Mutations in the gene encoding the transcriptional repressor methyl-CpG binding protein 2 (MeCP2) cause the neurodevelopmental disorder Rett syndrome. Loss of function as well as increased dosage of ...the MECP2 gene cause a host of neuropsychiatric disorders. To explore the molecular mechanism(s) underlying these disorders, we examined gene expression patterns in the hypothalamus of mice that either lack or overexpress MeCP2. In both models, MeCP2 dysfunction induced changes in the expression levels of thousands of genes, but unexpectedly the majority of genes (~85%) appeared to be activated by MeCP2. We selected six genes and confirmed that MeCP2 binds to their promoters. Furthermore, we showed that MeCP2 associates with the transcriptional activator CREB1 at the promoter of an activated target but not a repressed target. These studies suggest that MeCP2 regulates the expression of a wide range of genes in the hypothalamus and that it can function as both an activator and a repressor of transcription.
Ovarian cancer is the most lethal gynecological malignancy. It is usually diagnosed at a late stage, with a 5-yr survival rate of <30%. The majority of ovarian cancer cases are diagnosed after tumors ...have widely spread within the peritoneal cavity, limiting the effectiveness of debulking surgery and chemotherapy. Owing to a substantially lower survival rate at late stages of disease than at earlier stages, the major cause of ovarian cancer deaths is believed to be therapy-resistant metastasis. Although metastasis plays a crucial role in promoting ovarian tumor progression and decreasing patient survival rates, the underlying mechanisms of ovarian cancer spread have yet to be thoroughly explored. For many years, researchers have believed that ovarian cancer metastasizes via a passive mechanism by which ovarian cancer cells are shed from the primary tumor and carried by the physiological movement of peritoneal fluid to the peritoneum and omentum. However, the recent discovery of hematogenous metastasis of ovarian cancer to the omentum via circulating tumor cells instigated rethinking of the mode of ovarian cancer metastasis and the importance of the "seed-and-soil" hypothesis for ovarian cancer metastasis. In this review we discuss the possible mechanisms by which ovarian cancer cells metastasize from the primary tumor to the omentum, the cross-talk signaling events between ovarian cancer cells and various stromal cells that play crucial roles in ovarian cancer metastasis, and the possible clinical implications of these findings in the management of this deadly, highly metastatic disease.
Lung cancer is the most prevalent type of cancer and the leading cause of cancer-related deaths worldwide. Coherent anti-Stokes Raman scattering (CARS) is capable of providing cellular-level images ...and resolving pathologically related features on human lung tissues. However, conventional means of analyzing CARS images requires extensive image processing, feature engineering, and human intervention. This study demonstrates the feasibility of applying a deep learning algorithm to automatically differentiate normal and cancerous lung tissue images acquired by CARS. We leverage the features learned by pretrained deep neural networks and retrain the model using CARS images as the input. We achieve 89.2% accuracy in classifying normal, small-cell carcinoma, adenocarcinoma, and squamous cell carcinoma lung images. This computational method is a step toward on-the-spot diagnosis of lung cancer and can be further strengthened by the efforts aimed at miniaturizing the CARS technique for fiber-based microendoscopic imaging.
Big Data for Health Andreu-Perez, Javier; Poon, Carmen C. Y.; Merrifield, Robert D. ...
IEEE journal of biomedical and health informatics,
2015-July, 2015-Jul, 2015-7-00, 20150701, Volume:
19, Issue:
4
Journal Article
Peer reviewed
Open access
This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health ...informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging, clinical diagnosis, and long-term continuous physiological sensing of an individual. It is expected that recent advances in big data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to personalized treatment. The rise of big data, however, also raises challenges in terms of privacy, security, data ownership, data stewardship, and governance. This paper discusses some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled.
Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known ...drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data.
Using the proposed method, we predicted certain drug-protein interactions on the enzyme, ion channel, GPCRs, and nuclear receptor data sets. Some of them are confirmed by the latest publicly available drug targets databases such as KEGG.
We report encouraging results of using our method for drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.
Objective
To develop and validate a pretreatment magnetic resonance imaging (MRI)–based radiomic-clinical model to assess the treatment response of whole-brain radiotherapy (WBRT) by using SHapley ...Additive exPlanations (SHAP), which is derived from game theory, and can explain the output of different machine learning models.
Methods
We retrospectively enrolled 228 patients with brain metastases from two medical centers (184 in the training cohort and 44 in the validation cohort). Treatment responses of patients were categorized as a non-responding group vs. a responding group according to the Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria. For each tumor, 960 features were extracted from the MRI sequence. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. A support vector machine (SVM) model incorporating clinical factors and radiomic features wase used to construct the radiomic-clinical model. SHAP method explained the SVM model by prioritizing the importance of features, in terms of assessment contribution.
Results
Three radiomic features and three clinical factors were identified to build the model. Radiomic-clinical model yielded AUCs of 0.928 (95%CI 0.901–0.949) and 0.851 (95%CI 0.816–0.886) for assessing the treatment response in the training cohort and validation cohort, respectively. SHAP summary plot illustrated the feature’s value affected the feature’s impact attributed to model, and SHAP force plot showed the integration of features’ impact attributed to individual response.
Conclusion
The radiomic-clinical model with the SHAP method can be useful for assessing the treatment response of WBRT and may assist clinicians in directing personalized WBRT strategies in an understandable manner.
Key Points
• Radiomic-clinical model can be useful for assessing the treatment response of WBRT.
• SHAP could explain and visualize radiomic-clinical machine learning model in a clinician-friendly way.
Healthcare regulatory agencies have mandated a reduction in 30-day hospital readmission rates and have targeted COPD as a major contributor to 30-day readmissions. We aimed to develop and validate a ...simple tool deploying an artificial neural network (ANN) for early identification of COPD patients with high readmission risk. Using COPD patient data from eight hospitals within a large urban hospital system, four variables were identified, weighted and validated. These included the number of in-patient admissions in the previous 6 months, the number of medications administered on the first day, insurance status, and the Rothman Index on hospital day one. An ANN model was trained to provide a predictive algorithm and validated on an additional dataset from a separate time period. The model was implemented in a smartphone app (Re-Admit) incorporating four input risk factors, and a clinical care plan focused on high-risk readmission candidates was then implemented. Subsequent readmission data was analyzed to assess impact. The areas under the curve of receiver operating characteristics predicting readmission with ANN is 0.77, with sensitivity 0.75 and specificity 0.67 on the separate validation data. Readmission rates in the COPD high-risk subgroup after app and clinical intervention implementation saw a significant 48% decline. Our studies show the efficacy of ANN model on predicting readmission risks for COPD patients. The AI enabled Re-Admit smartphone app predicts readmission risk on day one of the patient's admission, allowing for early implementation of medical, hospital, and community resources to optimize and improve clinical care pathways.
Breast cancer bone micrometastases can remain asymptomatic for years before progressing into overt lesions. The biology of this process, including the microenvironment niche and supporting pathways, ...is unclear. We find that bone micrometastases predominantly reside in a niche that exhibits features of osteogenesis. Niche interactions are mediated by heterotypic adherens junctions (hAJs) involving cancer-derived E-cadherin and osteogenic N-cadherin, the disruption of which abolishes niche-conferred advantages. We elucidate that hAJ activates the mTOR pathway in cancer cells, which drives the progression from single cells to micrometastases. Human data set analyses support the roles of AJ and the mTOR pathway in bone colonization. Our study illuminates the initiation of bone colonization, and provides potential therapeutic targets to block progression toward osteolytic metastases.
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•Intrailiac artery injection allows studies of bone metastasis initiation•Bone micrometastases reside in the microenvironment niche with active osteogenesis•The niche cells boost tumor proliferation via adherens junctions (AJs)•The mTOR pathway lies downstream of AJs and mediates metastasis initiation
Wang et al. model breast cancer bone micrometastasis and identify an osteogenic niche that supports this process. Heterotypic adherens junctions form between cancer cells and stromal cells in these niches and activate mTOR to promote micrometastasis progression.
The way in which cells adopt different morphologies is not fully understood. Cell shape could be a continuous variable or restricted to a set of discrete forms. We developed quantitative methods to ...describe cell shape and show that Drosophila haemocytes in culture are a heterogeneous mixture of five discrete morphologies. In an RNAi screen of genes affecting the morphological complexity of heterogeneous cell populations, we found that most genes regulate the transition between discrete shapes rather than generating new morphologies. In particular, we identified a subset of genes, including the tumour suppressor PTEN, that decrease the heterogeneity of the population, leading to populations enriched in rounded or elongated forms. We show that these genes have a highly conserved function as regulators of cell shape in both mouse and human metastatic melanoma cells.
Irinotecan (CPT-11) induced diarrhea occurs frequently in patients with cancer and limits its usage. Bacteria β-glucuronidase (GUS) enzymes in intestines convert the nontoxic metabolite of CPT-11, ...SN-38G, to toxic SN-38, and finally lead to damage of intestinal epithelial cells and diarrhea. We previously reported amoxapine as a potent GUS inhibitor in vitro. To further understand the molecular mechanism of amoxapine and its potential for treatment of CPT-11-induced diarrhea, we studied the binding modes of amoxapine and its metabolites by docking and molecular dynamics simulation, and tested the in vivo efficacy on mice in combination with CPT-11.
The binding of amoxapine, its metabolites, 7-hydroxyamoxapine and 8-hydroxyamoxapine, and a control drug loxapine with GUS was explored by computational protocols. The in vitro potencies of metabolites were measured by Escherichia coli GUS enzyme and cell-based assay. Low-dosage daily oral administration was designed to use along with CPT-11 to treat tumor-bearing mice.
Computational modeling results indicated that amoxapine and its metabolites bound in the active site of GUS and satisfied critical pharmacophore features: aromatic features near bacterial loop residue F365' and hydrogen bond toward E413. Amoxapine and its metabolites were demonstrated as potent in vitro. Administration of low dosages of amoxapine with CPT-11 in mice achieved significant suppression of diarrhea and reduced tumor growth.
Amoxapine has great clinical potential to be rapidly translated to human subjects for irinotecan-induced diarrhea.
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