Identification of a unique temporal signature in blood and BAL associated with IPF progression

Proteomic profiling of bronchoalveolar lavage fluid uncovers protein clusters linked to survival in idiopathic forms of interstitial lung disease

Background

Idiopathic interstitial pneumonias (IIPs) such as idiopathic pulmonary fibrosis (IPF) and interstitial pneumonia with autoimmune features (IPAF), present diagnostic and therapeutic challenges due to their heterogeneous nature. This study aimed to identify intrinsic molecular signatures within the lung microenvironment of these IIPs through proteomic analysis of bronchoalveolar lavage fluid (BALF).

Methods

Patients with IIP (n=23) underwent comprehensive clinical evaluation including pre-treatment bronchoscopy and were compared to controls without lung disease (n=5). Proteomic profiling of BALF was conducted using label-free quantitative methods. Unsupervised cluster analyses identified protein expression profiles which were then analyzed to predict survival outcomes and investigate associated pathways.

Results

Proteomic profiling successfully differentiated IIP from controls. k-means clustering, based on protein expression revealed three distinct IIP clusters, which were not associated with age, smoking history, or baseline pulmonary function. These clusters had unique survival trajectories and provided more accurate survival predictions than the Gender Age Physiology (GAP) index (C-index 0.794 vs. 0.709). The cluster with the worst prognosis featured decreased inflammatory signaling and complement activation, with pathway analysis highlighting altered immune response pathways related to immunoglobulin production and B cell-mediated immunity.

Conclusions

The unsupervised clustering of BALF proteomics provided a novel stratification of IIP patients, with potential implications for prognostic and therapeutic targeting. The identified molecular phenotypes underscore the diversity within the IIP classification and the potential importance of personalized treatments for these conditions. Future validation in larger, multi-ethnic cohorts is essential to confirm these findings and to explore their utility in clinical decision-making for patients with IIP.

Idiopathic pulmonary fibrosis-specific Bayesian network integrating extracellular vesicle proteome and clinical information

Idiopathic pulmonary fibrosis (IPF) is a progressive disease characterized by severe lung fibrosis and a poor prognosis. Although the biomolecules related to IPF have been extensively studied, molecular mechanisms of the pathogenesis and their association with serum biomarkers and clinical findings have not been fully elucidated. We constructed a Bayesian network using multimodal data consisting of a proteome dataset from serum extracellular vesicles, laboratory examinations, and clinical findings from 206 patients with IPF and 36 controls. Differential protein expression analysis was also performed by edgeR and incorporated into the constructed network. We have successfully visualized the relationship between biomolecules and clinical findings with this approach. The IPF-specific network included modules associated with TGF-β signaling (TGFB1 and LRC32), fibrosis-related (A2MG and PZP), myofibroblast and inflammation (LRP1 and ITIH4), complement-related (SAA1 and SAA2), as well as serum markers, and clinical symptoms (KL-6, SP-D and fine crackles). Notably, it identified SAA2 associated with lymphocyte counts and PSPB connected with the serum markers KL-6 and SP-D, along with fine crackles as clinical manifestations. These results contribute to the elucidation of the pathogenesis of IPF and potential therapeutic targets.

A blood and bronchoalveolar lavage protein signature of rapid FEV1 decline in smoking-associated COPD

Accelerated progression of chronic obstructive pulmonary disease (COPD) is associated with increased risks of hospitalization and death. Prognostic insights into mechanisms and markers of progression could facilitate development of disease-modifying therapies. Although individual biomarkers exhibit some predictive value, performance is modest and their univariate nature limits network-level insights. To overcome these limitations and gain insights into early pathways associated with rapid progression, we measured 1305 peripheral blood and 48 bronchoalveolar lavage proteins in individuals with COPD [n = 45, mean initial forced expiratory volume in one second (FEV1) 75.6 ± 17.4% predicted]. We applied a data-driven analysis pipeline, which enabled identification of protein signatures that predicted individuals at-risk for accelerated lung function decline (FEV1 decline ≥ 70 mL/year) ~ 6 years later, with high accuracy. Progression signatures suggested that early dysregulation in elements of the complement cascade is associated with accelerated decline. Our results propose potential biomarkers and early aberrant signaling mechanisms driving rapid progression in COPD.

Idiopathic pulmonary fibrosis therapy development: a clinical pharmacology perspective

Drug development for idiopathic pulmonary fibrosis (IPF) has been challenging due to poorly understood disease etiology, unpredictable disease progression, highly heterogeneous patient populations, and a lack of robust pharmacodynamic biomarkers. Moreover, because lung biopsy is invasive and dangerous, making the extent of fibrosis as a direct longitudinal measurement of IPF disease progression unfeasible, most clinical trials studying IPF can only assess progression of fibrosis indirectly through surrogate measures. This review discusses current state-of-art practices, identifies knowledge gaps, and brainstorms development opportunities for preclinical to clinical translation, clinical populations, pharmacodynamic endpoints, and dose optimization strategies. This article highlights clinical pharmacology perspectives in leveraging real-world data as well as modeling and simulation, special population considerations, and patient-centric approaches for designing future studies.

Proteomic profiling of serum identifies a molecular signature that correlates with clinical outcomes in COPD

OBJECTIVE

Novel biomarkers related to main clinical hallmarks of Chronic obstructive pulmonary disease (COPD), a heterogeneous disorder with pulmonary and extra-pulmonary manifestations, were investigated by profiling the serum levels of 1305 proteins using Slow Off-rate Modified Aptamers (SOMA)scan technology.

METHODS

Serum samples were collected from 241 COPD subjects in the multicenter French Cohort of Bronchial obstruction and Asthma to measure the expression of 1305 proteins using SOMAscan proteomic platform. Clustering of the proteomics was applied to identify disease subtypes and their functional annotation and association with key clinical parameters were examined. Cluster findings were revalidated during a follow-up visit, and compared to those obtained in a group of 47 COPD patients included in the Melbourne Longitudinal COPD Cohort.

RESULTS

Unsupervised clustering identified two clusters within COPD subjects at inclusion. Cluster 1 showed elevated levels of factors contributing to tissue injury, whereas Cluster 2 had higher expression of proteins associated with enhanced immunity and host defense, cell fate, remodeling and repair and altered metabolism/mitochondrial functions. Patients in Cluster 2 had a lower incidence of exacerbations, unscheduled medical visits and prevalence of emphysema and diabetes. These protein expression patterns were conserved during a follow-up second visit, and substanciated, by a large part, in a limited series of COPD patients. Further analyses identified a signature of 15 proteins that accurately differentiated the two COPD clusters at the 2 visits.

CONCLUSIONS

This study provides insights into COPD heterogeneity and suggests that overexpression of factors involved in lung immunity/host defense, cell fate/repair/ remodelling and mitochondrial/metabolic activities contribute to better clinical outcomes. Hence, high throughput proteomic assay offers a powerful tool for identifying COPD endotypes and facilitating targeted therapies.

Prognostic value of tripartite motif (TRIM) family gene signature from bronchoalveolar lavage cells in idiopathic pulmonary fibrosis

BACKGROUND

Tripartite motif (TRIM) family genes get involved in the pathogenesis and development of various biological processes; however, the prognostic value of TRIM genes for idiopathic pulmonary fibrosis (IPF) needs to be explored.

METHODS

We acquired gene expression based on bronchoalveolar lavage (BAL) cells and clinical data of three independent IPF cohorts in the GSE70866 dataset from the Gene expression omnibus (GEO) database. Differentially expressed TRIM genes (DETGs) between IPF patients and healthy donors were identified and used to establish a risk signature by univariate and multivariate Cox regression analysis in the training cohort. The risk signature was further validated in other IPF cohorts, and compared with previously published signatures. Moreover, we performed functional enrichment analysis to explore the potential mechanisms. Eventually, the quantitative real time PCR was conducted to validate the expressions of the key genes in BAL from 12 IPF patients and 12 non-IPF controls from our institution.

RESULTS

We identified 4 DETGs including TRIM7, MEFV, TRIM45 and TRIM47 significantly associated with overall survival (OS) of IPF patients (P < 0.05). A multiple stepwise Cox regression analysis was performed to construct a 4-TRIM-gene prognostic signature. We categorized IPF patients into one low-risk group and the other high-risk group as per the average risk value of the TRIM prognostic signature in the training and validation cohorts. The IPF individuals in the low-risk group demonstrated an obvious OS advantage compared with the high-risk one (P < 0.01). The time-dependent receiver operating characteristic approach facilitated the verification of the predictive value of the TRIM prognostic signature in the training and validation cohorts, compared with other published signatures. A further investigation of immune cells and IPF survival displayed that higher proportion of resting memory CD4+ T cells and resting mast cells harbored OS advantage over lower proportion, however lower proportion of neutrophils, activated dendritic cells and activated NK cells indicated worse prognosis.

CONCLUSION

The TRIM family genes are significant for the prognosis of IPF and our signature could serve as a robust model to predict OS.

Reticulation pattern without honeycombing on high-resolution CT is associated with the risk of disease progression in interstitial lung diseases

Background

The disease course of idiopathic pulmonary fibrosis (IPF) is progressive and occasionally, other types of interstitial lung disease (ILD) may progress similarly to IPF. This study aimed to evaluate risk factors for disease progression within 24 months in patients with various ILDs.

Methods

This prospective study obtained 97 patients with a suspected ILD who underwent a transbronchial lung cryobiopsy. The extent of several high-resolution computed tomography (HRCT) patterns was assessed. Due to the inclusion criteria the study population presented a low extent of honeycombing and definite usual interstitial pneumonia (UIP) pattern on HRCT suggesting an early stage of ILD. Disease progression within 24 months despite treatment was defined as a relative decline of ≥ 10% in forced vital capacity (FVC), or a relative decline in FVC of ≥ 5% and one of the three additional criteria: (1) a decline in diffusion capacity to carbon monoxide (DLCO) ≥ 15%; (2) increased fibrosis on HRCT; (3) progressive symptoms, or progressive symptoms and increased fibrosis on HRCT. The same definition was utilized in patients with IPF and other ILDs. Risk factors for disease progression were evaluated in a multivariable logistic regression model.

Results

Disease progression was revealed in 52% of the patients with ILD, 51% of the patients with IPF, and 53% of the patients with other types of ILD. A high extent of reticulation on HRCT (Odds ratio [OR] 3.11, 95% Confidence interval [CI] 1.21–7.98, P = 0.019) and never smoking (OR 3.11, CI 1.12–8.63, P = 0.029) were associated with disease progression whereas platelet count (OR 2.06 per 100 units increase, CI 0.96–4.45, P = 0.065) did not quite reach statistical significance.

Conclusion

Higher extent of reticulation on HRCT and never smoking appeared to associate with the risk of disease progression within 24 months in ILD patients without honeycombing. Approximately half of the patients with ILD revealed disease progression, and similar proportions were observed in patients with IPF and in other types of ILD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-02105-9.

Association of Circulating Proteins with Death or Lung Transplant in Patients with Idiopathic Pulmonary Fibrosis in the IPF-PRO Registry Cohort

Idiopathic pulmonary fibrosis (IPF) is a progressive and ultimately fatal disease with a variable clinical course. Biomarkers that predict patient outcomes are needed. We leveraged data from 300 patients in the multicenter IPF-PRO Registry to determine associations between circulating proteins and the composite outcome of respiratory death or lung transplant. Plasma collected at enrollment was analyzed using aptamer-based proteomics (1305 proteins). Over a median follow-up of 30.4 months, there were 76 respiratory deaths and 26 lung transplants. In unadjusted univariable analyses, 61 proteins were significantly associated with the outcome (hazard ratio > 2 or < 0.5, corrected p ≤ 0.05). In multivariable analyses, a set of 4 clinical measures and 47 unique proteins predicted the probability of respiratory death or lung transplant with an optimism-corrected C-index of 0.76. Our results suggest that select circulating proteins strongly associate with the risk of mortality in patients with IPF and confer information independent of clinical measures.

ILRC: a hybrid biomarker discovery algorithm based on improved L1 regularization and clustering in microarray data

BACKGROUND

Finding significant genes or proteins from gene chip data for disease diagnosis and drug development is an important task. However, the challenge comes from the curse of the data dimension. It is of great significance to use machine learning methods to find important features from the data and build an accurate classification model.

RESULTS

The proposed method has proved superior to the published advanced hybrid feature selection method and traditional feature selection method on different public microarray data sets. In addition, the biomarkers selected using our method show a match to those provided by the cooperative hospital in a set of clinical cleft lip and palate data.

METHOD

In this paper, a feature selection algorithm ILRC based on clustering and improved L1 regularization is proposed. The features are firstly clustered, and the redundant features in the sub-clusters are deleted. Then all the remaining features are iteratively evaluated using ILR. The final result is given according to the cumulative weight reordering.

CONCLUSION

The proposed method can effectively remove redundant features. The algorithm's output has high stability and classification accuracy, which can potentially select potential biomarkers.

Biomarkers in Progressive Fibrosing Interstitial Lung Disease: Optimizing Diagnosis, Prognosis, and Treatment Response

Interstitial lung disease (ILD) comprises a heterogenous group of diffuse lung disorders that commonly result in irreversible pulmonary fibrosis. While idiopathic pulmonary fibrosis (IPF) is the prototypical progressive fibrosing ILD (PF-ILD), a high proportion of patients with other ILD subtypes develop a PF-ILD phenotype. Evidence exists for shared pathobiology leading to progressive fibrosis, suggesting that biomarkers of disease activity may prove informative across the wide spectrum of ILDs. Biomarker investigation to date has identified a number of molecular markers that predict relevant ILD endpoints, including disease presence, prognosis, and/or treatment response. In this review, we provide an overview of potentially informative biomarkers in patients with ILD, including those suggestive of a PF-ILD phenotype. We highlight the recent genomic, transcriptomic, and proteomic investigations that identified these biomarkers and discuss the body compartments in which they are found, including the peripheral blood, airway, and lung parenchyma. Finally, we identify critical gaps in knowledge within the field of ILD biomarker research and propose steps to advance the field toward biomarker implementation.