Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice

Why Pleiotropic Interventions are Needed for Alzheimer's Disease

Alzheimer's disease (AD) involves a complex pathological cascade thought to be initially triggered by the accumulation of β-amyloid (Aβ) peptide aggregates or aberrant amyloid precursor protein (APP) processing. Much is known of the factors initiating the disease process decades prior to the onset of cognitive deficits, but an unclear understanding of events immediately preceding and precipitating cognitive decline is a major factor limiting the rapid development of adequate prevention and treatment strategies. Multiple pathways are known to contribute to cognitive deficits by disruption of neuronal signal transduction pathways involved in memory. These pathways are altered by aberrant signaling, inflammation, oxidative damage, tau pathology, neuron loss, and synapse loss. We need to develop stage-specific interventions that not only block causal events in pathogenesis (aberrant tau phosphorylation, Aβ production and accumulation, and oxidative damage), but also address damage from these pathways that will not be reversed by targeting prodromal pathways. This approach would not only focus on blocking early events in pathogenesis, but also adequately correct for loss of synapses, substrates for neuroprotective pathways (e.g., docosahexaenoic acid), defects in energy metabolism, and adverse consequences of inappropriate compensatory responses (aberrant sprouting). Monotherapy targeting early single steps in this complicated cascade may explain disappointments in trials with agents inhibiting production, clearance, or aggregation of the initiating Aβ peptide or its aggregates. Both plaque and tangle pathogenesis have already reached AD levels in the more vulnerable brain regions during the “prodromal” period prior to conversion to “mild cognitive impairment (MCI).” Furthermore, many of the pathological events are no longer proceeding in series, but are going on in parallel. By the MCI stage, we stand a greater chance of success by considering pleiotropic drugs or cocktails that can independently limit the parallel steps of the AD cascade at all stages, but that do not completely inhibit the constitutive normal functions of these pathways. Based on this hypothesis, efforts in our laboratories have focused on the pleiotropic activities of omega-3 fatty acids and the anti-inflammatory, antioxidant, and anti-amyloid activity of curcumin in multiple models that cover many steps of the AD pathogenic cascade (Cole and Frautschy, Alzheimers Dement 2:284–286, 2006)

Getting a grip on sensorimotor effects in lexical-semantic processing

One of the strategies that researchers have used to investigate the role of sensorimotor information in lexical-semantic processing is to examine effects of words’ rated body-object interaction (BOI; the ease with which the human body can interact with a word’s referent). Processing tends to be facilitated for words with high BOI compared to words with low BOI, across a wide variety of tasks. Such effects have been referenced in debates over the nature of semantic representations, but their theoretical import has been limited by the fact that BOI is a fairly coarse measure of sensorimotor experience with words’ referents. In the present study we collected ratings for 621 words on seven semantic dimensions (graspability, ease of pantomime, number of actions, animacy, size, danger, and usefulness) in order to investigate which attributes are most strongly related to BOI ratings, and to lexical-semantic processing. BOI ratings were obtained from previous norming studies (Bennett, Burnett, Siakaluk, & Pexman, 2011; Tillotson, Siakaluk, & Pexman, 2008) and measures of lexical-semantic processing were obtained from previous behavioural megastudies involving the semantic categorization task (concrete/abstract decision; Pexman, Heard, Lloyd, & Yap, 2017) and the lexical decision task (Balota et al., 2007). Results showed that the motor dimension of graspability, ease of pantomime, and number of actions were all related to BOI and that these dimensions together explained more variance in semantic processing than did BOI ratings alone. These ratings will be useful for researchers who wish to study how different kinds of bodily interactions influence lexical-semantic processing and cognition

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

Structure of Simian Virus 40 at 3.8 Å Resolution.

The crystallographically determined structure of simian virus 40 shows that the 72 pentamers of viral protein VP1, which form the outer shell, have identical conformations except for the C-terminal arms of their subunits. Five arms emerge from each pentamer and insert into neighbouring pentamers. This tying together of standard building blocks allows for the required variability in packing geometry without sacrificing specificity

Yield Optimisation of Hepatitis B Virus Core Particles in E. coli Expression System for Drug Delivery Applications

An E. coli expression system offers a mean for rapid, high yield and economical production of Hepatitis B Virus core (HBc) particles. However, high-level production of HBc particles in bacteria is demanding and optimisation of HBc particle yield from E. coli is required to improve laboratory-scale productivity for further drug delivery applications. Production steps involve bacterial culture, protein isolation, denaturation, purification and finally protein assembly. In this study, we describe a modified E. coli based method for purifying HBc particles and compare the results with those obtained using a conventional purification method. HBc particle morphology was confirmed by Atomic Force Microscopy (AFM). Protein specificity and secondary structure were confirmed by Western Blot and Circular Dichroism (CD), respectively. The modified method produced ~3-fold higher yield and greater purity of wild type HBc particles than the conventional method. Our results demonstrated that the modified method produce a better yield and purity of HBc particles in an E. coli-expression system, which are fully characterised and suitable to be used for drug delivery applications