Transcriptomics of interstitial lung disease: a systematic review and meta-analysis.

Objective: Gene expression (transcriptomics) studies have revealed potential mechanisms of interstitial lung disease (ILD), yet sample sizes of studies are often limited and between-subtype comparisons are scarce. The aim of this study was to identify and validate consensus transcriptomic signatures of ILD subtypes.

Methods: We performed a systematic review and meta-analysis of fibrotic ILD transcriptomics studies using an individual participant data approach, and included studies examining bulk transcriptomics of human adult ILD samples and excluding those focusing on individual cell populations. Patient-level data and expression matrices were extracted from 43 studies and integrated using a multivariable integrative algorithm to develop ILD classification models.

Results: Using 1459 samples from 24 studies, we identified transcriptomic signatures for idiopathic pulmonary fibrosis (IPF), hypersensitivity pneumonitis (HP), idiopathic nonspecific interstitial pneumonia (NSIP), and systemic sclerosis-associated ILD (SSc-ILD) against control samples, which were validated on 308 samples from 8 studies (area under receiver operating curve [AUC]=0.99 [95% CI: 0.99-1.00], HP AUC=0.91 [0.84-0.99], NSIP AUC=0.94 [0.88-0.99], SSc-ILD AUC=0.98 [0.93-1.00]). Significantly, meta-analysis allowed, for the first time, identification of robust lung transcriptomics signatures to discriminate IPF (AUC=0.71 [0.63-0.79]) and HP (AUC=0.76 [0.63-0.89]) from other fibrotic ILDs, and unsupervised learning algorithms identified putative molecular endotypes of ILD associated with decreased forced vital capacity (FVC) and diffusing capacity of the lungs for carbon monoxide (D) % predicted. Transcriptomics signatures were reflective of both cell-specific and disease-specific changes in gene expression.

Conclusion: We present the first systematic review and largest meta-analysis of fibrotic ILD transcriptomics to date, identifying reproducible transcriptomic signatures with clinical relevance.