Mary Regina Boland

Birth month affects lifetime disease risk: a phenome-wide method

Objective An individual’s birth month has a significant impact on the diseases they develop during their lifetime. Previous studies reveal relationships between birth month and several diseases including atherothrombosis, asthma, attention deficit hyperactivity disorder, and myopia, leaving most diseases completely unexplored. This retrospective population study systematically explores the relationship between seasonal affects at birth and lifetime disease risk for 1688 conditions. Methods We developed a hypothesis-free method that minimizes publication and disease selection biases by systematically investigating disease-birth month patterns across all conditions. Our dataset includes 1 749 400 individuals with records at New York-Presbyterian/Columbia University Medical Center born between 1900 and 2000 inclusive. We modeled associations between birth month and 1688 diseases using logistic regression. Significance was tested using a chi-squared test with multiplicity correction. Results We found 55 diseases that were significantly dependent on birth month. Of these 19 were previously reported in the literature (P < .001), 20 were for conditions with close relationships to those reported, and 16 were previously unreported. We found distinct incidence patterns across disease categories. Conclusions Lifetime disease risk is affected by birth month. Seasonally dependent early developmental mechanisms may play a role in increasing lifetime risk of disease.

Mapping the effects of drugs on the immune system

Understanding how drugs affect the immune system has consequences for treating disease and minimizing unwanted side effects. Here we present an integrative computational approach for predicting interactions between drugs and immune cells in a system-wide manner. The approach matches gene sets between transcriptional signatures to determine their similarity. We apply the method to model the interactions between 1,309 drugs and 221 immune cell types and predict 69,995 interactions. The resulting immune-cell pharmacology map is used to predict how five drugs influence four immune cell types in humans and mice. To validate the predictions, we analyzed patient records and examined cell population changes from in vivo experiments. Our method offers a tool for screening thousands of interactions to identify relationships between drugs and the immune system.

Feasibility of feature-based indexing, clustering, and search of clinical trials. A case study of breast cancer trials from ClinicalTrials.gov.

BACKGROUND When standard therapies fail, clinical trials provide experimental treatment opportunities for patients with drug-resistant illnesses or terminal diseases. Clinical Trials can also provide free treatment and education for individuals who otherwise may not have access to such care. To find relevant clinical trials, patients often search online; however, they often encounter a significant barrier due to the large number of trials and in-effective indexing methods for reducing the trial search space. OBJECTIVES This study explores the feasibility of feature-based indexing, clustering, and search of clinical trials and informs designs to automate these processes. METHODS We decomposed 80 randomly selected stage III breast cancer clinical trials into a vector of eligibility features, which were organized into a hierarchy. We clustered trials based on their eligibility feature similarities. In a simulated search process, manually selected features were used to generate specific eligibility questions to filter trials iteratively. RESULTS We extracted 1,437 distinct eligibility features and achieved an inter-rater agreement of 0.73 for feature extraction for 37 frequent features occurring in more than 20 trials. Using all the 1,437 features we stratified the 80 trials into six clusters containing trials recruiting similar patients by patient-characteristic features, five clusters by disease-characteristic features, and two clusters by mixed features. Most of the features were mapped to one or more Unified Medical Language System (UMLS) concepts, demonstrating the utility of named entity recognition prior to mapping with the UMLS for automatic feature extraction. CONCLUSIONS It is feasible to develop feature-based indexing and clustering methods for clinical trials to identify trials with similar target populations and to improve trial search efficiency.

The digital revolution in phenotyping

Phenotypes have gained increased notoriety in the clinical and biological domain owing to their application in numerous areas such as the discovery of disease genes and drug targets, phylogenetics and pharmacogenomics. Phenotypes, defined as observable characteristics of organisms, can be seen as one of the bridges that lead to a translation of experimental findings into clinical applications and thereby support ‘bench to bedside’ efforts. However, to build this translational bridge, a common and universal understanding of phenotypes is required that goes beyond domain-specific definitions. To achieve this ambitious goal, a digital revolution is ongoing that enables the encoding of data in computer-readable formats and the data storage in specialized repositories, ready for integration, enabling translational research. While phenome research is an ongoing endeavor, the true potential hidden in the currently available data still needs to be unlocked, offering exciting opportunities for the forthcoming years. Here, we provide insights into the state-of-the-art in digital phenotyping, by means of representing, acquiring and analyzing phenotype data. In addition, we provide visions of this field for future research work that could enable better applications of phenotype data.

Systems biology approaches for identifying adverse drug reactions and elucidating their underlying biological mechanisms

Small molecules are indispensable to modern medical therapy. However, their use may lead to unintended, negative medical outcomes commonly referred to as adverse drug reactions (ADRs). These effects vary widely in mechanism, severity, and populations affected, making ADR prediction and identification important public health concerns. Current methods rely on clinical trials and postmarket surveillance programs to find novel ADRs; however, clinical trials are limited by small sample size, whereas postmarket surveillance methods may be biased and inherently leave patients at risk until sufficient clinical evidence has been gathered. Systems pharmacology, an emerging interdisciplinary field combining network and chemical biology, provides important tools to uncover and understand ADRs and may mitigate the drawbacks of traditional methods. In particular, network analysis allows researchers to integrate heterogeneous data sources and quantify the interactions between biological and chemical entities. Recent work in this area has combined chemical, biological, and large‐scale observational health data to predict ADRs in both individual patients and global populations. In this review, we explore the rapid expansion of systems pharmacology in the study of ADRs. We enumerate the existing methods and strategies and illustrate progress in the field with a model framework that incorporates crucial data elements, such as diet and comorbidities, known to modulate ADR risk. Using this framework, we highlight avenues of research that may currently be underexplored, representing opportunities for future work. WIREs Syst Biol Med 2016, 8:104–122. doi: 10.1002/wsbm.1323

An Integrated Model for Patient Care and Clinical Trials (IMPACT) to support clinical research visit scheduling workflow for future learning health systems

We describe a clinical research visit scheduling system that can potentially coordinate clinical research visits with patient care visits and increase efficiency at clinical sites where clinical and research activities occur simultaneously. Participatory Design methods were applied to support requirements engineering and to create this software called Integrated Model for Patient Care and Clinical Trials (IMPACT). Using a multi-user constraint satisfaction and resource optimization algorithm, IMPACT automatically synthesizes temporal availability of various research resources and recommends the optimal dates and times for pending research visits. We conducted scenario-based evaluations with 10 clinical research coordinators (CRCs) from diverse clinical research settings to assess the usefulness, feasibility, and user acceptance of IMPACT. We obtained qualitative feedback using semi-structured interviews with the CRCs. Most CRCs acknowledged the usefulness of IMPACT features. Support for collaboration within research teams and interoperability with electronic health records and clinical trial management systems were highly requested features. Overall, IMPACT received satisfactory user acceptance and proves to be potentially useful for a variety of clinical research settings. Our future work includes comparing the effectiveness of IMPACT with that of existing scheduling solutions on the market and conducting field tests to formally assess user adoption.

An Active Learning Framework for Efficient Condition Severity Classification

Understanding condition severity, as extracted from Electronic Health Records (EHRs), is important for many public health purposes. Methods requiring physicians to annotate condition severity are time-consuming and costly. Previously, a passive learning algorithm called CAESAR was developed to capture severity in EHRs. This approach required physicians to label conditions manually, an exhaustive process. We developed a framework that uses two Active Learning (AL) methods (Exploitation and Combination_XA) to decrease manual labeling efforts by selecting only the most informative conditions for training. We call our approach CAESAR-Active Learning Enhancement (CAESAR-ALE). As compared to passive methods,CAESAR-ALE’s first AL method, Exploitation, reduced labeling efforts by 64% and achieved an equivalent true positive rate, while CAESAR-ALE’s second AL method, Combination_XA, reduced labeling efforts by 48% and achieved equivalent accuracy. In addition, both these AL methods outperformed the traditional AL method (SVM-Margin). These results demonstrate the potential of AL methods for decreasing the labeling efforts of medical experts, while achieving greater accuracy and lower costs.

A Computational Drug Repositioning Approach for Targeting Oncogenic Transcription Factors

Mutations in transcription factor (TF) genes are frequently observed in tumors, often leading to aberrant transcriptional activity. Unfortunately, TFs are often considered undruggable due to the absence of targetable enzymatic activity. To address this problem, we developed CRAFTT, a computational drug-repositioning approach for targeting TF activity. CRAFTT combines ChIP-seq with drug-induced expression profiling to identify small molecules that can specifically perturb TF activity. Application to ENCODE ChIP-seq datasets revealed known drug-TF interactions, and a global drug-protein network analysis supported these predictions. Application of CRAFTT to ERG, a pro-invasive, frequently overexpressed oncogenic TF, predicted that dexamethasone would inhibit ERG activity. Dexamethasone significantly decreased cell invasion and migration in an ERG-dependent manner. Furthermore, analysis of electronic medical record data indicates a protective role for dexamethasone against prostate cancer. Altogether, our method provides a broadly applicable strategy for identifying drugs that specifically modulate TF activity.

Development and validation of a classification approach for extracting severity automatically from electronic health records.

BackgroundElectronic Health Records (EHRs) contain a wealth of information useful for studying clinical phenotype-genotype relationships. Severity is important for distinguishing among phenotypes; however other severity indices classify patient-level severity (e.g., mild vs. acute dermatitis) rather than phenotype-level severity (e.g., acne vs. myocardial infarction). Phenotype-level severity is independent of the individual patient’s state and is relative to other phenotypes. Further, phenotype-level severity does not change based on the individual patient. For example, acne is mild at the phenotype-level and relative to other phenotypes. Therefore, a given patient may have a severe form of acne (this is the patient-level severity), but this does not effect its overall designation as a mild phenotype at the phenotype-level.MethodsWe present a method for classifying severity at the phenotype-level that uses the Systemized Nomenclature of Medicine – Clinical Terms. Our method is called the Classification Approach for Extracting Severity Automatically from Electronic Health Records (CAESAR). CAESAR combines multiple severity measures – number of comorbidities, medications, procedures, cost, treatment time, and a proportional index term. CAESAR employs a random forest algorithm and these severity measures to discriminate between severe and mild phenotypes.ResultsUsing a random forest algorithm and these severity measures as input, CAESAR differentiates between severe and mild phenotypes (sensitivity = 91.67, specificity = 77.78) when compared to a manually evaluated reference standard (k = 0.716).ConclusionsCAESAR enables researchers to measure phenotype severity from EHRs to identify phenotypes that are important for comparative effectiveness research.

Ten Simple Rules to Enable Multi-site Collaborations through Data Sharing

1 Department of Biomedical Informatics, Columbia University, New York, New York, United States of America, 2 Department of Systems Biology, Columbia University, New York, New York, United States of America, 3 Department of Medicine, Columbia University, New York, New York, United States of America, 4 Observational Health Data Sciences and Informatics, Columbia University, New York, New York, United States of America, 5 Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America, 6 Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America