Developing better digital health measures of Parkinson's disease using free living data and a crowdsourced data analysis challenge.

One of the promising opportunities of digital health is its potential to lead to more holistic understandings of diseases by interacting with the daily life of patients and through the collection of large amounts of real-world data. Validating and benchmarking indicators of disease severity in the home setting is difficult, however, given the large number of confounders present in the real world and the challenges in collecting ground truth data in the home. Here we leverage two datasets collected from patients with Parkinson's disease, which couples continuous wrist-worn accelerometer data with frequent symptom reports in the home setting, to develop digital biomarkers of symptom severity. Using these data, we performed a public benchmarking challenge in which participants were asked to build measures of severity across 3 symptoms (on/off medication, dyskinesia, and tremor). 42 teams participated and performance was improved over baseline models for each subchallenge. Additional ensemble modeling across submissions further improved performance, and the top models validated in a subset of patients whose symptoms were observed and rated by trained clinicians.

COVID-19 drug repurposing: A review of computational screening methods, clinical trials, and protein interaction assays.

The situation of coronavirus disease 2019 (COVID-19) pandemic is rapidly evolving, and medical researchers around the globe are dedicated to finding cures for the disease. Drug repurposing, as an efficient way for drug development, has received a lot of attention. However, the huge amount of studies makes it challenging to keep up to date with the literature on COVID-19 therapeutic development. This review addresses this challenge by grouping the COVID-19 drug repurposing research into three large groups, including clinical trials, computational research, and in vitro protein-binding experiments. Particularly, to facilitate future drug discovery and the creation of effective drug combinations, drugs are organized by their mechanisms of action and reviewed by their efficacy measured by clinical trials. Providing this subtyping information, we hope this review would serve the scientists, clinicians, and the pharmaceutical industry who are looking at the new therapeutics for COVID-19 treatment.

TAIJI: approaching experimental replicates-level accuracy for drug synergy prediction.

MOTIVATION: Combination therapy is widely used in cancer treatment to overcome drug resistance. High-throughput drug screening is the standard approach to study the drug combination effects, yet it becomes impractical when the number of drugs under consideration is large. Therefore, accurate and fast computational tools for predicting drug synergistic effects are needed to guide experimental design for developing candidate drug pairs. RESULTS: Here, we present TAIJI, a high-performance software for fast and accurate prediction of drug synergism. It is based on the winning algorithm in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge, which is a unique platform to unbiasedly evaluate the performance of current state-of-the-art methods, and includes 160 team-based submission methods. When tested across a broad spectrum of 85 different cancer cell lines and 1089 drug combinations, TAIJI achieved a high prediction correlation (0.53), approaching the accuracy level of experimental replicates (0.56). The runtime is at the scale of minutes to achieve this state-of-the-field performance. AVAILABILITY AND IMPLEMENTATION: TAIJI is freely available on GitHub (https://github.com/GuanLab/TAIJI). It is functional with built-in Perl and Python. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Network Propagation Predicts Drug Synergy in Cancers.

Combination therapies are commonly used to treat patients with complex diseases that respond poorly to single-agent therapies. In vitro high-throughput drug screening is a standard method for preclinical prioritization of synergistic drug combinations, but it can be impractical for large drug sets. Computational methods are thus being actively explored; however, most published methods were built on a limited size of cancer cell lines or drugs, and it remains a challenge to predict synergism at a large scale where the diversity within the data escalates the difficulty of prediction. Here, we present a state-of-the-field synergy prediction algorithm, which ranked first in all subchallenges in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge. The model was built and evaluated using the largest drug combination screening dataset at the time of the competition, consisting of approximately 11,500 experimentally tested synergy scores of 118 drugs in 85 cancer cell lines. We developed a novel feature extraction strategy by integrating the cross-cell and cross-drug information with a novel network propagation method and then assembled the information in monotherapy and simulated molecular data to predict drug synergy. This represents a significant conceptual advancement of synergy prediction, using extracted features in the form of simulated posttreatment molecular profiles when only the pretreatment molecular profile is available. Our cross-tissue synergism prediction algorithm achieves promising accuracy comparable with the correlation between experimental replicates and can be applied to other cancer cell lines and drugs to guide therapeutic choices.Significance: This study presents a novel network propagation-based method that predicts anticancer drug synergy to the accuracy of experimental replicates, which establishes a state-of-the-field method as benchmarked by the pharmacogenomics research community involving models generated by 160 teams. Cancer Res; 78(18); 5446-57. ©2018 AACR.

Effects of Antioxidants in Human Cancers: Differential Effects on Non-Coding Intronic RNA Expression.

The notion that dietary antioxidants can help fight cancer is popular. However, the mechanism(s) behind the effect of antioxidants in cancer is still unclear. Previous studies indicate that supplements can influence gene expression; however, all of these studies were focused on the coding/exonic gene expression. Studies are now emerging to highlight critical functional roles for RNAs expressed from the non-coding regions. This project was designed to study the effect of antioxidant supplements on non-coding intronic RNA expression in human cancers. Vitamin E, N-Acetyl cysteine (NAC) and Sulforaphane are commonly used supplements to prevent diseases including cancers. We studied the effect of these antioxidant supplements on the non-coding intronic RNA expression using publicly available datasets from a mouse model for lung cancer and prostate cancer cell lines. Although high throughput polyA-enriched RNA-Seq data characterize spliced coding mRNA regions, recent studies reveal the expression of reads from the non-coding intronic regions. Our analyses indicate that cancer cells have higher expression of introns compared to that of normal cells and that treatment with antioxidant supplements reduces the increased expression of introns of several genes. However, we did find high expression of introns of multiple genes including many oncogenes in the supplement treated groups compared to that of the control; this effect was distinct depending on the cell type and the supplement studied. Using RT-PCRs, we validated the expression of introns of two oncogenes, DLK1 and LRG1, known to be key players in lung cancer progression, and demonstrate changed intronic expression with supplement treatment in cancer cells. With regard to the antioxidant system, supplements did not change the intronic RNAs for endogenous antioxidant enzymes except for a significant decrease in the expression of superoxide dismutase (SOD) intronic RNA. Concurrently, we also found that a prolonged (48 h) exposure to Vitamin C, Vitamin E and Green tea extract reduced the enzymatic activity of SOD in lung cancer cells. The results from this study reveal that the antioxidant supplements have a significant effect on the intronic RNA expression of many genes including cancer genes that are not directly linked to the body's antioxidant system. It is important to study this novel effect of antioxidant supplements in detail as it may have a significant role in disease progression.

Functional genomics complements quantitative genetics in identifying disease-gene associations.

An ultimate goal of genetic research is to understand the connection between genotype and phenotype in order to improve the diagnosis and treatment of diseases. The quantitative genetics field has developed a suite of statistical methods to associate genetic loci with diseases and phenotypes, including quantitative trait loci (QTL) linkage mapping and genome-wide association studies (GWAS). However, each of these approaches have technical and biological shortcomings. For example, the amount of heritable variation explained by GWAS is often surprisingly small and the resolution of many QTL linkage mapping studies is poor. The predictive power and interpretation of QTL and GWAS results are consequently limited. In this study, we propose a complementary approach to quantitative genetics by interrogating the vast amount of high-throughput genomic data in model organisms to functionally associate genes with phenotypes and diseases. Our algorithm combines the genome-wide functional relationship network for the laboratory mouse and a state-of-the-art machine learning method. We demonstrate the superior accuracy of this algorithm through predicting genes associated with each of 1157 diverse phenotype ontology terms. Comparison between our prediction results and a meta-analysis of quantitative genetic studies reveals both overlapping candidates and distinct, accurate predictions uniquely identified by our approach. Focusing on bone mineral density (BMD), a phenotype related to osteoporotic fracture, we experimentally validated two of our novel predictions (not observed in any previous GWAS/QTL studies) and found significant bone density defects for both Timp2 and Abcg8 deficient mice. Our results suggest that the integration of functional genomics data into networks, which itself is informative of protein function and interactions, can successfully be utilized as a complementary approach to quantitative genetics to predict disease risks. All supplementary material is available at http://cbfg.jax.org/phenotype.