ILD cohort

Pulmonary fibrosis (PF) is a general end-stage pathway activated in different forms of ILD, including Idiopathic/Familial Pulmonary Fibrosis (I/FPF), chronic (fibrotic) Hypersensitivity Pneumonitis (cHP), idiopathic non-specific interstitial pneumonia (iNSIP), Connective Tissue Related-ILD (CTD-ILD), and unclassifiable ILD (uILD). PF is characterized by the excessive deposition of extracellular matrix (ECM) in the lungs by activated fibroblasts, leading to irreversible lung function decline, respiratory failure, and eventually death. Patients suffering from progressive pulmonary fibrosis (PPF) might benefit from anti-inflammatory and/or antifibrotic therapy at certain disease stages, which can slow down lung function decline [1,2]. Currently the definition of PPF is based on changes in clinical parameters over time, meaning that at time of diagnosis and initiating therapy loss of pulmonary function due to irreversible fibrosis already has occurred. Consequently, the right (early) timing to start (antifibrotic) treatment is subject of ongoing debate. Early detection of PPF is therefore of utmost importance. More accurate distinction of progressive and non-progressive fibrosis patients and prediction of treatment response is essential for clinical diagnostics, treatment strategies and prognosis.

Next to this, it is important to gain a better understanding of which patients are at risk of developing PPF. Patients with Interstitial Lung Abnormalities (ILAs) can serve as a model for this. ILAs are increasingly recognized as a common finding on Computed Tomography (CT) of the lungs, with an incidence of 4-9% in elderly smokers and 2-7% in non-smokers [3]. ILAs are considered radiologic abnormalities that are characteristic for early-stage ILD, detected without clinical suspicion or symptoms of an ILD. ILAs have been associated with decreased lung capacity [4,5], decreased exercise capacity [6], impaired gas exchange [7,8], genetic variations among FPF and IPF [8,9] and increased all-cause mortality [10]. However, it is unknown which and when ILAs progress to clinically relevant ILD with PPF.

The P4O2 consortium aims to conduct a prospective, non-interventional observational cohort study to extensively phenotype a large cohort of ILD patients across three arms, at both clinical and pathophysiological levels:

  • Arm 1: IPF/FPF group (n=150).
  • Arm 2: Fibrotic ILD group (fILD) at risk for the development of PPF, including cHP, iNSIP, CTD-ILD and uILD (n=150)
  • Arm 3: ILA group (n=150).

General Objectives:

  1. Identify biomarkers and risk factors that associate with fibrosis progression or predict transformation into a rapid progressive (fibrosing) phenotype and acute exacerbations in IPF and FPF patients.
  2. Identify biomarkers and risk factors that predict the development of a PPF phenotype among different forms of fILD.
  3. Define biomarkers and risk factors for the development of clinically significant fILD among ILA patients.
  4. Identify biomarkers and define risk factors that might predict treatment response within fILD (secondary objective).

Methodology:
To address these general objectives we will instigate the following specific work-packages:

Ad 1: Identify biomarkers and risk factors that associate with fibrosis progression or predict transformation into a rapid progressive (fibrosing) phenotype and acute exacerbations in IPF and FPF patients. – We will recruit 150 patients diagnosed with IPF/FPF for the study and collect clinical data like pulmonary function test measurements, high-resolution (HR) CT-thorax, questionnaires and environmental exposures. Moreover, we will collect plasma, serum and urine samples at baseline and follow-up visits. Immunological bronchoalveolar lavage fluid (BALF) and lung tissue samples will be collected only when needed in the context of clinical care. Subsequently, a pre-defined panel of biomarkers related to pulmonary fibrosis will be measured in plasma, BALF and urine [11,12].

Primary endpoint: Biomarker levels will be correlated with time to event (i.e. rapid progression) to identify biomarkers that predict rapid progression defined by a disease course of less than 2 years [13].

Secondary endpoint analysis will be the correlation of biomarker concentrations over time in relation to time to rapid progression. To this end, we will apply competing risk joint models and landmark analyses on repeated biomarker measurements correlated to rapid or slow progression.

 

Ad 2: Identify biomarkers and risk factors that predict the development of a PPF phenotype among different forms of fILD. – We will recruit 150 fILD who  are at risk for the development of PPF (cHP, iNSIP, CTD-ILD and uILD) for the study. Clinical data, including pulmonary function test measurements, HRCT scans, questionnaires, and environmental exposures, will be collected. Moreover, we will collect plasma and serum and urine samples at baseline and follow-up visits. We will perform a bronchoscopic bronchoalveolar lavage (BAL) procedure on 30 participants to collect the supernatant for biomarker measurements and the cell fraction after 1 year follow-up. Lung tissue samples will be collected only when needed in the context of clinical care. A pre-defined panel of biomarkers potentially related to pulmonary fibrosis will be measured in plasma, BALF, and urine. In addition, plasma samples from all patients at baseline will be analyzed using the Explore 3072 Olink platform, which is high-throughput proteomics platform assay based on next-generation sequencing techniques, that determines the relative protein expression of 3072 different proteins in a single sample (for details, see www.olink.com).

Primary endpoint: Biomarker levels from both the predefined panel and the Explore 3072 panel will be correlated with the time to event (i.e., development of progressive fibrosis, as defined in table 2) to identify biomarkers associated with the development of progressive fibrosis.

Again, secondary endpoint analysis will be the correlation of biomarker concentrations over time (pre-defined panel only, but including significant candidates obtained at baseline from the Explore 3072 assay) in relation to time to the development of progressive fibrosis. Joint models for competing risks and landmark analyses on repeated biomarker measurements in relation to progressive fibrosis will be performed.

Ad 3: Define biomarkers and risk factors for the development of clinically significant fILD among ILA patients. – We will recruit 150 ILA patients for the study and collect clinical data, including pulmonary function measurements, HRCT scans, questionnaires, and environmental exposures. Plasma, serum, and urine samples will be collected at baseline and follow-up visits. A pre-defined panel of biomarkers potentially related to pulmonary fibrosis, including interesting candidates obtained in arm 1 and 2, will be measured in plasma and urine.

Primary endpoint: Biomarker levels will be correlated with the time to event (i.e., development of a clinically relevant ILD) to identify biomarkers that predict the development of an ILD.

For this arm, secondary endpoint analysis will be the correlation of biomarker concentrations over time in relation to time to the development of progressive fibrosis. Joint models for competing risks and landmark analyses on repeated biomarker measurements in relation to the development of a clinically relevant ILD will be performed.

 

For this arm, we will perform a bronchoscopic bronchoalveolar lavage (BAL) procedure on 30 ILA patients to collect the supernatant for biomarker measurements and the cell fraction for further analysis of the different immune cells present. This group of patients will be matched with respect to age and BMI. The expression levels of the predefined panel of biomarkers will be determined in BALF of these ILA patients and analyzed as described above for blood biomarker levels.

P4O2 parameters:

In all three arms of this cohort, next to biomarker analysis, we will collect the following samples and measurements (as listed below). These measurements will allow similar analyses as described above to find factors that predict either rapid progression in IPF/FPF, the development of  PPF in fILD, or the development of a clinically relevant ILD in ILA patients. These measurements align with the other P4O2 cohorts, including the PARASOL and COPD cohorts which will allow for data analysis over the different cohorts. Phenotyping and follow-up of this cohort, in comparison with the PARASOL cohort, may provide even more clinical and pathophysiological insight into risk factors for progression of ILAs to clinically relevant ILD, the development of progressive fibrosis within fILDs, or rapid progression versus slow progression in IPF/FPF. The additional measurements include:

  • Exhaled breath analysis including eNose measurement, PExA analysis, and volatile organic compounds (VOCs) analysis by gas chromatography-mass spectrometry (GC-MS). VOCs have the potential to mirror various metabolic processes locally within the respiratory system and systemically through the blood circulation. VOCs have been utilized as diagnostic, prognostic, and treatment response biomarkers for various respiratory illnesses, including ILD.
  • Peripheral Blood Mononuclear Cell (PBMC) populations in blood.
  • HRCT-scan analyses; Novel imaging analyses techniques may be used to stratify the severity of lung involvement and predict outcomes (in kind contribution by imaging partners of P4O2).
  • External exposome analyses; This includes lifestyle, dietary information (food diaries), and the evaluation of the physical/chemical environment. Previous studies have shown that built environment characteristics, such as ambient air pollution, may be a risk factors for ILD (optional: additional funding will be requested).
  • Metabolome analyses in urine (optional: additional funding will be requested).
  • Microbiome analyses in stool and nasal swabs. The gut and respiratory microbiome are important for effective immune response regulation. Analyzing the microbiome in stool and nasal swabs can provide insights into the microbial composition and its potential relationship to ILD. (optional: additional funding will be requested).
  • Genomics, epigenomics and transcriptome analysis in blood (optional: additional funding will be requested).

Feasibility:

The Amsterdam UMC center of expertise for ILD has established an extensive regional and national network to foster collaboration within the field of ILD. This network is well-defined and consists of linked community hospitals, aimed at improving referral patterns and shared care for patients with ILD in the Netherlands. Multidisciplinary meetings between the community hospitals and the Amsterdam UMC are organized on a regular basis to discuss patient cases. Next to this, the Amsterdam UMC has (research) collaborations with other large ILD centers within The Netherlands of which two other centers of expertise will be inclusion sites (Erasmus MC Rotterdam and St. Antonius hospital Nieuwegein). These centers of expertise play an active role in the ILD working group of the NVALT (Dutch Society of Pulmonologist). These networks and collaborations not only enhance patient-centered care but also facilitate research and ensure patient recruitment.

The recruitment of patients for the different arms within the cohort will be conducted as follows: 

    • Arm 1: IPF/FPF; Amsterdam UMC in collaboration with the national ILD network.
    • Arm 2: Fibrotic ILDs (fILD) at risk for the development of PPF (cHP, iNSIP, CTD-ILD and uILD) (n=150); Amsterdam UMC in collaboration with the national ILD network.
    • Arm 3: ILA; Amsterdam UMC in collaboration with the national ILD network, individuals that meet the criteria found in the PARASOL (healthy control) cohort within P4O2, individuals that meet the criteria found in the national lung cancer screening protocol, and in collaboration with the national radiology network.

Timeline P4O2. Time in months. Made with BioRender. Definition of abbreviations: BAL= bronchoalveolar lavage, HRCT= high-resolution computed tomography, ILA= interstitial lung abnormality, PBMC= peripheral blood mononuclear cells, VOC= volatile organic compound. 

 

Extra measurements and sample collections will be performed at clinical relevant events. Clinical relevant events are defined as follows:

  1. Additional diagnostic measurements e.g., bronchoscopic immunological BAL and/or bronchoscopic lung cryobiopsy/surgical lung biopsy.
  2. Rapid progression of disease outside the fixed time points.
  3. Treatment switch.
  4. Lung transplantation.
  5. Acute exacerbation.
  6. Pulmonary hypertension development.

Literature:

      1. Flaherty KR, Wells AU, Cottin V, et al. Nintedanib in Progressive Fibrosing Interstitial Lung Diseases. N Engl J Med. 2019;381:1718-1727.
      2. Behr J, Prasse A, Kreuter M, et al. Pirfenidone in patients with progressive fibrotic interstitial lung diseases other than idiopathic pulmonary fibrosis (RELIEF): a double-blind, randomised, placebo-controlled, phase 2b trial. Lancet Respir Med. 2021;S2213-2600:30554-3. Online ahead of print.
      3. Hatabu H, Hunninghake GM, Richeldi L, Brown KK, Wells AU et al. Interstitial lung abnormalities detected incidentally on CT: a Position Paper from the Fleischner Society Lancet Respir Med. 2020;8(7):726-737.
      4. Lederer DJ, Enright PL, Kawut SM, et al. Cigarette smoking is associated with subclinical parenchymal lung disease: the Multi-Ethnic Study of Atherosclerosis (MESA)-lung study. Am J Respir Crit Care Med. 2009; 180:407–414.
      5. Washko GR, Hunninghake GM, Fernandez IE, et al. Lung volumes and emphysema in smokers with interstitial lung abnormalities. N Engl J Med. 2011; 364:897–906.
      6. Doyle TJ, Washko GR, Fernandez IE, et al. Interstitial Lung Abnormalities and Reduced Exercise Capacity. Am J Respir Crit Care Med. 2012; 185(7):756–762.
      7. Tsushima K, Sone S, Yoshikawa S, Yokoyama T, Suzuki T, Kubo K. The radiological patterns of interstitial change at an early phase: over a 4-year follow-up. Respir Med. 2010; 104:1712–1721.
      8. Hunninghake GM, Hatabu H, Okajima Y, et al. MUC5B promoter polymorphism and interstitial lung abnormalities. N Engl J Med. 2013; 368:2192–2200.
      9. Kropski JA, Pritchett JM, Zoz DF, et al. Extensive Phenotyping of Individuals At-risk for Familial Interstitial Pneumonia Reveals Clues to the Pathogenesis of Interstitial Lung Disease. Am J Respir Crit Care Med. 2015; 191(4):417–426.
      10. Putman RK, Hatabu H, Araki T, et al. Association Between Interstitial Lung Abnormalities and All-Cause Mortality. JAMA. 2016; 315(7):672-81.
      11. Wu J, Li X, Zhao M, et al. Early Detection of Urinary Proteome Biomarkers for Effective Early Treatment of Pulmonary Fibrosis in a Rat Model. Proteomics Clin Appl. 2017; 11(11-12).
      12. M. Decaris, M. Rexhepaj, J. Vowinckel, et al. Urine Proteomics Identifies Novel Biomarkers of IPF Disease Progression and Resolution. American Thoracic Society International Conference Abstracts. 2019; A29. EMERGING CONCEPTS IN LUNG FIBROSIS.
      13. Kärkkäinen M, Kettunen HP, Nurmi H, Selander T, Purokivi M, Kaarteenaho R. Comparison of disease progression subgroups in idiopathic pulmonary fibrosis. BMC Pulm Med. 2019; 19(1):228.
      14. Jessen H, Hoyer N, Prior TS, et al. Turnover of type I and III collagen predicts progression of idiopathic pulmonary fibrosis. Respir Res. 2021; 22(1):205.
      15. Stainer A, Faverio P, Busnelli S, et al. Molecular Biomarkers in Idiopathic Pulmonary Fibrosis: State of the Art and Future Directions. Int J Mol Sci. 2021; 22(12):6255.
      16. Lagoutte P, Bettler E, Vadon-Le Goff S, Moali C. Procollagen C-proteinase enhancer-1 (PCPE-1), a potential biomarker and therapeutic target for fibrosis. Matrix Biol Plus. 2021; 11:100062.
      17. Majewski S, Szewczyk K, ┼╗al A, et al. Serial Measurements of Circulating KL-6, SP-D, MMP-7, CA19-9, CA-125, CCL18, and Periostin in Patients with Idiopathic Pulmonary Fibrosis Receiving Antifibrotic Therapy: An Exploratory Study. J Clin Med. 2021 Aug 28;10(17):3864.
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