A unified mediation analysis framework for integrative cancer proteogenomics with clinical outcomes.

MOTIVATION: Multilevel molecular profiling of tumors and the integrative analysis with clinical outcomes have enabled a deeper characterization of cancer treatment. Mediation analysis has emerged as a promising statistical tool to identify and quantify the intermediate mechanisms by which a gene affects an outcome. However, existing methods lack a unified approach to handle various types of outcome variables, making them unsuitable for high-throughput molecular profiling data with highly interconnected variables. RESULTS: We develop a general mediation analysis framework for proteogenomic data that include multiple exposures, multivariate mediators on various scales of effects as appropriate for continuous, binary and survival outcomes. Our estimation method avoids imposing constraints on model parameters such as the rare disease assumption, while accommodating multiple exposures and high-dimensional mediators. We compare our approach to other methods in extensive simulation studies at a range of sample sizes, disease prevalence and number of false mediators. Using kidney renal clear cell carcinoma proteogenomic data, we identify genes that are mediated by proteins and the underlying mechanisms on various survival outcomes that capture short- and long-term disease-specific clinical characteristics. AVAILABILITY AND IMPLEMENTATION: Software is made available in an R package (https://github.com/longjp/mediateR). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Network-Based Matching of Patients and Targeted Therapies for Precision Oncology.

The extensive acquisition of high-throughput molecular profiling data across model systems (human tumors and cancer cell lines) and drug sensitivity data, makes precision oncology possible - allowing clinicians to match the right drug to the right patient. Current supervised models for drug sensitivity prediction, often use cell lines as exemplars of patient tumors and for model training. However, these models are limited in their ability to accurately predict drug sensitivity of individual cancer patients to a large set of drugs, given the paucity of patient drug sensitivity data used for testing and high variability across different drugs. To address these challenges, we developed a multilayer network-based approach to impute individual patients' responses to a large set of drugs. This approach considers the triplet of patients, cell lines and drugs as one inter-connected holistic system. We first use the omics profiles to construct a patient-cell line network and determine best matching cell lines for patient tumors based on robust measures of network similarity. Subsequently, these results are used to impute the "missing link" between each individual patient and each drug, called Personalized Imputed Drug Sensitivity Score (PIDS-Score), which can be construed as a measure of the therapeutic potential of a drug or therapy. We applied our method to two subtypes of lung cancer patients, matched these patients with cancer cell lines derived from 19 tissue types based on their functional proteomics profiles, and computed their PIDS-Scores to 251 drugs and experimental compounds. We identified the best representative cell lines that conserve lung cancer biology and molecular targets. The PIDS-Score based top sensitive drugs for the entire patient cohort as well as individual patients are highly related to lung cancer in terms of their targets, and their PIDS-Scores are significantly associated with patient clinical outcomes. These findings provide evidence that our method is useful to narrow the scope of possible effective patient-drug matchings for implementing evidence-based personalized medicine strategies.

Semen Cuscutae Administration Improves Hepatic Lipid Metabolism and Adiposity in High Fat Diet-Induced Obese Mice.

Since arginase has been shown to compete with nitric oxide (NO) synthase, emerging evidence has reported that arginase inhibition improves obesity by increasing NO production. Semen cuscutae (SC), which is a well-known Chinese medicine, has multiple biological functions such as anti-oxidant function and immune regulation. In this study, we investigated whether the SC as a natural arginase inhibitor influences hepatic lipid abnormalities and whole-body adiposity in high-fat diet (HFD)-induced obese mice. The lipid accumulation was significantly reduced by SC treatment in oleic acid-induced hepatic steatosis in vitro. Additionally, SC supplementation substantially lowered HFD-induced increases in arginase activity and weights of liver and visceral fat tissue, while increasing hepatic NO. Furthermore, elevated mRNA expressions of sterol regulatory element-binding transcription factor 1 (SREBP-1c), fatty-acid synthase (FAS), peroxisome proliferator-activated receptor-gamma (PPAR-γ)1, and PPAR-γ2 in HFD-fed mice were significantly attenuated by SC supplementation. Taken together, SC, as a novel natural arginase inhibitor, showed anti-obesity properties by modulating hepatic arginase and NO production and metabolic pathways related to hepatic triglyceride (TG) metabolism.

iDINGO-integrative differential network analysis in genomics with Shiny application.

Motivation: Differential network analysis is an important way to understand network rewiring involved in disease progression and development. Building differential networks from multiple 'omics data provides insight into the holistic differences of the interactive system under different patient-specific groups. DINGO was developed to infer group-specific dependencies and build differential networks. However, DINGO and other existing tools are limited to analyze data arising from a single platform, and modeling each of the multiple 'omics data independently does not account for the hierarchical structure of the data. Results: We developed the iDINGO R package to estimate group-specific dependencies and make inferences on the integrative differential networks, considering the biological hierarchy among the platforms. A Shiny application has also been developed to facilitate easier analysis and visualization of results, including integrative differential networks and hub gene identification across platforms. Availability and implementation: R package is available on CRAN (https://cran.r-project.org/web/packages/iDINGO) and Shiny application at https://github.com/MinJinHa/iDINGO. Contact: mjha@mdanderson.org. Supplementary information: Supplementary data are available at Bioinformatics online.

Beneficial effects of voglibose administration on body weight and lipid metabolism via gastrointestinal bile acid modification.

This study was designed with the goal of examining the effects of voglibose administration on body weight and lipid metabolism and underlying mechanism high fat diet-induced obese mice. Male C57BL/6 mice were randomly assigned to one of four groups: a control diet (CTL), high-fat diet (HF), high-fat diet supplemented with voglibose (VO), and high fat diet pair-fed group (PF). After 12 weeks, the following characteristics were investigated: serum lipid and glucose levels, serum polar metabolite profiles, and expression levels of genes involved in lipid and bile acid metabolism. In addition, pyrosequencing was used to analyze the composition of gut microbiota found in feces. Total body weight gain was significantly lower in the VO group than in the CTL, HF, and PF groups. The VO group exhibited improved metabolic profiles including those of blood glucose, triglyceride, and total cholesterol levels. The 12-week voglibose administration decreased the ratio of Firmicutes to Bacteroidetes found in feces. Circulating levels of taurocholic and cholic acid were significantly higher in the VO group than in the HF and CTL groups. Deoxycholic acid levels tended to be higher in the VO group than in the HF group. Voglibose administration downregulated expression levels of CYP8B1 and HNF4α genes and upregulated those of PGC1α, whereas FXRα was not affected. Voglibose administration elicits changes in the composition of the intestinal microbiota and circulating metabolites, which ultimately has systemic effects on body weight and lipid metabolism in mice.