Abstract
We present a novel approach for the molecular transformation and analysis of patient clinical phenotypes. Building on the fact that drugs perturb the function of targets/genes, we integrated data from 8.2 million clinical reports detailing drug-induced side effects with the molecular world of drug-target information. Using this dataset, we extracted 1.8 million associations of clinical phenotypes to 770 human drug-targets. This collection is perhaps the largest phenotypic profiling reference of human targets to-date, and unique in that it enables rapid development of testable molecular hypotheses directly from human-specific information. We also present validation results demonstrating analytical utilities of the approach, including drug safety prediction, and the design of novel combination therapies. Challenging the long-standing notion that molecular perturbation studies cannot be performed in humans, our data allows researchers to capitalize on the vast tomes of clinical information available throughout the healthcare system.
Highlights
Deciphering molecular mechanisms connecting disease phenotypes to the underlying genotypes remains one of the most important endeavors in science and medicine
Using drug-to-target data from DrugBank [3] and the treatment information provided for each patient in 8.2 million FAERS reports, 1822 human proteins were mapped to associated clinical phenotypes
Examining the reaction profiles of tyrosine kinase inhibitors (TKIs) revealed that Sorafenib is more strongly associated with dermatological reactions than Sunitinib (Supplementary Table S2)
Summary
Deciphering molecular mechanisms connecting disease phenotypes to the underlying genotypes remains one of the most important endeavors in science and medicine. While certainly contributing to the identification of the key molecular protagonists of human disease, model systems suffer important limitations, the most fundamental of which remains their general lack of absolute congruence with the human condition that they are used to characterize [1] While this is understandable given the enormous complexity and cell-type specificity of biological systems, it does suggest that new levels of therapeutic innovation might be achieved through development of methods that allow us to decipher molecular mechanisms directly from human specific data. This is elegantly demonstrated by results from the TCGA project, where molecular data and phenotypic information have been analyzed to decipher novel targets and prognostic classifiers.
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