Early Recognition of Progressive Lung Fibrosis in Systemic Rheumatic Diseases: a Characterization of the Pulmonary Environment Through Extracellular Vesicles, Advanced and Functional Imaging
Early Recognition of Progressive Lung Fibrosis in Systemic Rheumatic Diseases: a Characterization of the Pulmonary Environment Through Extracellular Vesicles, Advanced and Functional Imaging
Connective tissue diseases (CTDs) cover a broad range of systemic rheumatic disorders characterized by abnormal immune activation, chronic inflammatory response, and fibrosis of internal organs. The most prevalent is interstitial lung disease (ILD), a severe pulmonary complication seen in 10 to 50% of CTDs and a major determinant of disability and death. Prevalence and clinical course of CTD-ILDs vary widely and seem to be independent of treatment. Current screening and prognosis prediction strategies based on clinical variables and auto-antibodies are inadequate, and disease biomarkers are lacking. The research project aims to identify biomarkers of ILD involvement in CTD patients by characterizing the proteome and transcriptome of extracellular vesicles (EVs) isolated from serum. This will be integrated with high-resolution computed tomography (HRCT) using artificial intelligence (AI)-based imaging assessment. These novel biomarkers are expected to address some current limitations of standard laboratory biomarkers and conventional HRCT imaging.
The investigator will involve a total of 200 CTD patients divided into two equal groups: those with ILD and those without. Serum EVs will be extracted from patient sera and characterized based on proteome and transcriptome content using mass spectrometry analysis and next-generation RNA-sequencing. The investigator will compare CTD patients with and without ILD, and progressive and non-progressive ILD patients according to OMERACT (Outcome Measures in Rheumatology) initiative criteria during a 12-month follow-up. HRCT features analyzed by a commercially available deep learning AI software will also be compared among CTD-ILD patients based on the occurrence of progression during follow-up.
An advanced approach combining EVs analysis in serum and AI algorithms of HRCT images, and functional fibrosis assessment in vivo, could enhance our understanding of CTD-ILDs pathogenesis.
The proposal aims to investigate for the first time the EVs proteomic and transcriptomic profile in serum of patients with CTDs to identify possible biomarkers of lung involvement. The integration of circulating EVs biomarkers with clinical phenotype and with advanced imaging technologies will provide novel diagnostic algorithms that early identify patients with lung involvement in CTD and patients at risk of pulmonary progression.
Background and rationale. Connective tissue diseases (CTDs) cover a broad range of systemic rheumatic disorders characterized by abnormal immune activation, chronic inflammatory response, and fibrosis of internal organs. The most prevalent is interstitial lung disease (ILD), a severe pulmonary complication seen in 10 to 50% of CTDs and a major determinant of disability and death. Prevalence and clinical course of CTD-ILDs vary widely and seem to be independent of treatment. Current screening and prognosis prediction strategies based on clinical variables and auto-antibodies are inadequate, and disease biomarkers are lacking. Extracellular vesicles (EVs) are lipid bilayer-bound particles secreted by most living cells and found in all body fluid. EVs have been proposed as biomarkers, therapy targets, or carriers due to unique properties including the possibility to be associated with producing cells and reflect their biology or cross biological barriers. The proteomic and transcriptomic analysis of EVs represents a very challenging procedure because EVs can circulate and cross biological barriers to reach specific target organs allowing a continuous cross-talk between serum and specific biologic niches, like lungs. Some promising data on EVs are available in idiopathic pulmonary fibrosis (IPF) and other chronic lung diseases, but data about their characteristics in CTD-ILD is still lacking.High-resolution computed tomography (HRCT) is the standard diagnostic tool in lung fibrosis, although interpreting the biological meaning of some abnormalities is often challenging. Inflammatory changes, metabolically active fibrosis, or established fibrosis could be difficult or impossible to distinguish. The use of artificial intelligence (AI) and functional chest imaging can overcome the intrinsic limitations of conventional imaging. AI-mediated evaluation of HRCT has the potential to quantitatively characterize lung texture, airways, and vessels, offering a non-invasive approach to comprehensively characterize pulmonary anatomy. Currently, accurate biological and clinical biomarkers for the early diagnosis and prognostic outcomes of CTD-ILDs are absent, posing a significant clinical challenge in managing these patients, including in therapeutic decision-making.In this scenario, our study will combine baseline serum EV-derived biomarkers and automated evaluation of HRCT scans via deep-learning AI, to characterize patients with CTD at high risk to develop ILD and patients that present a functional progression during 12 months of follow-up.
Objectives. The primary objective of this study is to compare the proteomic and transcriptomic profiles of serum EVs in CTD patients with and without ILD.
The secondary objective of this study is to compare the baseline proteomic and transcriptomic profiles of serum EVs in CTD-ILD and AI-detected difference in HRCT features between progressive and no progressive ILD patients at the 12-month follow-up.
Endpoints. The primary endpoint will be the differences in serum EV single-protein quantity and RNA expression between CTD patients with and without ILD, expressed as fold change.
The secondary endpoints will be the differences in serum EV single-protein quantity and RNA expression between progressive and non-progressive CTD-ILD patients, expressed as fold change, and AI-collected HRCT quantitative measures. The latter will include total lung volume, lung texture analysis (percentage of ground-glass opacities, reticulation, consolidation, and honeycombing), airways analysis (volume and wall thickness), and vessel analysis (vascular volume and vessel density).
Study design. Multicentric interventional without drug study with a longitudinal follow-up
Population. 200 consecutive CTD patients will be enrolled, divided into two equal groups of subjects with or without ILD. Based on the expected specific CTD and CTD-ILD prevalences in the general population, each group will include 60 patients with SSc, 50 patients with RA, 40 patients with SS, 20 patients with IIM, and 30 patients with UCTD, equally distributed between the two groups.bWe will select patients with CTDs that have a high risk of ILD presence and progression based on their autoantibody profile. ILD status will be determined by HRCT evaluation performed within 6 weeks from the enrolment.
Target population. Patients with connective tissue diseases at high risk of interstial lung disease
Inclusion criteria. Female and male aged between 18 and 75 years. Signature of informed consent. A clinical diagnosis of SSc, RA, SS, IIM, or UCTD that must adhere to internationally accepted classification criteria A high risk of ILD based on autoantibody profile, specifically: anti-Scl70+ or anti-RNAPIII+ for SSc, anti-CCP+ and/or RF+ for RA, anti-RoSSA+ and anti-LaSSB+ for primary SS, anti-synthetase+ for IIM. For UCTD patients, the enrollment criteria will be adapted to match those of Interstitial Pneumonia with Autoimmune Features (IPAF), with patients exhibiting one clinical feature of CTD and one serological domain criterion (e.g., ANA positive with nucleolar pattern, RF and anti-CCP positivity, anti-RoSSA and anti-LaSSB positivity, anti-Scl70 positivity) while not meeting the classification criteria for any other CTD. Evidence of ILD based on an HRCT documenting the presence of interstitial changes involving at least 10% of the parenchyma within the previous 6 weeks. An HRCT scan completely negative for ILD changes performed up to 6 weeks before enrollment will be evaluated for the group of CTD patients without ILD. Either naive to immunosuppressants or having been on a stable immunosuppressive regimen for the three months preceding blood collection for EV characterization. Treatment with rituximab must be not administered in the previous 24 weeks. Exclusion criteria The exclusion criteria for all the patients are as follows. Current treatment with corticosteroids >10 mg of prednisone.
Poor peripheral venus access that would interfere with blood sampling
Study duration . 92 weeks after the approval of EC. The expected enrollment period will be 32 weeks. The minimum follow-up will be 52 weeks for patient with CTD-ILD. 8 weeks will be used for statistical analysis
Interventional Assessment. Blood samples for EVs characterization in all patients.
Experimental investigations. AI-mediated HRCT assessment in patients with CTD-ILD laboratory biomarkers of ILD presence and progression (specifically IL-6, SPD, SP-A, KL-6, and CCL18) in all patients. Follow-up visits after 6 (T6) and 12 months (T12) for the assessment of functional lung progression in CTD-ILD patients.
Statistical analysis. Sample size calculation for proteomic and transcriptomic studies is generally challenging due to the complex nature of these data. A pragmatic sample size is therefore considered for the practical constraints of the study, such as disease and ILD prevalence and the number of subjects that can be recruited in three centers within the given time frame. We plan to enroll a total of 200 CTD patients. Given the high risk of ILD, determined by the specific diagnosis and autoantibody profile, approximately half of them (100 patients, 50%) are expected to exhibit ILD changes on the HRCT. Among these recruited CTD-ILD patients, at least 30% are projected to show pulmonary progression at the 12-month follow-up.
Inclusion Criteria:
Exclusion Criteria:
silvialaura.bosello@policlinicogemelli.it+390630159650
giulia.wlderk@policlinicogemelli.it+390630159650