Effects of eight neuropsychiatric copy number variants on human brain structure

Many copy number variants (CNVs) confer risk for the same range of neurodevelopmental symptoms and psychiatric conditions including autism and schizophrenia. Yet, to date neuroimaging studies have typically been carried out one mutation at a time, showing that CNVs have large effects on brain anatomy. Here, we aimed to characterize and quantify the distinct brain morphometry effects and latent dimensions across 8 neuropsychiatric CNVs. We analyzed T1-weighted MRI data from clinically and non-clinically ascertained CNV carriers (deletion/duplication) at the 1q21.1 (n = 39/28), 16p11.2 (n = 87/78), 22q11.2 (n = 75/30), and 15q11.2 (n = 72/76) loci as well as 1296 non-carriers (controls). Case-control contrasts of all examined genomic loci demonstrated effects on brain anatomy, with deletions and duplications showing mirror effects at the global and regional levels. Although CNVs mainly showed distinct brain patterns, principal component analysis (PCA) loaded subsets of CNVs on two latent brain dimensions, which explained 32 and 29% of the variance of the 8 Cohen’s d maps. The cingulate gyrus, insula, supplementary motor cortex, and cerebellum were identified by PCA and multi-view pattern learning as top regions contributing to latent dimension shared across subsets of CNVs. The large proportion of distinct CNV effects on brain morphology may explain the small neuroimaging effect sizes reported in polygenic psychiatric conditions. Nevertheless, latent gene brain morphology dimensions will help subgroup the rapidly expanding landscape of neuropsychiatric variants and dissect the heterogeneity of idiopathic conditions

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

The Need for a Developmentally Based Measure of Social Communication Skills

The ability to demonstrate and quantify changes in social communication skills has been hindered by a lack of existing measures with appropriate standardization and psychometric properties. Such a measure would be helpful for research in many populations but would be particularly crucial for detecting incremental changes in youth with neurodevelopmental disorders who might gain skills but still lag substantially behind same-age peers. Although study designs and statistical methods are under development to try to account for slow and/or nonlinear, but potentially meaningful, improvements,1 there is a dearth of measures designed to capture growth and loss of social communication skills. This opinion piece outlines the argument for such a measure and the primary issues to consider in its development

Sorting the Phenotypic Heterogeneity of Autism Spectrum Disorders: A Hierarchical Clustering Model

Autism spectrum disorder (ASD) is characterized by notable phenotypic heterogeneity, which is often viewed as an obstacle to the study of its etiology, diagnosis, treatment, and prognosis. Heterogeneity in ASD is multidimensional and complex including variability in phenotype as well as clinical, physiologic, and pathologic parameters. We apply a hierarchical clustering model suited to dealing with datasets of mixed data types to stratify children with ASD into more homogeneous subgroups in line with the Diagnostic and Statistical Manual of Mental Disorders (DSM)-5 model. The results of this cluster analysis will provide a better understanding the complex issue of ASD phenotypic heterogeneity and identify subgroups useful for further ASD genetic studies. Our goal is to provide insight into viable phenotypic and genotypic markers that would guide further cluster analysis of ASD genetic data. We suggest that analyzing the clusters in a hierarchical structure is a well-suited and meaningful model to unravel the complex heterogeneity of this disorder

Dr. Bishop et al. Reply

In "The Need for a Clinically Useful Schema of Social Communication," Blank et&nbsp;al. present an observation and coding method (The Initiating, Responding, Expectancy Violations [IREV] schema) for identifying "expectancy violations (EVs)," which may signal clinically significant departures from normal social communication behavior (eg, in individuals with autism spectrum disorder [ASD]).1 The authors point out that "historically, observation of a patient's (social communication) has not been part of the routine psychiatric mental status examination," and argue that this is an important missed opportunity for clinicians. Several direct observation methods exist for identifying and/or monitoring changes in social communication deficits associated with ASD.2 Despite their established diagnostic validity, it remains true that these measures used in isolation will result in a relatively high rate of "false positives"-usually comprising children who are better described with other diagnoses (eg, intellectual disability, language disorder, attention-deficit/hyperactivity disorder [ADHD]).2 This underscores the critical importance of context when interpreting observed social communication deficits

Ensemble Statistical and Subspace Clustering Model for Analysis of Autism Spectrum Disorder Phenotypes

Heterogeneity in Autism Spectrum Disorder (ASD) is complex including variability in behavioral phenotype as well as clinical, physiologic, and pathologic parameters. The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) now diagnoses ASD using a 2-dimensional model based social communication deficits and fixated interests and repetitive behaviors. Sorting out heterogeneity is crucial for study of etiology, diagnosis, treatment and prognosis. In this paper, we present an ensemble model for analyzing ASD phenotypes using several machine learning techniques and a k-dimensional subspace clustering algorithm. Our ensemble also incorporates statistical methods at several stages of analysis. We apply this model to a sample of 208 probands drawn from the Simon Simplex Collection Missouri Site patients. The results provide useful evidence that is helpful in elucidating the phenotype complexity within ASD. Our model can be extended to other disorders that exhibit a diverse range of heterogeneity

Extracting Latent Subdimensions of Social Communication: A Cross-Measure Factor Analysis

ObjectiveSocial communication deficits associated with autism spectrum disorder (ASD) are commonly represented as a single behavioral domain. However, increased precision of measurement of social communication is needed to promote more nuanced phenotyping, both within the autism spectrum and across diagnostic boundaries.MethodA large sample (N&nbsp;= 1,470) of 4- to 10-year-old children was aggregated from across 4 data sources, and then randomly split into testing and validation samples. A total of 57 selected social communication items from 3 widely used autism symptom measures (the Autism Diagnostic Observation Scale [ADOS], Autism Diagnostic Interview-Revised [ADI-R], and Social Responsiveness Scale [SRS]) were analyzed in the multi-trait/multi-method factor analysis framework. The selected model was then confirmed with the validation sample.ResultsThe 4-substantive factor model, with 3 orthogonal method factors, was selected using the testing sample based on fit indices and then confirmed with the validation sample. Two of the factors, "Basic Social Communication Skills" and "Interaction Quality," were similar to those identified in a previous analysis of the ADOS, Module 3. Two additional factors, "Peer Interaction and Modification of Behavior" and "Social Initiation and Affiliation," also emerged. Factor scores showed nominal correlations with age and verbal IQ.ConclusionIdentification of subdimensions could inform the creation of better conceptual models of social communication impairments, including mapping of how the cascading effects of social communication deficits unfold in ASD versus other disorders. Especially if extended to include both older and younger age cohorts and individuals with more varying developmental levels, these efforts could inform phenotype-based exploration for biological and genetic mechanisms by pinpointing specific mechanisms that contribute to various types of social communication deficits

Associations Between Parenting Stress and Quality Time in Families of Youth with Autism Spectrum Disorder

Increased stress among parents of youth with ASD has been well-documented. However, research on aspects of the parent-child relationship and subsequent links to parenting stress is limited. We assessed parents (N = 511) of youth with ASD to examine relations between parenting stress and parent-child quality time (amount of quality time, shared enjoyment, synchronicity). Elevated parenting stress was associated with less time spent engaging with youth in shared activities and decreased parent and child enjoyment during shared interactions. Parents with elevated stress reported engaging in shared activities and experiencing synchronicity with their child less often than parents below the clinical threshold. Future research should emphasize longitudinal efforts examining the directionality of this relationship to better inform family-focused intervention