Molecular profiling of signet ring cell colorectal cancer provides a strong rationale for genomic targeted and immune checkpoint inhibitor therapies

We would like to thank all patients whose samples were used in this study. We are also thankful to the Northern Ireland Biobank and Grampian Biorepository for providing us with tissue blocks and patient data; and Dr HG Coleman (Queen’s University Belfast) for her advice on statistical analyses. This work has been carried out with financial support from Cancer Research UK (grant: C11512/A18067), Experimental Cancer Medicine Centre Network (grant: C36697/A15590 from Cancer Research UK and the NI Health and Social Care Research and Development Division), the Sean Crummey Memorial Fund and the Tom Simms Memorial Fund. The Northern Ireland Biobank is funded by HSC Research and Development Division of the Public Health Agency in Northern Ireland and Cancer Research UK through the Belfast CRUK Centre and the Northern Ireland Experimental Cancer Medicine Centre; additional support was received from Friends of the Cancer Centre. The Northern Ireland Molecular Pathology Laboratory which is responsible for creating resources for the Northern Ireland Biobank has received funding from Cancer Research UK, Friends of the Cancer Centre and Sean Crummey Foundation.Peer reviewedPublisher PD

When One Size Does Not Fit All: A Simple Statistical Method to Deal with Across-Individual Variations of Effects

In science, it is a common experience to discover that although the investigated effect is very clear in some individuals, statistical tests are not significant because the effect is null or even opposite in other individuals. Indeed, t-tests, Anovas and linear regressions compare the average effect with respect to its inter-individual variability, so that they can fail to evidence a factor that has a high effect in many individuals (with respect to the intra-individual variability). In such paradoxical situations, statistical tools are at odds with the researcher’s aim to uncover any factor that affects individual behavior, and not only those with stereotypical effects. In order to go beyond the reductive and sometimes illusory description of the average behavior, we propose a simple statistical method: applying a Kolmogorov-Smirnov test to assess whether the distribution of p-values provided by individual tests is significantly biased towards zero. Using Monte-Carlo studies, we assess the power of this two-step procedure with respect to RM Anova and multilevel mixed-effect analyses, and probe its robustness when individual data violate the assumption of normality and homoscedasticity. We find that the method is powerful and robust even with small sample sizes for which multilevel methods reach their limits. In contrast to existing methods for combining p-values, the Kolmogorov-Smirnov test has unique resistance to outlier individuals: it cannot yield significance based on a high effect in one or two exceptional individuals, which allows drawing valid population inferences. The simplicity and ease of use of our method facilitates the identification of factors that would otherwise be overlooked because they affect individual behavior in significant but variable ways, and its power and reliability with small sample sizes (<30–50 individuals) suggest it as a tool of choice in exploratory studies

The possible benefits of reduced errors in the motor skills acquisition of children

An implicit approach to motor learning suggests that relatively complex movement skills may be better acquired in environments that constrain errors during the initial stages of practice. This current concept paper proposes that reducing the number of errors committed during motor learning leads to stable performance when attention demands are increased by concurrent cognitive tasks. While it appears that this approach to practice may be beneficial for motor learning, further studies are needed to both confirm this advantage and better understand the underlying mechanisms. An approach involving error minimization during early learning may have important applications in paediatric rehabilitation

Identifier mapping performance for integrating transcriptomics and proteomics experimental results

Background\ud Studies integrating transcriptomic data with proteomic data can illuminate the proteome more clearly than either separately. Integromic studies can deepen understanding of the dynamic complex regulatory relationship between the transcriptome and the proteome. Integrating these data dictates a reliable mapping between the identifier nomenclature resultant from the two high-throughput platforms. However, this kind of analysis is well known to be hampered by lack of standardization of identifier nomenclature among proteins, genes, and microarray probe sets. Therefore data integration may also play a role in critiquing the fallible gene identifications that both platforms emit.\ud \ud Results\ud We compared three freely available internet-based identifier mapping resources for mapping UniProt accessions (ACCs) to Affymetrix probesets identifications (IDs): DAVID, EnVision, and NetAffx. Liquid chromatography-tandem mass spectrometry analyses of 91 endometrial cancer and 7 noncancer samples generated 11,879 distinct ACCs. For each ACC, we compared the retrieval sets of probeset IDs from each mapping resource. We confirmed a high level of discrepancy among the mapping resources. On the same samples, mRNA expression was available. Therefore, to evaluate the quality of each ACC-to-probeset match, we calculated proteome-transcriptome correlations, and compared the resources presuming that better mapping of identifiers should generate a higher proportion of mapped pairs with strong inter-platform correlations. A mixture model for the correlations fitted well and supported regression analysis, providing a window into the performance of the mapping resources. The resources have added and dropped matches over two years, but their overall performance has not changed.\ud \ud Conclusions\ud The methods presented here serve to achieve concrete context-specific insight, to support well-informed decisions in choosing an ID mapping strategy for "omic" data merging

Large-Scale Whole-Genome Sequencing Reveals the Genetic Architecture of Primary Membranoproliferative GN and C3 Glomerulopathy

BACKGROUND: Primary membranoproliferative GN, including complement 3 (C3) glomerulopathy, is a rare, untreatable kidney disease characterized by glomerular complement deposition. Complement gene mutations can cause familial C3 glomerulopathy, and studies have reported rare variants in complement genes in nonfamilial primary membranoproliferative GN. METHODS: We analyzed whole-genome sequence data from 165 primary membranoproliferative GN cases and 10,250 individuals without the condition (controls) as part of the National Institutes of Health Research BioResource–Rare Diseases Study. We examined copy number, rare, and common variants. RESULTS: Our analysis included 146 primary membranoproliferative GN cases and 6442 controls who were unrelated and of European ancestry. We observed no significant enrichment of rare variants in candidate genes (genes encoding components of the complement alternative pathway and other genes associated with the related disease atypical hemolytic uremic syndrome; 6.8% in cases versus 5.9% in controls) or exome-wide. However, a significant common variant locus was identified at 6p21.32 (rs35406322) (P=3.29×10−8; odds ratio [OR], 1.93; 95% confidence interval [95% CI], 1.53 to 2.44), overlapping the HLA locus. Imputation of HLA types mapped this signal to a haplotype incorporating DQA1*05:01, DQB1*02:01, and DRB1*03:01 (P=1.21×10−8; OR, 2.19; 95% CI, 1.66 to 2.89). This finding was replicated by analysis of HLA serotypes in 338 individuals with membranoproliferative GN and 15,614 individuals with nonimmune renal failure. CONCLUSIONS: We found that HLA type, but not rare complement gene variation, is associated with primary membranoproliferative GN. These findings challenge the paradigm of complement gene mutations typically causing primary membranoproliferative GN and implicate an underlying autoimmune mechanism in most cases

Inferring stabilizing mutations from protein phylogenies : application to influenza hemagglutinin

One selection pressure shaping sequence evolution is the requirement that a protein fold with sufficient stability to perform its biological functions. We present a conceptual framework that explains how this requirement causes the probability that a particular amino acid mutation is fixed during evolution to depend on its effect on protein stability. We mathematically formalize this framework to develop a Bayesian approach for inferring the stability effects of individual mutations from homologous protein sequences of known phylogeny. This approach is able to predict published experimentally measured mutational stability effects (ΔΔG values) with an accuracy that exceeds both a state-of-the-art physicochemical modeling program and the sequence-based consensus approach. As a further test, we use our phylogenetic inference approach to predict stabilizing mutations to influenza hemagglutinin. We introduce these mutations into a temperature-sensitive influenza virus with a defect in its hemagglutinin gene and experimentally demonstrate that some of the mutations allow the virus to grow at higher temperatures. Our work therefore describes a powerful new approach for predicting stabilizing mutations that can be successfully applied even to large, complex proteins such as hemagglutinin. This approach also makes a mathematical link between phylogenetics and experimentally measurable protein properties, potentially paving the way for more accurate analyses of molecular evolution

C3 Glomerulopathy and Related Disorders in Children: Etiology-Phenotype Correlation and Outcomes

BACKGROUND AND OBJECTIVES: Membranoproliferative GN and C3 glomerulopathy are rare and overlapping disorders associated with dysregulation of the alternative complement pathway. Specific etiologic data for pediatric membranoproliferative GN/C3 glomerulopathy are lacking, and outcome data are based on retrospective studies without etiologic data. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: A total of 80 prevalent pediatric patients with membranoproliferative GN/C3 glomerulopathy underwent detailed phenotyping and long-term follow-up within the National Registry of Rare Kidney Diseases (RaDaR). Risk factors for kidney survival were determined using a Cox proportional hazards model. Kidney and transplant graft survival was determined using the Kaplan-Meier method. RESULTS: Central histology review determined 39 patients with C3 glomerulopathy, 31 with immune-complex membranoproliferative GN, and ten with immune-complex GN. Patients were aged 2-15 (median, 9; interquartile range, 7-11) years. Median complement C3 and C4 levels were 0.31 g/L and 0.14 g/L, respectively; acquired (anticomplement autoantibodies) or genetic alternative pathway abnormalities were detected in 46% and 9% of patients, respectively, across all groups, including those with immune-complex GN. Median follow-up was 5.18 (interquartile range, 2.13-8.08) years. Eleven patients (14%) progressed to kidney failure, with nine transplants performed in eight patients, two of which failed due to recurrent disease. Presence of >50% crescents on the initial biopsy specimen was the sole variable associated with kidney failure in multivariable analysis (hazard ratio, 6.2; 95% confidence interval, 1.05 to 36.6; P50% crescents on the initial biopsy specimen. CONCLUSIONS: Crescentic disease was a key risk factor associated with kidney failure in a national cohort of pediatric patients with membranoproliferative GN/C3 glomerulopathy and immune-complex GN. Presenting eGFR and crescentic disease help define prognostic groups in pediatric C3 glomerulopathy. Acquired abnormalities of the alternative pathway were commonly identified but not a risk factor for kidney failure

Active inference, sensory attenuation and illusions.

Active inference provides a simple and neurobiologically plausible account of how action and perception are coupled in producing (Bayes) optimal behaviour. This can be seen most easily as minimising prediction error: we can either change our predictions to explain sensory input through perception. Alternatively, we can actively change sensory input to fulfil our predictions. In active inference, this action is mediated by classical reflex arcs that minimise proprioceptive prediction error created by descending proprioceptive predictions. However, this creates a conflict between action and perception; in that, self-generated movements require predictions to override the sensory evidence that one is not actually moving. However, ignoring sensory evidence means that externally generated sensations will not be perceived. Conversely, attending to (proprioceptive and somatosensory) sensations enables the detection of externally generated events but precludes generation of actions. This conflict can be resolved by attenuating the precision of sensory evidence during movement or, equivalently, attending away from the consequences of self-made acts. We propose that this Bayes optimal withdrawal of precise sensory evidence during movement is the cause of psychophysical sensory attenuation. Furthermore, it explains the force-matching illusion and reproduces empirical results almost exactly. Finally, if attenuation is removed, the force-matching illusion disappears and false (delusional) inferences about agency emerge. This is important, given the negative correlation between sensory attenuation and delusional beliefs in normal subjects--and the reduction in the magnitude of the illusion in schizophrenia. Active inference therefore links the neuromodulatory optimisation of precision to sensory attenuation and illusory phenomena during the attribution of agency in normal subjects. It also provides a functional account of deficits in syndromes characterised by false inference and impaired movement--like schizophrenia and Parkinsonism--syndromes that implicate abnormal modulatory neurotransmission

Mate-Finding as an Overlooked Critical Determinant of Dispersal Variation in Sexually-Reproducing Animals

Dispersal is a critically important process in ecology, but robust predictive models of animal dispersal remain elusive. We identify a potentially ubiquitous component of variation in animal dispersal that has been largely overlooked until now: the influence of mate encounters on settlement probability. We use an individual-based model to simulate dispersal in sexually-reproducing organisms that follow a simple set of movement rules based on conspecific encounters, within an environment lacking spatial habitat heterogeneity. We show that dispersal distances vary dramatically with fluctuations in population density in such a model, even in the absence of variation in dispersive traits between individuals. In a simple random-walk model with promiscuous mating, dispersal distributions become increasingly ‘fat-tailed’ at low population densities due to the increasing scarcity of mates. Similar variation arises in models incorporating territoriality. In a model with polygynous mating, we show that patterns of sex-biased dispersal can even be reversed across a gradient of population density, despite underlying dispersal mechanisms remaining unchanged. We show that some widespread dispersal patterns found in nature (e.g. fat tailed distributions) can arise as a result of demographic variability in the absence of heterogeneity in dispersive traits across the population. This implies that models in which individual dispersal distances are considered to be fixed traits might be unrealistic, as dispersal distances vary widely under a single dispersal mechanism when settlement is influenced by mate encounters. Mechanistic models offer a promising means of advancing our understanding of dispersal in sexually-reproducing organisms