Background and Purpose: Our goal was to develop and validate radiomics and machine learning approaches for predicting molecular subgroups of pediatric medulloblastoma (MB). Materials and Methods: In this multi-institutional retrospective study, we evaluated MRI datasets of 109 pediatric MB patients from three children’s hospitals from January 2001 to January 2014. A computational framework was developed to extract MRI-based radiomic features from tumor segmentations, and two predictive models were tested: a double 10-fold cross validation using a combined dataset consisting of all three patient cohorts and a three-dataset cross-validation, in which training was performed on two cohorts and testing was performed on the third independent cohort. We used the Wilcoxon rank sum test for feature selection with assessment of area under the receiver operating characteristic curve (AUC) to evaluate model performance. Results: Of 590 MRI-derived radiomic features, including intensity-based histograms, tumor edge sharpness, Gabor features, and Local Area Integral Invariant (LAII) features, extracted from imaging-derived tumor segmentations, tumor edge sharpness was most significant for predicting sonic hedgehog (SHH) and group 4 tumors. ROC analysis revealed superior performance of the double 10-fold cross validation model for predicting SHH, group 3, and group 4 tumors when using combined T1- and T2-weighted images (AUC=0.79, 0.70, and 0.83, respectively). Using the independent three-dataset cross validation strategy, select radiomics features were predictive of SHH (AUC=0.70–0.73) and group 4 (AUC=0.76–0.80) MB. Conclusion: This study provides proof-of-concept results for the application of radiomics and machine learning approaches to a multi-institutional dataset for the prediction of MB subgroups.

Connectivity mapping resources consist of signatures representing changes in cellular state following systematic small-molecule, disease, gene, or other form of perturbations. Such resources enable the characterization of signatures from novel perturbations based on similarity; provide a global view of the space of many themed perturbations; and allow the ability to predict cellular, tissue, and organismal phenotypes for perturbagens. A signature search engine enables hypothesis generation by finding connections between query signatures and the database of signatures. This framework has been used to identify connections between small molecules and their targets, to discover cell-specific responses to perturbations and ways to reverse disease expression states with small molecules, and to predict small-molecule mimickers for existing drugs. This review provides a historical perspective and the current state of connectivity mapping resources with a focus on both methodology and community implementations.

Motivation Mining drug–disease association and related interactions are essential for developing in silico drug repurposing (DR) methods and understanding underlying biological mechanisms. Recently, large-scale biological databases are increasingly available for pharmaceutical research, allowing for deep characterization for molecular informatics and drug discovery. However, DR is challenging due to the molecular heterogeneity of disease and diverse drug–disease associations. Importantly, the complexity of molecular target interactions, such as protein–protein interaction (PPI), remains to be elucidated. DR thus requires deep exploration of a multimodal biological network in an integrative context. Results In this study, we propose BiFusion, a bipartite graph convolution network model for DR through heterogeneous information fusion. Our approach combines insights of multiscale pharmaceutical information by constructing a multirelational graph of drug–protein, disease–protein and PPIs. Especially, our model introduces protein nodes as a bridge for message passing among diverse biological domains, which provides insights into utilizing PPI for improved DR assessment. Unlike conventional graph convolution networks always assuming the same node attributes in a global graph, our approach models interdomain information fusion with bipartite graph convolution operation. We offered an exploratory analysis for finding novel drug–disease associations. Extensive experiments showed that our approach achieved improved performance than multiple baselines for DR analysis. Availability and implementation Source code and preprocessed datasets are at: https://github.com/zcwang0702/BiFusion.

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Korea (the Republic of) Vitamin D receptor (VDR) as a ligand-dependent transcription factor forms a heterodimer with retinoid X receptor (RXR) to activate vitamin D response elements-associated various cellular genes during hair growth. Lithocholic acid (LCA) is known to be a naturally produced VDR ligand to induce VDR-RXR conformation. Previous studies demonstrated that the absence of VDR and RXRa ultimately resulted in alopecia development in mouse. However, since the role of LCA is not clear in hair cycling, we examined the functional mechanism of LCA in human dermal papilla cells (hDPCs). Treatment of hDPC cells with LCA significantly increased cellular proliferation together with the enhanced expression of VDR, RXRa,

As new cells are generated in the skin, older cells must transit out of the basal layer in order to maintain homeostasis. Cells may be lost through apoptosis, but maintenance of a constant basal cell number is primary mediated through differentiation and delamination. The cellular mechanisms that maintain homeostasis in response to uncoupling of these events are not clear. Here we show that cells reorient their division axes in response to changes in the rate of proliferation and differentiation. Symmetric cell divisions were increased by hypoproliferation, while asymmetric cell divisions were increased by hyperproliferation. Strikingly, the rate of asymmetric cell divisions in the palmoplantar epidermis also increased upon expression of a constitutively active allele of Kras in a proliferation-independent manner. In this tissue, we found that asymmetric divisions are dependent on conserved spindle orientation machinery. Disruption of this machinery in a Kras mutant background resulted in massive tissue overgrowth and expansion of the progenitor cell population. In these mutants, the benign tumors that formed closely resembled Human papillomavirus-induced warts. Notably, we found that expression of the HPV E6 protein is sufficient to perturb spindle orientation in vitrodemonstrating a pathogenic disruption of the asymmetric cell division machinery. Together, these data reveal that epidermal progenitors use asymmetric cell divisions to protect against tissue overgrowth, and that this pathway may be targeted by viral pathogens in a tissuespecific manner to induce papilloma formation and HPV-induced cancers.

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