OP0199 RADIOMIC SIGNATURES REFLECT TREATMENT RESPONSE TO NINTEDANIB IN PRECLINICAL LUNG FIBROSIS MODEL

Background

Responses to anti-fibrotic drugs in preclinical disease models are difficult to quantify by histological analysis of single tissue sections. Quantitative in-depth analysis of imaging data, termed “radiomics”, may represent a more reliable and accurate measure of treatment response since the pathology of the whole organ is captured.

Objectives

To study the potential of µCT-derived radiomic features to reflect response to Nintedanib in the bleomycin (BLM)-induced murine model of fibrosing interstitial lung disease.

Methods

All C57BL/6J mice from both study groups were intratracheally instilled with 2 U/kg BLM on day 0 to induce lung fibrosis. Nintedanib was administered daily by gavage at 60 mg/kg for two weeks starting from day 7 (n=15). Controls received equivalent treatment with vehicle-only (n=19). Whole lung µCT scans (SkyScan 1176, Bruker) of each animal were acquired at baseline (day 0), pre-treatment (day 7), and post-treatment (day 21). The Ashcroft score was assessed on Sirius Red stained lung sections post-treatment. Lung volumes in µCTs were defined semi-automatically in MIM Software (6.9.2), followed by extraction of radiomic features with our in-house developed software Z-Rad (7.3.1). Each data set contained 1’386 features, describing image characteristics with histogram, texture, and wavelet functions. Data pre-processing involved removal of features sensitive to intra- and interobserver delineation variability (ICC<0.75), highly correlated features (Pearson’s r>0.95), and features not significantly changing between days 0 and 7 (p>0.05). Agglomerative clustering of radiomic temporal trajectories was performed on the Nintedanib group to identify distinct feature clusters. The identified feature sets were then used to plot average feature value trajectories for both study groups in each cluster. To identify features significantly different between a) Nintedanib vs. control, and b) pre- vs. post-treatment, Mann-Whitney U and Wilcoxon signed-rank tests were used, respectively. Samples were pooled from two independent experiments.

Results

Evaluation of tissue sections did not show a significant treatment-induced reduction of fibrosis with average Ashcroft scores of 3.7 (±1.2 s.d.) and 3.4 (±1.6 s.d.) in Nintedanib and control samples, respectively (p>0.05). Radiomics data analysis revealed two feature clusters in Nintedanib samples, composed of 52 features (cluster 1) and 96 features (cluster 2), the trajectories of which were then plotted for both study groups. In cluster 1, feature value trajectories significantly decreased in both Nintedanib and control group between pre-and post-treatment (p<0.001), whereas feature values in cluster 2 remained flat (p>0.05). Importantly, Nintedanib-treated mice displayed a much more pronounced feature value decrease post-treatment in cluster 1 compared to the control group (p<0.05). Here, feature values post-treatment resembled pre-disease baseline conditions in the Nintedanib group (p>0.05), whereas the control group remained significantly different from baseline (p<0.01). Cluster 1 was composed of 6 histogram, 11 texture, and 35 wavelet features, emphasizing the role of high-dimensional metrics for the detection of differences.

Conclusion

Histological quantification of lung fibrosis accounts only for a small fraction of the whole pathology in a spatially heterogeneous disease. We demonstrated that µCT-derived radiomic features identified significant differences on imaging level following Nintedanib treatment, which we could not reliably detect on tissue level using Ashcroft scoring. These findings hold great potential for the development of novel readouts for improved stratification of anti-fibrotic treatment effects in preclinical models.

Acknowledgements

This study received funding support from the Swiss Lung Association.

Disclosure of Interests

David Lauer Shareholder of: Roche (no relation to project), Employee of: Former employee of Roche (no relation to project), Janine Schniering: None declared, Hubert Gabrys: None declared, Malgorzata Maciukiewicz: None declared, Matthias Brunner: None declared, Oliver Distler Speakers bureau: Speaker fees in the area of systemic sclerosis and related complications from Bayer, Boehringer Ingelheim, Janssen, Medscape, Consultant of: Consultancies in the area of systemic sclerosis and its complications with Abbvie, Acceleron, Alcimed, Amgen, AnaMar, Arxx, AstraZeneca, Baecon, Blade, Bayer, Boehringer Ingelheim, Corbus, CSL Behring, 4P Science, Galapagos, Glenmark, Horizon, Inventiva, Kymera, Lupin, Miltenyi Biotec, Mitsubishi Tanabe, MSD, Novartis, Prometheus, Roivant, Sanofi and Topadur, Grant/research support from: Grant/research support from Kymera, Mitsubishi Tanabe, Boehringer Ingelheim, Thomas Frauenfelder: None declared, Stephanie Tanadini-Lang: None declared, Britta Maurer Speakers bureau: Received speaker fees from Boehringer-Ingelheim as well as congress support from Medtalk, Pfizer, Roche, Actelion, Mepha, and MSD, Consultant of: Consultancies with Novartis, Boehringer Ingelheim, Janssen-Cilag. Has a patent mir-29 for the treatment of systemic sclerosis issued (US8247389, EP2331143), Grant/research support from: Had grant/research support from AbbVie, Protagen, Novartis Biomedical Research.

The discovAIR project: a roadmap towards the Human Lung Cell Atlas

The Human Cell Atlas (HCA) consortium aims to establish an atlas of all organs in the healthy human body at single-cell resolution to increase our understanding of basic biological processes that govern development, physiology and anatomy, and to accelerate diagnosis and treatment of disease. The Lung Biological Network of the HCA aims to generate the Human Lung Cell Atlas as a reference for the cellular repertoire, molecular cell states and phenotypes, and cell–cell interactions that characterise normal lung homeostasis in healthy lung tissue. Such a reference atlas of the healthy human lung will facilitate mapping the changes in the cellular landscape in disease. The discovAIR project is one of six pilot actions for the HCA funded by the European Commission in the context of the H2020 framework programme. discovAIR aims to establish the first draft of an integrated Human Lung Cell Atlas, combining single-cell transcriptional and epigenetic profiling with spatially resolving techniques on matched tissue samples, as well as including a number of chronic and infectious diseases of the lung. The integrated Human Lung Cell Atlas will be available as a resource for the wider respiratory community, including basic and translational scientists, clinical medicine, and the private sector, as well as for patients with lung disease and the interested lay public. We anticipate that the Human Lung Cell Atlas will be the founding stone for a more detailed understanding of the pathogenesis of lung diseases, guiding the design of novel diagnostics and preventive or curative interventions.

The discovAIR project contributes to the Human Cell Atlas Lung Biological Network by establishing a first draft of the Human Lung Cell Atlas, advancing our insight into the cellular complexity and spatial organisation of the lung in health and diseasehttps://bit.ly/3zX4cad

CellRank for directed single-cell fate mapping

Computational trajectory inference enables the reconstruction of cell state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank (https://cellrank.org) for single-cell fate mapping in diverse scenarios, including regeneration, reprogramming and disease, for which direction is unknown. Our approach combines the robustness of trajectory inference with directional information from RNA velocity, taking into account the gradual and stochastic nature of cellular fate decisions, as well as uncertainty in velocity vectors. On pancreas development data, CellRank automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. Applied to lineage-traced cellular reprogramming data, predicted fate probabilities correctly recover reprogramming outcomes. CellRank also predicts a new dedifferentiation trajectory during postinjury lung regeneration, including previously unknown intermediate cell states, which we confirm experimentally.

CellRank infers directed cell state transitions and cell fates incorporating RNA velocity information into a graph based Markov process.

Computed tomography-based radiomics decodes prognostic and molecular differences in interstitial lung disease related to systemic sclerosis

Background

Radiomic features calculated from routine medical images show great potential for personalised medicine in cancer. Patients with systemic sclerosis (SSc), a rare, multiorgan autoimmune disorder, have a similarly poor prognosis due to interstitial lung disease (ILD). Here, our objectives were to explore computed tomography (CT)-based high-dimensional image analysis (“radiomics”) for disease characterisation, risk stratification and relaying information on lung pathophysiology in SSc-ILD.

Methods

We investigated two independent, prospectively followed SSc-ILD cohorts (Zurich, derivation cohort, n=90; Oslo, validation cohort, n=66). For every subject, we defined 1355 robust radiomic features from standard-of-care CT images. We performed unsupervised clustering to identify and characterise imaging-based patient clusters. A clinically applicable prognostic quantitative radiomic risk score (qRISSc) for progression-free survival (PFS) was derived from radiomic profiles using supervised analysis. The biological basis of qRISSc was assessed in a cross-species approach by correlation with lung proteomic, histological and gene expression data derived from mice with bleomycin-induced lung fibrosis.

Results

Radiomic profiling identified two clinically and prognostically distinct SSc-ILD patient clusters. To evaluate the clinical applicability, we derived and externally validated a binary, quantitative radiomic risk score (qRISSc) composed of 26 features that accurately predicted PFS and significantly improved upon clinical risk stratification parameters in multivariable Cox regression analyses in the pooled cohorts. A high qRISSc score, which identifies patients at risk for progression, was reverse translatable from human to experimental ILD and correlated with fibrotic pathway activation.

Conclusions

Radiomics-based risk stratification using routine CT images provides complementary phenotypic, clinical and prognostic information significantly impacting clinical decision making in SSc-ILD.

CT-based radiomics decodes phenotypic, prognostic and molecular differences in SSc-ILD, and predicts progression-free survival with a significant impact on future clinical decision making in SSc-ILDhttps://bit.ly/3zPaMOn

Elevated Fibronectin Levels in Profibrotic CD14+ Monocytes and CD14+ Macrophages in Systemic Sclerosis

Background

Systemic sclerosis (SSc) is an autoimmune disease characterized by overproduction of extracellular matrix (ECM) and multiorgan fibrosis. Animal studies pointed to bone marrow-derived cells as a potential source of pathological ECM-producing cells in immunofibrotic disorders. So far, involvement of monocytes and macrophages in the fibrogenesis of SSc remains poorly understood.

Methods and Results

Immunohistochemistry analysis showed accumulation of CD14⁺ monocytes in the collagen-rich areas, as well as increased amount of alpha smooth muscle actin (αSMA)-positive fibroblasts, CD68⁺ and mannose-R⁺ macrophages in the heart and lungs of SSc patients. The full genome transcriptomics analyses of CD14⁺ blood monocytes revealed dysregulation in cytoskeleton rearrangement, ECM remodeling, including elevated FN1 (gene encoding fibronectin) expression and TGF-β signalling pathway in SSc patients. In addition, single cell RNA sequencing analysis of tissue-resident CD14⁺ pulmonary macrophages demonstrated activated profibrotic signature with the elevated FN1 expression in SSc patients with interstitial lung disease. Peripheral blood CD14⁺ monocytes obtained from either healthy subjects or SSc patients exposed to profibrotic treatment with profibrotic cytokines TGF-β, IL-4, IL-10, and IL-13 increased production of type I collagen, fibronectin, and αSMA. In addition, CD14⁺ monocytes co-cultured with dermal fibroblasts obtained from SSc patients or healthy individuals acquired a spindle shape and further enhanced production of profibrotic markers. Pharmacological blockade of the TGF-β signalling pathway with SD208 (TGF-β receptor type I inhibitor), SIS3 (Smad3 inhibitor) or (5Z)-7-oxozeaenol (TGF-β-activated kinase 1 inhibitor) ameliorated fibronectin levels and type I collagen secretion.

Conclusions

Our findings identified activated profibrotic signature with elevated production of profibrotic fibronectin in CD14⁺ monocytes and CD14⁺ pulmonary macrophages in SSc and highlighted the capability of CD14⁺ monocytes to acquire a profibrotic phenotype. Taking together, tissue-infiltrating CD14⁺ monocytes/macrophages can be considered as ECM producers in SSc pathogenesis.

Staphylococcus aureus impairs dermal fibroblast functions with deleterious effects on wound healing

Chronic wounds are a major disease burden worldwide. The breach of the epithelial barrier facilitates transition of skin commensals to invasive facultative pathogens. Therefore, we investigated the potential effects of Staphylococcus aureus (SA) on dermal fibroblasts as key cells for tissue repair. In co-culture systems combining live or heat-killed SA with dermal fibroblasts derived from the BJ-5ta cell line, healthy individuals, and patients with systemic sclerosis, we assessed tissue repair including pro-inflammatory cytokines, matrix metalloproteases (MMPs), myofibroblast functions, and host defense responses. Only live SA induced the upregulation of IL-1β/-6/-8 and MMP1/3 as co-factors of tissue degradation. Additionally, the increased cell death reduced collagen production, proliferation, migration, and contractility, prerequisite mechanisms for wound closure. Intracellular SA triggered inflammatory and type I IFN responses via intracellular dsDNA sensor molecules and MyD88 and STING signaling pathways. In conclusion, live SA affected various key tissue repair functions of dermal fibroblasts from different sources to a similar extent. Thus, SA infection of dermal fibroblasts should be taken into account for future wound management strategies.

OP0150 MACHINE LEARNING APPROACHES FOR RISK MODELLING IN INTERSTITIAL LUNG DISEASE ASSOCIATED WITH SYSTEMIC SCLEROSIS USING HIGH DIMENSIONAL IMAGE ANALYSIS

Background:

The interstitial lung disease (ILD) associated with connective tissue diseases including systemic sclerosis (SSc) is heterogenous disease characterized by reduced survival of approximately 3 years (1). “Radiomics’’ is a field of research which describes the in-depth analysis of tissues by computational retrieval of high-dimensional quantitative features from medical images (2). Our previous study suggested capacity of radiomics features to differentiate between “high” and “low” risk groups for lung function decline in two independent cohorts (3).

Objectives:

  •bTo develop robust, machine learning (ML) workflow for “radiomics” data in SSc-ILD to select optimal methods for prediction.

  •oTo predict the time to individual lung function decline defined as defined by the time to a relative decline of ≥ 15% in Forced Vital Capacity (FVC)% as previously (3), using workflow.

Methods:

We investigated two cohorts of SSc-ILD: 90 patients (76.7% female, median age 57.5 years) from the University Hospital Zurich and 66 patients (75.8% female, median age 61.0 years) from Oslo University Hospital’s. Patients were retrospectively selected if (3): a) diagnosed with early/mild SSc according to the Very Early Diagnosis of Systemic Sclerosis (VEDOSS) criteria, b) presence of ILD on HRCT as determined by a senior radiologist. For every subject, we defined 1,355 robust radiomic features from HRCT images. The follow-up period was defined as the time interval between baseline visit and the last available follow-up visit.

We have developed a systematic computational workflow to build predictive ML models. To reduce the number of redundant radiomic features, we applied correlation thresholds. We applied distinct methods including 1) Lasso Penalized Regression for feature selection, and 2) Random Forest (RF) for modeling using the R package ‘caret’. To select the optimal ML model, we randomly divided derivation cohort into Training (70%) and Holdout (30%) sets and applied fivefold cross-validation (5kCV) for feature and classifier selection on Training set only.

Results:

We have investigated various methods to select the optimal set of predictive radiomic features. Since the ML model performance is affected by both, feature, and classifier selection, we assessed these factors first.

Results from feature filtering and selection, suggested that the combination of correlation threshold of 0.9 with Lasso regression proved best. As we perform feature selection in 5k CV workflow, features present in at least 2 sets entered model optimization step.

During model selection, we selected RF classifier. We detected positive correlation between actual and predicted values with Spearman’s rho = 0.313, p = 0.167 and Spearman’s rho = 0.341, p = 0.015 in Oslo and Holdout sets respectively, as shown on Figure 1. The percentage of variance remained modest for both Holdout (Rsq = 0.104) and Oslo (Rsq = 0.126) datasets.

Figure 1.

Performance of the best, RF classifier shown as scatterplot between actual and predicted values of individual time to lung decline.

Conclusion:

In summary, we: (1) developed ML workflow that allowed to select o optimal methodology for modeling (i.e., feature and classifier selection), and (2) provide models that predicted time to individual lung function decline, characterized by significant correlation between predicted and actual values.

References:

[1]Hansell DM, Goldin JG, King TE, Jr., Lynch DA, Richeldi L, Wells AU. CT staging and monitoring of fibrotic interstitial lung diseases in clinical practice and treatment trials: a position paper from the Fleischner Society. Lancet Respir Med. 2015;3(6):483-96.

[2]Lambin, P. et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 48, 441–446 (2012).

[3]Schniering J. et al. Resolving phenotypic and prognostic differences in interstitial lung disease related to systemic sclerosis by computed tomography-based radiomics. https://www.medrxiv.org/content/10.1101/2020.06.09.20124800v1

Disclosure of Interests:

None declared

POS0866 TWO-DIMENSIONAL HRCT-BASED RADIOMIC FEATURES IN SSC-ILD DISTINGUISH DRUG RESPONDERS FROM NON-RESPONDERS

Background:

Management of patients with systemic sclerosis-associated interstitial lung disease (SSc-ILD) is complicated by high inter-patient variability. To date, no validated predictors of treatment response are available for routine use. High resolution computed tomography (HRCT)-based radiomics, i.e. the high-dimensional, quantitative analysis of imaging metadata, have previously been shown to be successful in discriminating (SSc-)ILD phenotypes in preclinical and clinical studies1. Since HRCT is an integral part of the routine work-up in SSc, HRCT-based radiomic features may hold potential as non-invasive biomarkers.

Objectives:

To predict treatment response using two-dimensional (2D) HRCT-based radiomics in SSc-ILD patients from a prospectively followed cohort.

Methods:

Inclusion criteria were diagnosis of SSc-ILD in HRCT, availability of a suitable chest HRCT scan within 12 months prior to initiation of a new treatment, and availability of clinical baseline and follow-up information. Treatment response was defined as the absence of all of the following over a follow-up period of 12-24 months: relative decrease in forced vital capacity (FVC) ≥5%, increase of ILD in HRCT as assessed by a radiologist, change in treatment regimen due to insufficient response, ILD-related death or lung transplantation. Of each pre-treatment HRCT, 6 slices (15±5 mm apart, starting from the basal lung margin) were manually segmented and 1513 2D radiomic features were extracted using the in-house software Z-Rad (Python 2.7). Features were Z-score transformed and pre-filtered for inter- and intra-reader robustness (intraclass correlation coefficient >0.85) and inter-feature correlation (Spearman’s rho <0.9). A categorical linear regression model was created using 3-fold cross-validated elastic nets for feature selection. Features were then summarized and divided by their number. For generation of a score cut-off, Youden’s score was used. For two-group analyses of continuous variables, Wilcoxon’s test was performed, whereas categorical data was assessed using Fisher’s exact test.

Results:

A total of 64 pre-treatment HRCTs from 54 patients were analyzed. In 9 patients, >1 asynchronous treatments were assessed, while 45 patients had only 1 eligible treatment approach. The response rate within the assessed follow-up period was 45.3% (n=29). For score generation, 13 radiomic features were selected and an optimal cut-off value of -0.1589 was determined. Univariate linear regression showed significant association between our categorical radiomics-based score and treatment response (p=0.007, area under the curve = 0.65 (0.51-0.79), sensitivity=0.90, specificity=0.43), whereby a high score was predictive for treatment response.

No differences between patients with high (n=46) or low (n=18) scores were detected for baseline age (mean±SD=55.5±12.0 and 55.5±13.6 years, p=0.84), duration of SSc (mean±SD=6.2±8.4 and 4.7±4.4 years, p=0.79), time since ILD diagnosis (2.7±2.9 and 2.4±3.1 years, p=0.59), FVC (77.6±20.6 and 80.1±17.9, p=0.41) or DLco (54.4±21.0 and 57.6±18.9, p=0.40). Distribution of anti-Scl-70 positivity (45.7% vs. 55.6%, p=0.58) and diffuse cutaneous disease (47.7% vs. 61.1%, p=0.41) was not significantly different between patients with high and low scores, respectively, although a trend towards higher percentages in the high score group was observed.

Conclusion:

Our results indicate that, following validation in external cohorts, radiomics may be a promising tool for future pre-treatment patient stratification. Moreover, our radiomics-based score seems not to be associated with commonly studied clinical predictors such as anti-Scl-70 positivity or lung function, underlining a possible additive value to ‘traditional’ clinical parameters.

References:

[1]Schniering, J., et al. Resolving phenotypic and prognostic differences in interstitial lung disease related to systemic sclerosis by computed tomography-based radiomics. medRxiv [Preprint] doi:10.1101/2020.06.09.20124800 (2020).

Disclosure of Interests:

Chantal Meier: None declared, Malgorzata Maciukiewicz: None declared, Matthias Brunner: None declared, Janine Schniering: None declared, Hubert Gabrys: None declared, Anja Kühnis: None declared, Oliver Distler Speakers bureau: Speaker fee on Scleroderma and related complications: Bayer, Boehringer Ingelheim, Medscape, Novartis, Roche. Speaker fee on rheumatology topic other than Scleroderma: MSD, iQone, Novartis, Pfizer, Roche, Consultant of: Consultancy fee for Scleroderma and its complications: Abbvie, Acceleron Pharma, Amgen, AnaMar, Arxx Therapeutics, Bayer, Baecon Discovery, Boehringer, CSL Behring, ChemomAb, Corbus Pharmaceuticals, Horizon Pharmaceuticals, Galapagos NV, GSK, Glenmark Pharmaceuticals, Inventiva, Italfarmaco, iQvia, Kymera, Medac, Medscape, Mitsubishi Tanabe Pharma, MSD, Roche, Roivant Sciences, Sanofi, UCB. Consultancy fee for rheumatology topic other than Scleroderma: Abbvie, Amgen, Lilly, Pfizer, Grant/research support from: Research Grants to investigate the pathophysiology and potential treatment of Scleroderma and its complications: Kymera Therapeutics, Mitsubishi Tanabe, Thomas Frauenfelder: None declared, Stephanie Tanadini-Lang: None declared, Britta Maurer Speakers bureau: Speaker fees from Boehringer-Ingelheim, Grant/research support from: Grant/research support from AbbVie, Protagen, Novartis Biomedical Research, congress support from Pfizer, Roche, Actelion, mepha, and MSD

Integrative analysis of cell state changes in lung fibrosis with peripheral protein biomarkers

The correspondence of cell state changes in diseased organs to peripheral protein signatures is currently unknown. Here, we generated and integrated single-cell transcriptomic and proteomic data from multiple large pulmonary fibrosis patient cohorts. Integration of 233,638 single-cell transcriptomes (n = 61) across three independent cohorts enabled us to derive shifts in cell type proportions and a robust core set of genes altered in lung fibrosis for 45 cell types. Mass spectrometry analysis of lung lavage fluid (n = 124) and plasma (n = 141) proteomes identified distinct protein signatures correlated with diagnosis, lung function, and injury status. A novel SSTR2+ pericyte state correlated with disease severity and was reflected in lavage fluid by increased levels of the complement regulatory factor CFHR1. We further discovered CRTAC1 as a biomarker of alveolar type-2 epithelial cell health status in lavage fluid and plasma. Using cross-modal analysis and machine learning, we identified the cellular source of biomarkers and demonstrated that information transfer between modalities correctly predicts disease status, suggesting feasibility of clinical cell state monitoring through longitudinal sampling of body fluid proteomes.

Long noncoding RNA H19X is a key mediator of TGF-β-driven fibrosis

TGF-β is a master regulator of fibrosis, driving the differentiation of fibroblasts into apoptosis-resistant myofibroblasts and sustaining the production of extracellular matrix (ECM) components. Here, we identified the nuclear long noncoding RNA (lncRNA) H19X as a master regulator of TGF-β-driven tissue fibrosis. H19X was consistently upregulated in a wide variety of human fibrotic tissues and diseases and was strongly induced by TGF-β, particularly in fibroblasts and fibroblast-related cells. Functional experiments following H19X silencing revealed that H19X was an obligatory factor for TGF-β-induced ECM synthesis as well as differentiation and survival of ECM-producing myofibroblasts. We showed that H19X regulates DDIT4L gene expression, specifically interacting with a region upstream of the DDIT4L gene and changing the chromatin accessibility of a DDIT4L enhancer. These events resulted in transcriptional repression of DDIT4L and, in turn, in increased collagen expression and fibrosis. Our results shed light on key effectors of TGF-β-induced ECM remodeling and fibrosis.

Impact of CT convolution kernel on robustness of radiomic features for different lung diseases and tissue types

OBJECTIVES

In this study, we aimed to assess the impact of different CT reconstruction kernels on the stability of radiomic features and the transferability between different diseases and tissue types. Three lung diseases were evaluated, i.e. non-small cell lung cancer (NSCLC), malignant pleural mesothelioma (MPM) and interstitial lung disease related to systemic sclerosis (SSc-ILD) as well as four different tissue types, i.e. primary tumor, largest involved lymph node ipsilateral and contralateral lung.

METHODS

Pre-treatment non-contrast enhanced CT scans from 23 NSCLC, 10 MPM and 12 SSc-ILD patients were collected retrospectively. For each patient, CT scans were reconstructed using smooth and sharp kernel in filtered back projection. The regions of interest (ROIs) were contoured on the smooth kernel-based CT and transferred to the sharp kernel-based CT. The voxels were resized to the largest voxel dimension of each cohort. In total, 1386 features were analyzed. Feature stability was assessed using the intraclass correlation coefficient. Features above the stability threshold >0.9 were considered stable.

RESULTS

We observed a strong impact of the reconstruction method on stability of the features (at maximum 26% of the 1386 features were stable). Intensity features were the most stable followed by texture and wavelet features. The wavelet features showed a positive correlation between percentage of stable features and size of the ROI (R2 = 0.79, p = 0.005). Lymph node radiomics showed poorest stability (<10%) and lung radiomics the largest stability (26%). Robustness analysis done on the contralateral lung could to a large extent be transferred to the ipsilateral lung, and the overlap of stable lung features between different lung diseases was more than 50%. However, results of robustness studies cannot be transferred between tissue types, which was investigated in NSCLC and MPM patients; the overlap of stable features for lymph node and lung, as well as for primary tumor and lymph node was very small in both disease types.

CONCLUSION

The robustness of radiomic features is strongly affected by different reconstruction kernels. The effect is largely influenced by the tissue type and less by the disease type.

ADVANCES IN KNOWLEDGE

The study presents to our knowledge the most complete analysis on the impact of convolution kernel on the robustness of CT-based radiomics for four relevant tissue types in three different lung diseases. .

Serum metabolites as biomarkers in systemic sclerosis-associated interstitial lung disease

Systemic sclerosis (SSc) is a severe multi-organ disease with interstitial lung disease (ILD) being the major cause of death. While targeted therapies are emerging, biomarkers for sub-stratifying patients based on individual profiles are lacking. Herein, we investigated how levels of serum metabolites correlated with different stages of SSc and SSc-ILD. Serum samples of patients with SSc without ILD, stable and progressive SSc-ILD as well as of healthy controls (HC) were analysed using liquid targeted tandem mass spectrometry. The best discriminating profile consisted of 4 amino acids (AA) and 3 purine metabolites. L-tyrosine, L-tryptophan, and 1-methyl-adenosine distinguished HC from SSc patients. L-leucine, L-isoleucine, xanthosine, and adenosine monophosphate differentiated between progressing and stable SSc-ILD. In SSc-ILD, both, L-leucine and xanthosine negatively correlated with changes in FVC% predicted. Additionally, xanthosine was negatively correlated with changes in DLco% predicted and positively with the prognostic GAP index. Validation of L-leucine and L-isoleucine by an enzymatic assay confirmed both the sub-stratification of SSc-ILD patients and correlation with lung function and prognosis score. Serum metabolites may have potential as biomarkers for discriminating SSc patients based on the presence and severity of ILD. Confirmation in larger cohorts will be needed to appreciate their value for routine clinical care.

SAT0569 “IMAGES ARE MORE THAN PICTURES, THEY ARE DATA” [1] – EXPLORATION OF RADIOMICS ANALYSIS FOR SYSTEMIC SCLEROSIS-ASSOCIATED INTERSTITIAL LUNG DISEASE

Background:

Interstitial lung disease (ILD) affects 60% of patients with systemic sclerosis (SSc) and is the primary cause of death. Medical imaging is an integral part of the routine work-up for diagnosis and monitoring of SSc-ILD and includes high-resolution computed tomography (HRCT). Radiomics is a novel research area that describes the in-depth analysis of tissue phenotypes in medical images with computational retrieval of quantitative, mineable metadata appropriate for statistical analyses.

Objectives:

To explore the performance of HRCT-derived radiomic features for the assessment of SSc-associated ILD (i.e. diagnosis, staging, and lung function).

Methods:

Radiomics analysis was performed on HRCT scans from 98 SSc patients, including n=33 SSc patients without ILD, n=33 with limited and n=32 with extensive ILD as defined by 0%, <20% and ≥20% visual extent of fibrosis on HRCT, respectively. Following semi-automated segmentation of lung tissue on 3D reconstructed HRCT scans, 1386 radiomic features, including 17 intensity, 137 texture, and 1232 wavelet features were extracted using the in-house developed software Z-Rad (Python 2.7). In order to identify robust features, we conducted intra- and inter-reader correlation analysis (ICC) in a subgroup of patients. Only features with good reproducibility (ICC ≥ 0.75) entered subsequent analyses. We applied the Wilcoxon test, followed by Receiver Operating Characteristic ROC) curve analyses, to identify features significantly different between a) ILD and non-ILD and b) limited vs. extensive ILD patients. Spearman rank correlation was performed to reveal significant associations of radiomic features from a) and b) with lung function as measured by percentage of predicted forced vital capacity (FVC% predicted).

Results:

In total, 1355/1386 radiomic features passed the test of robustness and were eligible for further, exploratory analyses. Radiomic features with good performance (area under the ROC curve (AUC) ≥ 0.7 and p-value ≤ 0.05) were considered as potential candidate discriminators. Under this criterion, we identified 288/1355 (21.3%) radiomic features that were significantly different between ILD and non-ILD patients and 409/1355 (30.2%) features that significantly discriminated between limited and extensive ILD (Fig. 1). For diagnosis, the texture featuredependence count entropywas the top parameter to distinguish ILD patients from healthy controls (AUC = 0.89, p = 1.83x10-10), whereas for staging the wavelet featureHHH long run high grey level emphasisproved to be best suited to separate limited from extensive ILD (AUC = 0.88, p = 7.76x10-9).

Fig 1.

Correlation analysis of the most significant (best performing) discriminative radiomic features with lung function revealed a significant negative correlation ofdependence count entropy(rho = -0.51, p = 9.89x10-8) andHHH long run high grey level emphasis(rho = -0.51, p = 1.73x10-5) with FVC% predicted.

Conclusion:

Our study adds novelty to the field of SSc-ILD showing that radiomic features have great potential as quantitative imaging biomarkers for diagnosis and staging of SSc-ILD and that they may reflect lung function. As the next step, we are planning to build predictive models, using machine learning, for diagnosis, staging, and lung function and validate them in external patient cohorts. If validated such models will pave the way for computer-aided management in SSc-ILD and thus improve patients’ outcome.

References:

[1]Gillies, R. J., Kinahan, P. E. & Hricak, H. Radiomics: Images Are More than Pictures, They Are Data. Radiology 278, 563-577, doi:10.1148/radiol.2015151169 (2016).

Disclosure of Interests:

Janine Schniering: None declared, Malgorzata Maciukiewicz: None declared, Hubert Gabrys: None declared, Matthias Brunner: None declared, Christian Blüthgen: None declared, Oliver Distler Grant/research support from: Grants/Research support from Actelion, Bayer, Boehringer Ingelheim, Competitive Drug Development International Ltd. and Mitsubishi Tanabe; he also holds the issued Patent on mir-29 for the treatment of systemic sclerosis (US8247389, EP2331143)., Consultant of: Consultancy fees from Actelion, Acceleron Pharma, AnaMar, Bayer, Baecon Discovery, Blade Therapeutics, Boehringer, CSL Behring, Catenion, ChemomAb, Curzion Pharmaceuticals, Ergonex, Galapagos NV, GSK, Glenmark Pharmaceuticals, Inventiva, Italfarmaco, iQvia, medac, Medscape, Mitsubishi Tanabe Pharma, MSD, Roche, Sanofi and UCB, Speakers bureau: Speaker fees from Actelion, Bayer, Boehringer Ingelheim, Medscape, Pfizer and Roche, Matthias Guckenberger: None declared, Thomas Frauenfelder: None declared, Stephanie Tanadini-Lang: None declared, Britta Maurer Grant/research support from: AbbVie, Protagen, Novartis, congress support from Pfizer, Roche, Actelion, and MSD, Speakers bureau: Novartis

SAT0333 SERUM METABOLITES AS BIOMARKERS IN SYSTEMIC SCLEROSIS-ASSOCIATED INTERSTITIAL LUNG DISEASE

Background:

In fibrotic diseases, metabolic processes are altered with a tendency towards an anabolic state, which is partially reflected in serum. Circulating biomarkers for interstitial lung disease (ILD), the leading cause of death in systemic sclerosis (SSc), are still sparse and not established in routine care.

Objectives:

To assess the potential of serum metabolites as biomarkers for the presence and progression of SSc-ILD.

Methods:

Age and sex matched serum samples of SSc patients from the Zurich cohort and of healthy controls (HC) were analyzed. Progressive SSc-ILD was defined as either a relative decrease in forced vital capacity (FVC) >10%, a decrease in FVC of 5-9% and a concomitant decrease of carbon dioxide diffusion capacity >15%, or an increase of the extent of lung fibrosis on computed tomography from <20% to ≥20% compared to the last visit (mean follow-up interval = 14 months (range = 9-26)). Sera of HC, non-ILD SSc and stable vs. progressive SSc-ILD patients (n = 12 per group; total n = 48) were screened for 110 metabolites by targeted liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Peak areas were analyzed with R 3.6. For univariate analysis, FDR-corrected one-way ANOVA was used. In multivariate group-wise partial least squares discriminant analysis (PLS-DA), variable importance in the projection (VIP) scores ≥2 were considered significant.

Results:

In total, 85 metabolites were detected. Univariate analysis of all groups were suggestive of changes for 1-methyladenosine, L-tryptophan, L-tyrosine, L-leucine and xanthosine (p = 0.077, 0.028, 0.077, 0.028 and 0.032, respectively). In PLS-DA, HCs and SSc patients differed in their levels of L-tyrosine and L-tryptophan, while levels of L-threonine, 3-aminoisobutyric acid, adenosine monophosphate and xanthosine were changed when comparing non-ILD and SSc-ILD patients. Receiver operating curve (ROC) analysis of significant metabolites from uni- and multivariate testing resulted in separation of SSc patients from HCs by L-tyrosine (area under the curve (AUC) = 0.81, 95% confidence interval (CI): 0.67-0.96), L-tryptophan (AUC = 0.86, CI: 0.75-0.97) and 1-methyladenosine (AUC = 0.82, CI: 0.71-0.94). Progressive SSc-ILD patients were separated from stable patients by their levels of L-isoleucine, L-leucine, adenosine monophosphate and xanthosine (AUC = 0.83, 0.85, 0.79 and 0.77; CI: 0.66-1.00, 0.70-1.00, 0.60-0.97 and 0.55-0.99, respectively). Validation of increased values of the branched-chain amino acids L-leucine and L-isoleucine in progressive SSc-ILD vs. stable ILD using an enzymatic assay resulted in similar results as LC-MS/MS analysis, with higher values detected in progressive vs. stable patients (mean = 286.5 and 235.5 nM, respectively; p = 0.005). In ROC analysis (AUC = 0.81, CI: 0.62-1.00), a cut-off value of 250.3 nM separated stable from progressive patients with a sensitivity of 72.7% and a specificity of 83.3%.

Conclusion:

This study in SSc(-ILD) patients suggested alterations in serum metabolite levels corresponding with their current state of disease, indicating the potential use of serum metabolites as discriminating biomarkers upon further confirmation in larger multicenter studies.

Disclosure of Interests:

Chantal Meier: None declared, Katrin Freiburghaus: None declared, Cédric Bovet: None declared, Janine Schniering: None declared, Oliver Distler Grant/research support from: Grants/Research support from Actelion, Bayer, Boehringer Ingelheim, Competitive Drug Development International Ltd. and Mitsubishi Tanabe; he also holds the issued Patent on mir-29 for the treatment of systemic sclerosis (US8247389, EP2331143)., Consultant of: Consultancy fees from Actelion, Acceleron Pharma, AnaMar, Bayer, Baecon Discovery, Blade Therapeutics, Boehringer, CSL Behring, Catenion, ChemomAb, Curzion Pharmaceuticals, Ergonex, Galapagos NV, GSK, Glenmark Pharmaceuticals, Inventiva, Italfarmaco, iQvia, medac, Medscape, Mitsubishi Tanabe Pharma, MSD, Roche, Sanofi and UCB, Speakers bureau: Speaker fees from Actelion, Bayer, Boehringer Ingelheim, Medscape, Pfizer and Roche, Christos Nakas: None declared, Britta Maurer Grant/research support from: AbbVie, Protagen, Novartis, congress support from Pfizer, Roche, Actelion, and MSD, Speakers bureau: Novartis

18F-AzaFol for Detection of Folate Receptor-β Positive Macrophages in Experimental Interstitial Lung Disease-A Proof-of-Concept Study

Background: Interstitial lung disease (ILD) is a common and severe complication in rheumatic diseases. Folate receptor-β is expressed on activated, but not resting macrophages which play a key role in dysregulated tissue repair including ILD. We therefore aimed to pre-clinically evaluate the potential of 18F-AzaFol-based PET/CT (positron emission computed tomography/computed tomography) for the specific detection of macrophage-driven pathophysiologic processes in experimental ILD. Methods: The pulmonary expression of folate receptor-β was analyzed in patients with different subtypes of ILD as well as in bleomycin (BLM)-treated mice and respective controls using immunohistochemistry. PET/CT was performed at days 3, 7, and 14 after BLM instillation using the 18F-based folate radiotracer 18F-AzaFol. The specific pulmonary accumulation of the radiotracer was assessed by ex vivo PET/CT scans and quantified by ex vivo biodistribution studies. Results: Folate receptor-β expression was 3- to 4-fold increased in patients with fibrotic ILD, including idiopathic pulmonary fibrosis and connective tissue disease-related ILD, and significantly correlated with the degree of lung remodeling. A similar increase in the expression of folate receptor-β was observed in experimental lung fibrosis, where it also correlated with disease extent. In the mouse model of BLM-induced ILD, pulmonary accumulation of 18F-AzaFol reflected macrophage-related disease development with good correlation of folate receptor-β positivity with radiotracer uptake. In the ex vivo imaging and biodistribution studies, the maximum lung accumulation was observed at day 7 with a mean accumulation of 1.01 ± 0.30% injected activity/lung in BLM-treated vs. control animals (0.31 ± 0.06% % injected activity/lung; p < 0.01). Conclusion: Our preclinical proof-of-concept study demonstrated the potential of 18F-AzaFol as a novel imaging tool for the visualization of macrophage-driven fibrotic lung diseases.

Visualisation of interstitial lung disease by molecular imaging of integrin αvβ3 and somatostatin receptor 2

OBJECTIVE

To evaluate integrin αvβ3 (alpha-v-beta-3)-targeted and somatostatin receptor 2 (SSTR2)-targeted nuclear imaging for the visualisation of interstitial lung disease (ILD).

METHODS

The pulmonary expression of integrin αvβ3 and SSTR2 was analysed in patients with different forms of ILD as well as in bleomycin (BLM)-treated mice and respective controls using immunohistochemistry. Single photon emission CT/CT (SPECT/CT) was performed on days 3, 7 and 14 after BLM instillation using the integrin αvβ3-targeting ¹⁷⁷Lu-DOTA-RGD and the SSTR2-targeting ¹⁷⁷Lu-DOTA-NOC radiotracer. The specific pulmonary accumulation of the radiotracers over time was assessed by in vivo and ex vivo SPECT/CT scans and by biodistribution studies.

RESULTS

Expression of integrin αvβ3 and SSTR2 was substantially increased in human ILD regardless of the subtype. Similarly, in lungs of BLM-challenged mice, but not of controls, both imaging targets were stage-specifically overexpressed. While integrin αvβ3 was most abundantly upregulated on day 7, the inflammatory stage of BLM-induced lung fibrosis, SSTR2 expression peaked on day 14, the established fibrotic stage. In agreement with the findings on tissue level, targeted nuclear imaging using SPECT/CT specifically detected both imaging targets ex vivo and in vivo, and thus visualised different stages of experimental ILD.

CONCLUSION

Our preclinical proof-of-concept study suggests that specific visualisation of molecular processes in ILD by targeted nuclear imaging is feasible. If transferred into clinics, where imaging is considered an integral part of patients' management, the additional information derived from specific imaging tools could represent a first step towards precision medicine in ILD.

Evaluation of 99mTc-rhAnnexin V-128 SPECT/CT as a diagnostic tool for early stages of interstitial lung disease associated with systemic sclerosis

Background

Given the need for early detection of organ involvement in systemic sclerosis, we evaluated ^99mTc-rhAnnexin V-128 for the detection of early stages of interstitial lung disease (ILD) in respective animal models using single photon emission computed tomography (SPECT/CT).

Methods

In bleomycin (BLM)-challenged mice, fos-related antigen 2 (Fra-2) transgenic (tg) mice and respective controls, lung injury was evaluated by analysis of hematoxylin and eosin (HE) and Sirius red staining, with semi-quantification of fibrosis by the Ashcroft score. Apoptotic cells were identified by TUNEL assay, cleaved caspase 3 staining and double staining with specific cell markers. To detect early stages of lung remodeling by visualization of apoptosis, mice were injected intravenously with ^99mTc-rhAnnexin V-128 and imaged by small animal SPECT/CT. For confirmation, biodistribution and ex vivo autoradiography studies were performed.

Results

In BLM-induced lung fibrosis, inflammatory infiltrates occurred as early as day 3 with peak at day 7, whereas pulmonary fibrosis developed from day 7 and was most pronounced at day 21. In accordance, the number of apoptotic cells was highest at day 3 compared with saline controls and then decreased over time. Epithelial cells (E-cadherin+) and inflammatory cells (CD45+) were the primary cells undergoing apoptosis in the earliest remodeling stages of experimental ILD. This was also true in the pathophysiologically different Fra-2 tg mice, where apoptosis of CD45+ cells occurred in the inflammatory stage. In accordance with the findings on tissue level, at day 3 in the BLM and at week 16 in the Fra-2 tg model, biodistribution and/or ex vivo autoradiography showed increased pulmonary uptake of ^99mTc-rhAnnexin V-128 compared with controls. However, accumulation of the radiotracer and thus the signal intensity in lungs was too low to allow the differentiation of healthy and injured lungs in vivo.

Conclusion

At the tissue level, ^99mTc-rhAnnexin V-128 successfully demonstrated early stages of ILD in two animal models by detection of apoptotic epithelial and/or inflammatory cells. In vivo, however, we did not detect early lung injury. It remains to be investigated whether the same applies to human ILD.

Electronic supplementary material

The online version of this article (10.1186/s13075-018-1681-1) contains supplementary material, which is available to authorized users.

Tie2 as a novel key factor of microangiopathy in systemic sclerosis

BACKGROUND

The angiopoietin(Ang)/Tie2 system is a key regulator of vascular biology. The expression of membrane bound (mb) Tie2 and Ang-1 ensures vessel stability, whereas Ang-2, inducible by vascular endothelial growth factor (VEGF), hypoxia, and inflammation, acts as an antagonist. Tie2 signalling is also attenuated by soluble Tie2 (sTie2), the extracellular domain of the receptor, which is shed upon stimulation with VEGF. Herein, we investigate the role of Ang/Tie2 in the peripheral vasculopathy in systemic sclerosis (SSc) including animal models.

METHODS

The expression of Ang-1/-2 and Tie2 in skin/serum of SSc patients was compared with healthy controls by immunohistochemistry (IHC)/ELISA. Expression of Ang/Tie2 was analysed in different animal models: VEGF transgenic (tg) mice, hypoxia model, bleomycin-induced skin fibrosis, and tight skin 1 (TSK1) mice.

RESULTS

In SSc, dermal microvessels abundantly expressed Ang-2, but not Ang-1 compared with healthy controls. The percentage of mbTie2+ microvessels was profoundly decreased whereas the levels of sTie2 were increased already in early disease. Both in skin and sera of SSc patients, the Ang1/2 ratio was reduced, being lowest in patients with digital ulcers indicating vessel destabilizing conditions. We next studied potential influencing factors in animal models. The VEGF tg mouse model, the hypoxia, and the inflammation-dependent bleomycin model all showed a similar dysregulation of Ang/Tie2 as in SSc, which did not apply for the non-inflammatory TSK1 model.

CONCLUSION

Peripheral microvasculopathy in SSc results from a complex dysregulation of angiogenic signalling networks including the VEGF and the Ang/Tie2 system. The profoundly disturbed Ang-/Tie-2 balance might represent an important target for vascular therapeutic approaches in SSc.

Systemic sclerosis: The need for structured care

Autoimmune connective tissue diseases (CTDs) have a propensity to affect multiple organ systems as well as physical function, quality of life, and survival. Their clinical heterogeneity, multisystem involvement, and low worldwide prevalence present challenges for researchers to establish a study design to help better understand the course and outcomes of CTDs. Systemic sclerosis (SSc) is a notable example of a CTD, wherein longitudinal cohort studies (LCS) have enabled us to elucidate disease manifestations, disease course, and risk and prognostic factors for clinically important outcomes, by embedding research in clinical practice. Nevertheless, further efforts are needed to better understand SSc especially with regard to recognizing organ involvement early, developing new therapies, optimizing the use of existing therapies, and defining treatment targets. The heterogeneous multi-organ nature of SSc would lend itself well to a structured model of care, wherein step-up treatment algorithms are used with the goal of attaining a prespecified treatment target. In this chapter, we discuss the rationale for a structured treatment approach in SSc and propose possible treatment algorithms for three of the more common disease manifestations, namely skin involvement, digital ulcers and gastrointestinal tract involvement. We discuss possible strategies for evaluating and implementing these algorithms in the setting of LCS. We conclude by presenting a research agenda for the development of structured models of care in SSc.