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A central challenge in the genetics of autism spectrum disorders (ASDs) is advancing from gene discovery to the identification of actionable biological mechanisms. To investigate the function of ten genes that are strongly associated with ASD in the developing vertebrate brain, we performed behavioral, whole-brain structural, circuit, cellular and molecular analyses in zebrafish mutants of ASD genes. First, we identified both unique and overlapping effects of gene loss of function on basic sensory processing and arousal behaviors. Next, we identified the forebrain as the most significant contributor to brain size differences, while brain regions involved in sensory motor control, particularly dopaminergic regions, are associated with altered baseline brain activity. Third, using whole-brain RNA sequencing in zebrafish mutants of SCN2A and DYRK1A, we identified conservation of dysregulated pathways associated with ASD gene loss of function in zebrafish and mammals. Using hypothesis-driven gene set enrichment analysis, we found that dopaminergic genes are significantly enriched among downregulated genes in both mutants, while microglial genes are significantly enriched among upregulated genes. Finally, we show that both mutants display a significant increase in microglia throughout the brain, with DYRK1A mutants exhibiting a nearly two-fold increase in microglia. Therefore, our study implicates neuroimmune dysfunction as an important pathway downstream of select ASD genes. As a next step, we are using pharmaco-behavioral profiling to identify pharmacological suppressors of mutant behavioral phenotypes. Together, this study demonstrates the power of in vivo functional analyses in zebrafish to identify biologically relevant pathways downstream of ASD genes. [ABSTRACT FROM AUTHOR]

Background: Radiomics can quantify tumor phenotypic characteristics non-invasively by applying feature algorithms to medical imaging data. In this study of lung cancer patients, we investigated the association between radiomic features and the tumor histologic subtypes (adenocarcinoma and squamous cell carcinoma). Furthermore, in order to predict histologic subtypes, we employed machine-learning methods and independently evaluated their prediction performance. Methods: Two independent radiomic cohorts with a combined size of 350 patients were included in our analysis. A total of 440 radiomic features were extracted from the segmented tumor volumes of pretreatment CT images. These radiomic features quantify tumor phenotypic characteristics on medical images using tumor shape and size, intensity statistics, and texture. Univariate analysis was performed to assess each feature's association with the histological subtypes. In our multivariate analysis, we investigated 24 feature selection methods and 3 classification methods for histology prediction. Multivariate models were trained on the training cohort and their performance was evaluated on the independent validation cohort using the area under ROC curve (AUC). Histology was determined from surgical specimen. Results: In our univariate analysis, we observed that fifty-three radiomic features were significantly associated with tumor histology. In multivariate analysis, feature selection methods ReliefF and its variants showed higher prediction accuracy as compared to other methods. We found that Naive Baye's classifier outperforms other classifiers and achieved the highest AUC (0.72; p-value=2.3 x 10-7) with five features: Stats_min, Wavelet_HLL_rlgl_lowGrayLevelRunEmphasis, Wavelet_HHL_stats_median, Wavelet_HLL_stats_skewness, and Wavelet_HLH_glcm_clusShade. Conclusion: Histological subtypes can influence the choice of a treatment/therapy for lung cancer patients. We observed that radiomic features show significant association with the lung tumor histology. Moreover, radiomics-based multivariate classifiers were independently validated for the prediction of histological subtypes. Despite achieving lower than optimal prediction accuracy (AUC 0.72), our analysis highlights the impressive potential of non-invasive and cost-effective radiomics for precision medicine. Further research in this direction could lead us to optimal performance and therefore to clinical applicability, which could enhance the efficiency and efficacy of cancer care. [ABSTRACT FROM AUTHOR]