Unraveling mechanistic insights through interstitial lung disease multiomics.

Abstract

Purpose of review: Interstitial lung disease (ILD) and consequent pulmonary fibrosis are associated with significant morbidity and mortality with limited treatment options. There are more than 200 different etiologies that can lead to ILD. As a result, diagnostic accuracy and delay, prognostication, and treatment responses are still rife with challenges. The integration of bioinformatics with clinical practice is gaining momentum, evolving from a research tool to a practical resource with potential applications at the bedside. Work in this field has opened avenues into the pursuit of precision medicine in ILD.

Recent findings: Across various 'omics-based technologies, numerous studies highlight the potential of using molecular data to disentangle the complex processes that lead to pulmonary fibrosis. Recent studies point toward integrating signals across domains to filter out noise and identify true signals. However, there is still a need for functional work to connect the high-dimensional signals to the biology underlying pulmonary fibrosis.

Summary: Pursuing a multiomic approach across multiple domains in ILD holds promise for better biomarkers, clinical trial enrichment, and developing a deeper understanding of disease pathology.