DrivR-Base: a feature extraction toolkit for variant effect prediction model construction.

MOTIVATION: Recent advancements in sequencing technologies have led to the discovery of numerous variants in the human genome. However, understanding their precise roles in diseases remains challenging due to their complex functional mechanisms. Various methodologies have emerged to predict the pathogenic significance of these genetic variants. Typically, these methods employ an integrative approach, leveraging diverse data sources that provide important insights into genomic function. Despite the abundance of publicly available data sources and databases, the process of navigating, extracting, and pre-processing features for machine learning models can be highly challenging and time-consuming. Furthermore, researchers often invest substantial effort in feature extraction, only to later discover that these features lack informativeness. RESULTS: In this article, we introduce DrivR-Base, an innovative resource that efficiently extracts and integrates molecular information (features) related to single nucleotide variants. These features encompass information about the genomic positions and the associated protein positions of a variant. They are derived from a wide array of databases and tools, including structural properties obtained from AlphaFold, regulatory information sourced from ENCODE, and predicted variant consequences from Variant Effect Predictor. DrivR-Base is easily deployable via a Docker container to ensure reproducibility and ease of access across diverse computational environments. The resulting features can be used as input for machine learning models designed to predict the pathogenic impact of human genome variants in disease. Moreover, these feature sets have applications beyond this, including haploinsufficiency prediction and the development of drug repurposing tools. We describe the resource's development, practical applications, and potential for future expansion and enhancement. AVAILABILITY AND IMPLEMENTATION: DrivR-Base source code is available at https://github.com/amyfrancis97/DrivR-Base.

The tumor multi-omic landscape of endometrial cancers developed on a germline genetic background of adiposity.

High body mass index (BMI) is a causal risk factor for endometrial cancer but the tumor molecular mechanisms affected by adiposity and their therapeutic relevance remain poorly understood. Here we characterize the tumor multi-omic landscape of endometrial cancers that have developed on a background of lifelong germline genetic exposure to elevated BMI. We built a polygenic score (PGS) for BMI in women using data on independent, genome-wide significant variants associated with adult BMI in 434,794 women. We performed germline (blood) genotype quality control and imputation on data from 354 endometrial cancer cases from The Cancer Genome Atlas (TCGA). We assigned each case in this TCGA cohort their genetically predicted life-course BMI based on the BMI PGS. Multivariable generalized linear models adjusted for age, stage, microsatellite status and genetic principal components were used to test for associations between the BMI germline PGS and endometrial cancer tumor genome-wide genomic, transcriptomic, proteomic, epigenomic and immune traits in TCGA. High BMI germline PGS was associated with (i) upregulated tumor gene expression in the IL6-JAK-STAT3 pathway (FDR=4.2×10-7); (ii) increased estimated intra-tumor activated mast cell infiltration (FDR=0.008); (iii) increased single base substitution (SBS) mutational signatures 1 (FDR=0.03) and 5 (FDR=0.09) and decreased SBS13 (FDR=0.09), implicating age-related and APOBEC mutagenesis, respectively; and (iv) decreased tumor EGFR protein expression (FDR=0.07). Alterations in IL6-JAK-STAT3 signaling gene and EGFR protein expression were, in turn, significantly associated with both overall survival and progression-free interval. Thus, we integrated germline and somatic data using a novel study design to identify associations between genetically predicted lifelong exposure to higher BMI and potentially actionable endometrial cancer tumor molecular features. These associations inform our understanding of how high BMI may influence the development and progression of this cancer, impacting endometrial tumor biology and clinical outcomes.

In what ways might group clinical supervision affect the development of resilience in hospice nurses?

BACKGROUND: Resilience is important to sustain hospice nurses through a challenging career. Clinical supervision is a commonly cited support strategy, but there is limited evidence which focuses on its influence on the development of resilience in hospice nurses. AIMS: To explore how group clinical supervision might affect the development of resilience in hospice nurses. METHOD: A pragmatic approach and mixed methods research design was employed. Quantitative questionnaire data and qualitative focus group data were collected from community hospice nurses participating in group clinical supervision. FINDINGS: The findings identified the importance of an effective group reflective process on the benefits to be gained from clinical supervision. Clinical supervision was found to affect the development of resilience by developing confidence at work, regulating emotions, offering a coping strategy, managing expectations, and developing self-awareness. This was dependent upon individual preference and experience, the local organisational context, and wider social and political factors. CONCLUSION: This research contributes insight into group clinical supervision as an intervention to support resilience in hospice nurses. It offers recommendations for practice, to enhance the development of resilience through clinical supervision, and recommendations for future research.

Children with autism spectrum disorder and epilepsy.

Autism spectrum disorder (ASD) is a biologically based neurodevelopmental disability characterized by qualitative and persistent deficits in social communication and social interaction and by the presence of restricted, repetitive, and stereotyped patterns of behavior. Symptoms must be present in early childhood and they must limit and impair everyday functioning. There is an increased prevalence of epilepsy and/or epileptiform electroencephalography (EEG) abnormalities in children with ASD. It is estimated that approximately one-third of children and adolescents with ASD experience seizures, but the relationship between epilepsy and autism is controversial. This article reviews the types of seizures associated with ASD, the EEG findings, and current treatment strategies. The article also describes syndromes associated with the autism phenotype and epilepsy.