Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge

Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95)

Using peptide-level proteomics data for detecting differentially expressed proteins

The expression of proteins can be quantified in high-throughput means using different types of mass spectrometers. In recent years, there have emerged label-free methods for determining protein abundance. Although the expression is initially measured at the peptide level, a common approach is to combine the peptide-level measurements into protein-level values before differential expression analysis. However, this simple combination is prone to inconsistencies between peptides and may lose valuable information. To this end, we introduce here a method for detecting differentially expressed proteins by combining peptide-level expression-change statistics. Using controlled spike-in experiments, we show that the approach of averaging peptide-level expression changes yields more accurate lists of differentially expressed proteins than does the conventional protein-level approach. This is particularly true when there are only few replicate samples or the differences between the sample groups are small. The proposed technique is implemented in the Bioconductor package PECA, and it can be downloaded from http://www.bioconductor.org

SimPhospho: a software tool enabling confident phosphosite assignment

Motivation: Mass spectrometry combined with enrichment strategies for phosphorylated peptides has been successfully employed for two decades to identify sites of phosphorylation. However, unambiguous phosphosite assignment is considered challenging. Given that site-specific phosphorylation events function as different molecular switches, validation of phosphorylation sites is of utmost importance. In our earlier study we developed a method based on simulated phospho-peptide spectral libraries, which enables highly sensitive and accurate phosphosite assignments. To promote more widespread use of this method, we here introduce a software implementation with improved usability and performance. Results: We present SimPhospho, a fast and user-friendly tool for accurate simulation of phospho-peptide tandem mass spectra. Simulated phosphopeptide spectral libraries are used to validate and supplement database search results, with a goal to improve reliable phosphoproteome identification and reporting. The presented program can be easily used together with the TransProteomic Pipeline and integrated in a phosphoproteomics data analysis workflow. Availability and implementation: SimPhospho is open source and it is available for Windows, Linux and Mac operating systems. The software and its user's manual with detailed description of data analysis as well as test data can be found at https://sourceforge.net/projects/simphospho/. Contact: [email protected] or G.L. [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

Liver lipid metabolism is altered by increased circulating estrogen to androgen ratio in male mouse

Estrogens are suggested to lower the risk of developing metabolic syndrome in both sexes. In this study, we investigated how the increased circulating estrogen-to-androgen ratio (E/A) alters liver lipid metabolism in males. The cytochrome P450 aromatase (P450arom) is an enzyme converting androgens to estrogens. Male mice overexpressing human aromatase enzyme (AROM + mice), and thus have high circulating E/A, were used as a model in this study. Proteomics and gene expression analyses indicated an increase in the peroxisomal β-oxidation in the liver of AROM + mice as compared with their wild type littermates. Correspondingly, metabolomic analysis revealed a decrease in the amount of phosphatidylcholines with long-chain fatty acids in the plasma. With interest we noted that the expression of Cyp4a12a enzyme, which specifically metabolizes arachidonic acid (AA) to 20-hydroxy AA, was dramatically decreased in the AROM + liver. As a consequence, increased amounts of phospholipids having AA as a fatty acid tail were detected in the plasma of the AROM + mice. Overall, these observations demonstrate that high circulating E/A in males is linked to indicators of higher peroxisomal β-oxidation and lower AA metabolism in the liver. Furthermore, the plasma phospholipid profile reflects the changes in the liver lipid metabolism

A DREAM challenge to build prediction models for short-term discontinuation of docetaxel in metastatic castration-resistant prostate cancer.

Background: Docetaxel has a demonstrated survival benefit for metastatic castration-resistant prostate cancer (mCRPC). However, 10-20% of patients discontinue docetaxel prematurely because of toxicity-induced adverse events, and managing risk factors for toxicity remains an ongoing challenge for health care providers and patients. Prospective identification of high-risk patients for early discontinuation has the potential to assist clinical decision-making and can improve the design of more efficient clinical trials. In partnership with Project Data Sphere (PDS), a non-profit initiative facilitating clinical trial data-sharing, we designed an open-data, crowdsourced DREAM (Dialogue for Reverse Engineering Assessments and Methods) Challenge for developing models to predict early discontinuation of docetaxel. Methods: Data from the comparator arms of four phase III clinical trials in first-line mCRPC were obtained from PDS, including 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 598 patients treated with docetaxel, prednisone/prednisolone, and placebo in the VENICE trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, and 528 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Early discontinuation was defined as treatment stoppage within three months due to adverse treatment effects. Over 150 clinical features including laboratory values, medical history, lesion measures, prior treatment, and demographic variables were curated and made freely available for model building for all four trials. The ASCENT2, VENICE, and MAINSAIL trial data sets formed the training set that also included patient discontinuation status. The ENTHUSE 33 trial, with patient discontinuation status hidden, was used as an independent validation set to evaluate model performance. Prediction performance was assessed using area under the precision-recall curve (AUPRC) and the Bayes factor was used to compare the performance between prediction models. Results: The frequency of early discontinuation was similar between training (ASCENT2, VENICE, and MAINSAIL) and validation (ENTHUSE 33) sets, 12.3% versus 10.4% of docetaxel-treated patients, respectively. In total, 34 independent teams submitted predictions from 61 different models. AUPRC ranged from 0.088 to 0.178 across submissions with a random model performance of 0.104. Seven models with comparable AUPRC scores (Bayes factor ≤ 3) were observed to outperform all other models. A post-challenge analysis of risk predictions generated by these seven models revealed three distinct patient subgroups: patients consistently predicted to be at high-risk or low-risk for early discontinuation and those with discordant risk predictions. Early discontinuation events were two-times higher in the high- versus low-risk subgroup and baseline clinical features such as presence/absence of metastatic liver lesions, and prior treatment with analgesics and ACE inhibitors exhibited statistically significant differences between the high- and low-risk subgroups (adjusted P < 0.05). An ensemble-based model constructed from a post-Challenge community collaboration resulted in the best overall prediction performance (AUPRC = 0.230) and represented a marked improvement over any individual Challenge submission. Findings: Our results demonstrate that routinely collected clinical features can be used to prospectively inform clinicians of mCRPC patients' risk to discontinue docetaxel treatment early due to adverse events and to the best of our knowledge is the first to establish performance benchmarks in this area. This work also underscores the "wisdom of crowds" approach by demonstrating that improved prediction of patient outcomes is obtainable by combining methods across an extended community. These findings were made possible because data from separate trials were made publicly available and centrally compiled through PDS

Crowdsourcing digital health measures to predict Parkinson’s disease severity: The Parkinson’s Disease Digital Biomarker DREAM Challenge.

Consumer wearables and sensors are a rich source of data about patients’ daily disease and symptom burden, particularly in the case of movement disorders like Parkinson’s disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95)