Novel Therapeutic Approaches in Connective Tissue Disease-Associated Interstitial Lung Disease.

Connective tissue diseases (CTD) comprise a group of autoimmune diseases that can affect multiple organs in the body including the lungs. The most common form of pulmonary involvement is interstitial lung disease (ILD). CTD-associated ILD (CTD-ILD) can take one of several courses including nonprogressive, chronically progressive, or rapidly progressive. Chronically and rapidly progressive patterns are associated with increased mortality. Limited randomized controlled trial data are available for treatment of CTD-ILD, with most data coming from systemic sclerosis-related ILD. The current first-line treatment for all CTD-ILD is immunosuppression with consideration of antifibrotics, stem cell transplant, and lung transplant in progressive disease. In this article, we review data for ILD treatment options in systemic sclerosis, rheumatoid arthritis, myositis, and primary Sjögren's syndrome-related ILDs.

Dendritic Cell - Fibroblast Crosstalk via TLR9 and AHR Signaling Drives Lung Fibrogenesis.

Idiopathic pulmonary fibrosis (IPF) is characterized by progressive scarring and loss of lung function. With limited treatment options, patients succumb to the disease within 2-5 years. The molecular pathogenesis of IPF regarding the immunologic changes that occur is poorly understood. We characterize a role for non-canonical aryl-hydrocarbon receptor signaling (ncAHR) in dendritic cells (DCs) that leads to production of IL-6 and IL-17, promoting fibrosis. TLR9 signaling in myofibroblasts is shown to regulate production of TDO2 which converts tryptophan into the endogenous AHR ligand kynurenine. Mice with augmented ncAHR signaling were created by crossing floxed AHR exon-2 deletion mice (AHR Δex2 ) with mice harboring a CD11c-Cre. Bleomycin was used to study fibrotic pathogenesis. Isolated CD11c+ cells and primary fibroblasts were treated ex-vivo with relevant TLR agonists and AHR modulating compounds to study how AHR signaling influenced inflammatory cytokine production. Human datasets were also interrogated. Inhibition of all AHR signaling rescued fibrosis, however, AHR Δex2 mice treated with bleomycin developed more fibrosis and DCs from these mice were hyperinflammatory and profibrotic upon adoptive transfer. Treatment of fibrotic fibroblasts with TLR9 agonist increased expression of TDO2. Study of human samples corroborate the relevance of these findings in IPF patients. We also, for the first time, identify that AHR exon-2 floxed mice retain capacity for ncAHR signaling.

Genetics and Genomics of Pulmonary Fibrosis: Charting the Molecular Landscape and Shaping Precision Medicine.

Recent genetic and genomic advancements have elucidated the complex etiology of idiopathic pulmonary fibrosis (IPF) and other progressive fibrotic interstitial lung diseases (ILDs), emphasizing the contribution of heritable factors. This state-of-the-art review synthesizes evidence on significant genetic contributors to pulmonary fibrosis (PF), including rare genetic variants and common single nucleotide polymorphisms (SNPs). The MUC5B promoter variant is unusual, a common SNP that markedly elevates the risk of early and established PF. We address the utility of genetic variation in enhancing understanding of disease pathogenesis, clinical phenotypes, improving disease definitions, and informing prognosis and treatment response. Critical research gaps are highlighted, particularly the underrepresentation of non-European ancestries in PF genetic studies and the exploration of PF phenotypes beyond usual interstitial pneumonia (UIP)/IPF. We discuss the role of telomere length, often critically short in PF, and its link to progression and mortality, underscoring the genetic complexity involving telomere biology genes (TERT, TERC) and others like SFTPC and MUC5B. Additionally, we address the potential of gene-by-environment interactions to modulate disease manifestation, advocating for precision medicine in PF. Insights from gene expression profiling studies and multi-omic analyses highlight the promise for understanding disease pathogenesis and offer new approaches to clinical care, therapeutic drug development, and biomarker discovery. Finally, we discuss the ethical, legal, and social implications of genomic research and therapies in PF, stressing the need for sound practices and informed clinical genetic discussions. Looking forward, we advocate for comprehensive genetic testing panels and polygenic risk scores to improve the management of PF and related ILDs across diverse populations.

Deep Learning-based Segmentation of CT Scans Predicts Disease Progression and Mortality in IPF.

RATIONALE: Despite evidence demonstrating a prognostic role for CT scans in IPF, image-based biomarkers are not routinely used in clinical practice or trials. OBJECTIVES: Develop automated imaging biomarkers using deep learning based segmentation of CT scans. METHODS: We developed segmentation processes for four anatomical biomarkers which were applied to a unique cohort of treatment-naive IPF patients enrolled in the PROFILE study and tested against a further UK cohort. The relationship between CT biomarkers, lung function, disease progression and mortality were assessed. MEASUREMENTS AND MAIN RESULTS: Data was analysed from 446 PROFILE patients. Median follow-up was 39.1 months (IQR 18.1-66.4) with cumulative incidence of death of 277 over 5 years (62.1%). Segmentation was successful on 97.8% of all scans, across multiple imaging vendors at slice thicknesses 0.5-5mm. Of 4 segmentations, lung volume showed strongest correlation with FVC (r=0.82, p<0.001). Lung, vascular and fibrosis volumes were consistently associated across cohorts with differential five-year survival, which persisted after adjustment for baseline GAP score. Lower lung volume (HR 0.98, CI 0.96-0.99, p=0.001), increased vascular volume (HR 1.30, CI 1.12-1.51, p=0.001) and increased fibrosis volume (HR 1.17, CI 1.12-1.22, p=<0.001) were associated with reduced two-year progression-free survival in the pooled PROFILE cohort. Longitudinally, decreasing lung volume (HR 3.41; 95% CI 1.36-8.54; p=0.009) and increasing fibrosis volume (HR 2.23; 95% CI 1.22-4.08; p=0.009) were associated with differential survival. CONCLUSIONS: Automated models can rapidly segment IPF CT scans, providing prognostic near and long-term information, which could be used in routine clinical practice or as key trial endpoints. This article is open access and distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).

A multidimensional classifier to support lung transplant referral in patients with pulmonary fibrosis.

BACKGROUND: Lung transplantation remains the sole curative option for patients with idiopathic pulmonary fibrosis (IPF), but donor organs remain scarce, and many eligible patients die before transplant. Tools to optimize the timing of transplant referrals are urgently needed. METHODS: Least absolute shrinkage and selection operator was applied to clinical and proteomic data generated as part of a prospective cohort study of interstitial lung disease (ILD) to derive clinical, proteomic, and multidimensional logit models of near-term death or lung transplant within 18 months of blood draw. Model-fitted values were dichotomized at the point of maximal sensitivity and specificity, and decision curve analysis was used to select the best-performing classifier. We then applied this classifier to independent IPF and non-IPF ILD cohorts to determine test performance characteristics. Cohorts were restricted to patients aged ≤72 years with body mass index 18 to 32 to increase the likelihood of transplant eligibility. RESULTS: IPF derivation, IPF validation, and non-IPF ILD validation cohorts consisted of 314, 105, and 295 patients, respectively. A multidimensional model comprising 2 clinical variables and 20 proteins outperformed stand-alone clinical and proteomic models. Following dichotomization, the multidimensional classifier predicted near-term outcome with 70% sensitivity and 92% specificity in the IPF validation cohort and 70% sensitivity and 80% specificity in the non-IPF ILD validation cohort. CONCLUSIONS: A multidimensional classifier of near-term outcomes accurately discriminated this end-point with good test performance across independent IPF and non-IPF ILD cohorts. These findings support refinement and prospective validation of this classifier in transplant-eligible individuals.

Machine Learning of Plasma Proteomics Classifies Diagnosis of Interstitial Lung Disease.

RATIONALE: Distinguishing connective tissue disease associated interstitial lung disease (CTD-ILD) from idiopathic pulmonary fibrosis (IPF) can be clinically challenging. OBJECTIVES: Identify proteins that separate and classify CTD-ILD from IPF patients. METHODS: Four registries with 1247 IPF and 352 CTD-ILD patients were included in analyses. Plasma samples were subjected to high-throughput proteomics assays. Protein features were prioritized using Recursive Feature Elimination (RFE) to construct a proteomic classifier. Multiple machine learning models, including Support Vector Machine, LASSO regression, Random Forest (RF), and imbalanced-RF, were trained and tested in independent cohorts. The validated models were used to classify each case iteratively in external datasets. MEASUREMENT AND MAIN RESULTS: A classifier with 37 proteins (PC37) was enriched in biological process of bronchiole development and smooth muscle proliferation, and immune responses. Four machine learning models used PC37 with sex and age score to generate continuous classification values. Receiver-operating-characteristic curve analyses of these scores demonstrated consistent Area-Under-Curve 0.85-0.90 in test cohort, and 0.94-0.96 in the single-sample dataset. Binary classification demonstrated 78.6%-80.4% sensitivity and 76%-84.4% specificity in test cohort, 93.5%-96.1% sensitivity and 69.5%-77.6% specificity in single-sample classification dataset. Composite analysis of all machine learning models confirmed 78.2% (194/248) accuracy in test cohort and 82.9% (208/251) in single-sample classification dataset. CONCLUSIONS: Multiple machine learning models trained with large cohort proteomic datasets consistently distinguished CTD-ILD from IPF. Identified proteins involved in immune pathways. We further developed a novel approach for single sample classification, which could facilitate honing the differential diagnosis of ILD in challenging cases and improve clinical decision-making.

Association study of human leukocyte antigen variants and idiopathic pulmonary fibrosis.

Introduction: Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial pneumonia marked by progressive lung fibrosis and a poor prognosis. Recent studies have highlighted the potential role of infection in the pathogenesis of IPF, and a prior association of the HLA-DQB1 gene with idiopathic fibrotic interstitial pneumonia (including IPF) has been reported. Owing to the important role that the human leukocyte antigen (HLA) region plays in the immune response, here we evaluated if HLA genetic variation was associated specifically with IPF risk. Methods: We performed a meta-analysis of associations of the HLA region with IPF risk in individuals of European ancestry from seven independent case-control studies of IPF (comprising 5159 cases and 27 459 controls, including a prior study of fibrotic interstitial pneumonia). Single nucleotide polymorphisms, classical HLA alleles and amino acids were analysed and signals meeting a region-wide association threshold of p<4.5×10-4 and a posterior probability of replication >90% were considered significant. We sought to replicate the previously reported HLA-DQB1 association in the subset of studies independent of the original report. Results: The meta-analysis of all seven studies identified four significant independent single nucleotide polymorphisms associated with IPF risk. However, none met the posterior probability for replication criterion. The HLA-DQB1 association was not replicated in the independent IPF studies. Conclusion: Variation in the HLA region was not consistently associated with risk in studies of IPF. However, this does not preclude the possibility that other genomic regions linked to the immune response may be involved in the aetiology of IPF.

Genome-wide SNP-sex interaction analysis of susceptibility to idiopathic pulmonary fibrosis.

Background: Idiopathic pulmonary fibrosis (IPF) is a chronic lung condition that is more prevalent in males than females. The reasons for this are not fully understood, with differing environmental exposures due to historically sex-biased occupations, or diagnostic bias, being possible explanations. To date, over 20 independent genetic variants have been identified to be associated with IPF susceptibility, but these have been discovered when combining males and females. Our aim was to test for the presence of sex-specific associations with IPF susceptibility and assess whether there is a need to consider sex-specific effects when evaluating genetic risk in clinical prediction models for IPF. Methods: We performed genome-wide single nucleotide polymorphism (SNP)-by-sex interaction studies of IPF risk in six independent IPF case-control studies and combined them using inverse-variance weighted fixed effect meta-analysis. In total, 4,561 cases (1,280 females and 2,281 males) and 23,500 controls (8,360 females and 14,528 males) of European genetic ancestry were analysed. We used polygenic risk scores (PRS) to assess differences in genetic risk prediction between males and females. Findings: Three independent genetic association signals were identified. All showed a consistent direction of effect across all individual IPF studies and an opposite direction of effect in IPF susceptibility between females and males. None had been previously identified in IPF susceptibility genome-wide association studies (GWAS). The predictive accuracy of the PRSs were similar between males and females, regardless of whether using combined or sex-specific GWAS results. Interpretation: We prioritised three genetic variants whose effect on IPF risk may be modified by sex, however these require further study. We found no evidence that the predictive accuracy of common SNP-based PRSs varies significantly between males and females.

A Deep Learning-Based Radiomic Classifier for Usual Interstitial Pneumonia.

BACKGROUND: Because chest CT scan has largely supplanted surgical lung biopsy for diagnosing most cases of interstitial lung disease (ILD), tools to standardize CT scan interpretation are urgently needed. RESEARCH QUESTION: Does a deep learning (DL)-based classifier for usual interstitial pneumonia (UIP) derived using CT scan features accurately discriminate radiologist-determined visual UIP? STUDY DESIGN AND METHODS: A retrospective cohort study was performed. Chest CT scans acquired in individuals with and without ILD were drawn from a variety of public and private data sources. Using radiologist-determined visual UIP as ground truth, a convolutional neural network was used to learn discrete CT scan features of UIP, with outputs used to predict the likelihood of UIP using a linear support vector machine. Test performance characteristics were assessed in an independent performance cohort and multicenter ILD clinical cohort. Transplant-free survival was compared between UIP classification approaches using the Kaplan-Meier estimator and Cox proportional hazards regression. RESULTS: A total of 2,907 chest CT scans were included in the training (n = 1,934), validation (n = 408), and performance (n = 565) data sets. The prevalence of radiologist-determined visual UIP was 12.4% and 37.1% in the performance and ILD clinical cohorts, respectively. The DL-based UIP classifier predicted visual UIP in the performance cohort with sensitivity and specificity of 93% and 86%, respectively, and in the multicenter ILD clinical cohort with 81% and 77%, respectively. DL-based and visual UIP classification similarly discriminated survival, and outcomes were consistent among cases with positive DL-based UIP classification irrespective of visual classification. INTERPRETATION: A DL-based classifier for UIP demonstrated good test performance across a wide range of UIP prevalence and similarly discriminated survival when compared with radiologist-determined UIP. This automated tool could efficiently screen for UIP in patients undergoing chest CT scan and identify a high-risk phenotype among those with known ILD.

Associations of Plasma Omega-3 Fatty Acids With Progression and Survival in Pulmonary Fibrosis.

BACKGROUND: Preclinical experiments suggest protective effects of omega-3 fatty acids and their metabolites in lung injury and fibrosis. Whether higher intake of omega-3 fatty acids is associated with disease progression and survival in humans with pulmonary fibrosis is unknown. RESEARCH QUESTION: What are the associations of plasma omega-3 fatty acid levels (a validated marker of omega-3 nutritional intake) with disease progression and transplant-free survival in pulmonary fibrosis? STUDY DESIGN AND METHODS: Omega-3 fatty acid levels were measured from plasma samples of patients with clinically diagnosed pulmonary fibrosis from the Pulmonary Fibrosis Foundation Patient Registry (n = 150), University of Virginia (n = 58), and University of Chicago (n = 101) cohorts. The N-3 index (docosahexaenoic acid + eicosapentaenoic acid) was the primary exposure variable of interest. Linear-mixed effects models with random intercept and slope were used to examine associations of plasma omega-3 fatty acid levels with changes in FVC and diffusing capacity for carbon monoxide over a period of 12 months. Cox proportional hazards models were used to examine transplant-free survival. Stratified analyses by telomere length were performed in the University of Chicago cohort. RESULTS: Most of the cohort were patients with idiopathic pulmonary fibrosis (88%) and male patients (74%). One-unit increment in log-transformed N-3 index plasma level was associated with a change in diffusing capacity for carbon monoxide of 1.43 mL/min/mm Hg per 12 months (95% CI, 0.46-2.41) and a hazard ratio for transplant-free survival of 0.44 (95% CI, 0.24-0.83). Cardiovascular disease history, smoking, and antifibrotic usage did not significantly modify associations. Omega-3 fatty acid levels were not significantly associated with changes in FVC. Higher eicosapentaenoic acid plasma levels were associated with longer transplant-free survival among University of Chicago participants with shorter telomere length (P value for interaction = .02). INTERPRETATION: Further research is needed to investigate underlying biological mechanisms and whether omega-3 fatty acids are a potential disease-modifying therapy.

Proteomic Biomarkers of Survival in Idiopathic Pulmonary Fibrosis.

RATIONALE: Idiopathic pulmonary fibrosis (IPF) causes progressive lung scarring and high mortality. Reliable and accurate prognostic biomarkers are urgently needed. OBJECTIVE: To identify and validate circulating protein biomarkers of IPF survival. METHODS: High-throughput proteomic data were generated using prospectively collected plasma samples from patients with IPF from the Pulmonary Fibrosis Foundation Patient Registry (discovery cohort) and the Universities of California-Davis, Chicago, and Virginia (validation cohort). Proteins associated with three-year transplant-free survival (TFS) were identified using multivariable Cox proportional hazards regression. Those associated with TFS after adjustment for false discovery in the discovery cohort were advanced for testing in the validation cohort, with proteins maintaining TFS association with consistent effect direction considered validated. After combining cohorts, functional analyses were performed, and machine learning used to derive a proteomic signature of TFS. MAIN RESULTS: Of 2921 proteins tested in the discovery cohort (n=871), 231 were associated with differential TFS. Of these, 140 maintained TFS association with consistent effect direction in the validation cohort (n=355). After combining cohorts, validated proteins with strongest TFS association were latent-transforming growth factor beta-binding protein 2 (HR 2.43, 95% CI 2.09-2.82), collagen alpha-1(XXIV) chain (HR 2.21; 95% CI 1.86-2.39) and keratin 19 (HR 1.60; 95% CI 1.47-1.74). In decision curve analysis, a proteomic signature of TFS outperformed a similarly derived clinical prediction model. CONCLUSIONS: In largest proteomic investigation of IPF outcomes performed to date, we identified and validated 140 protein biomarkers of TFS. These results shed important light on potential drivers of IPF progression.

Walking the path of treatable traits in interstitial lung diseases.

Interstitial lung diseases (ILDs) are complex and heterogeneous diseases. The use of traditional diagnostic classification in ILD can lead to suboptimal management, which is worsened by not considering the molecular pathways, biological complexity, and disease phenotypes. The identification of specific "treatable traits" in ILDs, which are clinically relevant and modifiable disease characteristics, may improve patient's outcomes. Treatable traits in ILDs may be classified into four different domains (pulmonary, aetiological, comorbidities, and lifestyle), which will facilitate identification of related assessment tools, treatment options, and expected benefits. A multidisciplinary care team model is a potential way to implement a "treatable traits" strategy into clinical practice with the aim of improving patients' outcomes. Multidisciplinary models of care, international registries, and the use of artificial intelligence may facilitate the implementation of the "treatable traits" approach into clinical practice. Prospective studies are needed to test potential therapies for a variety of treatable traits to further advance care of patients with ILD.

Design of a phase III, double-blind, randomised, placebo-controlled trial of BI 1015550 in patients with progressive pulmonary fibrosis (FIBRONEER-ILD).

INTRODUCTION: Progressive pulmonary fibrosis (PPF) includes any diagnosis of progressive fibrotic interstitial lung disease (ILD) other than idiopathic pulmonary fibrosis (IPF). However, disease progression appears comparable between PPF and IPF, suggesting a similar underlying pathology relating to pulmonary fibrosis. Following positive results in a phase II study in IPF, this phase III study will investigate the efficacy and safety of BI 1015550 in patients with PPF (FIBRONEER-ILD). METHODS AND ANALYSIS: In this phase III, double-blind, placebo-controlled trial, patients are being randomised 1:1:1 to receive BI 1015550 (9 mg or 18 mg) or placebo twice daily over at least 52 weeks, stratified by background nintedanib use. Patients must be diagnosed with pulmonary fibrosis other than IPF that is progressive, based on predefined criteria. Patients must have forced vital capacity (FVC) ≥45% predicted and haemoglobin-corrected diffusing capacity of the lung for carbon monoxide ≥25% predicted. Patients must be receiving nintedanib for at least 12 weeks, or not receiving nintedanib for at least 8 weeks, prior to screening. Patients on stable treatment with permitted immunosuppressives (eg, methotrexate, azathioprine) may continue their treatment throughout the trial. Patients with clinically significant airway obstruction or other pulmonary abnormalities, and those using immunosuppressives that may confound FVC results (cyclophosphamide, tocilizumab, mycophenolate, rituximab) or high-dose steroids will be excluded. The primary endpoint is absolute change from baseline in FVC (mL) at week 52. The key secondary endpoint is time to the first occurrence of any acute ILD exacerbation, hospitalisation for respiratory cause or death, over the duration of the trial. ETHICS AND DISSEMINATION: The trial is being carried out in accordance with the ethical principles of the Declaration of Helsinki, the International Council on Harmonisation Guideline for Good Clinical Practice and other local ethics committees. The study results will be disseminated at scientific congresses and in peer-reviewed publications. TRIAL REGISTRATION NUMBER: NCT05321082.

Predicting New-onset Exertional and Resting Hypoxemia in Fibrotic Interstitial Lung Disease.

Rationale: Hypoxemia in fibrotic interstitial lung disease (ILD) indicates disease progression and is of prognostic significance. The onset of hypoxemia signifies disease progression and predicts mortality in fibrotic ILD. Accurately predicting new-onset exertional and resting hypoxemia prompts appropriate patient discussion and timely consideration of home oxygen. Objectives: We derived and externally validated a risk prediction tool for both new-onset exertional and new-onset resting hypoxemia. Methods: This study used ILD registries from Canada for the derivation cohort and from Australia and the United States for the validation cohort. New-onset exertional and resting hypoxemia were defined as nadir oxyhemoglobin saturation < 88% during 6-minute-walk tests, resting oxyhemoglobin saturation < 88%, or the initiation of ambulatory or continuous oxygen. Candidate predictors included patient demographics, ILD subtypes, and pulmonary function. Time-varying Cox regression was used to identify the top-performing prediction model according to Akaike information criterion and clinical usability. Model performance was assessed using Harrell's C-index and goodness-of-fit (GoF) likelihood ratio test. A categorized risk prediction tool was developed. Results: The best-performing prediction model for both new-onset exertional and new-onset resting hypoxemia included age, body mass index, a diagnosis of idiopathic pulmonary fibrosis, and percent predicted forced vital capacity and diffusing capacity of carbon monoxide. The risk prediction tool exhibited good performance for exertional hypoxemia (C-index, 0.70; GoF, P = 0.85) and resting hypoxemia (C-index, 0.77; GoF, P = 0.27) in the derivation cohort, with similar performance in the validation cohort except calibration for resting hypoxemia (GoF, P = 0.001). Conclusions: This clinically applicable risk prediction tool predicted new-onset exertional and resting hypoxemia at 6 months in the derivation cohort and a diverse validation cohort. Suboptimal GoF in the validation cohort likely reflected overestimation of hypoxemia risk and indicated that the model is not flawed because of underestimation of hypoxemia.

Lung function trajectories in patients with idiopathic pulmonary fibrosis.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive fibrosing interstitial lung disease characterised by decline in lung function. We evaluated trajectories of forced vital capacity (FVC) and diffusing capacity (DLco) in a cohort of patients with IPF. METHODS: Patients with IPF that was diagnosed or confirmed at the enrolling centre in the previous 6 months were enrolled into the IPF-PRO Registry between June 2014 and October 2018. Patients were followed prospectively, with lung function data collected as part of routine clinical care. Mean trajectories of FVC and DLco % predicted in all patients and in subgroups by characteristics assessed at enrolment were estimated using a joint model that accounted for factors such as disease severity and visit patterns. RESULTS: Of 1002 patients in the registry, 941 had ≥ 1 FVC and/or DLco measurement after enrolment. The median (Q1, Q3) follow-up period was 35.1 (18.9, 47.2) months. Overall, mean estimated declines in FVC and DLco % predicted were 2.8% and 2.9% per year, respectively. There was no evidence that the mean trajectories of FVC or DLco had a non-linear relationship with time at the population level. Patients who were male, white, had a family history of ILD, were using oxygen, or had prior/current use of antifibrotic therapy at enrolment had greater rates of decline in FVC % predicted. Patients who were male or white had greater rates of decline in DLco % predicted. CONCLUSIONS: Data from the IPF-PRO Registry suggest a constant rate of decline in lung function over a prolonged period, supporting the inexorably progressive nature of IPF. A graphical abstract summarising the data in this manuscript is available at: https://www.usscicomms.com/respiratory/IPF-PRORegistry_LungFunctionTrajectories . TRIAL REGISTRATION: NCT01915511.