The healthcare industry generates massive amounts of data originating from a variety of sources, among them patient health records, pharmacies, clinical trials, insurance providers, regulatory bodies, hospitals and clinics, and wearable devices and sensors. Advances in data processing, analysis, and deep learning techniques have made it possible to leverage this wealth of healthcare data to optimize patient care. In this series, reviews discuss the potential for large-scale datasets to provide valuable insights that help personalize therapies, diagnose and understand rare diseases, optimize clinical trial design, facilitate drug discovery and development, reduce healthcare costs, and more. The reviews also discuss the limitations of existing analysis methods, asserting that analyses of large-scale datasets can complement traditional preclinical and clinical research. As healthcare data increases in volume and complexity, so does its potential to transform medical practice and research.
High-throughput technologies for genomics, transcriptomics, proteomics, and metabolomics, and integrative analysis of these data, enable new, systems-level insights into disease pathogenesis. Mitochondrial diseases are an excellent target for hypothesis-generating omics approaches, as the disease group is mechanistically exceptionally complex. Although the genetic background in mitochondrial diseases is in either the nuclear or the mitochondrial genome, the typical downstream effect is dysfunction of the mitochondrial respiratory chain. However, the clinical manifestations show unprecedented variability, including either systemic or tissue-specific effects across multiple organ systems, with mild to severe symptoms, and occurring at any age. So far, the omics approaches have provided mechanistic understanding of tissue-specificity and potential treatment options for mitochondrial diseases, such as metabolome remodeling. However, no curative treatments exist, suggesting that novel approaches are needed. In this Review, we discuss omics approaches and discoveries with the potential to elucidate mechanisms of and therapies for mitochondrial diseases.
Sofia Khan, Gulayse Ince-Dunn, Anu Suomalainen, Laura L. Elo
Advanced phenotyping of cardiovascular diseases has evolved with the application of high-resolution omics screening to populations enrolled in large-scale observational and clinical trials. This strategy has revealed that considerable heterogeneity exists at the genotype, endophenotype, and clinical phenotype levels in cardiovascular diseases, a feature of the most common diseases that has not been elucidated by conventional reductionism. In this discussion, we address genomic context and (endo)phenotypic heterogeneity, and examine commonly encountered cardiovascular diseases to illustrate the genotypic underpinnings of (endo)phenotypic diversity. We highlight the existing challenges in cardiovascular disease genotyping and phenotyping that can be addressed by the integration of big data and interpreted using novel analytical methodologies (network analysis). Precision cardiovascular medicine will only be broadly applied to cardiovascular patients once this comprehensive data set is subjected to unique, integrative analytical strategies that accommodate molecular and clinical heterogeneity rather than ignore or reduce it.
Jane A. Leopold, Bradley A. Maron, Joseph Loscalzo
Real-world data (RWD) continue to emerge as a new source of clinical evidence. Although the best-known use case of RWD has been in drug regulation, RWD are being generated and used by many other parties, including biopharmaceutical companies, payors, clinical researchers, providers, and patients. In this Review, we describe 21 potential uses for RWD across the spectrum of health care. We also discuss important challenges and limitations relevant to the translation of these data into evidence.
Vivek A. Rudrapatna, Atul J. Butte
Technological advances in rapid data acquisition have transformed medical biology into a data mining field, where new data sets are routinely dissected and analyzed by statistical models of ever-increasing complexity. Many hypotheses can be generated and tested within a single large data set, and even small effects can be statistically discriminated from a sea of noise. On the other hand, the development of therapeutic interventions moves at a much slower pace. They are determined from carefully randomized and well-controlled experiments with explicitly stated outcomes as the principal mechanism by which a single hypothesis is tested. In this paradigm, only a small fraction of interventions can be tested, and an even smaller fraction are ultimately deemed therapeutically successful. In this Review, we propose strategies to leverage large-cohort data to inform the selection of targets and the design of randomized trials of novel therapeutics. Ultimately, the incorporation of big data and experimental medicine approaches should aim to reduce the failure rate of clinical trials as well as expedite and lower the cost of drug development.
Eugene Melamud, D. Leland Taylor, Anurag Sethi, Madeleine Cule, Anastasia Baryshnikova, Danish Saleheen, Nick van Bruggen, Garret A. FitzGerald