacy and combat acquired resistance. Here, we experimentally tested dynamic proteomic changes and phenotypic responses in diverse AML cell lines treated with pan-PIM kinase inhibitor and fms-related tyrosine kinase 3 (FLT3) inhibitor as single agents and in combination. We constructed cell-specific executable models of the signaling axis, connecting genetic aberrations in FLT3, tyrosine kinase 2 (TYK2), platelet-derived growth factor receptor alpha (PDGFRA), and fibroblast growth factor receptor 1 (FGFR1) to cell proliferation and apoptosis via the PIM and PI3K kinases. The models capture key differences in signaling that later enabled them to accurately predict the unique proteomic changes and phenotypic responses of each cell line. Furthermore, using cell-specific models, we tailored combination therapies to individual cell lines and successfully validated their efficacy experimentally. Specifically, we showed that cells mildly responsive to PIM inhibition exhibited increased sensitivity in combination with PIK3CA inhibition. We also used the model to infer the origin of PIM resistance engineered through prolonged drug treatment of MOLM16 cell lines and successfully validated experimentally our prediction that this resistance can be overcome with AKT1/2 inhibition. Cancer Res; 77(4); 827–38. ©2016 AACR.

The challenge of developing effective pharmacodynamic biomarkers for preclinical and clinical testing of FGFR signaling inhibition is significant. Assays that rely on the measurement of phospho-protein epitopes can be limited by the availability of effective antibody detection reagents. Transcript profiling enables accurate quantification of many biomarkers and provides a broader representation of pathway modulation. To identify dynamic transcript biomarkers of FGFR signaling inhibition by AZD4547, a potent inhibitor of FGF receptors 1, 2, and 3, a gene expression profiling study was performed in FGFR2-amplified, drug-sensitive tumor cell lines. Consistent with known signaling pathways activated by FGFR, we identified transcript biomarkers downstream of the RAS-MAPK and PI3K/AKT pathways. Using different tumor cell lines in vitro and xenografts in vivo, we confirmed that some of these transcript biomarkers (DUSP6, ETV5, YPEL2) were modulated downstream of oncogenic FGFR1, 2, 3, whereas others showed selective modulation only by FGFR2 signaling (EGR1). These transcripts showed consistent time-dependent modulation, corresponding to the plasma exposure of AZD4547 and inhibition of phosphorylation of the downstream signaling molecules FRS2 or ERK. Combination of FGFR and AKT inhibition in an FGFR2-mutated endometrial cancer xenograft model enhanced modulation of transcript biomarkers from the PI3K/AKT pathway and tumor growth inhibition. These biomarkers were detected on the clinically validated nanoString platform. Taken together, these data identified novel dynamic transcript biomarkers of FGFR inhibition that were validated in a number of in vivo models, and which are more robustly modulated by FGFR inhibition than some conventional downstream signaling protein biomarkers. Mol Cancer Ther; 15(11); 2802–13. ©2016 AACR.

Although cancers are considered stem cell diseases, mechanisms involving stem cell alterations are poorly understood. Squamous cell carcinoma (SQCC) is the second most common lung cancer, and its pathogenesis appears to hinge on changes in the stem cell behavior of basal cells in the bronchial airways. Basal cells are normally quiescent and differentiate into mucociliary epithelia. Smoking triggers a hyperproliferative response resulting in progressive premalignant epithelial changes ranging from squamous metaplasia to dysplasia. These changes can regress naturally, even with chronic smoking. However, for unknown reasons, dysplasias have higher progression rates than earlier stages. We used primary human tracheobronchial basal cells to investigate how copy number gains in SOX2 and PIK3CA at 3q26-28, which co-occur in dysplasia and are observed in 94% of SQCCs, may promote progression. We find that SOX2 cooperates with PI3K signaling, which is activated by smoking, to initiate the squamous injury response in basal cells. This response involves SOX9 repression, and, accordingly, SOX2 and PI3K signaling levels are high during dysplasia, while SOX9 is not expressed. By contrast, during regeneration of mucociliary epithelia, PI3K signaling is low and basal cells transiently enter a SOX2LoSOX9Hi state, with SOX9 promoting proliferation and preventing squamous differentiation. Transient reduction in SOX2 is necessary for ciliogenesis, although SOX2 expression later rises and drives mucinous differentiation, as SOX9 levels decline. Frequent coamplification of SOX2 and PIK3CA in dysplasia may, thus, promote progression by locking basal cells in a SOX2HiSOX9Lo state with active PI3K signaling, which sustains the squamous injury response while precluding normal mucociliary differentiation. Surprisingly, we find that, although later in invasive carcinoma SOX9 is generally expressed at low levels, its expression is higher in a subset of SQCCs with less squamous identity and worse clinical outcome. We propose that early pathogenesis of most SQCCs involves stabilization of the squamous injury state in stem cells through copy number gains at 3q, with the pro-proliferative activity of SOX9 possibly being exploited in a subset of SQCCs in later stages., A study of tracheobronchial basal cells reveals a mechanism by which lung squamous cell carcinoma is almost universally initiated by exploitation of a stem cell injury response involving SOX2 and PI3K., Author Summary Squamous cell carcinoma (SQCC) is a deadly and common form of lung cancer. How it develops from stem cells is poorly understood. SQCCs predominantly arise in bronchial epithelia, likely from basal cells, stem cells that normally generate mucinous and ciliated cells. Smoking, however, causes normally quiescent basal cells to proliferate and generate protective squamous epithelia. Continuous smoking eventually causes precancerous changes and, ultimately, SQCC. However, some precancerous changes regress to normal epithelia, suggesting that the natural stem cell injury response is not sustainable indefinitely. Here, we describe how the SOX2 transcription factor and PI3K signaling, which is activated by smoking, induce the squamous injury response in basal cells. We also provide evidence that regeneration of mucociliary epithelia after injury requires basal cells to enter a period of low SOX2 expression and PI3K signaling. Ninety-four percent of SQCCs have copy number gains in chromosome 3 that affect SOX2 and PIK3CA, a catalytic PI3K subunit. These gains occur at a point when precancerous tissue has an increased likelihood of progressing to SQCC. Our data suggest that SQCC is genetically initiated by events that sustain a normally self-limiting injury state, which forces stem cells to proliferate indefinitely in the presence of smoking-associated mutagens.

Cell type-specific gene expression in humans involves complex interactions between regulatory factors and DNA at enhancers and promoters. Mapping studies for expression quantitative trait loci (eQTLs), transcription factors (TFs) and chromatin markers have become widely used tools for identifying gene regulatory elements, but prediction of target genes remains a major challenge. Here, we integrate genome-wide data on TF-binding sites, chromatin markers and functional annotations to predict genes associated with human eQTLs. Using the random forest classifier, we found that genomic proximity plus five TF and chromatin features are able to predict >90% of target genes within 1 megabase of eQTLs. Despite being regularly used to map target genes, proximity is not a good indicator of eQTL targets for genes 150 kilobases away, but insulators, TF co-occurrence, open chromatin and functional similarities between TFs and genes are better indicators. Using all six features in the classifier achieved an area under the specificity and sensitivity curve of 0.91, much better compared with at most 0.75 for using any single feature. We hope this study will not only provide validation of eQTL-mapping studies, but also provide insight into the molecular mechanisms explaining how genetic variation can influence gene expression.

Background Cancer is responsible for elevated HIV-related morbidity and mortality. Research on HIV-infected patients with concurrent cancer is rare in China. The purpose of our study was to investigate the prevalence and risk factors associated with cancer among HIV-infected inpatients in Beijing, and to investigate the mortality and risk factors among HIV-infected inpatients with cancer. Methods Hospital records from a total of 1946 HIV-infected patients were collected from the Beijing Ditan Hospital. The data, from 2008 to 2013, were collected retrospectively. The cancer diagnoses included AIDS-defining cancers (ADC) and non-AIDS defining cancers (NADC). Logistic regression was used to identify risk factors predicting the concurrence of cancer with HIV. Mortality was examined using Kaplan-Meier estimates and Cox proportional hazards models. Results 7.7 % (149 cases) of all HIV-infected inpatients had concurrent cancer at their first hospital admission; of those, 33.6 % (50 cases) had ADCs, and 66.4 % (99 cases) had NADCs. The most prevalent NADCs were Hodgkin’s lymphoma, gastrointestinal cancer, liver cancer, and lung cancer. Patients who did not accept antiretroviral therapy (ART) were more likely to suffer from cancer [AOR = 2.07 (1.42–3.01), p = 0.001]. Kaplan-Meier curves indicated that the survival probability of HIV-positive cancer patients was significantly lower than that of HIV-positive cancer-free patients (log-rank test, p

To investigate the interaction between human cytomegalovirus infection and PAX9 gene polymorphisms in low birth weight (LBW) infants.Nested-PCR was used to detect human cytomegalovirus (HCMV) infection and restriction fragment length polymorphism PCR (RFLP-PCR) was then used to detect polymorphisms at rs2073244 and rs4904210 on the PAX9 gene in 65 HCMV-positive infant cases and 65 HCMV-negative infant controls. Cases and controls were compared for differences in the rates of LBW using the χ(2) test. Genetic and environmental interactions were assessed by comparing rates of LBW between the cases and controls in stratified analyses and logistic regressions.The study confirmed that HCMV infection is significantly associated with LBW (OR = 5.519, χ(2) = 20.924, p 0.05) and suggested that some PAX9 polymorphisms may promote the induction of LBW by HCMV infection. The AG and GG genotypes at rs2073244 and the CC genotype at rs4904210, with interaction coefficients greater than unity suggest that they may strengthen the association between HCMV infection and LBW.Congenital HCMV infection can cause LBW and some PAX9 polymorphisms can increase the risk of LBW in HCMV-infected infants. The results may provide new clues into the relationship among HCMV infection, PAX9 polymorphisms and LBW as well as a foundation for additional molecular epidemiological and biochemical research in the future.

Pancreatic ductal adenocarcinoma (PDAC) is the fourth most common cause of cancer death in North America. Activating KRAS mutations and Smad4 loss occur in approximately 90% and 55% of PDAC, respectively. While their roles in the early stages of PDAC development have been confirmed in genetically modified mouse models, their roles in the multistep malignant transformation of human pancreatic duct cells have not been directly demonstrated. Here, we report that Smad4 represents a barrier in KRAS-mediated malignant transformation of the near normal immortalized human pancreatic duct epithelial (HPDE) cell line model. Marked Smad4 downregulation by shRNA in KRAS (G12V) expressing HPDE cells failed to cause tumorigenic transformation. However, KRAS-mediated malignant transformation occurred in a new HPDE-TGF-β resistant (TβR) cell line that completely lacks Smad4 protein expression and is resistant to the mito-inhibitory activity of TGF-β. This transformation resulted in tumor formation and development of metastatic phenotype when the cells were implanted orthotopically into the mouse pancreas. Smad4 restoration re-established TGF-β sensitivity, markedly increased tumor latency by promoting apoptosis, and decreased metastatic potential. These results directly establish the critical combination of the KRAS oncogene and complete Smad4 inactivation in the multi-stage malignant transformation and metastatic progression of normal human HPDE cells.

Background The epidermal growth factor receptor (EGFR) kinase domain T790M (amino acid substitution at position 790 in EGFR from threonine [T] to methionine [M]) mutation in non–small-cell lung cancer (NSCLC) results in resistance to EGFR tyrosine kinase inhibitors (TKIs). We used a patient-derived tumor xenograft (PDX) model containing an EGFR exon 19 deletion/T790M mutation to assess response to the EGFR-directed antibody cetuximab. Changes in the EGFR signaling pathway and ligand expression after treatment were investigated. Methods PDX were randomized into control and treatment arms. Pharmacodynamic studies were performed at 2 and 24 hours and at 4 days after a single administration of cetuximab, erlotinib, or dacomitinib. Changes in the EGFR signaling pathway were assessed using Western blot analysis, and baseline mRNA expression of EGFR ligands using microarray analysis. Relative changes after treatment were assessed using quantitative polymerase chain reaction. Results The xenograft showed a dramatic response to cetuximab. A complete reduction of total EGFR and phosphorylated EGFR occurred after cetuximab treatment. The PDX had increased baseline levels of heparin-binding epidermal growth factor-like growth factor ( HB-EGF ) compared with other PDX models with or without EGFR mutations. Amphiregulin was significantly reduced 2 hours after treatment with cetuximab. Compared with control mice, cetuximab- and EGFR–TKI-treated mice had significantly reduced HB-EGF gene expression at 2 hours, however, by day 4 the level of HB-EGF expression was higher. The effect of cetuximab compared with EGFR TKI on HB-EGF gene expression levels differed significantly at 2 and 24 hours but not at 4 days. Conclusion We showed a dramatic tumor response with cetuximab in an exon 19 deletion/T790M EGFR mutant lung adenocarcinoma PDX model, which suggests a role for the autocrine feedback loop in the mutant EGFR signaling pathway. Further investigation using cetuximab in NSCLC with T790M mutation is warranted.

Background Cellular development requires the precise control of gene expression states. Transcription factors are involved in this regulatory process through their combinatorial binding with DNA. Information about transcription factor binding sites can help determine which combinations of factors work together to regulate a gene, but it is unclear how far the binding data from one cell type can inform about regulation in other cell types. Results By integrating data on co-localized transcription factor binding sites in the K562 cell line with expression data across 38 distinct hematopoietic cell types, we developed regression models to describe the relationship between the expression of target genes and the transcription factors that co-localize nearby. With K562 binding sites identifying the predictors, the proportion of expression explained by the models is statistically significant only for monocytic cells (p-value< 0.001), which are closely related to K562. That is, cell type specific binding patterns are crucial for choosing the correct transcription factors for the model. Comparison of predictors obtained from binding sites in the GM12878 cell line with those from K562 shows that the amount of difference between binding patterns is directly related to the quality of the prediction. By identifying individual genes whose expression is predicted accurately by the binding sites, we are able to link transcription factors FOS, TAF1 and YY1 to a sparsely studied gene LRIG2. We also find that the activity of a transcription factor may be different depending on the cell type and the identity of other co-localized factors. Conclusion Our approach shows that gene expression can be explained by a modest number of co-localized transcription factors, however, information on cell-type specific binding is crucial for understanding combinatorial gene regulation.

Background As the primary sites of synaptic or sensory input in the nervous system, dendrites play an essential role in processing neuronal and sensory information. Moreover, the specification of class specific dendrite arborization is critically important in establishing neural connectivity and the formation of functional networks. Cytoskeletal modulation provides a key mechanism for establishing, as well as reorganizing, dendritic morphology among distinct neuronal subtypes. While previous studies have established differential roles for the small GTPases Rac and Rho in mediating dendrite morphogenesis, little is known regarding the direct regulators of these genes in mediating distinct dendritic architectures. Methodology/Principal Findings Here we demonstrate that the RhoGEF Trio is required for the specification of class specific dendritic morphology in dendritic arborization (da) sensory neurons of the Drosophila peripheral nervous system (PNS). Trio is expressed in all da neuron subclasses and loss-of-function analyses indicate that Trio functions cell-autonomously in promoting dendritic branching, field coverage, and refining dendritic outgrowth in various da neuron subtypes. Moreover, overexpression studies demonstrate that Trio acts to promote higher order dendritic branching, including the formation of dendritic filopodia, through Trio GEF1-dependent interactions with Rac1, whereas Trio GEF-2-dependent interactions with Rho1 serve to restrict dendritic extension and higher order branching in da neurons. Finally, we show that de novo dendritic branching, induced by the homeodomain transcription factor Cut, requires Trio activity suggesting these molecules may act in a pathway to mediate dendrite morphogenesis. Conclusions/Significance Collectively, our analyses implicate Trio as an important regulator of class specific da neuron dendrite morphogenesis via interactions with Rac1 and Rho1 and indicate that Trio is required as downstream effector in Cut-mediated regulation of dendrite branching and filopodia formation.

Background The epidermal growth factor receptor (EGFR) signaling pathway plays a key role in regulation of cellular growth and development. While highly studied, it is still not fully understood how the signal is orchestrated. One of the reasons for the complexity of this pathway is the extensive network of inter-connected components involved in the signaling. In the aim of identifying critical mechanisms controlling signal transduction we have performed extensive analysis of an executable model of the EGFR pathway using the stochastic pi-calculus as a modeling language. Results Our analysis, done through simulation of various perturbations, suggests that the EGFR pathway contains regions of functional redundancy in the upstream parts; in the event of low EGF stimulus or partial system failure, this redundancy helps to maintain functional robustness. Downstream parts, like the parts controlling Ras and ERK, have fewer redundancies, and more than 50% inhibition of specific reactions in those parts greatly attenuates signal response. In addition, we suggest an abstract model that captures the main control mechanisms in the pathway. Simulation of this abstract model suggests that without redundancies in the upstream modules, signal transduction through the entire pathway could be attenuated. In terms of specific control mechanisms, we have identified positive feedback loops whose role is to prolong the active state of key components (e.g., MEK-PP, Ras-GTP), and negative feedback loops that help promote signal adaptation and stabilization. Conclusions The insights gained from simulating this executable model facilitate the formulation of specific hypotheses regarding the control mechanisms of the EGFR signaling, and further substantiate the benefit to construct abstract executable models of large complex biological networks.

The BioMoby project was initiated in 2001 from within the model organism database community. It aimed to standardize methodologies to facilitate information exchange and access to analytical resources, using a consensus driven approach. Six years later, the BioMoby development community is pleased to announce the release of the 1.0 version of the interoperability framework, registry Application Programming Interface and supporting Perl and Java code-bases. Together, these provide interoperable access to over 1400 bioinformatics resources worldwide through the BioMoby platform, and this number continues to grow. Here we highlight and discuss the features of BioMoby that make it distinct from other Semantic Web Service and interoperability initiatives, and that have been instrumental to its deployment and use by a wide community of bioinformatics service providers. The standard, client software, and supporting code libraries are all freely available at http://www.biomoby.org/.

Background Neurons are one of the most structurally and functionally diverse cell types found in nature, owing in large part to their unique class specific dendritic architectures. Dendrites, being highly specialized in receiving and processing neuronal signals, play a key role in the formation of functional neural circuits. Hence, in order to understand the emergence and assembly of a complex nervous system, it is critical to understand the molecular mechanisms that direct class specific dendritogenesis. Methodology/Principal Findings We have used the Drosophila dendritic arborization (da) neurons to gain systems-level insight into dendritogenesis by a comparative study of the morphologically distinct Class-I (C-I) and Class-IV (C-IV) da neurons. We have used a combination of cell-type specific transcriptional expression profiling coupled to a targeted and systematic in vivo RNAi functional validation screen. Our comparative transcriptomic analyses have revealed a large number of differentially enriched/depleted gene-sets between C-I and C-IV neurons, including a broad range of molecular factors and biological processes such as proteolytic and metabolic pathways. Further, using this data, we have identified and validated the role of 37 transcription factors in regulating class specific dendrite development using in vivo class-specific RNAi knockdowns followed by rigorous and quantitative neurometric analysis. Conclusions/Significance This study reports the first global gene-expression profiles from purified Drosophila C-I and C-IV da neurons. We also report the first large-scale semi-automated reconstruction of over 4,900 da neurons, which were used to quantitatively validate the RNAi screen phenotypes. Overall, these analyses shed global and unbiased novel insights into the molecular differences that underlie the morphological diversity of distinct neuronal cell-types. Furthermore, our class-specific gene expression datasets should prove a valuable community resource in guiding further investigations designed to explore the molecular mechanisms underlying class specific neuronal patterning.

The effectiveness of most cancer targeted therapies is short lived since tumors evolve and develop resistance. Combinations of drugs offer the potential to overcome resistance, however the number of possible combinations is vast necessitating data-driven approaches to find optimal treatments tailored to a patient’s tumor. AstraZeneca carried out 11,576 experiments on 910 drug combinations across 85 cancer cell lines, recapitulating in vivo response profiles. These data, the largest openly available screen, were hosted by DREAM alongside deep molecular characterization from the Sanger Institute for a Challenge to computationally predict synergistic drug pairs and associated biomarkers. 160 teams participated to provide the most comprehensive methodological development and subsequent benchmarking to date. Winning methods incorporated prior knowledge of putative drug target interactions. For >60% of drug combinations synergy was reproducibly predicted with an accuracy matching biological replicate experiments, however 20% of drug combinations were poorly predicted by all methods. Genomic rationale for synergy predictions were identified, including antagonism unique to combined PIK3CB/D inhibition with the ADAM17 inhibitor where synergy is seen with other PI3K pathway inhibitors. All data, methods and code are freely available as a resource to the community.