Phenotype-Driven Plasma Biobanking Strategies and Methods

Towards a Novel Frontier in the Use of Epigenetic Clocks in Epidemiology

Health problems associated with aging are a major public health concern for the future. Aging is a complex process with wide intervariability among individuals. Therefore, there is a need for innovative public health strategies that target factors associated with aging and the development of tools to assess the effectiveness of these strategies accurately. Novel approaches to measure biological age, such as epigenetic clocks, have become relevant. These clocks use non-sequential variable information from the genome and employ mathematical algorithms to estimate biological age based on DNA methylation levels. Therefore, in the present study, we comprehensively review the current status of the epigenetic clocks and their associations across the human phenome. We emphasize the potential utility of these tools in an epidemiological context, particularly in evaluating the impact of public health interventions focused on promoting healthy aging. Our review describes associations between epigenetic clocks and multiple traits across the life and health span. Additionally, we highlighted the evolution of studies beyond mere associations to establish causal mechanisms between epigenetic age and disease. We explored the application of epigenetic clocks to measure the efficacy of interventions focusing on rejuvenation.

Revealing polygenic pleiotropy using genetic risk scores for asthma

In this study we examined how genetic risk for asthma associates with different features of the disease and with other medical conditions and traits. Using summary statistics from two multi-ancestry genome-wide association studies of asthma, we modeled polygenic risk scores (PRSs) and validated their predictive performance in the UK Biobank. We then performed phenome-wide association studies of the asthma PRSs with 371 heritable traits in the UK Biobank. We identified 228 total significant associations across a variety of organ systems, including associations that varied by PRS model, sex, age of asthma onset, ancestry, and human leukocyte antigen region alleles. Our results highlight pervasive pleiotropy between asthma and numerous other traits and conditions and elucidate pathways that contribute to asthma and its comorbidities.

Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts

Polygenic risk scores (PRSs) have been widely explored in precision medicine. However, few studies have thoroughly investigated their best practices in global populations across different diseases. We here utilized data from Global Biobank Meta-analysis Initiative (GBMI) to explore methodological considerations and PRS performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRSs using pruning and thresholding (P + T) and PRS-continuous shrinkage (CS). For both methods, using a European-based linkage disequilibrium (LD) reference panel resulted in comparable or higher prediction accuracy compared with several other non-European-based panels. PRS-CS overall outperformed the classic P + T method, especially for endpoints with higher SNP-based heritability. Notably, prediction accuracy is heterogeneous across endpoints, biobanks, and ancestries, especially for asthma, which has known variation in disease prevalence across populations. Overall, we provide lessons for PRS construction, evaluation, and interpretation using GBMI resources and highlight the importance of best practices for PRS in the biobank-scale genomics era.

High-throughput phenotyping with temporal sequences

OBJECTIVE

High-throughput electronic phenotyping algorithms can accelerate translational research using data from electronic health record (EHR) systems. The temporal information buried in EHRs is often underutilized in developing computational phenotypic definitions. This study aims to develop a high-throughput phenotyping method, leveraging temporal sequential patterns from EHRs.

MATERIALS AND METHODS

We develop a representation mining algorithm to extract 5 classes of representations from EHR diagnosis and medication records: the aggregated vector of the records (aggregated vector representation), the standard sequential patterns (sequential pattern mining), the transitive sequential patterns (transitive sequential pattern mining), and 2 hybrid classes. Using EHR data on 10 phenotypes from the Mass General Brigham Biobank, we train and validate phenotyping algorithms.

RESULTS

Phenotyping with temporal sequences resulted in a superior classification performance across all 10 phenotypes compared with the standard representations in electronic phenotyping. The high-throughput algorithm's classification performance was superior or similar to the performance of previously published electronic phenotyping algorithms. We characterize and evaluate the top transitive sequences of diagnosis records paired with the records of risk factors, symptoms, complications, medications, or vaccinations.

DISCUSSION

The proposed high-throughput phenotyping approach enables seamless discovery of sequential record combinations that may be difficult to assume from raw EHR data. Transitive sequences offer more accurate characterization of the phenotype, compared with its individual components, and reflect the actual lived experiences of the patients with that particular disease.

CONCLUSION

Sequential data representations provide a precise mechanism for incorporating raw EHR records into downstream machine learning. Our approach starts with user interpretability and works backward to the technology.

Each patient is a research biorepository: informatics-enabled research on surplus clinical specimens via the living BioBank

The ability to analyze human specimens is the pillar of modern-day translational research. To enhance the research availability of relevant clinical specimens, we developed the Living BioBank (LBB) solution, which allows for just-in-time capture and delivery of phenotyped surplus laboratory medicine specimens. The LBB is a system-of-systems integrating research feasibility databases in i2b2, a real-time clinical data warehouse, and an informatics system for institutional research services management (SPARC). LBB delivers deidentified clinical data and laboratory specimens. We further present an extension to our solution, the Living µBiome Bank, that allows the user to request and receive phenotyped specimen microbiome data. We discuss the details of the implementation of the LBB system and the necessary regulatory oversight for this solution. The conducted institutional focus group of translational investigators indicates an overall positive sentiment towards potential scientific results generated with the use of LBB. Reference implementation of LBB is available at https://LivingBioBank.musc.edu.

Harnessing the potential of blood donation archives for influenza surveillance and control

Many blood donation services around the globe maintain large archives of serum and/or plasma specimens of blood donations which could potentially be used for serologic surveillance and risk assessment of influenza. Harnessing this potential requires robust evidence that the outcomes of influenza serology in plasma, which is rarely used, is consistent with that in serum, which is the conventional choice of specimens for influenza serology. We harvested EDTA-plasma specimens from the blood donation archives of Hong Kong Red Cross Transfusion Services, where EDTA is the type of anticoagulant used for plasma collection, compared their antibody titers and responses to that in serum. Influenza A/H1N1/California/7/2009 and A/H3N2/Victoria/208/2009 were the test strains. Our results showed that antibody titers in 609 matched serum/EDTA-plasma specimens (i.e. obtained from the same donor at the same time) had good agreement inferred by Intraclass Correlation Coefficient, the value of which was 0.82 (95% CI: 0.77-0.86) for hemagglutination inhibition assay and 0.95 (95% CI: 0.93-0.96) for microneutralization assay; seroconversion rates (based on hemagglutination inhibition titers) during the 2010 and 2011 influenza seasons in Hong Kong inferred from paired EDTA-plasma were similar to that inferred from paired sera. Our study provided the proof-of-concept that blood donation archives could be leveraged as a valuable source of longitudinal blood specimens for the surveillance, control and risk assessment of both pandemic and seasonal influenza.

Accelerating Biomarker Discovery Through Electronic Health Records, Automated Biobanking, and Proteomics

BACKGROUND

Circulating biomarkers can facilitate diagnosis and risk stratification for complex conditions such as heart failure (HF). Newer molecular platforms can accelerate biomarker discovery, but they require significant resources for data and sample acquisition.

OBJECTIVES

The purpose of this study was to test a pragmatic biomarker discovery strategy integrating automated clinical biobanking with proteomics.

METHODS

Using the electronic health record, the authors identified patients with and without HF, retrieved their discarded plasma samples, and screened these specimens using a DNA aptamer-based proteomic platform (1,129 proteins). Candidate biomarkers were validated in 3 different prospective cohorts.

RESULTS

In an automated manner, plasma samples from 1,315 patients (31% with HF) were collected. Proteomic analysis of a 96-patient subset identified 9 candidate biomarkers (p < 4.42 × 10^-5). Two proteins, angiopoietin-2 and thrombospondin-2, were associated with HF in 3 separate validation cohorts. In an emergency department-based registry of 852 dyspneic patients, the 2 biomarkers improved discrimination of acute HF compared with a clinical score (p < 0.0001) or clinical score plus B-type natriuretic peptide (p = 0.02). In a community-based cohort (n = 768), both biomarkers predicted incident HF independent of traditional risk factors and N-terminal pro-B-type natriuretic peptide (hazard ratio per SD increment: 1.35 [95% confidence interval: 1.14 to 1.61; p = 0.0007] for angiopoietin-2, and 1.37 [95% confidence interval: 1.06 to 1.79; p = 0.02] for thrombospondin-2). Among 30 advanced HF patients, concentrations of both biomarkers declined (80% to 84%) following cardiac transplant (p < 0.001 for both).

CONCLUSIONS

A novel strategy integrating electronic health records, discarded clinical specimens, and proteomics identified 2 biomarkers that robustly predict HF across diverse clinical settings. This approach could accelerate biomarker discovery for many diseases.

Integration of complex data sources to provide biologic insight into pulmonary vascular disease (2015 Grover Conference Series)

The application of complex data sources to pulmonary vascular diseases is an emerging and promising area of investigation. The use of -omics platforms, in silico modeling of gene networks, and linkage of large human cohorts with DNA biobanks are beginning to bear biologic insight into pulmonary hypertension. These approaches to high-throughput molecular phenotyping offer the possibility of discovering new therapeutic targets and identifying variability in response to therapy that can be leveraged to improve clinical care. Optimizing the methods for analyzing complex data sources and accruing large, well-phenotyped human cohorts linked to biologic data remain significant challenges. Here, we discuss two specific types of complex data sources-gene regulatory networks and DNA-linked electronic medical record cohorts-that illustrate the promise, challenges, and current limitations of these approaches to understanding and managing pulmonary vascular disease.

Targeting the untargeted in molecular phenomics with structurally-selective ion mobility-mass spectrometry

Systems-wide molecular phenomics is rapidly expanding through technological advances in instrumentation and bioinformatics. Strategies such as structural mass spectrometry, which utilizes size and shape measurements with molecular weight, serve to characterize the sum of molecular expression in biological contexts, where broad-scale measurements are made that are interpreted through big data statistical techniques to reveal underlying patterns corresponding to phenotype. The data density, data dimensionality, data projection, and data interrogation are all critical aspects of these approaches to turn data into salient information. Untargeted molecular phenomics is already having a dramatic impact in discovery science from drug discovery to synthetic biology. It is evident that these emerging techniques will integrate closely in broad efforts aimed at precision medicine.

Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health

The key question of precision medicine is whether it is possible to find clinically actionable granularity in diagnosing disease and classifying patient risk. The advent of next-generation sequencing and the widespread adoption of electronic health records (EHRs) have provided clinicians and researchers a wealth of data and made possible the precise characterization of individual patient genotypes and phenotypes. Unstructured text-found in biomedical publications and clinical notes-is an important component of genotype and phenotype knowledge. Publications in the biomedical literature provide essential information for interpreting genetic data. Likewise, clinical notes contain the richest source of phenotype information in EHRs. Text mining can render these texts computationally accessible and support information extraction and hypothesis generation. This chapter reviews the mechanics of text mining in precision medicine and discusses several specific use cases, including database curation for personalized cancer medicine, patient outcome prediction from EHR-derived cohorts, and pharmacogenomic research. Taken as a whole, these use cases demonstrate how text mining enables effective utilization of existing knowledge sources and thus promotes increased value for patients and healthcare systems. Text mining is an indispensable tool for translating genotype-phenotype data into effective clinical care that will undoubtedly play an important role in the eventual realization of precision medicine.