Computational methods to analyze image-based siRNA knockdown screens

Neuroblastoma is the most common extra-cranial solid tumor of early childhood. Standard therapies are not effective in case of poor prognosis and chemotherapy resistance. To improve drug therapy, it is imperative to discover new targets that play a substantial role in tumorigenesis of neuroblastoma. The mitotic machinery is an attractive target for therapeutic interventions and inhibitors can be developed to target mitotic entry, spindle apparatus, spindle activation checkpoint, and mitotic exit. Thus, we performed a study to find genes that cause mitosis linked cell death upon inhibition in neuroblastoma cells. We investigated gene expression studies of neuroblastoma tumors and selected 240 genes relevant for tumorigenesis and cell cycle. With these genes we performed image-based time-lapse screening of gene knockdowns in neuroblastoma cells. We developed a classifier to classify images into cellular phenotypes, using SVM, performing manual evaluation and automatic corrections. This classifier yielded better predictions of cellular phenotypes than the standard classification protocol. We further developed an elaborated analysis pipeline based on the phenotype kinetics from the gene knockdown screening to identify genes with vital role in mitosis to identify therapeutic targets for neuroblastoma. We developed two methods (1) to generate clusters of genes with similar phenotype profiles and (2) to track the sequence of phenotype events, particularly mitosis-linked-celldeath. We identified six genes (DLGAP5, DSCC1, SMO, SNRPD1, SSBP1, and UBE2C) that cause mitosis-linked-cell-death upon knockdown in both of the neuroblastoma cell lines tested (SH-EP and SK-N-BE(2)-C). Gene expression analysis of neuroblastoma patients show that these genes are up-regulated in aggressive tumors and they show good prediction performance for overall survival. Four of these hits (DLGAP5, DSCC1, SSBP1, UBE2C) are directly involved in cell cycle and one (SMO) indirectly which is involved in cell cycle regulation. Functional association and gene-expression analysis of these hits indicated that monitoring cell cycle dynamics enabled finding promising drug targets for neuroblastoma cells. In summary, we present a bioinformatics pipeline to determine cancer specific therapeutic targets by first performing a focused gene expression analysis to select genes followed by a gene knockdown screening assay of live cells

De novo pathway-based biomarker identification

Gene expression profiles have been extensively discussed as an aid to guide the therapy by predicting disease outcome for the patients suffering from complex diseases, such as cancer. However, prediction models built upon single-gene (SG) features show poor stability and performance on independent datasets. Attempts to mitigate these drawbacks have led to the development of network-based approaches that integrate pathway information to produce meta-gene (MG) features. Also, MG approaches have only dealt with the two-class problem of good versus poor outcome prediction. Stratifying patients based on their molecular subtypes can provide a detailed view of the disease and lead to more personalized therapies. We propose and discuss a novel MG approach based on de novo pathways, which for the first time have been used as features in a multi-class setting to predict cancer subtypes. Comprehensive evaluation in a large cohort of breast cancer samples from The Cancer Genome Atlas (TCGA) revealed that MGs are considerably more stable than SG models, while also providing valuable insight into the cancer hallmarks that drive them. In addition, when tested on an independent benchmark non-TCGA dataset, MG features consistently outperformed SG models. We provide an easy-touse web service at http:// pathclass. compbio. sdu. dk where users can upload their own gene expression datasets from breast cancer studies and obtain the subtype predictions from all the classifiers

Enteral lactoferrin supplementation for very preterm infants : a randomised placebo-controlled trial

Background: Infections acquired in hospital are an important cause of morbidity and mortality in very preterm infants. Several small trials have suggested that supplementing the enteral diet of very preterm infants with lactoferrin, an antimicrobial protein processed from cow's milk, prevents infections and associated complications. Methods: In this randomised, placebo-controlled trial, very preterm infants (born before 32 weeks' gestation) in 37 UK hospitals were allocated randomly (1:1) within 72 hours after birth to receive enteral bovine lactoferrin (150 mg/kg/day; maximum 300 mg/day) versus sucrose (same dose) once daily until 34 weeks' postmenstrual age. Web-based randomisation minimised for recruitment site, gestation (completed weeks), sex, and single versus multifetal pregnancy. Parents, caregivers and outcomes assessors were unaware of group assignment. The primary outcome was microbiologically-confirmed or clinically-suspected lateonset infection (occurring >72 hours after birth). The trial was registered with the International Standard Randomised Controlled Trial Number 88261002. Findings: We recruited 2203 participants between May 2014 and September 2017. Four infants had consent withdrawn or unconfirmed leaving 1098 infants in the lactoferrin group and 1101 in the sucrose group. Primary outcome data for 2182 infants were available for inclusion in the intention-to-treat analyses. In the intervention group, 316/1093 (28.9%) infants acquired a late-onset infection versus 334/1089 (30.7%) in the control group: risk ratio (RR) adjusted for minimisation factors 0.95 (95% confidence interval [CI] 0.86, 1.04). Pre-specified subgroup analyses did not show statistically significant interactions for gestation at birth (completed weeks') or type of enteral milk received (human, formula, or both). Interpretation: Enteral supplementation with bovine lactoferrin does not reduce the incidence of late-onset infection in very preterm infants. Funding: UK National Institute for Health Research Health Technology Assessment programme (10/57/49)

Health, education, and social care provision after diagnosis of childhood visual disability

Aim: To investigate the health, education, and social care provision for children newly diagnosed with visual disability.Method: This was a national prospective study, the British Childhood Visual Impairment and Blindness Study 2 (BCVIS2), ascertaining new diagnoses of visual impairment or severe visual impairment and blindness (SVIBL), or equivalent vi-sion. Data collection was performed by managing clinicians up to 1-year follow-up, and included health and developmental needs, and health, education, and social care provision.Results: BCVIS2 identified 784 children newly diagnosed with visual impairment/SVIBL (313 with visual impairment, 471 with SVIBL). Most children had associated systemic disorders (559 [71%], 167 [54%] with visual impairment, and 392 [84%] with SVIBL). Care from multidisciplinary teams was provided for 549 children (70%). Two-thirds (515) had not received an Education, Health, and Care Plan (EHCP). Fewer children with visual impairment had seen a specialist teacher (SVIBL 35%, visual impairment 28%, χ2p < 0.001), or had an EHCP (11% vs 7%, χ2p < 0 . 01).Interpretation: Families need additional support from managing clinicians to access recommended complex interventions such as the use of multidisciplinary teams and educational support. This need is pressing, as the population of children with visual impairment/SVIBL is expected to grow in size and complexity.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited

Shared heritability and functional enrichment across six solid cancers

Correction: Nature Communications 10 (2019): art. 4386 DOI: 10.1038/s41467-019-12095-8Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (r(g) = 0.57, p = 4.6 x 10(-8)), breast and ovarian cancer (r(g) = 0.24, p = 7 x 10(-5)), breast and lung cancer (r(g) = 0.18, p = 1.5 x 10(-6)) and breast and colorectal cancer (r(g) = 0.15, p = 1.1 x 10(-4)). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis.Peer reviewe

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe