Abstract
While the term asthma has long been known to describe heterogeneous groupings of patients, only recently have data evolved which enable a molecular understanding of the clinical differences. The evolution of transcriptomics (and other 'omics platforms) and improved statistical analyses in combination with large clinical cohorts opened the door for molecular characterization of pathobiologic processes associated with a range of asthma patients. When linked with data from animal models and clinical trials of targeted biologic therapies, emerging distinctions arose between patients with and without elevations in type 2 immune and inflammatory pathways, leading to the confirmation of a broad categorization of type 2-Hi asthma. Differences in the ratios, sources, and location of type 2 cytokines and their relation to additional immune pathway activation appear to distinguish several different (sub)molecular phenotypes, and perhaps endotypes of type 2-Hi asthma, which respond differently to broad and targeted anti-inflammatory therapies. Asthma in the absence of type 2 inflammation is much less well defined, without clear biomarkers, but is generally linked with poor responses to corticosteroids. Integration of "big data" from large cohorts, over time, using machine learning approaches, combined with validation and iterative learning in animal (and human) model systems is needed to identify the biomarkers and tightly defined molecular phenotypes/endotypes required to fulfill the promise of precision medicine.